Agenda packet, special meeting, first session — San Mateo City Council, January 24, 2023 (part 3 of 6)

Jurisdiction Name Site
Address/Intersection 5 Digit ZIP Code Assessor Parcel
Number
Consolidated
Sites
General Plan
Designation (Current)
Zoning
Designation
(Current)
Minimum Density
Allowed (units/acre)
Max Density
Allowed (units/acre) Parcel Size (Acres) Existing
Use/Vacancy Infrastructure Publicly-Owned Site Status Identified in Last/Last Two Planning Cycle(s) Lower Income Capacity
Moderate
Income Capacity
Above Moderate
Income Capacity Total Capacity Optional
Information1
Optional
Information2
Optional
Information3
SAN MATEO 94403 039‐360‐140 Transit‐Oriented Develop mTOD 0 50 1.33 1 story/parking YES ‐ Current NO ‐ Privately‐Owned Avai lable Not Used in Prior Housing Element 27 11 29 67 Owner interst in redevelopment, TOD ZonAdjacent to the railw SAN MATEO 3520 El Camino Real 94403 042‐121‐040 Regional/Community ComC3‐1/R4 0 50 1.81 1 story retail YES ‐ Current NO ‐ Privately‐Owned Av ailable Not Used in Prior Housing Element 25 11 27 63 One story build ing, larger than .5 acre Laurel Creek passes t SAN MATEO 41 Hillsdale Blvd 94403 039‐490‐170 X Regional/Community Co mC2‐2/Q5 0 50 28.91 Hillsdale Mall YES ‐ Current NO ‐ Privately‐Owned Pending Project Not Used in Prior Housing Element 485 199 515 1199 Ow ner interested in Developer DiscussionLaurel Creek passes t SAN MATEO 2950 El Camino Real 94403 039‐353‐010 X Regional/Community ComC3‐1/R4 0 50 0.31 2 story retail YES ‐ Current NO ‐ Privately‐Owned Pe nding Project Not Used in Prior Housing Element 6 3 6 15 Owner intere sted in Developer DiscussionLaurel Creek passes t SAN MATEO 94403 039‐353‐020 X Regional/Community Co mC3‐1/R4 0 50 0.73 Parking ramp and pYES ‐ Current NO ‐ Privately‐Owned Pending Project Not Used in Pri or Housing Element 14 6 16 36 Owner intersted in r eDeveloper DiscussionLaurel Creek passes t SAN MATEO 94403 039‐353‐030 X Regional/Community Co mC3‐1/R4 0 50 0.44 Hillsdale Parking L oYES ‐ Current NO ‐ Privately‐Owned Pending Project Not Used in Pri or Housing Element 9 4 9 22 Owner intersted in r eDeveloper DiscussionLaurel Creek passes t SAN MATEO 94403 039‐353‐040 X Regional/Community Co mC3‐1/R4 0 50 0.14 Hillsdale Parking L oYES ‐ Current NO ‐ Privately‐Owned Pending Project Not Used in Pri o r H o u s i n g E l e m e n t 3137 O w n e r i n t e r s t e d i n r eDeveloper DiscussionLaurel Creek passes t SAN MATEO 3590 El Camino Real 94403 042‐121‐080 X Regional/Community ComC3‐1/R4 0 50 0.65 1‐2 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 13 5 14 32 Ownership inte rest, one story building, la Laurel Creek passes t SAN MATEO 36th Ave/Colegrove St 94403 042‐121‐060 X Regional/Communi ty ComC3/R4 0 50 1.43 Parking Lot YES ‐ Potential NO ‐ Privately‐Owned Pendi ng Project Used in Two Consecutive Prior Housing Elements ‐ Vac a 29 12 31 72 Ownership interest, Developer Discussio nLaurel Creek passes t SAN MATEO 2600 S Deleware St 94403 040‐031‐230 Transit‐Oriented Dev elopmBMSP 0 50 1.17 vacant former race YES ‐ Planned NO ‐ Privately‐Owned Pending Project Not Used in Prior Housing Element 7 60 67 PA20‐053; SPAR app Entitlement ApproveNo constraints found SAN MATEO 341 H illsdale Blvd 94403 040‐102‐580 Y Regional/Community Co mC2‐0.5 0 50 0.33 Car wash YES ‐ Current NO ‐ Privately‐Owned Pending P roject Not Used in Prior Housing Element 23 207 230 PA20‐046; H illsdale IPre‐Application SubmNeeds consolidation, SAN MATEO 477 H illsdale Blvd 94403 040‐102‐620 Y Regional/Community Co mC2‐0.5 0 50 2.1 Hillsdale Inn hotel YES ‐ Current NO ‐ Privately‐Owne d Pending Project Not Used in Prior Housing Element SAN MATEO 94403 040‐102‐630 Y Regional/Community Co mC2‐0.5 0 50 0.62 Hillsdale Inn hotel YES ‐ Current NO ‐ Privately‐Own ed Pending Project Not Used in Prior Housing Element SAN MATEO 2988 Campus Dr 94403 041‐521‐010 Z Executive Office E1‐1 0 35 2.83 3 story Office YES ‐ Current NO ‐ Privately‐Owned Pending Proj ect Not Used in Prior Housing Element 29 261 290 PA‐2020‐012; Penin sConstruction‐in‐ProgNon‐continuous parc SAN MATEO 2800 Campus Dr 94403 041‐521‐020 Z Executive Office E1‐1 0 35 4.28 2 story Office YES ‐ Current NO ‐ Privately‐Owned Pending Proj ect Not Used in Prior Housing Element SAN MATEO 2655 Campus Dr 94403 041‐522‐010 Z Executive Office E1‐1 0 35 3.38 2 story Office YES ‐ Current NO ‐ Privately‐Owned Pending Proj ect Not Used in Prior Housing Element SAN MATEO 2755 Campus Dr 94403 041‐522‐020 Z Executive Office E1‐1 0 35 5.03 3 story Office YES ‐ Current NO ‐ Privately‐Owned Pending Proj ect Not Used in Prior Housing Element SAN MATEO 401 Concar Dr. 94402 035‐200‐998 Transit‐Oriented Develop mTOD 0 50 2.82 Hayward Park Trai nYES ‐ Potential YES ‐ County‐Owned Pending Project Not Used in Pri or Housing Element 16 12 163 191 PA21‐033; Hayward Entitlement Appro veAdjacent to the railro SAN MATEO 19 Kingston St 94404 033‐191‐040 AA High Density Multi‐Fam ily R4 0 50 0.44 parking YES ‐ Potential NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 24 10 25 59 Large parking lot, sam e ownership, larger High risk flood zone ( SAN MATEO 25 Kingston St 94404 033‐191‐060 AA High Density Multi‐Fam ily R4 0 50 0.13 1 story restaurant YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 3 Kingston St 94404 033‐191‐070 AA High Density Multi‐Fami ly R4 0 50 0.45 1 story retail YES ‐ Current NO ‐ Privately‐Owned Avail able Not Used in Prior Housing Element SAN MATEO 210 S. San Mateo Dr 94010 034‐142‐200 AB Downtown Retail C ore CBD 0 30 0.43 Retail YES ‐ Current NO ‐ Privately‐Owned Available No t Used in Prior Housing Element 14 6 15 35 Downtown, parking lot, tw o stories, old b No constraints found SAN MATEO 94010 034‐142‐220 AB Downtown Retail Core CBD 0 30 0.26 Parki ng lot YES ‐ Potential NO ‐ Privately‐Owned Available Not Used in P rior Housing Element SAN MATEO 1495 El Camino Real 94401 034‐302‐140 Executive Office/ H igh DenE2‐1/R4 0 50 0.68 1 story retail/office YES ‐ Current YES ‐ Other Pu blicly‐Owned Pending Project Not Used in Prior Housing Element 14 6 15 35 PA‐2017‐0030; pre‐ aPre‐Application SubmNo constraints found SAN MATEO 1850 NORFOLK S T 94403 035‐381‐020 AC Neighborhood Commerci aC1‐0.5/R4 0 50 0.58 Parkside Plaza sho pYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 134 55 143 332 General interst in redevelopment, old bu iAdjacent to highway SAN MATEO 1826 NORFOLK S T 94403 035‐381‐030 AC Neighborhood Commerci aC1‐0.5/R4 0 50 6.07 Parkside Plaza sho pYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element SAN MATEO 220 W 20th Ave 94403 039‐030‐400 Executive Office/ High D enE1‐1/R4 0 50 1.54 Single story office‐ AYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 31 13 33 77 Developer owned, approvals for commer cNo constraints found SAN MATEO 150 W 20th Ave 94403 039‐030‐220 Executive Office/ High D enE1‐1/R4 0 50 1.98 Single story office‐ SYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 32 13 34 79 Owners have considered mixed use with rNo constraints found SAN MATEO 2900 El Camino Real 94403 039‐353‐050 Regional/Community ComC3‐1/R4 0 50 1.08 One story commer cYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 22 9 23 54 One story building, larger than .5 acre, la rNo constraints found SAN MATEO 2850 El Camino Real 94403 039‐353‐060 Regional/Community ComC3‐1/R4 0 50 0.64 1 story commercial YES ‐ Current NO ‐ Privately‐Own ed Pending Project Not Used in Prior Housing Element 3 15 18 PA‐19‐02 1; pre‐appliPre‐Application SubmNo constraints found SAN MATEO 2838 El Camino Real 94403 039‐353‐070 Regional/Community ComC3‐1/R4 0 50 1.18 2 story retail YES ‐ Current NO ‐ Privately‐Owned Av ailable Not Used in Prior Housing Element 24 10 25 59 Larger than .5 acre, one story, old buildingNo constraints found SAN MATEO 4060 El Camino Real 94403 042‐241‐180 Regional/Community ComC3‐1/R4 0 50 1.02 One story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 21 8 22 51 Larger than .5 acre, one story, large parkinNo constraints found SAN MATEO 4107 Piccad illy Ln 94403 042‐242‐060 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.25 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 120 49 127 296 Ownership interest in specific plan redeveNon‐continuous parc SAN MATEO 11 41st Ave 94403 042‐242‐070 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.24 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 40 42nd Ave 94403 042‐242‐160 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.2 1 story retail YES ‐ Current NO ‐ Privately‐Owned A vailable Not Used in Prior Housing Element SAN MATEO 49 42nd Ave 94403 042‐243‐020 AD Neighborhood Commerci aC1‐1.5/R4 0 50 2.09 Olympic Parking Lo tYES ‐ Potential NO ‐ Privately‐Owned Available Not Used in Prior H ousing Element SAN MATEO 42nd/El Camino Real 94403 042‐244‐040 AD Neighborhood Comm erciaC1‐1.5/R4 0 50 0.13 parking section adj aYES ‐ Potential NO ‐ Privately‐Owned Available Not Used in Prior H ousing Element SAN MATEO 4242 El Camino Real 94403 042‐244‐050 AD Neighborhood Comm erciaC1‐1.5/R4 0 50 1.19 CVS YES ‐ Current NO ‐ Privately‐Owned Available No t Used in Prior Housing Element SAN MATEO 43rd Ave 94403 042‐245‐040 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.12 parking section adj aYES ‐ Potential NO ‐ Privately‐Owned Available Not Used in Prior H ousing Element SAN MATEO 61 43rd Ave 94403 042‐245‐050 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.12 One story retail wit YES ‐ Current NO ‐ Privately ‐Owned Available Not Used in Prior Housing Element SAN MATEO 55 43rd Ave 94403 042‐245‐060 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.12 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 53 43rd Ave 94403 042‐245‐070 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.12 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 45 43rd Ave 94403 042‐245‐080 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.12 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 37 43rd Ave 94403 042‐245‐090 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.12 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 25 43rd Ave 94403 042‐245‐100 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.24 2 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 17 43rd Ave 94403 042‐245‐110 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.24 2 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 4300 S El Camino Real 94403 042‐245‐120 AD Neighborhood Co mmerciaC1/R4 0 50 0.3 Existing 2‐story co mYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element SAN MATEO 85 43rd Ave 94403 042‐245‐130 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.36 Mollie Stone/CVS S YES ‐ Current NO ‐ Privately‐O wned Available Not Used in Prior Housing Element SAN MATEO 89 43rd Ave 94403 042‐263‐010 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.73 Olympic Village YES ‐ Current NO ‐ Privately‐Owne d Available Not Used in Prior Housing Element SAN MATEO 4330 Olympic Ave 94403 042‐264‐010 AD Neighborhood Commerc iaC1‐1.5/R4 0 50 1.05 Bel Mateo Bowl YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 19 8 22 49 Ownership inte rest in specific plan redeveNon‐contiguous parc SAN MATEO 4150 Piccad illy Ln 94403 042‐242‐050 AD Neighborhood Commerci aC1‐1.5/R4 0 50 1.08 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 19 8 22 49 Ownership inte rest in specific plan redeveNon‐contiguous parc SAN MATEO 20 42nd Ave 94403 042‐242‐180 AD Neighborhood Commerci aC1‐1.5/R4 0 50 0.21 Retail Commercial YES ‐ Current NO ‐ Privately‐Ow n e d A v a i l a b l e N o t U s e d i n P r i o r H o u s i n g E l e m e n t 3148 O w n e r s h i p i n t e rest in specific plan redeveNon‐contiguous parc SAN MATEO 2028 El Camino Real 94403 039‐060‐430 Regional/Community ComC3‐1/R4 0 50 0.38 The Great Entertai nYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 19 19 Large parking lot, underut ilized, two storySmaller than 0.5 acre SAN MATEO 16 Hobart Ave 94402 034‐381‐230 AE Medium Density Multi‐Fa mR3 0 35 1.48 10 unit ‐ 1 story ap t YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 18 7 19 44 One/two story, very old buildings, larger tNeeds consolidation, SAN MATEO 1102 El Camino Real 94402 034‐381‐240 AE Regional/Communit y ComC2‐1/R4 0 50 2 story retail/office YES ‐ Current NO ‐ Privately‐Owne d Available Not Used in Prior Housing Element SAN MATEO 1110 El Camino Real 94402 034‐381‐250 AE Regional/Communit y ComC2‐1/R5 0 50 2 story retail offiic e YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element SAN MATEO 1114 El Camino Real 94402 034‐381‐260 AE Regional/Communit y ComC2‐1/R6 0 50 2 story retail offiic e YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element SAN MATEO 1116 El Camino Real 94402 034‐381‐320 AE Regional/Communit y ComC2‐1/R7 0 50 2 story retail offiic e YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element SAN MATEO 1200 El Camino Real 94402 034‐382‐150 AE Regional/Communit y ComC2‐1/R8 0 50 Retail Commercial YES ‐ Current NO ‐ Privately‐Owned Ava ilable Not Used in Prior Housing Element SAN MATEO 1212 El Camino Real 94402 034‐382‐160 AE Regional/Communit y ComC2‐1/R9 0 50 Retail Commercial YES ‐ Current NO ‐ Privately‐Owned Ava ilable Not Used in Prior Housing Element SAN MATEO 901 El Camino Real 94402 034‐275‐220 Executive Office E2‐1 0 50 0.57 Medical office YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 7 3 7 17 Larger than .5 acre, one story, large parkinNo constraints found SAN MATEO 801 Woodside Way 94401 032‐122‐240 Medium Density Multi‐F amR3 0 35 0.31 warehouse YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 6 6 Keep with 18.2 density, R‐3 zoning, very uSmaller than 0.5 acre SAN MATEO 719 Woodside Way 94401 032‐122‐250 Medium Density Multi‐F amR3 0 35 0.2 warehouse YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 4 4 Keep with 18.2 density, R‐3 zoning, very uSmaller than 0.5 acre SAN MATEO 717 Woodside Way 94401 032‐122‐210 Medium Density Multi‐F amR3 0 35 0.14 auto repair YES ‐ Current NO ‐ Privately‐Owned Available N ot Used in Prior Housing Element 3 3 Keep with 18.2 density, R‐3 zoning, very uSmaller than 0.5 acre SAN MATEO 3600 S El Camino Real 94403 042‐123‐420 Regional/Communit y ComC3‐1/R4 0 50 0.5 FedEx YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 6 2 6 14 .5 acre, large parking lot, one story No constraints foun d SAN MATEO 1311 S El Camino Real 94402 034‐301‐200 Executive Office/ High DenE2‐1/R4 0 50 0.54 Bright Horizon Pre s YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 6 3 7 16 Larger than .5 acre, Housing overlay, maj oNo constraints found SAN MATEO 350 N San Mateo Dr 94401 032‐182‐120 AF Regional/Community ComC2‐1 0 35 0.63 vacant gas station YES ‐ Current NO ‐ Privately‐Owned A vailable Not Used in Prior Housing Element 8 3 8 19 Remediated former gas station, larger thaClean up needed (cle SAN MATEO 220 E Poplar Ave 94401 032‐182‐130 AF Regional/Community C omC2‐2 0 35 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 1 Baywood 94402 032‐441‐270 Executive Office/ High De nE2‐2/R5 0 50 0.5 2 story medical YES ‐ Current NO ‐ Privately‐Owned Av ailable Not Used in Prior Housing Element 6 6 12 .5 acre, large park ing lot, two stories, und No constraints found SAN MATEO 406 1st Ave 94401 034‐157‐140 Downtown Retail Core CBD‐S 0 50 0.38 1 story office YES ‐ Current NO ‐ Privately‐Owned Available N ot Used in Prior Housing Element 6 6 Downtown, next to CALTrain station, 1 st oAdjacent to railroad, SAN MATEO 600 S B St 94401 034‐194‐140 AG Neighborhood Commerci aC1‐3/R5 0 50 0.64 2‐story retail/offic e YES ‐ Current NO ‐ Privately‐Owned Pending Project Not Used in Pri or Housing Element 5 43 48 Former PA 2021‐03 6 Application SubmitteNo constraints found SAN MATEO 616 S B St 94401 034‐194‐030 AG Neighborhood Commerci aC1‐3/R5 0 50 1 story retail YES ‐ Current NO ‐ Privately‐Owned Pendi ng Project Not Used in Prior Housing Element SAN MATEO 93 Bovet Rd 94402 039‐011‐450 AH Neighborhood Commerci aC1‐2 0 35 0.68 parking lot YES ‐ Potential NO ‐ Privately‐Owned Availa ble Not Used in Prior Housing Element 13 6 15 34 Same ownership as Bo vet, parking lot, lar Adjacent to highway SAN MATEO 71‐77 Bovet Rd 94402 039‐011‐460 AH Neighborhood Commerci aC1‐2 0 35 5.97 1 story retail YES ‐ Current NO ‐ Privately‐Owned Avail able Not Used in Prior Housing Element 84 35 90 209 Ownership interes t in redevelopment; Pa Adjacent to highway SAN MATEO 71‐77 Bovet Rd 94402 039‐011‐470 AH Neighborhood Commerci aC1‐2 0 35 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 71‐77 Bovet Rd 94402 039‐011‐480 AH Neighborhood Commerci aC1‐2 0 35 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 1750 El Camino Real 94402 039‐011‐500 AH Neighborhood Comm erciaC1‐2 0 35 1 story retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 71‐77 Bovet Rd 94402 039‐011‐510 AH Neighborhood Commerci aC1‐2 0 35 parking YES ‐ Potential NO ‐ Privately‐Owned Available Not U sed in Prior Housing Element SAN MATEO 1650 Borel Pl 94402 039‐011‐400 Executive Office E1‐2 0 35 2. 51 2 story office YES ‐ Current NO ‐ Privately‐Owned Available Not U sed in Prior Housing Element 30 12 32 74 Larger than .5 acre, two st ory, large parki Adjacent to highway SAN MATEO 3880 S El Camino Real 94403 042‐165‐130 Regional/Communit y ComC3‐1/R4 0 50 1.43 1 story retail YES ‐ Current NO ‐ Privately‐Owned Av ailable Not Used in Prior Housing Element 23 9 25 57 Larger than .5 a cre, one story, large parkinNo constraints found SAN MATEO 2000 Winward Way 94404 035‐610‐030 Regional/Community Co mC2‐0.62 0 50 4.27 Residence Inn 160 hYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 24 136 160 Owner inquiries to convert to residential A djacent to highway SAN MATEO 1900 S Norfolk St 94403 035‐391‐090 Executive Office E1‐0. 5 0 35 8.18 2 story office class BYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 99 41 105 245 Owner interest in redevelopment, two st oAdjacent to highway SAN MATEO 1801 Grant St 94402 035‐243‐050 Regional/Community Co mC3‐1/R 0 50 1.17 1 story retail Video YES ‐ Current NO ‐ Privately‐O wned Available Not Used in Prior Housing Element 14 6 15 35 Larger tha n .5 acre, 1 story, old building, uNo constraints found SAN MATEO 2030 S Delaware St 94403 035‐320‐270 TOD TOD 0 50 1.03 plumbin g supply waYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 21 9 22 52 TOD Zone, larger than .5 acre, 1 story, ol dNo constraints found SAN MATEO 477 9th Ave 94402 033‐281‐130 Executive Office E2‐2 0 50 1.6 1 story office YES ‐ Current NO ‐ Privately‐Owned Pending Project No t Used in Prior Housing Element 12 108 120 PA‐2022‐047; Mixed Applic ation SubmitteNo constraints found SAN MATEO 733 N San Mateo Dr 94401 032‐151‐300 AI Regional/Community ComC3‐2 0 50 0.34 Vacant commercial YES ‐ Current NO ‐ Privately‐Owned Av ailable Not Used in Prior Housing Element 14 6 14 34 Property owner i nterested in assisted livinNo constraints found SAN MATEO 727 N San Mateo Dr 94401 032‐151‐130 AI Regional/Community ComC3‐2 0 50 0.17 Vacant commercial YES ‐ Current NO ‐ Privately‐Owned Av ailable Not Used in Prior Housing Element Property owner interested in assisted livinNo constraints found SAN MATEO 723 N San Mateo Dr 94401 032‐151‐320 AI Executive Office E2 ‐1.5 0 50 0.63 Vacant commercial YES ‐ Current NO ‐ Privately‐Owned Av ailable Not Used in Prior Housing Element Property owner interested in assisted livinNo constraints found SAN MATEO 1017 3rd Ave 94404 033‐134‐100 AJ Medium Density Multi‐Fa mR3 0 50 0.14 Vacant YES ‐ Potential NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 13 5 14 32 Half vacant, larger than .5 acre consolidatNo constraints found SAN MATEO 1015 3rd Ave 94404 033‐134‐110 AJ Medium Density Multi‐Fa mR3 0 50 0.2 Vacant site YES ‐ Potential NO ‐ Privately‐Owned Available Not Used in Prior Housing Element SAN MATEO 245 Humboldt St 94404 033‐134‐240 AJ Medium Density Multi‐ FamR3 0 50 0.3 4 unit ‐ 2 story resi dYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element SAN MATEO 480 S Ellsworth Ave 94401 034‐173‐040 AK Downtown Retail C ore CBD/R 0 50 0.11 Retail YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 2 2 Downtown, combined lot size larger than Needs consolidation , SAN MATEO 159 E 5th Ave 94402 034‐173‐050 AK Downtown Retail Core CBD /R 0 50 0.09 retail YES ‐ Current NO ‐ Privately‐Owned Available Not Us ed in Prior Housing Element 1 1 Downtown, combined lot size larger than Needs consolidation , SAN MATEO 150 E 4th Ave 94401 034‐173‐090 AK Downtown Retail Core CBD /R 0 50 0.12 Restaurant YES ‐ Current NO ‐ Privately‐Owned Available No t Used in Prior Housing Element 2 2 Downtown, combined lot size larger than Needs consolidation , SAN MATEO 155 E 5th Ave 94401 034‐173‐130 AK Downtown Retail Core CBD /R 0 50 0.18 Retail YES ‐ Current NO ‐ Privately‐Owned Available Not Us ed in Prior Housing Element 3 3 Downtown, combined lot size larger than Needs consolidation , SAN MATEO 2621 Palm Pl 94403 039‐085‐280 AL Regional/Community Co mC3‐2 0 50 1.72 Warehouse‐greenh YES ‐ Current NO ‐ Privately‐Owned Ava ilable Not Used in Prior Housing Element 35 14 37 86 Ownership intere st in redevelopment, lar Adjacent to the railw SAN MATEO 2645 El Camino Real 94403 039‐085‐290 AL Regional/Communit y ComC3‐2 0 50 0.57 Retail‐Ah Sam Flori sYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 8 3 8 19 Ownership interest in redevelopment, la r Adjacent to the railw SAN MATEO 1670 Amphlett Blvd 91201 035‐241‐240 AM Executive Office E2 ‐1 0 50 5.78 Low Rise Office/Pa rYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 117 48 124 289 Ownership interest in redevelopment, la r Adjacent to highway SAN MATEO 1700 Amphlett Blvd 91201 035‐241‐250 AM Executive Office E2 ‐1 0 50 4.07 Low Rise Office/Pa rYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 82 34 87 203 Ownership interest in redevelopment, la r Adjacent to highway SAN MATEO 1720 Amphlett Blvd 91201 035‐241‐260 AM Executive Office E2 ‐1 0 50 4.61 Low Rise Office/Pa rYES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Hou sing Element 93 38 99 230 Ownership interest in redevelopment, la r Adjacent to highway

Page 2 of 3 ends

Page 3 of 3 ends

Table B: Housing Sites Inventory Edits
Revision Date Excel Row Site Address Change Note
10/25/2022 42 2745 S El Camino Real Under optional info, added note: "Owner interested in housing redevelopment, see letter dated 10/24/22" 10/19/2022 14 145 Kingston Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 21 678 Concar Dr Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 49 200 S Fremont St Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 51 222 S Fremont St Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 52 480 E 4th Ave Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 53
3069 Kyne St (BMSP ‐
Residential Block 6) Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 62 222 E 4th Ave Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 66 034‐179‐050 Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 68 435 E 3rd Ave Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 69 312 Delaware St Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 80 5 Hayward Ave Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 81 1600 El Camino Real Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 88 303 Baldwin Ave Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 90 1863 S Norfolk St Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 98 1919 O'Farrell St Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 100 2700 El Camino Real Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 107 41 Hillsdale Blvd Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 114 040‐031‐230 Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 115 341 Hillsdale Blvd Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 118 2988 Campus Dr Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 122 035‐200‐998 Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 128 1495 El Camino Real Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 134 2850 El Camino Real Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 176 600 S B St Under optional info 2, added the progress of the pending project and how affordability was calculated 10/19/2022 190 477 9th Ave Under optional info 2, added the progress of the pending project and how affordability was calculated 11/30/2022 All Rows All Sites Under optional info 3, added environmental and developmental constraints 12/12/2022 208 ADUs Updated total ADU estimates
12/14/2022 212 4 W Santa Inez Ave Added 4 Santa Inez, under construction 12/14/2022 207 500 E 4th Ave Added Block 20 development project 12/14/2022 n/a 2200 Bridgepointe Pkwy Removed ice rink from Bridgepointe sites (see sites consolidated under V) 12/14/2022 n/a 480 N Bayshore Blvd Removed Best Western hotel 12/15/2022 175 93 Bovet Rd Updated Borel Square density from 30 du/a to 35 du/a; added ownership interest note 12/15/2022 104 41 Hillsdale Blvd
Updated Hillsdale Mall density from 50 du/a to 35 du/a, for a max realistic capacity of 1,200 units (see sites consolidated under X)
12/15/2022 n/a 19 11th Ave Removed 19 11th Ave (R1 zone)

APPENDIX D

AFFIRMATIVELY FURTHERING FAIR

HOUSING NARRATIVE

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-2
APPENDIX D | AFFIRMATIVELY FURTHERING FAIR

HOUSING NARRATIVE

0.1 Table of Content

0.1 Table of Content ............................................................................................................................ 2

0.2 List of Figures ................................................................................................................................ 3

0.3 List of Tables.................................................................................................................................. 3

0.4 List of Attachments ....................................................................................................................... 4 1 WHAT IS AFFIRMATIVELY FURTHERING FAIR HOUSING? ................................ ............................... 5 2 HISTORY OF SEGREGATION IN THE REGION ................................ ................................ ..................6 3 PRIMARY FINDINGS ................................ ................................ ................................ .................. 10

3.1 Resident Needs Collected Through Local Survey ......................................................................... 12

3.2 Contributing factors and Fair Housing Action Plan ..................................................................... 12

4. SECTION I. Fair Housing Enforcement and Outreach Capacity ................................ ..................... 16

4.1 Fair Housing Legal Cases and Inquiries ....................................................................................... 16

4.2 Outreach and Capacity ................................................................................................................ 21

4.3 Compliance with State Law ......................................................................................................... 22

4.4 Housing Specific Policies Enacted Locally .................................................................................... 22

5. SECTION II. Integration and Segregation ................................ ................................ .................... 24

5.1 Race and Ethnicity ....................................................................................................................... 24

5.2 Dissimilarity and Isolation Indices ............................................................................................... 31

5.3 Segregation Between City of San Mateo and Other Jurisdictions in the Bay .............................. 32

5.3.1 Area Region ............................................................................................................................. 32

5.3.2 Disability Status ....................................................................................................................... 32

5.3.3 Familial Status ......................................................................................................................... 35

5.3.4 Household Income .................................................................................................................. 38

5.4 Racially or Ethnically Concentrated Areas of Poverty and Affluence .......................................... 41

6. SECTION III. Access to Opportunity ................................ ................................ ............................ 50

6.1 Education .................................................................................................................................... 51

6.2 Employment ................................................................................................................................ 52

6.3 Transportation ............................................................................................................................ 52

6.4 Environment ................................................................................................................................ 53

6.5 Disparities in access to opportunity ............................................................................................ 54

6.6 Disparities specific to the population living with a disability ...................................................... 54

7. SECTION IV. Disproportionate Housing Needs ................................ ................................ ............ 57

7.1 Housing Needs ............................................................................................................................ 57

7.2 Cost Burden and Severe Cost Burden .......................................................................................... 58

7.3 Overcrowding .............................................................................................................................. 63

7.4 Substandard Housing .................................................................................................................. 65 C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-3

7.5 Homelessness .............................................................................................................................. 67

7.6 Displacement ............................................................................................................................... 67

8. Site Inventory Analysis ................................ ................................ ................................ .............. 72

8.1 Segregation and Integration ....................................................................................................... 72

8.2 Disparities in Access to Opportunity ........................................................................................... 75

8.3 Disproportionate Housing Needs ................................................................................................ 75

9. Contributing Factors and Fair Housing Action Plan ................................ ................................ ..... 78

0. 2 List of Figures
Figure 1: AFFH definition ........................................................................................................................................................ 5 Figure 2: Major Public and Legal Actions that Influence Fair Access to Housing ............................................ 8 Figure 3: Population by Race ............................................................................................................................................. 19 Figure 4: Population by Race, 2000-2019 ..................................................................................................................... 20 Figure 5: Fair Housing Complaints and Inquiries ....................................................................................................... 21 Figure 6: Local policies affecting housing issues ....................................................................................................... 23 Figure 7: Integration and Segregation ........................................................................................................................... 24 Figure 8: Racial Demographics by Block Group ......................................................................................................... 30 Figure 9: Population with a Disability ............................................................................................................................. 34 Figure 10: Familial Status ..................................................................................................................................................... 36 Figure 11: Median Household Income ........................................................................................................................... 39 Figure 12: Segregation and Integration......................................................................................................................... 40 Figure 13: R/ECAP definition .............................................................................................................................................. 41 Figure 14: R/ECAPs, 2010 .................................................................................................................................................... 43 Figure 15: R/ECAPs, 2019 .................................................................................................................................................... 44 Figure 16: White Majority Tracts ...................................................................................................................................... 46 Figure 17: RCAAs .................................................................................................................................................................... 49 Figure 18: Access to Opportunity Definition ............................................................................................................... 50 Figure 19: Disability ............................................................................................................................................................... 55 Figure 20: Access to Opportunity ..................................................................................................................................... 56 Figure 21: Disproportionate Housing Needs definition .......................................................................................... 57 Figure 22: Cost Burden by Income Level ....................................................................................................................... 58 Figure 23: Cost Burden by Race ........................................................................................................................................ 59 Figure 24: Cost Burden by Tenure ................................................................................................................................... 60 Figure 25: Cost Burdened Homeowners........................................................................................................................ 61 Figure 26: Cost Burdened Renters ................................................................................................................................... 62 Figure 27: Overcrowding ..................................................................................................................................................... 64 Figure 28: Substandard Housing ...................................................................................................................................... 66 Figure 29: Displacement Sensitive Communities ....................................................................................................... 67 Figure 30: Sensitive Communities .................................................................................................................................... 69 Figure 31: Disproportionate Housing Needs ............................................................................................................... 71

0. 3 List of Tables
Table 1: Hispanic Population, City of San Mateo, 2000, 2010, and 2019 ......................................................... 25

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-4
Table 2: Asian Population, City of San Mateo, 2000, 2010, and 2019 ................................................................ 26 Table 3: Black Population, City of San Mateo, 2000, 2010, 2019 ......................................................................... 27 Table 4: Female Headed Households, City of San Mateo, 2000, 2010, and 2019 ......................................... 37 Table 5: Race, Population, and Median Income ......................................................................................................... 47 Table 6: RCAA Census Tracts Near City of San Mateo, 2019 ................................................................................. 47 Table 7: Non-RCAAs .............................................................................................................................................................. 48 Table 8: Share of RHNA Units by Income and Share Households Earning less than 80% AMI ............... 73 Table 9: Share of RHNA Units by Income and Share of People of Color ......................................................... 73 Table 10: Share of RHNA Units by Income and Share of People with a Disability ....................................... 74 Table 11: Share of RHNA Units by Income and Share of Households with Children .................................. 74 Table 12: Share of RHNA Units by TCAC Resource Area ........................................................................................ 75 Table 13: Share of RHNA Units by Income and Share of Cost Burdened Households ............................... 76 Table 14: Share of RHNA Units by Income and Share of Overcrowded Households .................................. 76 Table 15: Share of RHNA Units by Displacement Risk ............................................................................................. 77

0. 4 List of Attachments
▪ Fair Housing Action Plan
▪ AFFH Maps and Data
▪ Access to Educational Opportunities
▪ UC Merced Segregation Report
▪ Quotes and Narrative from Outreach
▪ State Fair Housing Laws

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-5
1 WHAT IS AFFIRMATIVELY FURTHERING FAIR HOUSING?
The State of California’s 2018 Assembly Bill (AB 686) requires that all public agencies in the state affirmatively further fair housing (AFFH) beginning January 1, 2019. Public agencies receiving funding from the U.S. Department of Housing and Urban Develo pment (HUD) are also required to demonstrate their commitment to AFFH. The federal obligation stems from the fair housing component of the federal Civil Rights Act mandating federal fund recipients to take “meaningful actions” to address segregation and related barriers to fair housing choice.

AB 686 requires all public agencies to “administer programs and activities relating to housing and community development in a manner that affirmatively furthers fair housing, and take no action inconsistent with this obligation”1
AB 686 also makes changes to Housing Element Law to incorporate requirements to AFFH as part of the housing element and general plan to include an analysis of fair housing outreach and capacity, integration and segregation, access to opportunity, disparate housing needs, and current fair housing practices.

Affirmatively Furthering Fair Housing
“Affirmatively furthering fair housing” means taking meaningful actions, in addition to combating discrimination, that overcome patterns of segregation and foster inclusive communities free from barriers that restrict access to opportunity based on protected characteristics. Specifically, affirmatively furthering fair housing means taking meaningful actions that, taken together, address significant disparities in housing needs and in access to opportunity, replacing segregated living patterns with truly integrated and balanced living patterns, t ransforming racially and ethnically concentrated areas of poverty into areas of opportunity, and fostering and maintaining compliance with civil rights and fair housing laws. The duty to affirmatively further fair housing extends to all of a public agency’s activities and programs relating to housing and community development. (Gov. Code, § 8899.50, subd. (a)(1).)”
Figure 1: AFFH definition
Source: California Department of Housing and Community Development Guidance, 2021, page 14.

1 California Department of Housing and Community Development Guidance, 2021, page 9.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-6
2 HISTORY OF SEGREGATION IN THE REGION
The United States’ oldest citie s have a history of mandating segregated living patterns—and Northern California cities are no exception. ABAG, in its recent Fair Housing Equity
Assessment, attributes segregation in the Bay Area to
historically discriminatory practices —highlighting redli ning and discriminatory mortgage approvals—as well as “structural inequities” in society, and “self-segregation” (i.e., preferences to live near similar people).

Researcher Richard Rothstein’s 2017 book The Color of Law: A Forgotten History of How Our Go vernment Segregated
America chronicles how the public sector contributed to the segregation that exists today. Rothstein highlights several significant developments in the Bay Area region that played a large role in where the region’s non-White residents settled.

Pre-civil rights San Mateo County faced resistance to racial integration, yet it was reportedly less direct than in some Northern California communities, taking the form of
“blockbusting” and “steering” or intervention by public officials. These l ocal discriminatory practices were
exacerbated by actions of the Federal Housing Administration which excluded low -income neighborhoods, where the majority of people of color lived, from its mortgage loan program.

According to the San Mateo County Histori cal Association. San Mateo County’s early African Americans worked in a variety of industries, from logging, to agriculture, to restaurants and entertainment.

Expansion of jobs, particularly related to shipbuilding during and after World War II attracted m any new residents into the Peninsula, including the first sizable migration of African Americans. Enforcement of racial covenants after the war forced the migration of the county’s African Americans into neighborhoods where they were allowed to occupy hous ing—housing segregated into less desirable areas, next to highways, and concentrated in public housing and urban renewal developments.

The private sector contributed to segregation through activities that discouraged (blockbusting) or prohibited (restrictive covenants) integrated neighborhoods. In the City of San Mateo, David Bohannon, developer of the Hillsdale neighborhood following World War II, when housing was in short supply, recorded deeds that specified that only “members of the Caucasian or White race shall be permitted” to occupy sold homes —the exception being “domestics in the employ[ ment] on the premises.”2 This practice was the norm at the time, since the federal government refused to insure large- scale single-family developments throughout the country, unless they specifically prohibited non-Whites from purchasing the new single -family homes. Henry Doelger in Daly City, as well as Bohannon in San Mateo, both developed large tracts of single-family homes with racially-based restrictive covenants. City staff has uncovered restrictive covenants still extant on properties being provided rehabilitation assistance, even though such covenants have long been unenforceable. From Hillsdale Boulevard to about 20th Avenue, between Alameda de las Pulgas and El Camino Real, single -family homes in the area almost exclusively had restrictive covenants. Bohannon went on to develop many race-restricted neighborhoods in the Bay Area, became president of the National Association of Home Builders (NAHB), became national This history of segregation in the
region is important not only to
understand how residential
settlement patterns came
about—but, more importantly, to
explain differences in housing
opportunity among residents
today. In sum, not all residents
had the ability to build housing
wealth or achieve economic
opportunity. This historically
unequal playing field in part
determines why residents have
different housing needs today.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-7
president of the Urban Land Institute (ULI), and was inducted into California’s Homebuilding Foundation Hall of Fame.

The segregator effect of blockbusting activities is well-documented in East Palo Alto. In 1954, after a White family in East Palo Alto sold their home to an African American family, the then-president of the California Real Estate Association set up an office in East Palo Alto to scare White familie s into selling their homes (“for fear of declining property values”) to agents and speculators. These agents then sold these homes at over-inflated prices to African American buyers, some of whom had trouble making their payments.

Within six years, East Pa lo Alto—initially established with “whites only” neighborhoods —became 82% African American. The FHA prevented re-integration by refusing to insure mortgages held by White buyers residing in East Palo Alto.

Throughout the county, neighborhood associations and City leaders attempted to thwart integration of communities. Although some neighborhood residents supported integration, most did not, and it was not unusual for neighborhood associations to require acceptance of all new buyers. Builders with intention s to develop for all types of buyers (regardless of race) found that their development sites were rezoned by planning councils, required very large minimum lot sizes, and \or were denied public infrastructure to support their developments or charged prohibitively high amounts for infrastructure.

In addition to historical discriminatory practices that embedded segregation into living patterns throughout the Bay Area, it’s also necessary to recognize the historical impacts of colonization and genocide on Indigenous populations and how the effects of those atrocities are still being felt today. The original inhabitants of present -day San Mateo County are the Ramaytush Ohlone, who have “…lived on the San Francisco Peninsula for thousands of years and continue to live here as respectful stewards of the land.”2 However, “[d]ue to the devastating policies and practices of a succession of explorers, missionaries, settlers, and various levels of government over the centuries since European expansion, the Ramaytush Ohlone lost the vast majority of their population as well as their land.”3 The lasting influence of these policies and practices have contributed directly to the disparate housing and economic outcomes collectively experienced by Native populations today.4
The timeline of major federal Acts and court decisions related to fair housing choice and zoning and land use appeared to be on the same page as these discriminatory practices for most of the 20 th century. As shown in the timeline, exclusive zoning practices were common in the early 1900s. Courts struck down only the mo st discriminatory practices and allowed those that would be considered today to have a “disparate impact” on classes protected by the Fair Housing Act. For example, the 1926 case Village of Euclid v. Amber Realty Co. (272 U.S. 365) supported the segregation of residential, business, and industrial uses, justifying separation by characterizing apartment buildings as “mere parasite(s)” with the potential to “utterly destroy” the character and desirability of neighborhoods. At that time, multifamily apartments were the only housing options for people of color, including immigrants.

The Federal Fair Housing Act was not enacted until nearly 60 years after the first racial zoning ordinances appeared in U.S. cities. This coincided with a shift away from federal control over low-income housing toward locally-tailored approaches (block grants) and market-oriented choice (Section 8 subsidies)—the latter of which is only effective when adequate affordable rental units are available.

2 https://www.smcoe.org/for-communities/indigenous-people-of-san-mateo-county.html 3 https://www.smcoe.org/for-communities/indigenous-people-of-san-mateo-county.html 4 https://www.americanprogress.org/article/systemic-inequality-displacement-exclusion-segregation/

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T

Figure 2: Major Public and Legal Actions that Influence Fair Access to Housing

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T

Maps and data referenced in this section. Throughout this section, there are references to maps created by HCD to support the AFFH and data tables created by HCD, the Association of Bay Area Governments (ABAG), and the consultant team. Those maps and tables appear in an Attachment 2 and f ollow the organization of this section and the state guidance. The maps, in particular, are useful in demonstrating how the City of San Mateo compares with surrounding jurisdictions and the county overall in offering housing choices and access to opportunity.

Report content and organization. This Fair Housing Assessment follows the April 2021 State of California State Guidance for AFFH. The study was conducted as part of the 21 Elements process, which facilitates the completion of Housing Elements for all San Mateo County jurisdictions.

Primary Findings, Contributing Factors, and Fair Housing Action Plan (Appendix 1) identifies the primary factors contributing to fair housing challenges and the plan for taking meaningful actions to improve access to housing and economic opportunity.

Section I. Fair Housing Enforcement and Outreach Capacity reviews lawsuits/enforcement actions/complaints against the jurisdiction; compliance with state fair housing laws and regulations; and jurisdictional capacity to conduct fair housing outreach and education.

Section II. Integration and Segregation identifies areas of concentrated segregation, degrees of segregation, and the groups that experience the highest levels of segregation Section III. Access to Opportunity examines differences in access to education, transportation, economic development, and healthy environments.

Section IV. Disparate Housing Needs identifies which groups have disproportionate housing needs including displacement risk.

Attachments:
• Access to Educational Opportunities (Attachment 3)—findings from a countywide analysis of access to education and educational outcomes by protected class. • State Fair Housing Laws (Attachment 6)—summary of key State laws and regulations related to mitigating housing discrimination and expanding housing choice.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-10
3 PRIMARY FINDINGS
This section summarizes the primary findings from the Fair Housing Assessment for the City of San Mateo including the following sections: fair housing enforcement and outreach capacity, integration and segregation, access to opportunity, disparate housing needs, and co ntributing factors and the City’s fair housing action plan.
• 16% of fair housing complaints filed in San Mateo County from 2017 to 2021 (57 total) were in the City of San Mateo (9 total), which is approximately aligned with the city share of the county’s population (14%). The most common issues cited in the City were refusal to rent and discrimination in terms, conditions, privileges relating to rental. Most complaints were on the basis of disability status (6 complaints) and race (3 complaints) in the City.

Racial and ethnic minority populations are disproportionately impacted by poverty, low household incomes, overcrowding, and homelessness compared to the non -Hispanic White population in the City of San Mateo. Additionally, racial and ethnic minorities are more likely to live in moderate resources areas and be denied for a home mortgage loan.
▪ Racial and ethnic minority populations generally have higher rates of poverty (Figure II-5) and lower household incomes (Figure II -4) compared to the non -Hispanic White population in the City of San Mateo.
▪ Racial and ethnic minorities are more likely than non -Hispanic White households to experience overcrowding (Figure IV-17). Low- and moderate-income households are also more likely to be overcrowded (Figure IV-18).
▪ People who identify as American Indian or Alaskan Native, Black, White, and Hispanic are overrepresented in the homeless population compared to their share of the general population (Figure IV-22).
▪ Hispanic, Asian, and Black residents are more likely to live in moderate resource areas compared to high resour ce areas (Figure III -12). It is important to note there are no designated low resource areas in the City of San Mateo. ▪ Hispanic and American Indian or Alaska Native households have the highest denial rates for mortgage loan applications in 2018 and 2019 (Figure IV-33).

Geospatially, the northeast area of the City is disproportionately impacted by high poverty, low education opportunity, low economic opportunity, low environmental scores, high social vulnerability scores, concentrations of cost burdened households, overcrowding, and moderate resource scores. These areas are generally on either side of Highway 101 and stretch to the San Francisco Bay waterfront , encompassing the North Central and Shoreview neighborhoods. These areas have:
▪ Higher poverty rates between 10% and 20% (Figure II-28). ▪ Education opportunity scores between 0.25 and 0.5—meaning they have lower education scores compared to the rest of the City (Figure III-1). ▪ Low economic opportunity scores between zero and 0.5 (Figure III-7).

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-11
▪ Low environmental score s—which account for PM2.5, diesel PM, drinking water, pesticides, toxic release, traffic, cleanup sites, groundwater threats, hazardous waste, impaired water bodies, and solid waste sites (Figure III-9). The northeast area of the City of San Mateo has particularly poor environmental outcomes for traffic, impaired water, groundwater threats, hazardous waste, and asthma.
▪ The composite opportunity score for the City of San Mateo shows Census Tracts in the northeast area of the City fall within moderate resource areas while the rest of the City is within high or highest resource areas (Figure III-14).
▪ The Social Vulnerability Index (SVI) provided by the Centers for Disease Control and Prevention (CDC) ranks census tracts based on their ability to respond to a disaster and includes four themes of socioeconomic status, household composition, race or ethnicity, and housing and transportation . T he northeast area of the City is most vulnerable according to the SVI (Figure III-15).
▪ Concentration (60% to 80% of households) of cost burdened households (Figure IV-13). ▪ Overcrowded households are concentrated in the same areas as cost burdened households (Figure IV-19).
▪ These areas are also within Special Flood Hazard Areas (Figure IV-31) and are vulnerable to displacement (Figure IV-28).

The City of San Mateo has a slight concentration of residents with a disability with 9% of the population compared to 8% in the county (Figure III -17). Residents living with a disability in the City are more likely to be unemployed and are largely concentrated in areas around Highway 101. Finally, the aging population is putting a strain on paratransit access countywide.
▪ Unemployment is disproportionately high among residents living with a disability at 12% compared to 3% for residents without a disability in the City of San Mateo — particularly when compared to the county (Figure III-20).

Racial and ethnic minority students in the City of San Mateo—served by the San Mateo Union High School District and the San Mateo-Foster Elementary School District—experience lower educational outcomes compared to other students. Many high schoolers in the county met admission standards for a University of California (UC) or California State University (CSU) school. However, Pacific Islander, Hispanic, and Black students in the San Mateo Union district were less likely to meet the admission standards .

Although San Mateo Union High School has relatively low dropout rates —4% of students—compared to other districts in the c ounty, dropout rates among Hispanic (7%), Black (6%), and Pacific Islander students are higher (Figures have been included in the access to education Attachment 3). • Nearly half of all renter households in the City of San Mateo are cost burdened —spending more than 30% of their gross income on housing costs—and one in four are extremely cost burdened—spending more than 50% of their gross income on housing costs (Figure IV-9). There are disparities in housing cost burden in the City of San Mateo by race and ethnicity and family size (Figure IV-11 and Figure IV-12). • 15% of respondents to the resident survey conducted for this AFFH said that schools in their neighborhood were of poor quality.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-12

3.1 Resident Needs Collected Through Local Survey
A survey administered to capture residents’ needs and support the AFFH found the following housing challenges. Nearly 150 residents completed the survey:
• About 26% of residents said their house or apartment is too small for their family; ▪ 36% for racial and/or ethnic minority households;
▪ 42% for single parent households
• 14% of renters said they worry that if they request a repair they will experience rent increase or get evicted;
▪ 16% for racial and/or ethnic minority households;
▪ 21% for single parent households;
• 27% of respondents indicated they had been discriminated against when looking for housing in San Mateo County;
▪ 31% for racial and/or ethnic minority respondents;
▪ 43% for residents with a disability;
• 10% (14% for single parent households) of renters are often late on rent and 14% (20% for residents with a disability) can’t keep up with utilities.

3. 2 Contributing factors and Fair Housing Action Plan

The disparities in housing choice and access to opportunity discussed above stem from historical actions, socioeconomic factors that limit employment and income growth, the inability of the broader region to respond to housing demand, regional barriers to open housing choice, and, until recently, very limited resources to respond to needs. Specifically,
Fair housing issue: Hispanic households have disproportionate housing needs. These needs are evident in mortgage denial gaps, geographic distribution of affordable housing, cost burden, and overcrowding.

Contributing factors:
▪ Higher rates of mortgage denial rates among Hispanic households stems from decades of discrimination in housing markets and challenges building wealth through economic mobility and homeownership.
▪ Although voucher holders and affordable housing (as captured in the HCD L ocation Affordability Index) are not as highly concentrated in the City of San Mateo as in many surrounding jurisdictions, the northern portion of the City offers the most affordable homes. As such, residents living in these areas have lower incomes and higher rates of poverty. Preference may be at play as well: A recent article in Cityscape found that Hispanic homebuyers —when controlled for demographics, lo an characteristics, and C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-13
finances—are more likely to purchase homes in neighborhoods with fewer non-Hispanic White homeowners and lower economic opportunity.5
▪ Hispanic residents are more likely than others to work low wage jobs that do not support the City’s or region’s housing prices, resulting in higher rates of cost burden and overcrowding. Although, it is customary for Hispanic households to live in multigenerational settings, which may account for higher rates of perceived overcrowding, overcrowding is also an indicator of lack of access to affordable and right- sized housing.
▪ Hispanic residents are primarily concentrated in the northeastern area of the City where residents face higher poverty and cost burden as well as poor opportunity outcomes according to TCAC’s opportunity maps.

Fair housing issue: Hispanic residents and single female parent households are concentrated in census tracts with higher poverty, low economic and environmental opportunity, high cost burden, overcrowding, and flood hazards compared to the rest of the City of San Mateo.

Contributing factors:
▪ Concentration of naturally occurring affordable ownership and rental housing opportunities in the northeast areas of the City further concentrates poverty, cost burden, and overcrowding in areas with low economic and environmental outcomes. ▪ There is a relative lack of affordable housing opportunities in higher resourced areas of the City.
▪ Highway 101 creates a major barrier between the Shoreview neighborhood —where the geographic concentrations of these groups exist—and the rest of the City of San Mateo.

Fair housing issue: Persons with disabilities have higher housing needs due to challenges accessing employment and housing discrimination and are concentrated in areas with lower environme ntal and economic opportunity scores.

Contributing factors:
▪ The unemployment rate for the City of San Mateo’s residents with a disability is four times that of persons without a disability. The exact reasons for this disparity are unclear and are likely related to limited job opportunities, access to employment, and market discrimination.
▪ The undersupply of accessible housing units, particularly for renters, creates a scarcity of units for residents living with a disability.
▪ There were six complaints—out of the nine total complaints in the City—filed with HUD in the City of San Mateo from 2017 to 2020 where the issues cited included a failure to

5 Sanchez-Moyano, R. (2021). Achieving spatial equity through suburban homeownership? Neighborhood attributes of Hispanic homebuyers. Cityscape: A Journal of Policy Development and Research. Volume 23(3).

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-14
make reasonable accommodations. Landlords and property owners are required to provide reasonable accommodations to residents living with a disability upon request. ▪ There are concentrations of the population living with a disability west of Highway 101 in the North Central neighborhood. This area of the City has a concentration of low and moderate income households (more than 50% per census tract) and scores low on TCAC’s environmental and economic opportunity scores.

Fair housing issue: Persons with disabilities and persons of color are most likely to file complaints of housing discrimination due to discriminatory terms, conditions, privileges, or services and facilities and failure to make reasonable accommodations.

Contributing factors:
▪ Housing discrimination residents with disabilities and Hispanic households. ▪ Lack of understanding of reasonable a ccommodation requirements by landlords and property owners.

The prioritization of contributing factors is based on the ability of the City to make significant impacts on the concerns. They are as follows:

Fair housing issue: Hispanic households have disproportionate housing needs. These needs are evident in mortgage denial gaps, geographic distribution of affordable housing, cost burden, and overcrowding.

Contributing factors:

LOW: Higher rates of mortgage denial rates among Hispanic households stems from decades of discrimination in housing markets and challenges building wealth through economic mobility and homeownership.

LOW: Although voucher holders and affordable housing (as captured in the HCD Location Affordability Index) are not as highly concentrated in the City of San Mateo as in many surrounding jurisdictions, the northern portion of the City offers the most affordable homes. As such, residents living in these areas have lower incomes and higher rates of poverty.

MEDIUM: Hispanic residents are more likely than others to work low wage jobs that do not support the City’s or region’s housing prices, resulting in higher rates of cost burden and overcrowding. Although, it is customary for Hispanic households to live in multigenerational settings, which may account for higher rates of perceived overcrowding, overcrowding is also an indicator of lack of access to affordable and right-sized housing.

HIGH: Hispanic residents are primarily concentrated in the northeastern area of the City where residents face higher poverty and cost burden as well as poor opportunity outcomes according to TCAC’s opportunity maps.

Contributing factors:

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-15
HIGH: Concentration of naturally occurring affordable ownership and rental housing opportunities in the northeast areas of the City further concentrates poverty, cost burden, and overcrowding in areas with low economic and environmental outcomes.

HIGH: There is a relative lack of affordable housing opportunities in higher resourced areas of the City.

LOW: Highway 101 creates a major barrier between the Shoreview neighborhood —where the geographic concentrations of these groups exist—and the rest of the City of San Mateo.

Contributing factors:

LOW: The unemployment rate for the City of San Mateo’s residents with a disability is four times that of persons without a disability. The exact reasons for this disparity are unclear and are likely related to limited job opportunities, access to employment, and market discrimination.

HIGH: The undersupply of accessible housing units, particularly for renters, creates a scarcity of units for residents living with a disability.

MEDIUM: There were six complaints—out of the nine total complaints in the City—filed with HUD in the City of San Mateo from 2017 to 2020 where the issues cited included a failure to make reasonable accommodations. Landlords and property owners are required to provide reasonable accommodations to residents living with a disability upon request.

HIGH: There are concentrations of the population living with a disability west of Highway 101 in the North Central neighborhood. This area of the City has a concentration of low- and moderate -income households (more than 50% per census tract) and score s low on TCAC’s environmental and economic opportunity scores.

Contributing factors:

HIGH: Housing discrimination residents with disabilities and Hispanic households.

HIGH: Lack of understanding of reasonable accommodation requirements by landlords and pro perty owners.

The Fair Housing Action Plan (FHAP) at the end of this report details how the City of San Mateo proposes to respond to the factors contributing to the fair housing challenges identified in this analysis.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-16

4. SECTION I. FAIR HOUSING ENFORCEMENT AND OUTREACH CAPACITY This section discusses fair housing legal cases and inquiries, fair housing protections and enforcement, and outreach capacity.

4.1 Fair Housing Legal Cases and Inquiries

California fair housing law extends beyond the pr otections in the Federal Fair Housing Act (FHA). In addition to the FHA protected classes —race, color, ancestry/national origin, religion, disability, sex, and familial status—California law offers protections for age, sexual orientation, gender identity or expression, genetic information, marital status, military or veteran status, and source of income (including federal housing assistance vouchers).

The California Department of Fair Employment in Housing (DFEH) was established in 1980 and is now the largest civil rights agency in the United States . According to their website, the DFEH’s mission is, “to protect the people of California from unlawful discrimination in employment, housing and public accommodations (businesses) and from hate violence and human trafficking in accordance with the Fair Employment and Housing Act (FEHA), Unruh Civil Rights Act, Disabled Persons Act, and Ralph Civil Rights Act”.6
DFEH receives, evaluates, and investigates fair housing complaints. DFEH plays a particularly signi ficant role in investigating fair housing complaints against protected classes that are not included in federal legislation and therefore not investigated by HUD. DFEH’s website provides detailed instructions for filing a complaint, the complaint process, appealing a decision, and other frequently asked questions. 7 Fair housing complaints can also be submitted to HUD for investigation.

Additionally, San Mateo County has a number of local enforcement organizations including Project Sentinel, the Legal Aid So ciety of San Mateo County, and Community Legal Services of East Palo Alto.

These organizations receive funding from the County and participating jurisdictions to support fair housing enforcement and outreach and education in the County.

From 2017 to 2021, 57 fair housing complaints in San Mateo County were filed with the U.S. Department of Housing and Urban Development (HUD) (Figure I-2)—16% of complaints were in the City of San Mateo (9 complaints) (Figure I -3). Most complaints submitted to HUD cited disab ility status as the bias (56%) followed by race (19%), and familial status (14%). In the City of San Mateo, the most common issues cited were refusal to rent and discrimination in terms, conditions, privileges relating to rental.

Countywide, no cause dete rmination was found in 27 complaints followed by successful conciliation or settlement with 22 complaints. Fair housing inquiries in 2020 were primarily submitted to HCD from the City of San Mateo, Redwood City, Daly City, and Menlo Park (Figure I-3, Figure I-4, and Figure I-5).

Of the 146 City of San Mateo respondents to the resident survey, 95 residents have looked for housing seriously, of those, 23 (24%) indicated that a “ Landlord did not return calls and/or emails asking about a

6 https://www.dfeh.ca.gov/aboutdfeh/
7 https://www.dfeh.ca.gov/complaintprocess/
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-17
unit”, and 41 (46%) indicated they have been denied housing to rent or buy in the past 5 years. The main reason for denial (40%) was “income too low.”
Similarly, of the 28 voucher holders responding to the survey, the majority (69%) indicated that finding an affordable unit is somewhat or very difficult. Seven of them indicated this is due to “ Landlords have policies of not renting to voucher holders.” Fair housing complaints filed with HUD by San Mateo County residents have been on a declining trend since 2018, when 18 complaints were filed. In 2019, complaints dropped to 5, increased to 11 in 2020, and had reached 6 by mid-2021.

Nationally, the National Fair Housing Alliance (NFHA) reported a “negligible” decrease in the number of complaints filed between 2019 and 202 0. The primary bases for complaints nationally were nearly identical to San Mateo County’s: disability (55%) and race (17%). Familial status represented 8% of complaints nationally, whereas this basis comprised 14% of cases in the county.

NFHA identifies three significant trends in 2020 that are relevant for San Mateo County:
• First, fair lending cases referred to the Department of Justice from federal banking regulators has been declining, indicating that state and local government entities may want to play a larger role in examining fair lending barriers to homeownership.
• Second, NFHA identified a significant increase in the number of complaints of harassment —1,071 complaints in 2020 compared to 761 in 2019.
• Finally, NFHA found that 73% of all fair housin g complaints in 2020 were processed by private fair housing organizations, rather than state, local, and federal government agencies —reinforcing the need for local, active fair housing organizations and increased funding for such organizations.8 Factors that contribute to fair housing issues are grounded in the history of the City of San Mateo and the broader region. A summary of historical land use, investment practices, and resulting demographic trends are below:

Historical land use. Black residents who migrated to the City of San Mateo to escape violence in the South and as part of their military duty in the 1930s quickly found that e conomic capital did not guarantee homeownership in the City of San Mateo. Real estate agents guided transactions and excluded many Black and Asian residents from homeownership, regardless of their financial means and community standing.

In one instance, a Black family did succeed in buying a house in the city. When news spread of the purchase, the Ku Klux Klan in San Mateo placed ads in the San Mateo Times in response to encourage membership. Family and friends of the Black family guarded their house day and night watching for Ku Klux Klan members.9
The Black community was also restricted from buying developments for veterans. The San Mateo Historical Society told the story of Mr. Cullen, the city’s second Black employee in its history. He ran the San Mateo city dump and was excited to see that a deve lopment for veterans was being constructed across from it, as his son had served in the military. He went over to explore and was confronted by a sign

8 https://nationalfairhousing.org/2021/07/29/annual-fair-housing-report-shows-increase-in-housing-harassment/ 9 La Peninsula, The Journal of the San Mateo County Historical Association, vol. xliv, no. 1. https://historysmc.org/wp- content/uploads/2020/08/La-Peninsula-Migration-Spring-2016.pdf

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-18
that read “RESTRICTED.” His son could not access these homes because he was Black.10 Similar restrictive covenants expanded across neighborhoods. Coupled with out of reach, equally exclusionary government loans for single family homes, people of color were effectively excluded from land zoned for such homes across the Bay Area.

Investment practices. Low rent and public housing developments were hampered in the 20th century by a California law passed in 1950 that required a referendum be held in a city to approve these developments. In James v. Valtierra , citizens of San Mateo County and San Jose who were eligible for public housing challenged the law as they felt they had been denied housing opportunities and thus the law violated the Equal Protection Clause. The District Court agreed with the plaintiffs and said that because the referendum applied only to public housing, it violated the Fourteenth Amendment, but the Supreme Court disagreed. They said that the referendum process “ensures that all the people in the community will have a voice in the decision, which may lead to large expenditures of local governmental funds for increased public services and to lower tax revenues.”11 Therefore, low-income people could not have a say in the future in housing if they were the minority of voters. Although there are now more workarounds to develop subsidized housing, such as federal and state tax credits, it is still time consuming and expensive for developers to avoid a city-wide vote. This has stemmed investment in public and low rent housing development in the state. California voters will decide to repeal the article or not in 2024.12 The accumulation of exclusionary and discriminatory practices has led to disinvestment in neighborhoods of color. According to a study by UC Berkeley, although Black and Latino neighborhoods participate in the labor force at similar rates as segregated white areas, they earn 39% less income than white neighborhoods. Houses are valued $131,000 less , impacting wealth acquisition , generational assets , school resources, and commercial investment.

The lack of investment has caused out-migration of Black residents, particularly in the East Palo Alto and San Francisco area. East Palo Alto has lost 66% of its Black population and San Francisco has lost 43% since 1990.13 Overt racism, violence, displacement from eviction and foreclosure have forced ma ny Black families out of the city and even the state. As San Mateo County loses diversity, there are still many who cannot afford to move nor do they want to leave their communities . Ensuring housing stability is one investment that aims to restore stability and counteract the many burdens white communities have placed onto Californians of color.

Demographic trends. The City of San Mateo has demographics largely in line with San Mateo County and the Bay Area. The City has a slightly higher wh ite population compared to the County and the Bay Area (41% versus 39%). The Hispanic/ Latinx community makes up a quarter of the City, County and Bay Area population. The parity in population with the Bay Area suggests that the City of San Mateo hosts a diverse community and provides housing that attracts and retains residence from different economic and racial backgrounds. Notably, however, the Bay Ar ea has a small percentage of Black Californians at 6%. San Mateo County and San Mateo City have less than 6%. This is emblematic of California’s past housing

10 La Peninsula, The Journal of the San Mateo County Historical Association, vol. xliv, no. 1. https://historysmc.org/wp- content/uploads/2020/08/La-Peninsula-Migration-Spring-2016.pdf 11 Roots, Race, and Place: A History of Racially Exclusionary Housing in the San Francisco Bay Area. Haas Institute. 12 California voters to decide on repeal of anti-public housing measure in 2024 (msn.com) 13 California's Black exodus comes with a hidden toll for transplants (calmatters.org) C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-19
policies that discriminated against Black communities that have an enduring segregative effect seen today.

Source: ABAG HESS Tool and Root Policy Research.

The figure below shows demographic trends by race from 2000 to 2019. Since 2000, the white population in San Mateo City has gone down as the Hispanic, Asian, and multiple race populations have increased.

Native American and Black populations have remained low over time. As mentioned in the investment section, Black populat ions elsewhere in San Mateo County have drastically decreased. Given that San Mateo City has not seen an increase in the Black population, this further confirms that many Black families have been displaced entirely from San Mateo County and that San Mateo City does not offer affordable choices.
26% 30% 27%
6%
41%
39% 39%
6% 4% 5%
25% 24% 24%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%
100.0%
San Mateo San Mateo County Bay Area
Percent of Population
American Indian or Alaska Native, Non-Hispanic Asian / API, Non-Hispanic Black or African American, Non-Hispanic White, Non-Hispanic Other Race or Multiple Races, Non-Hispanic Hispanic or Latinx Figure 3: Population by Race

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-20

Source: ABAG HESS Tool and Root Policy Research.

17%
21%
26%
58%
47%
41%
6%
21%
27% 25%
0 10000
20000
30000
40000
50000
60000
2000 2010 2019
Share of Population
American Indian or Alaska Native, Non-Hispanic Asian / API, Non-Hispanic Black or Af rican American, Non-Hispanic White, Non-Hispanic Ot her R ace or Multiple Races, Non-Hispanic Hispanic or Latinx Figure 4: Population by Race, 2000-2019
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-21

Figure 5: Fair Housing Complaints and Inquiries

4.2 Outreach and Capacity
The City of San Mateo could improve the accessibility of fair housing information on their website and resources for residents experiencing housing discrimination . The City’s website provides a link to the Regional Assessment of Fair Housing—approved by HUD in November 2017—and AFFH goals specific to the City of San Mateo.14 Housing resources are also available on the City’s website but there is not specific information or resources for residents experiencing discrimination in housing or the Fair Housing Act. 15

14 https://www.cityofsanmateo.org/3764/Fair-Housing-Assessment 15 https://www.cityofsanmateo.org/2506/Other-Resources
Fair Housing Complaints and Inquiries
Fair Housing Complaints, by Basis, San Mateo County, 2017-2021 Number Percent
Disability 32 56%
Race 11 19%
Familial Status 8 14%
National Origin 3 5%
Religion 2 4%
Sex 1 2%
Total cases 57 100%
Total Inquiries by Jurisdiction, 2020
27 24
17 11
9 9
7 7
6 6
5 4
2 1
San Mateo
Redwood City
Daly City
Menlo Park
Belmont
Pacifica
East Palo Alto
Foster City
Burlingame
South San Francisco
San Bruno
San Carlos
Woodside
Half Moon Bay

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-22
The Draft Housing Element 2023 -2031 incorporates additional measures for providing access and education efforts as a specific program H 4.3.

4.3 Compliance with State Law

The City of San Mateo is – or will be -- compliant with the following state laws that promote fair and affordable housing. There is no active litigation related to fair housing against the City currently. The City has not been alleged or found in violation of the following:
• State Density Bonuses and Other Incentives Law (Gov. Code. Title 7. Division 1. Chapter 4.3 Density Bonuses and Other Incentives, amended and effective January 1, 2021)(revisions are included in program H 1.3)
• Housing Accountability Act (Gov Code Section 65589.5) requiring adoption of a Housing Element and compliance with RHNA allocations;
• No Net Loss Law (Gov Code Section 65863) requiring that adequate sites be maintained to accommodate unmet RHNA allocations, including among income levels; • Least Cost Zoning Law (Gov Code Section 65913.1);
• Excessive Subdivision Standards Law (Gov Code Section 65913.2); • Limits on Growth Controls Law (Gov Code Section 65589.5).

4.4 Housing Specific Policies Enacted Locally
The City of San Mateo identified the following local policies that contribute to the regulatory environment for affordable housing development in the City.

Local policies in place to encourage housing
development.
• Mixed Use Zoning
• Density Bonus Ordinances
• Condominium Conversion Ordinance
• Homeowner Rehabilitation program
• General Fund Allocation Incl. former
RDA “Boomerang” Funds
• Commercial Development Impact Fee
• Locally Funded Homebuyer Assistance
Programs
Local barriers to affordable housing
development.
• Height limits on multifamily
developments
• Voter initiatives that restrict multifamily
developments, rezoning for higher
density, height limits or similar
measures
• Low floor area ratios (FAR) allowed for
multifamily housing
• Excessive parking requirements
• Extensive time period/requirements to
develop multi-family properties
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-23

Local policies that are NOT in place but would
provide the best outcomes in addressing
housing shortages.
• Development and/or permit
streamlining
• Objective design standards

Local policies that are NOT in place, but have
potential Council interest for further
exploration.
• Community land trusts
• Acquisition of affordable units with
expiring subsidies
Local policies in place to mitigate or prevent
displacement of low-income households.
• Condominium conversion regulations
• Affordable housing impact/linkage fee
on new commercial development
• Inclusionary zoning
• Living wage employment ordinances
• Promoting streamlined processing of
ADUs
• Fair housing legal services
• Acquisition of unsubsidized properties
with affordable rents
• Dedicating surplus land for affordable
housing
• Ordinance on replacement units that
exceed State standards
Figure 6: Local policies affecting housing issues
According to the California Department of Housing and Community Development AFFH Data Viewer (HCD data viewer), the City of San Mateo does not have any public housing buildings. However, the City does have three census tracts with a moderate share of house holds using housing vouchers (5% to 15%) and most other areas of the City have some (5% or less) housing voucher utilization.

Compared to nearby Millbrae, Burlingame, and Hillsborough, the City of San Mateo appears accommodating to renters with housing vouchers because the City has a greater share of voucher holders compared to the surrounding communities. The presence of housing voucher users indicates available rental supply to house these residents and a lack of exclusionary behavior from landlords in the City.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-24

5. SECTION II. INTEGRATION AND SEGREGATION
This section discusses integration and segregation of the population by protected classes including race and ethnicity, disability status, familial status, and income status. The section concludes with an analysis of racially and ethnically concentrated areas of poverty and affluence.

Integration and Segregation
“Integration generally means a condition in which there is not a high concentration of persons of a particular race, color, religion, sex, familial status, national origin, or having a disability or a particular type of disability when compared to a broader geographic area.

Segregation generally means a condition in which there is a high concentration of persons of a particular race, color, religion, sex, familial status, national origin, or having a disability or a type of disability in a particular geographic area when compared to a broader geographic area.” Figure 7: Integration and Segregation
Source: California Department of Housing and Community Development Guidance, 2021, page 31.

5.1 Race and Ethnicity
Generally, the demographic characteristics of the City of San Mateo are consistent with the overall characteristics of San Mateo County. The population distribution by race and ethnicity is similar to the county with the largest proportion of the population being non-Hispanic White (41%) followed by Asian (26%), Hispanic (25%), other or multiple races (6%), and Black (2%).16 Older residents are less diverse with 67% of the population older than 65 years identifying as White compared to only 46% of t he population for children less than 18 years old.

Racial and ethnic minority populations generally have higher rates of poverty and lower household incomes compared to the non-Hispanic White population in the City of San Mateo.

Geospatially, the City of San Mateo has three White majority census tracts17 and one census tract (6062) that has a slim Hispanic majority.18
The 6062 Census tract covers the North Central neighborhood, which is located just north of Downtown, east of El Camino Real and west of Highway 101, and is identified as an equity priority neighborhood.

As shown in the table below, the most ethnically diverse Census tract in San Mateo City is 6062, where the slightly majority of residents are of Hispanic descent. This Census tract became less diverse between 2010 and 2019, however, with a decline of more than 900 residents of Hispanic descent. This was a change from trends between 2000 and 2010, when the Hispanic population increased by 400 residents.

This Census tract is also the tract with the highest proportion of low to moderate income households in the city and where the majority of renter s are cost burdened (59%) —both indicators of displacement

16 The share of the population that identifies as American Indian or Alaska Native is less than 1%. 17 Majority census tracts show the predominant racial or ethnic group by tract compared to the next most populous. 18 Redlining maps, otherwise known as Home Owners’ Loan Corporation (HOLC) maps, are not available for San Mateo County.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-25
vulnerability. This Census tr act abuts Highway 101 which separates the tract from higher income neighborhoods on the Bay. It is likely a refuge for residents seeking relatively affordable housing.

Other Census tracts show stable or increasing ethnic diversity , including those with the lowest Low to Moderate income proportions.

Since 2000, except for Tract 6062, all census tracts in San Mateo have seen modest increases in Hispanic population, including those with the lowest proportions of low and moderate income households (6073, 6078). This is a positive trend, indicative of integration and furthering access to opportunity.

Table 1: Hispanic Population, City of San Mateo, 2000, 2010, and 2019 Census
Tract
Characteristic
s of Tract
Low-
mod
%
2000
Hispanic
populatio
n and
%age
2010
Hispanic
populatio
n and
%age
#
chang
e 2000
to
2010
2019
Hispanic
populatio
n and
%age
#
chang
e
2000-
2019
#
chang
e
2010-
2019
6062 Hispanic
majority
(slight and
declining); on
the edge of
R/ECAP status
63% 4743/ 60% 5146/ 68% 403 4235/ 54% -508 -911
6063 White
majority
(slight)
46% 852/ 23% 676/ 20% -176 993 / 24% 141 317
6066 White
majority
35% 448/ 14% 717/ 22% 269 812 / 22% 364 95
6073 White
majority
19% 240/ 7% 278/ 8% 38 461 / 13% 221 183
6074 White
majority
47% 569/ 13% 641/ 15% 72 1062/ 22% 493 421
6075 More
balanced
race/ethnic
distribution
38% 485/ 22% 509/ 15% 24 1466/ 24% 981 957
6076 White
majority
(slight)
40% 585/ 16% 863/ 24% 278 862 / 24% 277 -1
6077.0
1 More
balanced
race/ethnic
distribution
41% 1103/ 31% 1465/ 36% 362 1646/ 37% 543 181

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-26
6077.0
2 More
balanced
race/ethnic
distribution;
on the edge of
R/ECAP status
45% 796/ 28% 966/ 33% 170 1114/ 34% 318 148
6078 More
balanced
race/ethnic
distribution

30.5 %
580/ 18% 566/ 22% -14 672 / 20% 92 106

The following table shows the same information for the Asian population —the second largest racial and ethnic group in the city. Between 2010 and 2019, Asian residents increased across Census tracts, with the largest increase in tract 6075—a gain of more than 1,000 Asian residents. This is a similar level of growth to Hispanic residents.

Table 2: Asian Population, City of San Mateo, 2000, 2010, and 2019 Census
Tract
Characteristic
s of Tract
Low-
mod
%
2000
Asian
populatio
n and
%age
2010 Asian
populatio
n and
%age
#
chang
e 2000
to
2010
2019 Asian
populatio
n and
%age
#
chang
e
2000-
2019
#
chang
e
2010-
2019
6062 Hispanic
majority
(slight and
declining); on
the edge of
R/ECAP status
63% 933/ 12% 1056/ 35% 123 1381/ 39% 448 325
6063 White
majority
(slight)
46% 444/ 12% 735/ 25% 291 1051/ 34% 607 315
6066 White
majority
35% 478/ 15% 624/ 21% 146 643/ 22% 165 19
6073 White
majority
19% 509/ 15% 616/ 19% 107 823/ 26% 314 207
6074 White
majority
47% 638/ 15% 917/ 26% 279 1228/ 32% 590 311
6075 More
balanced
race/ethnic
distribution
38% 259/ 12% 931/ 33% 672 2124/ 45% 1865 1193
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-27
6076 White
majority
(slight)
40% 672/ 18% 554/ 22%
-118
936/ 34% 264 382
6077.0
1 More
balanced
race/ethnic
distribution
41% 658/ 18% 803/ 32% 145 881/ 32% 223 78
6077.0
2 More
balanced
race/ethnic
distribution;
on the edge of
R/ECAP status
45% 483/ 17% 494/ 27% 11 630/ 29% 147 148
6078 More
balanced
race/ethnic
distribution

30.5 %
639/ 20% 664/ 28% 25 901/ 33% 262 106

The Black population, on t he other hand, has seen a decline in population in half of the census tracts in the City of San Mateo. Although data from 2000 is only available from census tract 6062, it shows a substantial decline in just two decades from 876 Black households to 473 in 2019. Other tracts have extremely small numbers . Some have seen modest increases while others continue to see the Black population decline.

Table 3: Black Population, City of San Mateo, 2000, 2010, 2019 Census
Tract
Characteristic
s of Tract
Low-
mod
%
2000 Black
population
/ %
2010 Black
population
/ %
#
chang
e 2000
to
2010
2019 Black
population
/ %
#
chang
e
2000-
2019
#
chang
e
2010-
2019
6062 Hispanic
majority
(slight and
declining); on
the edge of
R/ECAP status
63% 876/ 11% 552/ 7% -324 473/ 6% -403 -79
6063 White
majority
(slight)
46% . 46/ 1% . 4/ 0.001% . -42
6066 White
majority
35% . 93/ 3% . 107/ 3% . 14

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-28
6073 White
majority
19% . 29/ 0.1% . 0/ 0% . -29
6074 White
majority
47% . 86/ 2% . 50/ 1% . -36
6075 More
balanced
race/ethnic
distribution
38% . 87/ 3% . 213/ 3% . 126
6076 White
majority
(slight)
40% . 34/ 1% . 68/ 2% . 34
6077.0
1 More
balanced
race/ethnic
distribution
41% . 79/ 2% . 27/ 1% . -52
6077.0
2 More
balanced
race/ethnic
distribution;
on the edge
of R/ECAP
status
45% . 54/ 2% . 102/ 3% . 48
6078 More
balanced
race/ethnic
distribution

30.5 %
. 34/ 1% . 63/ 2% . 29

The map below shows concentration by race. Noticeably, the block groups with the largest concentration of non-white residents are east of El Camino Real. The neighborhoods east of El Camino Real include the North Central, Central, Sunnybrae, North Shoreview, Shoreview/Parkside, and San Mateo Glendale Village neighborhood areas. North Central and Central neighborhoods are two of the oldest neighbor hoods in the city and are characterized by smaller lots with a range of housing types including single-family homes, duplexes, tri-plexes, quad-plexes as well as larger multi-family buildings located next to each other. It should be noted that discriminatory real estate practices excluded non-whites from many neighborhoods in the city and provided little options for minorit y populations to live or purchase housing with the exception of these two neighborhoods. One result is that t here is higher concentration of religious institutions, ethnic markets, and multi-generational minority families in these two neighborhoods.

Many of the homes in the Sunnybrae, North Shoreview, Shoreview/Parkside, and San Mateo Glendale Village neighborhood areas were developed following World War II . The postwar housing boom saw a rapid increase in housing production to house the millions of returning war veterans. In communities across the nation, h ousing developers addressed the need through the advent of master plan developments which mass produc ed modest-size, one-story track homes on smaller lots (i.e. typically C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-29
5,000 square feet) that differed from the custom built housing on larger lots which was the norm before WWII. Multi-family apartment buildings have been built along major streets , which serve as a transition between neighborhood serving commercial uses and the single-family homes, in these neighborhoods .

While the homes were more affordable , discriminatory real estate practices that excluded non-whites continued until the Fair Housing Act of 1968 . Since the 1970’s, homes in these neighborhoods continue to remain more affordable than custom the built homes on larger lots that are located in other parts of the city.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-30

Figure 8: Racial Demographics by Block Group

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-31

5.2 Dissimilarity and Isolation Indices

The Dissimilarity Index, or DI, is a common tool that measures segregation in a community. The DI in an index that measures the degree to which two distinct groups are evenly distributed across a geographic area. The DI represents the percentage of a group’s population that would have to move for each area in the county to have the same percentage of that group as the county overall.

DI values range from 0 to 100 —where 0 is perfect integration and 100 is complete segregation.

Dissimilarity index values between 0 and 39 generally indicate low segregation, values between 40 and 54 generally indicate moderate segregation, and values betwe en 55 and 100 generally indicate a high level of segregation.

The isolation index is interpreted as the probability that a randomly drawn minority resident shares an area with a member of the same minority, it ranges from 0 to 100 and higher values of isol ation tend to indicate higher levels of segregation. The isolation index measures the segregation of a single group, and the dissimilarity index measures segregation between two different groups. The Theil’s H -Index can be used to measure segregation between all racial or income groups across the City at once.

ABAG and UC Merced completed an analysis of segregation in San Mateo. Several indices were used to assess segregation in the City and determine how the City differs from patterns of segregation and integration in the region overall. The following is the summary from the UC Merced report (Attachment 4):
• As of 2020, white residents are the most segregated compared to other racial groups in San Mateo, as measured by the isolation index. White residents live in neighborhoods where they are less likely to come into contact with other racial groups. • Among all racial groups, the white population’s isolation index value has changed the most over time, becoming less segregated from other racial groups between 2000 and 2020. • According to the dissimilarity index, the highest level of racial segregation is between Latinx and white residents within San Mateo.19
• According to the Theil’s H-Index, neighborhood racial segregation in San Mateo declined between 2010 and 2020.
• Neighborhood income segregation stayed about the same between 2010 and 2015. • Above Moderate-income residents are the most segregated compared to other income groups in San Mateo. Above Moderate-income residents live in neighborhoods where they are less likely to encounter residents of other income groups.

19 The analysis conducted for this report suggests that dissimilarity index values are unreliable for a population group if that group represents approximately less than 5% of the jurisdiction’s total population. ABAG/MTC recommends that when cities have population groups that are less than 5% of the jurisdiction’s population (see Table Error! Reference source not found. in Appendix 2), jurisdiction staff could focus on the isolation index or Thiel’s H-Index to gain a more accurate understanding of neighborhood-level racial segregation in their jurisdiction.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-32
• Among all income groups, the Very Low -income population’s segregation measure has changed the most over time, becoming more segregated from other income groups between 2010 and 2015.
• According to the dissimilarity index, segregation between lower -income residents and residents who are not lower-income has not substantively changed between 2010 and 2015. In 2015, the income segregation in San Mateo between lower -income residents and other residents was higher than the average value for Bay Area jurisdictions.

5.3 Segregation Between City of San Mateo and Other Jurisdictions in the Bay

5.3.1 Area Region
• San Mateo has a higher share of white residents than other jurisdictions in the Bay Area as a whole, a higher share of Latinx residents, a lower share of Black residents, and a lower share of Asian/Pacific Islander residents.
• Regarding income groups, San Mateo has a higher share of very low-income residents than other jurisdictions in the Bay Area as a whole, a higher share of low-income residents, a higher share of moderate-income residents, and a lower share of above moderate-income residents.

These findings illustrate the need to provide housing, especially affordable housing, throughout the community, rather than in any single area. The inventory of opportunity sites demonstrates that the City has assumed affordable housing in areas where there a re not existing concentrations of lower -income households, but rather in locations rich in service, transit, and other resources to ensure availability to these households. As such, the City does not anticipate the new housing to increase segregation withi n the City.

Further, the City anticipates that planning for approximately 3, 616 units of housing affordable to low and very low-income households, as shown in the site inventory (Appendix C), will provide housing for resident groups who are more racially and ethnically diverse than the C i t y overall due to their disproportionate needs. The City is prepared to pair the construction of new affordable housing with affirmative marketing and other programs to ensure that residents with disproportionate needs in the region benefit from the housing.

5.3.2 Disability Status
The share of the population living with at least one disability is 9% in the City of San Mateo compared to 8% in San Mateo County (Figure II-13).

Concentrated disability status. The map below shows the concentration of those with disabilities using 2019 ACS 5-year data. San Mateo City has a low concentration with less than 10 percent, however, areas nearby have concentrations of 10 to 20 percent. These areas of concentration push up against El Camino Real and Bayshore Freeway.

Over time, the number of those with disabilities has stayed relatively stagnant. In 2012, 8.4% of San Mateo City identified a disability and in 2020, 9% did. The low percentage over time of those with disabilities suggests that San Mateo City lacks affordable, accessible units to accommodate more than 9% of their population. Trends in disability are largely in line with the Bay Area and County, where, respectively, 10% and 8% of the population has a disability.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-33
As the population ages and is more likely to have a disability, there are increased concerns of displacement over time. Those who have a disability have a higher unemployment rate of 12% compared to those without a disability at 3%. Those who use fixed income due to a disability or retirement are more susceptible to displacement when housing costs rise. Populations over 65 are more likely to report having a disability in the City of San Mateo. This cohort has also gone up 13% from 92,482 in 2000 to 104,333 in 2019. The growth is a reminder that affordable and accessible housing are a growing need in the region.

There are a handful of census tracts in the City that have a 10% to 20% share of the population living with a disability (Figure II-14). Geographic concentrations of people living with a disability may indicate the area has ample access to services, amenities, and transportation that support this population. The concentration of disability largely is in line with concentration of non-white racial groups and low-to- moderate income census tracts around Bayshore freeway.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-34

Figure 9: Population with a Disability

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-35

5.3.3 Familial Status
The City of San Mateo is home to more single-person households than the county, with 28% of households compared to only 22% in the County (Figure II-16). Additionally, there are fewer married-couple families and families with children in the City (Figure II-17 and Figure II-18).

Familial status can indicate specific housing needs and preferences. A larger number of nonfamily or single person households indicates a higher share of seniors living alone, young adults living alone or with roommates, and unmarried partners. Higher shares of nonfamily households indicate an increased need for one- and two-bedroom units.

The majority of married couple households and slim majority of residents living alone live-in owner - occupied housing (Figure II-19). The number of housing units available by number of bedrooms and tenure is consistent with the familial status of the households that live in the City of San Mateo (Figure II-16 and Figure II-20). Compared to the county, the City of San Mateo has a smaller proportion of family households and greater proportion of single person households—which is reflected in the number of bedr ooms and tenure of the housing stock in the City (Figure II-19 and Figure II -20). The distribution of households by family type are mapped at the census tract level in Figures 21, 22, 23, and 24.

Female-headed households with children are more likely to be below the poverty level. In the City of San Mateo, 76 female -headed households with no children are below the poverty level compared to 309 female-headed households with children. Single-mother households are also less likely to be homeowners in the City. Concentrations of female-headed households with children and no partner are shown in the map below. Most of San Mateo has census tracts less than 20% female -headed households with the exception of four census tracts next to Bayshore Freeway.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-36

Figure 10: Familial Status
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-37
Table 4: Female Headed Households, City of San Mateo, 2000, 2010, and 2019 Census
Tract
Characteristic
s of Tract
Low-
mod
%
2000
Female-
headed
household
s
2010
Female-
headed
household
s
#
chang
e 2000
to
2010
2019
Female-
headed
household
s
#
chang
e
2000-
2019
#
chang
e
2010-
2019
6062 Hispanic
majority
(slight and
declining); on
the edge of
R/ECAP status
63% 246 304 58 557 311 253
6063 White
majority
(slight)
46% 145 72
-73
52 -93
-20
6066 White
majority
35% 126 40 -86 55 -71 -15
6073 White
majority
19% 43 60 17 0 -43
-60
6074 White
majority
47% 77 60 -17 99 22 33
6075 More
balanced
race/ethnic
distribution
38% 99 117 18 85 -14 -32
6076 White
majority
(slight)
40% 114 66 -48 69 -45 3
6077.0
1 More
balanced
race/ethnic
distribution
41% 105 111 6 49 -56 -62
6077.0
2 More
balanced
race/ethnic
distribution;
on the edge of
R/ECAP status
45% 50 0 -50 193 193 143
6078 More
balanced

30.5 %
36 58 22 70 34 12

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-38
race/ethnic
distribution

The table above details the changes in female -headed households from 2000 to 2019. Most of these households are in tract 6062, an area that, as previously shown, has the highest proportion of those with low to moderate income. 311 female-headed households have been added to the census tract from 2000 to 2019. Other tracts have seen decreases in female-headed households. This suggests that more married couples are moving to the area, who generally have higher incomes, and that unaffordability has pushed female-headed households elsewhere in the City or County.

5.3.4 Household Income
The household income distribution by percent of area median income (AMI) in the City of San Mateo is similar to the county (Figure II -25). There are several census block groups in the City that have median incomes below the 2020 state median income of $87, 100 for a family of four, but the majority of block groups have median incomes well above that (Figure II-26 and Figure II-27). Poverty rates are highest in the City of San Mateo —between 10% and 20% –in census tracts along the San Francisco Bay and Highway 101 (Figure II -28). The map below shows median household income by census tract. Blue represents income less than the state median income ($87,100), bright green represents income less than $125,000 and dark green represents more than $125,000. A majority of census tracts in the San Mateo City area have an income more than $125,000 . Incomes les s than $125,000 are along El Camino Real , Railroad Avenue, and Bayshore Freeway. Lower incomes are in similar areas as previous maps showing disability and female headed households.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-39

Figure 11: Median Household Income

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-40

Figure 12: Segregation and Integration

Segregation and Integration
Population by Protected Class
City of San Mateo San Mateo County
Race and Ethnicity
American Indian or Alaska Native, NH 0% 0%
Asian / API, NH 26% 30%
Black or African American, NH 2% 2%
White, Non-Hispanic (NH) 41% 39%
Other Race or Multiple Races, NH 6% 4%
Hispanic or Latinx 25% 24%
Disability Status
With a disability 9% 8%
Without a disability 91% 92%
Familial Status
Female-Headed Family Households 9% 10%
Male-headed Family Households 4% 5%
Married-couple Family Households 51% 55%
Other Non-Family Households 8% 8%
Single-person Households 28% 22%
Household Income
0%-30% of AMI 13% 13%
31%-50% of AMI 12% 11%
51%-80% of AMI 16% 16%
81%-100% of AMI 11% 10%
Greater than 100% of AMI 49% 49%
0%
26%
2%
41%
6%
25%
0%
30%
2%
39%
4%
24%
9%
91%
8%
92%
9%
4%
51%
8%
28%
10%
5%
55%
8%
22%
13%
12%
16%
11%
49%
13%
11%
16%
10%
49%

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-41

5.4 Racially or Ethnically Concentrated Areas of Poverty and Affluence Racially Concentrated Area of Poverty or an Ethnically Concentrated Area of Poverty (R/ECAP) and Racially Concentrated Areas of Affluence (RCAAs) represent opposing ends of the segregation spectrum from racially or ethnically segregated areas with high poverty rates to affluent , predominantly White , neighborhoods. Historically, HUD has paid parti cular attention to R/ECAPs as a focus of policy and obligations to AFFH. Recent research out of the University of Minnesota Humphrey School of Public Affairs argues for the inclusion of RCAAs to acknowledge current and past policies that created and perpet uate these areas of high opportunity and exclusion.20
It is important to note that R/ECAPs and RCAAs are not areas of focus because of racial and ethnic concentrations alone. This study recognizes that racial and ethnic clusters can be a part of fair housin g choice if they occur in a non -discriminatory market. Rather, R/ECAPs are meant to identify areas where residents may have historically faced discrimination and continue to be challenged by limited economic opportunity, and conversely, RCAAs are meant to identify areas of particular advantage and exclusion.

R/ECAPs
HCD and HUD’s definition of a Racially/Ethnically Concentrated Area of Poverty is:
• A census tract that has a non-White population of 50 percent or more (majority-minority) or, for non-urban areas, 20 percent, AND a poverty rate of 40 percent or more; OR • A census tract that has a non -white population of 50 percent or more (majority -minority) AND the poverty rate is three times the average tract poverty rate for the County, whichever is lower.

Figure 13: R/ECAP definition
Source: California Department of Housing and Community Development Guidance, 2021.

For this study, the poverty threshold used to qualify a tract as an R/ECAP was three times the average census tract poverty rate countywide —or 19.1%. In 2010 there were three census tracts that qualify as R/ECAPs (19.4% poverty rate) in the county. None of the R/ECAPs wer e located in the City of San Mateo in 2010 (Figure II-29).

In 2019 there were two census tracts that qualify as R/ECAPs (19.1% poverty rate) in the county —with one in the North County and one in the South County . Those R/ECAPs are located in Daly City and Redwood City. None were located in the City of San Mateo (Figure II-30).

No R/ECAPs were located in San Mateo City in 2010. In 2010, there were three R/ECAPs, all located in the South County in Redwood City, and East Palo Alto.

The small number of R/ECAPs reveals that poverty is either more equally distributed across neighborhoods in the City of San Mateo or that there are not enough racial or ethnic communities that can access the City. The displacement and subsequent decline of Black residents in San Mateo County is indicative of this trend, however, the rising Hispanic and Asian populations in the City of San Mateo suggests that poverty is not ethnically concentrated. The lack of R/ECAPs in the City over time is driven

20 Goetz, E. G., Damiano, A., & Williams, R. A. (2019). Racially Concentrated Areas of Affluence: A Preliminary Investigation.

Cityscape: A Journal of Policy Development and Research, 21(1), 99–124

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-42
by economic factors and lack of deeply affordable housing. Persons living in poverty encounter barriers to accessing the majority of cities within San Mateo County because of the lack of deeply subsidized housing. The county has no public housing , and Housing Choice Voucher utilization is low due to the extremely limited rental stock available to both voucher and non-voucher holders.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-43
Figure 14: R/ECAPs, 2010

Note: R/ECAPs are census tracts that have a non-white population of 50 percent or more (majority-minority) AND the poverty rate is three times the average tract poverty rate for the County (19.4% in 2010).

Source: 2010 and 2019 5-year ACS and Root Policy Research

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-44
Figure 15: R/ECAPs, 2019

Source: 2010 and 2019 5-year ACS and Root Policy Research

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-45
Racially Concentrated Areas of A ffluence. The map below shows the locations of RCAAs. RCAAs are census tracts where the white population more than 80% and median income exc eeds $125,000. 21 Analysis of RCAAs force cities to confront how concentrations of wealthy, wh ite communities were established. As discussed previously, racial covenants , discriminatory lending, and reactionary intimidation of non -white homeowners effectively excluded other rac es from acces sing neighborhoods which today are largely still majority white and affluent. The story of concentrated poverty in neighborhoods of color cannot be told without the knowledge of active exclusion from those with more political, social and economic power.

The map below shows the white-majority tracts in the City of San Mateo. Almost all tracts have a majority white population. In some areas, there is a gap of more than 50% between the white population and the second most populous race.

21 Goets, Edward, Damiano and Williams (2019). Racially Concentrated Areas of Affluence: A Preliminary Investigation. University of Minnesota.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-46

Figure 16: White Majority Tracts

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-47
The western portion of the City of San Mateo is identified as an RCAA and marks the beginning of a stretch of RCAAs all the way to the Pacific Ocean. There is noticeable overlap between the white-majority tracts and RCAAs given that its measurement includes white populations over 80%. RCAAs further distinguish these areas as both white and affluent. The table below shows median income by race and the population distribution in the City of San Mateo and San Mateo County. White households have slightly higher incomes than households overall. Black and Hispanic households have significantly less income than households overall. In the City of San Mateo, there is a $43,115 gap between Hispanic households and households overall. Th is is concerning from a housing af fordability standpoint. There must be a broad spectrum of housing prices to sustain diversity in both the City and County of San Mateo.

Table 5: Race, Population, and Median Income

Source: ACS 2019 5-year data in 2019 inflation adjusted dollars.

On the census tract level, RCAAs have less households in pove rty than non -RCAAs. The tables below compare the white population, income, and number of households below poverty. The maximum number of those under poverty level is 207 in census tracts near the City. The maximum number in non-RCAAs is 1,426 in census tract 6062 in the City of San Mateo. It is likely that those below poverty level cannot access housing in RCAA tracts and therefore congregate in the tracts with the most affordable housing.

Table 6: RCAA Census Tracts Near City of San Mateo, 2019 Census Tract White population and
%
Median Income # Households Below
Poverty Level
6058 1861/ 72% $184625 38
6064 2896/ 56% $167165 128
6065 2318/ 66% >$250000 73
6067 1524/ 62% $144479 74
6068 2013/ 57% $183173 114
6070 1850/ 52% $153125 134
6071 1889/ 61% $216037 52
6073 2111/ 58% $177927 207

Median Income Population Median Income Population
White $125,086 41% $127,921 39%
Hispanic $81,427 25% $79,761 24%
Asian $142,083 26% $141,341 27%
Black $86,818 2% $70,519 6%
Two or more races $139,937 6% $119,801 5%
All Households $124,842 100% $122,641 100%
City of San Mateo San Mateo County
Race/ Ethnicity

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-48
Table 7: Non-RCAAs
6062 1071/ 14% $81231 1426
6063 1796/ 43% $92007 294
6066 1904/ 51% $151369 223
6073 2111/ 58% $177927 207
6074 2159/ 44% $109355 466
6075 1918/ 31% $133438 386
6076 1634/ 45% $122614 201
6077.01 1089/ 24% $107625 344
6077.02 781/ 23% $128068 509

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-49

Figure 17: RCAAs

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-50

6. SECTION III. ACCESS TO OPPORTUNITY
This section discusses disparities in access to opportunity among protected classes including access to quality education, employment, transportation, and environment.

Access to Opportunity
“Access to opportunity is a concept to approximate place -based characteristics linked to critical life outcomes. Access to opportunity oftentimes means both improving the quality of life for residents of low-income communities, as well as supporting mobility and access to ‘high resource’ neighborhoods.

This encompasses education, employment, economic development, safe and decent housing, low rates of violent crime, transportation, and other opportunities, including recreation, food and healthy environment (air, water, safe neighborhood, safety from environmental hazards, social services, and cultural institutions).”
Figure 18: Access to Opportunity Definition
Source: California Department of Housing and Community Development Guidance, 2021, page 34.

Local knowledge: resident survey questions about access to opportunity. Residents were asked about several resources that would improve their living situation in the survey conducted to support this AFFH.

When asked what type of help they need to improve their housing security, top answers where:
• Help me with a down payment/purchase (35%);
• Help me with the housing search (26%); and
• Help me get a loan to buy a house (24%).

When asked what type of help they need to improve their neighborhood, top answers where:
• Better lighting (34%);
• Improve street crossings (29%); and
• Reduce crime (27%).

When asked what type of help they need to improve their health, top answers where:
• Make it easier to exercise (40%);
• More healthy food (37%); and
• Better/access to mental health care (23%).

When asked what type of help they need to improve their job situation, top answers where:
• Increase wages (46%);
• Find a job near my apartment/house (26%); and
• Help paying for college (20%).

When asked what type of help they need to improve children’s education, top answers where:
• Stop bullying/crime/drug use at school (26%);
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-51
• Make school more challenging (25%); and
• Have more activities afterschool (24%).

The California Tax Credit Allocation Committee (TCAC) in collaboration with HCD developed a series of opportunity maps that help to identify areas of the community with good or poor access to opportunity for residents. These maps were developed to align funding allocations with the goal of improving outcomes for low-income residents—particularly children.

The opportunity maps highlight areas of highest resource, high resource, moderate resource, moderate resource (rapidly changing), low resource and high segregation and poverty. TCAC provides opportunity maps for access to opportunity in quality education, employment, transportation, and environment.

Opportunity scores are presented on a scale from zero to one and the higher the number, the more positive the outcomes.

6.1 Education
TCAC’s education score is based on math proficiency, reading proficiency, hig h school graduation rates, and the student poverty rate. According to TCAC’s educational opportunity map, most Census Tracts in the City of San Mateo score between 0.5 and 0.75—opportunity scores are presented on a scale from zero to one and the higher the number, the more positive the outcomes. However, there are a handful of Census Tracts along Highway 101 and the San Francisco Bay that score between 0.25 and 0.5—meaning they have lower education scores compared to the rest of the City. This area also has higher poverty rates, lower economic opportunity scores, and a greater share of minority households compared to the rest of the City.

The attached “Access to Education” (Attachment 4) includes findings from a countywide analysis of access to education and educational outcomes by protected class. Preliminary findings from this analysis are shared below.

According to the Disparate Access to Educational Opportunities Appendix, the City of San Mateo is served by the San Mateo Union High School Di strict and the San Mateo -Foster City Elementary School District.

San Mateo Union increased enrollment by 16% from 2010 to 2020 and the elementary district enrollment increased by 1% over the same time. However, both districts lost students during the COVID pandemic.

San Mateo Union enrollment by race and ethnicity is similar to the countywide distribution. However, there is a higher proportion of Asian students in San Mateo Union (23% compared to 17% countywide), a smaller proportion of Filipino students ( 5% compared to 8% countywide) and Hispanic students (32% compared to 38% countywide).

The San Mateo-Foster City Elementary District has the second highest share of homeless students, with 2% of students experiencing homelessness. The district also has a high share of English learners compared to the countywide proportion (26% compared to 20% countywide). Overall, the elementary district is more diverse than the countywide average.

Many high schoolers in the county met admission standards for a University of California (UC) or California State University (CSU) school. Of the high school districts in San Mateo County , Sequoia Union had the highest rate of graduates who met such admission standards at 69% followed by San Mateo Union High with 68%. Pacific Islander, Hispanic, and Black students in the San Mateo Union district were less likely to meet the admission standards, with rates of 29%, 46%, and 46% respectively.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-52
Although San Mateo Union High School has relatively low dropout rates —4% of students—compared to other districts in the county, dropout rates among Hispanic (7%), Black (6%), and Pacific Islander students are higher.

6.2 Employment
The top three industries by number of jobs in the City of San Mateo include professional and managerial services, health and educational services, and arts and recreation services (Figure III-2 and Figure III-3).

The City of San Mateo has a lower job-to-household ratio when compared to the county at 1.45 and 1.59 respectively—which means there are fewer employment opportunities per household in the City of San Mateo (Figure III -4 and Figure III -5). The City also has a slightly l ower unemployment rate of 5.2% compared to the county at 5.9% (Figure III-6).

TCAC’s economic opportunity score is comprised of poverty, adult educational attainment, employment, job proximity, and median home value. The western portions of the City of Sa n Mateo, adjacent to the City of Hillsborough and Belmont, score more than 0.75 for economic opportunity, whereas tracts in the central City score between 0.5 and 0.75 (Figure III-7). Finally, the lowest economic opportunity scores in the City are within tracts along the waterfront in the northeast area of the City of San Mateo.

HUD’s job proximity index shows the City of San Mateo is in relatively close proximity to jobs (Figure III- 8). On a scale from zero to 100 where 100 is the closest proximity to jobs the majority of the City scores above 60.

6.3 Transportation
[TCAC’s transportation opportunity score and maps were not available at the time of this draft report] This section provides a summary of the transportation system that serves the City of San Mateo and the broader region including emerging trends and data relevant to transportation access in the City. The San Mateo County Transit District acts as the admini strative body for transit and transportation programs in the county including SamTrans and the Caltrain commuter rail. SamTrans provides bus services in San Mateo County, including Redi-Wheels paratransit service.

In 2018, the Metropolitan Transportation Commission (MTC), which covers the entire Bay Area, adopted a coordinated public transit and human services transportation plan. While developing the coordinated plan, the MTC conducted extensive community outreach about transportation within the area. Tha t plan—which was developed by assessing the effectiveness of how well seniors, persons with disabilities, veterans, and people with low incomes are served —was reviewed to determine gaps in services in San Mateo and the county overall. Below is a summary of comments relevant to the City of San Mateo and San Mateo County.
“San Mateo’s [Paratransit Coordinating Council] PCC and County Health System, as well as the Peninsula Family Service Agency provided feedback. The most common themes expressed had to do with pedestrian and bicycle needs at specific locations throughout the county, though some covered more general comments such as parked cars blocking sidewalk right-of-way and a desire for bike lanes to accommodate motorized scooters and wheelchairs. Transportation information, emerging mobility providers, and transit fares were other common themes.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-53
While some comments related to the use of car share, transportation network companies (TNCs), or autonomous vehicles as potential solutions, other comments called for the increased accessibility and affordability of these services in the meantime.”22 Transit improvements recommended for the City of San Mateo include:
• “More access to the College of San Mateo is needed. There is no direct service to Canada College [from the College of San Mateo] and other local colleges from the Coastside. • Many sidewalks in the county are uneven and inaccessible to individuals using mobility devices. • Some people with disabilities need personalized assistance (escort service) that is not available. • Transfers into San Mateo County [from transit services outside of the county] continue to be very difficult. SFMTA and SamTrans need a cost sharing agreement.” A partnership between the World Institute on Disability and the MTC created the research and community engagement project TRACS (Transportation Resilience, Accessibility & Climate Sustainability). The project’s overall goal is to, “stimulate connection and communication between the community of seniors and people with disabilities together with the transportation system – the agencies in the region local to the San Francisco bay, served by MTC.”23
As part of the TRACS outreach process, respondents were asked to share their compliments or good experiences with MTC transit. One respondent who had used multiple services said, “it is my sense that SamTrans is the best Bay Area transit provider in terms of overall disability accommodation.” The San Mateo County Transit District updated their Mobility Plan for Older Adults and People with Disabilities in 2018. According to the district, the county’s senior population is expected to grow more than 70% over the next 20 years and the district is experiencing unprecedented increases in paratransit ridership. The plan is targeted at developing effective mobility programs for residents with disabilities and older adults including viable alternatives to paratransit, partnerships, and leveraging funding sources.24 MTC also launched Clipper START—an 18-month pilot project— in 2020 which provides fare discounts on single transit rides for riders whose household income is no more than double the federal poverty level.25

6.4 Environment
TCAC’s opportunity areas environmental scores are based on the CalEnviroScreen 3.0 indicators, which identify areas disproportionately vulnerable to pollution sources such as ozone, PM2.5, diesel PM, , pesticides, toxic release, traffic, cl eanup sites, groundwater threats, hazardous waste, impaired water bodies, and solid waste sites.

Generally, all census tracts in the City of San Mateo score moderate to poorly on environmental outcomes. Census tracts surrounding Highway 101 and 92 have th e lowest environmental scores in the

22 https://mtc.ca.gov/sites/default/files/MTC_Coordinated_Plan.pdf 23 https://wid.org/transportation-accessibility/
24 https://www.samtrans.com/Planning/Planning_and_Research/Mobility_Plan_for_Older_Adults_and_People_with_Disabilities. html
25 https://mtc.ca.gov/planning/transportation/access-equity-mobility/clipperr-startsm

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-54
City—primarily due to traffic on the highways, groundwater threats, and impaired water bodies (Figure III-9 and Figure III -10). However, the City scores relatively high compared to other areas of San Mateo County on the California Healthy Places Index (HPI) developed by the Public Health Alliance of Southern California (PHASC) (Figure III-11).

The HPI includes 25 community characteristics in eight categories including economic, social, education, transportation, neighborhood, housing, clean environment, and healthcare. 26 The northeast area of the City of San Mateo score the lowest on the HPI (Figure III-11).

6.5 Disparities in access to opportunity
Data show that racial and ethnic minorities are more likely to live in moderate resource areas compared to non-Hispanic White residents (Figure III-12). Nearly half (47%) of the population living in high resource areas are non-Hispanic White, compared to one in three (33%) in moderate resource areas.

Conversely, Hispanic, Asian, and Black residents are more likely to live in moderate resource areas. It is important to note that the City of San Mateo does not include any census tracts that are designated as low resource areas. The share of the population with Limited English Proficiency (LEP) is 8% compared to 7% in the county (Figure III-13).

TCAC’s composite opportunity score for the City of San Mateo shows census tracts in the northeast area of the City fall within moderate resource areas while the rest of the City is within high or highest resource areas (Figure III -14). The Social Vulnerability Index (SVI) provided by the Center for Disease Control (CDC)—ranks census tracts based on their ability to resp ond to a disaster —includes four themes of socioeconomic status, household composition, race or ethnicity, and housing and transportation. Again, the northeast area of the City—encompassing the neighborhoods North Central, Shoreview, and North Shoreview—is most vulnerable according to the SVI (Figure III-15).

The City of San Mateo does not have any disadvantaged communities as defined under SB 535 as, “the top 25% scoring areas from CalEnviroScreen along with other areas with high amounts of pollution and low populations.”27 (Figure III-16)

6.6 Disparities specific to the population living with a disability Nine percent of the population in the City of San Mateo are living with at least one disability, compared to 8% in the county (Figure III -17). The most co mmon disabilities in the City are ambulatory (4.2%), independent living (3.6%), and cognitive (3.5%) (Figure III-18).

Of residents with a disability responding to the residents’ survey, 30% said that their home does not meet the needs of their household member.

26 https://healthyplacesindex.org/about/
27 https://oehha.ca.gov/calenviroscreen/sb535
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-55
Disability
“Disability types include hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty.” Figure 19: Disability
Source: California Department of Housing and Community Development Guidance, 2021, page 36.

For the population 65 and over, the share of the population with an ambulatory or independent living difficulty increases (Figure III-19). As mentioned above under access to transportation, San Mateo County is rapidly aging; therefore, this population with a disability is likely to increase.

Unemployment is disproportionately high among residents living with a disability with an unemployment rate of 12%, compared with 3% for residents without a disability in the City of San Mateo—particularly when compared to the county where the disparity is not as high. Countywide, the unemployment rate for residents with a disability is 4%, compared to 3% for residents without a disability (Figure III-20). High unemployment rates among this population points to a need for increased services and resources to connect this population with employment opportunities.

Residents living with a disability are primarily concentrated geographically along the Highway 101 corridor (Figure III-21).

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-56

Figure 20: Access to Opportunity

Access to Opportunity
Regional Access
City of San Mateo San Mateo County
J obs to Household Ratio 1.45 1.59
Unemployment Rate 5% 6%
LEP Population 8% 7%
Share of Population by Race in Resource Areas in the City of San Mateo Employment by Disability Status
0%
0%
27%
25%
3%
1%
33%
47%
5%
6%
32%
20%
Moderate Resource Area
High/Highest Resource Area
American Indian or Alaska Native, NH Asian / API, NH
Black or African American, NH White, Non-Hispanic (NH)
Other Race or Multiple Races, NH Hispanic or Latinx
97%
88%
3%
12%
No Disability
With A Disability
City of San Mateo
97%
96%
3%
4%
No Disability
With A Disability
Employed Unemployed
San Mateo County
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-57

7. SECTION IV. DISPROPORTIONATE HOUSING NEEDS
This section discusses disparate housing needs for protected classes including cost burden and severe cost burden, overcrowding, substandard housing conditions, homelessness, displacement, and other considerations.

Disproportionate Housing Needs
“Disproportionate housing needs generally refers to a condition in which there are significant disparities in the proportion of members of a protected class experiencing a category of housing need when compared to the proportion of members of any other rel evant groups, or the total population experiencing that category of housing need in the applicable geographic area. For purposes of this definition, categories of housing need are based on such factors as cost burden and severe cost burden, overcrowding, homelessness, and substandard housing conditions.” Figure 21: Disproportionate Housing Needs definition
Source: California Department of Housing and Community Development Guidance, 2021, page 39.

7.1 Housing Needs
Population growth in the City of San Mateo has generally kept up with the pace of growth countywide, except the City did not lose population during the great recession whereas the county did (Figure IV -1).

Population growth slowed again from 2019 to 2020, likely due to the emergence of the COVID -19 pandemic in the Spring of 2020.

Since 2015, the housing that has received permits to accommodate growth has largely been priced for above moderate-income households, with 1,545 units permitted for above moderate-income households compared to 94 permits for moderate income households; 59 permits for low-income households; and 126 permitted for very low-income households (Figure IV-2). The Housing Needs Data Report for the City of San Mateo indicates new construction has not kept pace with demand throughout the Bay Area, “resulting in longer commutes, increasing prices, and exacerbating issues of displacement and homelessness.” 28
The variety of housing types available in the City in 2020 are predominately single family (44%) and medium to large scale multifamily (39%). From 2010 to 2020, the multifamily inventory increased more than single family, and the City has a greater share of multifamily housing compared to other communities in the region. 29
The majority of the housing inventory in the City of San Mateo was constructed from 1940 to 1980 (Figure IV-3). As such, the City’s units are older, lack energy efficiency, could be costly to adapt for disability accessibility, and may have deferred maintenance if households cannot afford to make improvements.

Compared to San Mateo County, the City’s owner-occupied housing market has a greater share of units priced between $1 and $1.5 million—29% of units in the City fall within this price range compared to 23%

28 Housing Needs Data Report: San Mateo, ABAG/MTC Staff and Baird + Driskell Community Planning, 2021. 29 Housing Needs Data Report: San Mateo, ABAG/MTC Staff and Baird + Driskell Community Planning, 2021.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-58
in the county (Figure IV-4). Conversely, units priced above $2 million make up a smaller proportion of the City’s housing stock compared to the county with 14% and 19% respectively. According to the Zillow home value index, home prices have experienced remarkable growth in the City and county (Figure IV-5).

Rents have increased at a slower pace compared to the for-sale market—however, median rents increased more rapidly from 2017 to 2019 (Figure IV-7). Rent increases have likely been dampened by the COVID-19 pandemic. Compared to the county, the City of San Mateo has more luxury rental units—27% of units rent for more than $3,000 in the City compared to 22% in the county (Figure IV-6).

7.2 Cost Burden and Severe Cost Burden
Nearly half of all renter households in the City of San Mateo are cost burdened —spending more than 30% of their gross income on housing costs —and one in four are extremely cost burdened —spending more than 50% of their gross income on housing costs (Figure IV-9). Cost burdened households have less money to spend on other essentials like groc eries, transportation, education, healthcare, and childcare.

Extremely cost burdened households are considered at risk for homelessness.

A greater portion of households in the City of San Mateo (39%) struggle with cost burden compared to the county (37%) ( Figure IV-8). Lower income households are more likely to experience housing cost burden. Nearly three out of every four households earning less than 30% AMI—considered extremely low- income households—are severely cost burdened, compared to only 1% of households earning more than 100% of AMI Even in the second highest income category, almost one -third of households are cost burdened.

Figure 22: Cost Burden by Income Level

Source: ABAG HESS Tool and Root Policy Research.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-59
There are disparities in housing cost burden in the City of San Mateo by race and ethnicity and family size. Black or African American (59%) and Hispanic households (55%) experience the highest rates of cost burden in the City. Non-Hispanic households of other races (28% cost burdened), Asian households (33%), and non-Hispanic White households (34%) experience the lowest cost burden (Figure IV-11).

Figure 23: Cost Burden by Race

Source: ABAG HESS Tool and Root Policy Research.

Large family households—considered households with five or more persons—experience cost burden at a rate of 46% compared to all other households at 37% (Figure IV -12). Cost burdened households are primarily concentrated along the waterfront and Highway 101 (Figure IV-13 and Figure IV-14).

Renter occupied households are more likely to be cost burdened compared with owner occupiers. 48% of renters are cost burdened compared with 31% of owner occupiers. Owners ten d to have more income, and as previously established, those in high AMI categories pay less housing costs as a proportion of income. As revealed in discussion regarding RCAAs, Hispanic and Black households have substantially less income than white households and therefore are more likely to be cost-burdened.

100%
67%
41%
66%
72%
45%
20%
38%
19%
14%
26%
12%
21% 15% 13%
29%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
American Indian or
Alaska Native, Non-
Hispanic
Asian / API, Non-
Hispanic
Black or African
American, Non-
Hispanic
White, Non-Hispanic Other Race or
Multiple Races, Non-
Hispanic
Hispanic or Latinx
Percent of Households
0%-30% of Income Used for Housing 30%-50% of Income Used for Housing 50%+ of Income Used for Housing Cost Burden Not computed

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-60
Figure 24: Cost Burden by Tenure

Source: ABAG HESS Tool and Root Policy Research.

Cost burden is concentrated geographically in the City of San Mateo. Census tracts with higher rates of cost burden also align with previous maps that show concentration of disability and female -household head status. Cost burden for renters is more severe, with more census tracts showing 60 -80% renters in the Northeast tracts facing housing cost burden. There are no tracts showing 60-80% of homeowners are rent burdened.
69%
50%
19%
23%
12%
25%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Owner Occupied Renter Occupied
Percent of Households
0%-30% of Income Used for Housing 30%-50% of Income Used for Housing 50%+ of Income Used for Housing Not Computed
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-61

Figure 25: Cost Burdened Homeowners

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-62

Figure 26: Cost Burdened Renters

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-63

7.3 Overcrowding
The vast majority of households (93%) in the City of San Mateo are not overcrowded—indicated by more than one occupant per room (Figure IV -15). However, renter households are more likely to be overcrowded with 13% of households with more than one occupant per room compared to 2% of owner households (Figure IV-16).

The resident survey shows higher needs: 26% of respondents said that their house or apartment isn’t big enough for their family members.

Racial and ethnic minorities are more likely than non -Hispanic White households to experience overcrowding. Other races (27% of households), Hispanic households (26%), and American Indian or Alaskan Native households (12%) experience the highest rates of overcrowding (Figure IV-17). Low- and moderate-income households are also more likely to be overcrowded (Figure IV-18).

Geographically, overcrowded households are concentrated in the same areas as cost burdened households, along the waterfront and Highway 1 01 (Figure IV -19). This could indicate that people are attempting to save on housing by splitting costs with more roommates or family members.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-64

Figure 27: Overcrowding

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-65

7.4 Substandard Housing
Data on housing condition are very limited, with the most consistent data available across jurisdictions found in the American Community Survey (ACS) —which captures units in substandard condition as self - reported in Census surveys. In the City of San Mateo , renter households are also more likely to have substandard kitchen and plumbing facilities compared to owner households. Generally, a low share of households are lacking kitchen or plumbing. For renters, 1.3% are lacking kitchen facilities while less than one percent are lacking plumbing. For owners, less than one percent are lacking either kitchen or plumbing facilities (Figure IV-20).

In the City, 20 to 40% of all households that suffer from severe overcrowding, severe cost burden, or an incomplete kitchen or plumbing. Any of the four severe housing problems place households at risk of displacement and adverse physical and mental health. Regionally, the City of San Mateo is surrounded by municipalities with less than 20% of households with substandard housing issues. It is plausible that the high Hispanic population relative to other surrounding census tracts is behind the concentration. This population is more likely to rent and live in multigenerational homes . Both can be factors that indicate substandard and overcrowding conditions. While some tracts in the City of San Mateo likely offers refuge for low -income renters who cannot afford other areas of the County, it is important to note that substandard housing conditions place residents at risk of displacement from the City, as discussed in the following section.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-66

Figure 28: Substandard Housing

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-67

7.5 Homelessness
In 2019, 1,512 people were experiencing homelessness in the county (74 people in the City of San Mateo) during the One-Day Count, with 40% of people in emergency or transitional shelter while the remaining 60% were unsheltered. The majority of unsheltered people experiencing homelessness were in households without children. The majority of people in transitional housing were in households with children (Figure IV-21).

People who identify as American Indian or Alaskan Native (6% of the homeless population c ompared to less than 1% of the total population), Black (13%, 2%), White (67%, 51%), and Hispanic (38%, 28%) are overrepresented in the homeless population compared to their share of the general population (Figure IV -22 and Figure IV -23). People struggling with chronic substance abuse (112 people), severe mental illness (305), and domestic violence (127) represented a substantial share of the homeless population in 2019 (Figure IV-24).

7.6 Displacement
Owner households generally experience a greater amount of housing stability whereas renter households are more mobile (i.e., move more frequently). Households in the City were more likely to have moved in the past year compared to the households in the county (14% compared to 12% in the county) (Figure IV- 25 and Figure IV-26).

In the City of San Mateo 10% of income assisted rental units are at high or very high risk for displacement, a total of 72 out of 702 total units in the City. In San Mateo County, 417 units are at risk— 8% of the total assisted housing units in the county (Figure IV-27).

Displacement Sensitive Communities
“According to the Urban Displacement Project, communities were designated sensitive if they met the following criteria:
• They currently have populations vulnerable to displacement in the event of increased redevelopment and drastic shifts in housing cost. Vulnerability is defined as:
▪ Share of very low-income residents is above 20%, 2017 ▪ AND
▪ The tract meets two of the following criteria:
− Share of renters is above 40%, 2017
− Share of people of color is above 50%, 2017
− Share of very low -income households (50% AMI or below) that are severely rent burdened households is above the county median, 2017
− They or areas in close proximity have been experiencing displacement pressures.

Displacement pressure is defined as:
• Percent change in rent above county median for rent increases, 2012 -2017 OR
• Difference between tract median rent and median rent for surrounding tracts above median for all tra cts in county (rent gap), 2017”
Figure 29: Displacement Sensitive Communities
Source: https://www.sensitivecommunities.org/.

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-68
The resident survey conducted for this study found that 31% of residents in the City of San Mateo have been displaced in the past 5 years. The top reason for displacement was “Rent increased more than I could pay” (42%).Sixteen census tracts in the City are currently vulnerable to displacement—these same Tracts have high shares of renter households (Figure IV-28). The map below shows the concentration of sensitive communities. To the west o f the City of San Mateo, there are few communities at risk of displacement.

This does not indicate that there are adequate resources for those of all income groups, but rather that these communities are not obtainable to those with less resources.

Analysis of the factors contributing to displacement. As the resident survey indicated, wages hav e not kept pace with housing costs in the City. The COVID-19 pandemic highlighted how quickly employment and income status can change ; those who are not able to save because of housing cost burden are far more likely to be involuntarily displaced from their homes. Those who live in substandard housing also likely live in properties with l ow appraised value. This puts them at risk of displacement as developers look for cheap land to build profitable, expensive housing.

Areas of the City with the highest cost burden and overcrowding—along the waterfront—are included in the Special Flood Hazard Areas determined by the Federal Emergency Management Agency (FEMA) as having a 1% chance of flooding annually (Figure IV-29, IV -30, and IV -31). 30 As such, displacement caused by flooding is a very unlikely event. Increased rent is a much more significant factor.

Instead, future displacement is more likely a factor of rents rising faster than incomes and a very limited supply of affordable units. Developing additional affordable and income -targeted units in displacement sensitive areas is the surest method of mitigating continued displacement pressure.

30The analysis in this section is based on displacement data provided by ABAG, which includes only current and future displacement risk, rather than a historical analysis of changes in these populations by Census tract. Therefore, it is presumed that there has been displacement of lower income and minority populations over time in the past. For example, according to Census data, one particular tract in North Central was 18% African American in 1990, but by 2017 that tract’s African American population was reduced to just 4%.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-69

Figure 30: Sensitive Communities

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-70
Access to mortgage loans. Disparities by race and ethnicity are also prevalent for home mortgage applications, particularly in denial rates (Figure IV-32). Hispanic (32% denial rate) and American Indian or Alaska Native households (27%) had the highest denial rates for mortgage loan applications in 2018 and 2019. Conversely, non-Hispanic Asian (17%), Black (18%), and White households (19%) have the lowest denial rates during the same time (Figure IV-33).

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-71
Figure 31: Disproportionate Housing Needs
Disproportionate Housing Needs
Cost Burden, City of San Mateo, 2019
Area Median Income (AMI)
Overcrowding, City of San Mateo, 2019
Occupants per Room by Tenure
Substandard Housing, City of San Mateo, 2019
Incomplete Kitchen and Plumbing Facilities by Tenure
Homelessness, City of San Mateo, 2019
Race and Ethnicity
Share of Homeless
Population
Share of Overall
Population
American Indian or Alaska Native 6% 0%
Asian / API 6% 30%
Black or African American 13% 2%
White 67% 51%
Other Race or Multiple Races 8% 17%
Displacement, 2020
Assisted Units at High or Very
High Risk of Displacement City of San Mateo San Mateo County Number of Units 72 417
% of Assisted Units 10% 8%
14%
23%
44%
65%
89%
13%
39%
39%
32%
10%
73%
37%
18%
3%
1%
0%-30% of AMI
31%-50% of AMI
51%-80% of AMI
81%-100% of AMI
100%+ of AMI
0%-30% of Income Used for Housing 30%-50% of Income Used for Housing 50%+ of Income Used for Housing

0.4%

0.3%

1.3%

0.4%
Kitchen
Plumbing
Owner Renter

7.5%

5.5%

1.5%

0.5%

1.0 to 1.5 Occupants per Room
More than 1.5 Occupants per Room
Owner Renter Series3

1.5+ Occupants
per Room
1-1.5 Occupants
per Room

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-72

8. SITE INVENTORY ANALYSIS
AB 686 requires an analysis of sites identified to meet RHNA obligations for their ability to affirmatively further fair housing.

This supplement provides a summary of the data available through ABAG’s HESS mapping tool for evaluating the fair housing impacts of the RHNA sites chosen.

8.1 Segregation and Integration
This section summarizes the distribution of RHNA units in the City of San Mateo by income target in relation to four factors of segregation including household income, people of color, households with a disability, and households with children. The following figures show the share of units by income within areas that have a concentration of household types compared to the Citywide rate.

1) Table 1 shows how many units are allocated to areas of the City (census tracts) with a share of Low-Moderate Income (LMI) households (earning less than 80% AMI) greater than or less than the Citywide rate of 41% of households. Generally, proposed units are split between areas with a greater than average share of LMI households with 45% of units compared to areas with a lower than average share with 47% of proposed units. The equal share of units across above and below LMI areas does not exacerbate concentrations of low income units in low income areas.

2) Table 2 shows how many units are estimated in areas of the City with a percent of the population that identified as a Person of Color (non -White population) greater than and less than the Citywide share of 59% of the population. Again, units are roughly split between 48% of units in areas with a concentration of People of Color and 44% of proposed units in areas with a lower share of People of Color. Units are equally distributed amon g areas where people of color are located which should mitigate exacerbating concentrations. Low income units are more likely to be located in areas with a greater than citywide rate of people of color, although the difference is low. Although the types of households who will occupy units is speculative, the City will work with developers to embrace best practices in affirmative marketing to facilitate occupancy in locations that are preferred by people of color including areas with established historical cultural histories and affinities.

3) Table 3 shows the share of the proposed units that are located in areas with a concentration of population with a disability compared to the Citywide rate of 9% of the population living with a disability. Most units (71%) are located in areas of the City with a concentration of residents living with a disability.

4) Table 4 shows how many units are allocated to areas of the City with a greater share of households with children compared to the Citywide rate of 30% of households. Most units (61%) are not within areas with a concentration of households that have children. Only 31% of proposed units are located in areas with a concentration of families with children.

C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-73
Table 8: Share of RHNA Units by Income and Share Households Earning less than 80% AMI31 Note:
41% of households in the
City of San Mateo earn less
than 80% AMI.

Source:

ABAG HESS tool and Root
Policy Research.

Table 9: Share of RHNA Units by Income and Share of People of Color Note:
59% of the population in
the City of San Mateo is a
Person of Color.

Source:

ABAG HESS tool and Root
Policy Research.

31Units with “no data” are not within what ABAG/HESS defined as the City of San Mateo. There is no data in the ABAG/HESS tool for these sites.

Total 4,718 4,963 790
Very Low Income Units 1,096 894 122
Low Income Units 791 547 100
Moderate Income Units 641 556 82
Above Moderate Income Units 2,190 2,966 486
Total 45% 47% 8%
Very Low Income Units 52% 42% 6%
Low Income Units 55% 38% 7%
Moderate Income Units 50% 43% 6%
Above Moderate Income Units 39% 53% 9%
% LMI Households
Greater than
Citywide rate
Less than
Citywide rate No data
Total 5,034 4,647 790
Very Low Income Units 1,089 901 122
Low Income Units 828 510 100
Moderate Income Units 649 548 82
Above Moderate Income Units 2,468 2,688 486
Total 48% 44% 8%
Very Low Income Units 52% 43% 6%
Low Income Units 58% 35% 7%
Moderate Income Units 51% 43% 6%
Above Moderate Income Units 44% 48% 9%
% People of Color
Greater than
Citywide rate
Less than
Citywide rate No data

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-74
Table 10: Share of RHNA Units by Income and Share of People with a Disability Note:
9% of the population in the
City of San Mateo has a
disability.

Source:

ABAG HESS tool and Root
Policy Research.

Table 11: Share of RHNA Units by Income and Share of Households with Children Note:
30% of households in the
City of San Mateo have
child(ren).

Source:

ABAG HESS tool and Root
Policy Research.

Racially and Ethnically Concentrated Areas of Poverty and Affluence. None of the proposed units are within an R/ECAP or RCAA.

Total 7,486 2,195 790
Very Low Income Units 1,516 474 122
Low Income Units 1,054 284 100
Moderate Income Units 895 302 82
Above Moderate Income Units 4,021 1,135 486
Total 71% 21% 8%
Very Low Income Units 72% 22% 6%
Low Income Units 73% 20% 7%
Moderate Income Units 70% 24% 6%
Above Moderate Income Units 71% 20% 9%
% People with a Disability
Greater than
Citywide rate
Less than
Citywide rate No data
Total 3,298 6,383 790
Very Low Income Units 523 1,467 122
Low Income Units 296 1,042 100
Moderate Income Units 295 902 82
Above Moderate Income Units 2,184 2,972 486
Total 31% 61% 8%
Very Low Income Units 25% 69% 6%
Low Income Units 21% 72% 7%
Moderate Income Units 23% 71% 6%
Above Moderate Income Units 39% 53% 9%
% Households with Children
Greater than
Citywide rate
Less than
Citywide rate No data
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-75

8.2 Disparities in Access to Opportunity
This section summarizes the distribution of RHNA units in the City of San Mateo by income target by TCAC defined resource areas.

5) Table 5 shows the proposed units by TCAC resource areas including moderate, high, and highest resource areas in the City of San Mateo. The vast majority of units (87%) are in moderate resources areas compared to high (5%) or highest (<1%) resource areas. There are no low resource areas in the City of San Mateo.

Table 12: Share of RHNA Units by TCAC Resource Area

Source: ABAG HESS Tool and Root Policy Research.

8.3 Disproportionate Housing Needs
This section summarizes the distribution of RHNA units in the City of San Mateo by income target based on three indicators of disproportionate housing needs including housing cost burden, overcrowding, and displacement risk.

6) Figure 6 shows the estimated share of units in areas of the City with a higher rate of cost burden among households compared to the Citywide rate of 39%. Almost all of the units (90%) are proposed in areas of the City with a lower than average rate of housing cost burden.

7) Figure 7 shows the proposed share of units in areas of the City with a higher or lower rate of overcrowding compared to the Citywide rate of 7%. Again, almost all of the proposed units (92%) are in areas that have lower than average rates of overcrowding.

8) Figure 8 shows the estimated share of units by displacement risk. Most units (60%) are within areas that are at risk of becoming exclusive or already exclusive. The remaining units (32%) are in Total 9,106 531 44 790
Very Low Income Units 1,890 89 11 122
Low Income Units 1,254 77 7 100
Moderate Income Units 1,127 63 7 82
Above Moderate Income Units 4,835 302 19 486
Total 87% 5% 0% 8%
Very Low Income Units 89% 4% 1% 6%
Low Income Units 87% 5% 0% 7%
Moderate Income Units 88% 5% 1% 6%
Above Moderate Income Units 86% 5% 0% 9%
TCAC Resource Areas
Moderate
Resource
Highest
Resource No data
High
Resource

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-76
moderate or mixed stable neighborhoods an d less than 1% are in stable or advanced exclusive neighborhoods.

Table 13: Share of RHNA Units by Income and Share of Cost Burdened Households Note:
39% of households in the
City of San Mateo are cost
burdened.

Source:

ABAG HESS tool and Root
Policy Research.

Table 14: Share of RHNA Units by Income and Share of Overcrowded Households Note:
7% of households in the
City of San Mateo are
overcrowded.

Source:

ABAG HESS tool and Root
Policy Research.

Total 260 9,421 790
Very Low Income Units 46 1,944 122
Low Income Units 33 1,305 100
Moderate Income Units 34 1,163 82
Above Moderate Income Units 147 5,009 486
Total 2% 90% 8%
Very Low Income Units 2% 92% 6%
Low Income Units 2% 91% 7%
Moderate Income Units 3% 91% 6%
Above Moderate Income Units 3% 89% 9%
% Households Cost Burdened
Greater than
Citywide rate
Less than
Citywide rate No data
Total 26 9,655 790
Very Low Income Units 7 1,983 122
Low Income Units 4 1,334 100
Moderate Income Units 4 1,193 82
Above Moderate Income Units 11 5,145 486
Total 0% 92% 8%
Very Low Income Units 0% 94% 6%
Low Income Units 0% 93% 7%
Moderate Income Units 0% 93% 6%
Above Moderate Income Units 0% 91% 9%
% Households Overcrowded
Greater than
Citywide rate
Less than
Citywide rate No data
C I T Y O F S A N M A T E O 2 0 31 H O U S I N G E L E M E N T Page H-D-77
Table 15: Share of RHNA Units by Displacement Risk

Source: ABAG HESS Tool and Root Policy Research.

Total 4,383 1,881 3,373 44 790
Very Low Income Units 990 229 760 11 122
Low Income Units 756 106 469 7 100
Moderate Income Units 578 108 504 7 82
Above Moderate Income Units 2,059 1,438 1,640 19 486
Total 42% 18% 32% 0% 8%
Very Low Income Units 47% 11% 36% 1% 6%
Low Income Units 53% 7% 33% 0% 7%
Moderate Income Units 45% 8% 39% 1% 6%
Above Moderate Income Units 36% 25% 29% 0% 9%
Displacement Risk
At Risk of
Becoming
Exclusive
Becoming
Exclusive
Stable/
Advanced
Exclusive No data
Stable
Moderate/
Mixed
Income

C I T Y O F S A N M A T E O
20 4 0 G E N E R A L P L A N

H O U S I N G E L E M E N T
Page H-D-78

9. CONTRIBUTING FACTORS AND FAIR HOUSING ACTION PLAN
Based on the research and analysis above, Attachment 1 – Fair Housing Action Plan contains the specific actions the City will take to address AFFH concerns throughout the community.

APPENDIX D | Attachment 1 – Fair Housing Action Plan
Actions Fair Housing Issues Contributing Factors Fair Housing Cate gory Action Type of Action Responsible Party Objectives Quantified O bjectives Timeline Action 1.1: Adjust the city's Below Market Rate
(inclusionary) program to provide larger density bonuses, and/or increased city support in exchange for affordable units that address the needs of residents with
disproportionate housing needs (e.g., accessible/visitable units for persons with disabilities, child‐friendly
developments with day care on site for single parents,
and 3‐4 bedroom units for larger families).

Hispanic and single female parent
households are concentrated in low
opportunity census tracts.

Lack of affordable housing in high
opportunity areas; Lack of accessible
affordable units
Disparities in access to
opportunities
Assist in development of
housing for low income
households and households
with special needs
Land use resources City of San Mateo Expand the variety of housin g units produced under the inclusionary housing
program. Currently developments of 11 or
more units require 15% affordable to
moderate income families for ownership
and 15% for low income families for renters.

Perform a feasibility analysis to redesign the
program to allow a menu of options. Ensure
analysis includes review housing for households
with disproportionate housing needs, along with
income levels. This item is connected to Policy H

1.3, Policy H 1.5., Policy H 1.14, and Policy H 1.15.

Complete feasibility analysis
by Fall 2023; Implement
redesigned program by Spring
2024.

Action 1.2: Participate in a regional downpayment
assistance program with affirmative marketing to
households with disproportionate housing needs
including persons with disab ilities, single parents, and Hispanic households (e.g., Spanish and English, targeted to northeast neighborhoods).

Hispanic households have disproportionate
housing needs.

Historic discrimination and
continued mortgage denials;
Concentration in low opportunity
census tracts; High housing costs and
low wages
Disparities in access to
opportunities
Promote equal housing
opportunity
Financial resources Regional Partnership with
HEART (San Mateo County has
program with them)
Improve accessibility to home mortgage
loans for Hispanic households who have the
highest loan denial rates. Provide wealth
building through homeownership for
moderate income households.

Affirmatively market down payment assistance to
20 Hispanic households; Provide down payment
assistance to 30 total households; Provide
homebuyer education to 200 households. In
addition, the City will work with other jurisdictions
to conduct outreach and education. Ensure that
programs target/affirmatively market to households
in impacted neighborhoods, including North Central
and North Shoreview, among others.

This item is connected to Policy H 4.4.

Meet quantified objectives by
the end of the Housing
Element period in 2031;
Conduct homebuyer/outreach
and education quarterly in
partnership with HEART
Action 1.3: Support the design a regional forgivable loan program for homeowners to construct an ADU that is
held affordable for extremely low income households for 15 years.

Hispanic and single female parent
households are concentrated in low
opportunity census tracts.

Lack of affordable housing in high
opportunity areas; Lack of accessible
affordable units
Disparities in access to
opportunities
Incentivize accessory dwelling
units (ADUs)
Land use resources 21 Elements/HEART Increase opportunities for l ower‐income households to find housing that is
affordable.

Design a regional loan forgiveness program. Ensure
that programs target/affirmatively market to
households in impacted neighborhoods, including
North Central and North Shoreview, among others.

Target those with disproportionate housing needs,
with a goal to reach 5 households annually.

This item is connected to Policy H 1.4.

Begin design in Summer 2025
and complete by winter 2026.

Action 2.1: Add more city supported housing with
affordability restrictions in moderate and high resource areas. Affirmatively market the housing to households
with disproportionate housing needs including persons
with disabilities, single parents, and Hispanic households (e.g., Spanish and English, targeted to northeast
neighborhoods).

Hispanic and single female parent
households are concentrated in low
opportunity census tracts.

Lack of affordable housing in high
opportunity areas; Lack of accessible
affordable units; Concentration of
NOAH in low opportunity census
tracts.

Disproportionate housing
need for low income
households and protected
classes
Assist in development of
housing for low income
households and households
with special needs
Financial resources City of San Mateo Affirmatively market the ho using to households with disproportionate housing
needs including persons with disabilities,
single parents, and Hispanic households
(e.g., Spanish and English, targeted
identified neighborhoods).

Require developers to affirmatively market 1,000
units to those with disproportionate housing needs
over the eight‐year period (approximately 125
annually). Ensure that programs target/affirmatively
market to households with disproportionate
housing needs. Continue to update the housing
inventory and provide on the City’s website so that
developers can target housing in moderate and high
opportunity areas.

This item is connected to Policy H 1.2. and Policy H

4.4. 2031 (Annually); as
development projects come in
for approvals/financing
Action 2.2: Incentivize developers through direct
subsidies, fee waivers, and/or density bonuses, to
increase accessibility requirements beyond the federal
requirement of 5% for subsidized developments.

Persons with disabilities have
disproportionate housing needs.

AND
Persons with disabilities and persons of
color are most likely to file fair housing
complaints with HUD.

Lack of accessible affordable units;
Lack of access to economic
opportunity; Concentration in low
income and low opportunity census
tracts.

Disproportionate housing
need for low income
households and protected
classes
Promote equal housing
opportunity
Financial resources City of San Mateo Increase development of acc essible units
beyond minimum requirements
a. Update development agreements for projects
with City subsidies to include additional accessible
units.
b. Update the City’s Inclusionary Housing Policy to
require projects that receive City subsidies to
increase the percentage of units that meet
accessibility requirements. This item is connected to
Policy H1.3
2023 ‐ 2031 (ongoing) as
development opportunities
come available. Review
developer agreements as
projects come in (annually);
2025 ‐ 2026 Draft Update
Inclusionary Housing Policy
Action 2.3: Prioritize city funding proposals for city
funded affordable housing that are committed to serving hard to serve residents (e.g., extremely low income,
special needs, on site services)
Persons with disabilities have
disproportionate housing needs.

AND
Persons with disabilities and persons of
color are most likely to file fair housing
complaints with HUD.

Lack of accessible affordable units;
Lack of access to economic
opportunity; Concentration in low
income and low opportunity census
tracts.

Disparities in access to
opportunity
Promote equal housing
opportunity
Financial resources City of San Mateo Create more housing for har d to serve households.

Conduct a best practices review and develop a
program to prioritize City funding for housing
projects.
2027 ‐ 2028 Conduct a review
of best practices; and develop
a draft program for City
Council adoption
Action Area 1. Enhancing housing mobility strategies: consist of removing barriers to housing in areas of opportunity and strategically enhancing access.

Action Area 3. Improving place‐based strategies to encourage community conservation and revitalization including preservation of existing affordable housing: involves approaches that are focused on conserving and improvin g assets in areas of lower opportunity Action Area 2. Encouraging new housing choices and affordability in high resource areas: promoting housing supply, choices and affordability in areas of high opportunity and outside of areas of concentrated poverty.

Page 1 of 3 ends

Actions Fair Housing Issues Contributing Factors Fair Housing Cate gory Action Type of Action Responsible Party Objectives Quantified O bjectives Timeline Action 3.1: As part of the General Plan, conduct an area plan for the North Shoreview and North Central
neighborhoods and prioritize land use and design around Highway 101 to improve access and reduce the division
of the urban form produced by the highway.

Hispanic and single female parent
households are concentrated in low
opportunity census tracts.

Lack of affordable housing in high
opportunity areas; Lack of accessible
affordable units; Concentration of
NOAH in low opportunity census
tracts.

Segregation/ integration
patterns; disparities in access
to opportunities
Conserve and improve the
existing affordable housing
stock
Land use resources City of San Mateo Reduce overcrowding, improve health and safety, and improve mobility and access to
services in impacted neighborhoods.

Prepare an area plan for North Shoreview and
North Central neighborhoods.

Create plan through the
General Plan Update
implementation process
Action 3.2: Continue to fund minor home repairs and
implement a preference for projects in low opportunity
census tracts identified in the analysis.

Hispanic and single female parent
households are concentrated in low
opportunity census tracts.

Lack of affordable housing in high
opportunity areas; Lack of accessible
affordable units
Disparities in access to
opportunity
Conserve and improve the
existing affordable housing
stock
Financial resources City of San Mateo Fund minor home repairs and accessibility improvements. Provide opportunity for
home rehabilitation lo ans for low income
residents. Allow accessibility improvements
on rental properties with owner permission.

Complete annual goals of 10 minor home repairs
and 14 accessibility modifications through grants for
low‐income residents. Provide home rehabilitation
loans for low‐ income residents. Affirmatively
market to Hispanic and single female heads of
household biannually; ensure that programs
target/affirmatively market to households in
impacted neighborhoods, including North Central
and North Shoreview, among others.

This item is connected to Policy H2.1 and Policy
H2.3.
2023‐2031 (Fund Annually;
consistent with Policy H2.1;
outreach biannually in target
neighborhoods)
Action 3.3: Monitor affordable housing projects that are at risk of conversion to market rate. Support regional and local efforts to examine displacement of affordable
housing and lower income households. Assist with the
retention of special needs housing that is at risk of
expiring affordability requirements.

Hispanic households have disproportionate
housing needs.

Historic discrimination and
continued mortgage denials;
Concentration in low opportunity
census tracts; High housing costs and
low wages
Outreach capacity and
enforcement
Conserve and improve the
existing affordable housing
stock
Human resources City of San Mateo Monitor affordable units whose subsidies are set to expire within the planning period
develop a plan for preservation of the units
to keep them affordable long term.

Bridgepointe Condominiums affordability
requirements for 59 affordable units expire in 2027,
out of which 24 are very low‐ income units (35 are
at 120% AMI). Belmont Building affordab ility
requirements for 6 units expire in 2032. The rental
property is owned by a for‐profit entity, potential
for loss of units is high.

Proactively coordinate with owners to preserve the
24 very low‐ income units as affordable, including
identifying potential funding sources, advertise
conversion units to non‐profits, provide conduct
tenant outreach and education, add a displacement
preference for new affordable housing for people
displaced, including those displaced as a result of
conversion. Provide noticing to tenants and affected
public entities in accordance with Gov. Code, §
65863.10, 65863.11, and 65863.13 Outreach and
negotiate with owners for affordability extensions
beginning at least two years prior to the
affordability expiration date. This item is connected
to Policy H2.2
a) 2025‐2027 (Bridgepointe
Condominiums)
b) 2030‐2032 (Belmont
Building); Consistent with
Policy # H2.2
Action 4.1: Establish tenant protections in local
ordinance to extend measures of AB1482 related to
relocation, documentation, and right to return policy in eviction cases.

Persons with disabilities have
disproportionate housing needs.

AND
Persons with disabilities and persons of
color are most likely to file fair housing
complaints with HUD.

AND
Hispanic households have disproportionate
housing needs.

Lack of accessible affordable units;
Lack of access to economic
opportunity; Concentration in low
income and low opportunity census
tracts; Historic discrimination and
continued mortgage denials; High
housing costs and low wages
Disparities in access to
opportunity
Address governmental and
non‐governmental constraints
Human resources City of San Mateo Increase tenant protections to prevent dislpacement of those with
disproportionate housing needs.
a)    Extend AB1482 provisions to require tenant
relocation payments for No Fault evictions for those
with tenure less than one year.
b)    Make recommendations to the City Council for
establishing tenant protection policies that include
the requirement of documentation from landlords
who use the substantial remodel exemption to evict
tenants and a Right to Return policy for tenants
displaced from homes due to demolition or
substantial remodels.
c)    Amend the Code to strengthen enforcement
penalty structure to aid in protecting tenants from
unsafe or substandard units. This item is connected
to Policy H 3.4.
a)   2023 – 2024
b)   2025 – 2026
c)   2023 – 2024; consistent
with Policy #H 3.4
Action 4.2: Partner with Project Sentinel to perform fair housing training for landlords and tenants. Focus
enforcement efforts on race based discrimination and
reasonable accommodations.

Persons with disabilities have
disproportionate housing needs.

AND
Persons with disabilities and persons of
color are most likely to file fair housing
complaints with HUD.

Lack of accessible affordable units;
Lack of access to economic
opportunity; Concentration in low
income and low opportunity census
tracts; Lack of understanding of
reasonable accommodation
requirements by landlords and
property owners.

Outreach capacity and
enforcement
Promote equal housing
opportunity
Human resources Project Sentinel Increase awareness of fair housi ng laws and tenants' rights to reduce unlawful
discrimination and displacement.

Provide annual funding to Project Sentinel to
provide training every two years in the Spring,
targeting 200 landlords each training. Awareness
will be increased through outreach to landlords.

Annually as part of CDBG
allocation in the spring
Action Area 4. Protecting existing residents from displacement: strategies that protects residents in areas of lower or moderat e opportunity and concentrated poverty and preserves housing ch oices and affordability.

Page 2 of 3 ends

Actions Fair Housing Issues Contributing Factors Fair Housing Cate gory Action Type of Action Responsible Party Objectives Quantified O bjectives Timeline Action 4.3: Create a webpage specific to fair housing
including resources for residents who feel they have
experienced discrimination, in formation about filing fair housing complaints with HCD or HUD, and information
about protected classes under the Fair Housing Act.

Persons with disabilities have
disproportionate housing needs.

AND
Persons with disabilities and persons of
color are most likely to file fair housing
complaints with HUD.

Outreach capacity and
enforcement
Promote equal housing
opportunity
Human resources City of San Mateo Increase awareness of fair hous ing laws and tenants' rights to reduce unlawful
discrimination and displacement.

Provide information on the City's website about
housing discrimination, laws, and protections.

Update the webpage every two years, along with
other transparency updates.
2024 and bi‐annually
thereafter; consistent with
general Policy# H3.4
Action 4.4: Ensure that all multifamily residential
developments contain signage to explain the right to
request reasonable accommodations for persons with
disabilities. Make this information available and clearly transparent on the city's website and fund landlord
training and outreach on reasonable accommodations.

Persons with disabilities have
disproportionate housing needs.

AND
Persons with disabilities and persons of
color are most likely to file fair housing
complaints with HUD.

Initially, create ongoing condition of approval to
ensure both BMR and all‐affordable developments
contain this information. Explore options for
recording against the property and/or including in
the affordable housing agreement.

Create ongoing conditions of
approval by fall 2024; conduct
best practices review on
options to record reasonable
accommodation language by
January 2025, and implement
a program by January 2026
Action 4.5: Ensure that future improvements in
disadvantaged communities will not produce a net loss of affordable housing or the displacement of residents and seek to increase the amount of affordable housing in
disadvantaged communities.
(NOTE: New action to address Environmental Justice)
Persons with disabilities and persons of
color have disproportionate housing needs
Lack of accessible affordable units;
Lack of access to economic
opportunity; Concentration in low
income and low opportunity census
tracts; Lack of investment in older
housing stock.

Disparities in access to
opportunity
Promote equal housing
opportunity
Human resources City of San Mateo Ensure that lower‐income and pr otected class households are not displaced becaise
of community improvements
In collaboration with nonprofit and for‐profit
housing developers, study the feasibility of
collaborating with the Northern California Land
Trust, or establishing a new community land trust,
that will support long‐term community ownership
and housing affordability in disadvantaged
communities. Implement findings as part of the
General Plan Update.

To be completed as part of
the larger General Plan
Update, with the expected
date of completion by 20217

Page 3 of 3 ends

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 1
APPENDIX D | Attachment 2 – AFFH Maps and Data
SECTION I. Fair Housing Enforcement and Outreach Capacity

Figure I-1: Fair Housing Assistance Organizations, San Mateo County Source: Organization Websites

Figure I-2: Fair Housing Complaints Filed with HUD by Basis, San Mateo County, 2017-2021 Source: HUD

Name
Project
Sentinel Northern California
1490 El Camino
Real, Santa Clara,
CA 95050
(800) 339-6043 https://www.housing.org/
Le ga l Aid
Society of San
Mateo County
San Mateo County
330 Twin Dolphin
Drive, Suite 123,
Redwood City, CA
94065
(650) 558-0915 https://www.legalaidsm c.org/h
ousing-resources
Co mmunity
Leg al Services
of East Palo
Alto
East Palo Alto,
Menlo Park,
Burlingame,
Mountain View,
Redwood City, and
San Francisco
1861 Bay Road,
East Palo Alto, CA
94303
(650)-326-6440 https://clsepa.org/services/# ho
using
WebsiteService Area Address Phone
Disability 8 9 3 9 3 32 56%
Race 3 5 2 1 11 19%
Familial Status 4 3 1 8 14%
National Origin 2 1 3 5%
Religion 1 1 2 4%
Sex 1 1 2%
Total cases 17 18 5 11 6 57 100%
201 7-2021 Total
2017 2018 2019 2020 2021 Cases % of Total
ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 2

Figure I-3: HCD Fair Housing Inquiries (2013- 2021) and HUD Fair Housing Complaints (2017- 2021) Source: Organization Websites

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 3

Figure I-4: FHEO Inquiries by City to HCD, San Mateo County, 2013-2021 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 4

Figure I-5: HCD Fair Housing Inquiries by Bias, January 2013-March 2021 Source: California Department of Housing and Community Development AFFH Data Viewer

Jurisdiction
Atherton 0 0 0 0 0 0 0 0 0
Belm ont 2 0 1 0 0 0 0 6 9
Brisbane 0 0 0 0 0 0 0 0 0
B u r lin ga m e 3 0 2 0 0 0 0 1 6
Colm a 0 0 0 0 0 0 0 0 0
Daly City 1 2 1 3 0 0 0 9 16
East Palo Alto 1 1 0 0 0 0 0 5 7
Foster City 4 0 0 0 0 0 0 3 7
Half Moon Bay 0 0 0 0 0 0 0 1 1
Hillsborough 0 0 0 0 0 0 0 0 0
Menlo Park 3 0 0 0 0 1 0 5 9
Millbrae 0 0 0 0 0 0 0 0 0
Pacifica 3 0 0 1 0 1 0 4 9
Portola Valley 0 0 0 0 0 0 0 0 0
Redwood City 5 1 1 1 0 1 0 15 24
San Bruno 0 0 0 0 0 0 0 5 5
San Carlos 1 0 1 0 0 0 0 2 4
San Mateo 4 2 2 2 0 0 0 16 26
South San Francisco 0 0 0 1 0 0 0 5 6
Woodside 0 0 0 0 0 0 0 2 2
TotalDisability Race
Fam ilial
Status
National
Origin Religion Sex Color
None
Cited
ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 5

Figure I-6: Public Housing Buildings, San Mateo County
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 6

Figure I-7: Housing Choice Vouchers by Census Tract
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 7
SECTION II. Integration and Segregation

Race and ethnicity.

Figure II-1: Population by Race and Ethnicity, 2019
Source: ABAG Housing Needs Data Workbook

Figure II-2: Population by Race and Ethnicity, City of San Mateo, 2000-2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 8

Figure II-3: Senior and Youth Population by Race, City of San Mateo, 2000-2019 Source: ABAG Housing Needs Data Workbook

Figure II-4: Area Median Income by Race and Ethnicity, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 9

Figure II-5: Poverty Rate by Race and Ethnicity, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 10

Figure II-6: % Non-White Population by Census Block Groups, 2018 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 11

Figure II-7:White Majority Census Tracts
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 12

Figure II-8: Asian Majority Census Tracts
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 13

Figure II-9: Hispanic Majority Census Tracts
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 14

Figure II-10: Neighborhood Segregation by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 15

Figure II-11: Diversity Index by Block Group, 2010
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 16

Figure II-12: Diversity Index by Block Group, 2018
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 17
Disability status.

Figure II-13: Share of Population by Disability Status, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 18

Figure II-14: % of Population with a Disability by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 19
Familial status.

Figure II-15: Age Distribution, City of San Mateo, 2000-2019 Source: ABAG Housing Needs Data Workbook

Figure II-16: Share of Households by Size, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 20

Figure II-17: Share of Households by Type, 2019
Source: ABAG Housing Needs Data Workbook

Figure II-18: Share of Households by Presence of Children (Less than 18 years old), 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 21

Figure II-19: Housing Type by Tenure, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

Figure II-20: Housing Units by Number of Bedrooms and Tenure, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 22

Figure II-21: % of Children in Married Couple Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 23

Figure II-22: % Households with Single Female with Children by Census Tract, 2019 [legend missing in HCD provided map]
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 24

Figure II-23.: % of Married Couple Households by Census Tract, 2019 [legend missing in HCD provided map]
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 25

Figure II-24: % of Adults Living Alone by Census Tract, 2019 [legend missing in HCD provided map] Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 26
Household income.

Figure II-25: Share of Households by Area Median Income (AMI), 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 27

Figure II-26: Median Household Income by Block Group, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 28

Figure II-27: Low to Moderate Income Population by Block Group Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 29

Figure II-28: Poverty Status by Census Tract, 2019
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 30

Figure II-29: R/ECAPs, 2010
Source: 2010 and 2019 5-year ACS and Root Policy Research Note: R/ECAPs are census tracts that have a non-white population of 50 percent or more (majority-minority) AND the poverty rate is three times the average tract poverty rate for the County (19.4% in 2010).

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 31

Figure II-30: R/ECAPs, 2019
Source: 2010 and 2019 5-year ACS and Root Policy Research Note: R/ECAPs are census tracts that have a non-white population of 50 percent or more (majority-minority) AND the poverty rate is three times the average tract poverty rate for the County (19.1% in 2010).

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 32
SECTION III. Access to Opportunity
Education

Figure III-1: TCAC Opportunity Areas Education Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 33
Employment

Figure III-2: Jobs by Industry, City of San Mateo, 2002-2018 Source: ABAG Housing Needs Data Workbook

Figure III-3: Job Holders by Industry, City of San Mateo, 2002-2018 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 34

Figure III-4: Jobs to Household Ratio, City of San Mateo, 2002-2018 Source: ABAG Housing Needs Data Workbook

Figure III-5: Jobs to Worker Ratio by Wage, City of San Mateo, 2002-2018 Source: ABAG Housing Needs Data Workbook

Figure III-6: Unemployment Rate, 2010-2021
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 35

Figure III-7: TCAC Opportunity Areas Economic Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 36

Figure III-8: Jobs Proximity Index by Block Group, 2017 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 37
Transportation
[TCAC’s transportation opportunity score and maps were not available at the time of this report]

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 38
Environment

Figure III-9: TCAC Opportunity Areas Environmental Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 39

Figure III-10: CalEnviroScreen by Census Tract, 2021
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 40

Figure III-11: Healthy Places Index by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 41
Patterns in disparities in access to opportunity.

Figure III-12: Population Living in Moderate and High Resource Ares by Race and Ethnicity, City of San Mateo, 2019
Source: ABAG Housing Needs Data Workbook

Figure III-13: Population with Limited English Proficiency, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 42

Figure III-14: TCAC Opportunity Areas Composite Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 43

Figure III-15: Social Vulnerability Index by Census Tract, 2018 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 44

Figure III-16: SB 535 Disadvantaged Communities
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 45
Disparities in access to opportunity for persons with disabilities.

Figure III-17: Population by Disability Status, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

Figure III-18: Disability by Type for the Non-Institutionalized Population 18 Years and Over, City of San Mateo, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 46

Figure III-19: Disability by Type for Seniors (65 years and over), City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

Figure III-20: Employment by Disability Status, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 47

Figure III-21: Share of Population with a Disability by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 48

Figure III-2: San Mateo County Housing Policies and Programs Analysis [PLACEHOLDER] Source: ABAG

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 49
SECTION IV. Disproportionate Housing Needs
Housing needs.

Figure IV-1: Population Indexed to 1990
Source: ABAG Housing Needs Data Workbook

Figure IV-2: Housing Permits Issued by Income Group, City of San Mateo, 2015-2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 50

Figure IV-3: Housing Units by Year Built, City of San Mateo Source: ABAG Housing Needs Data Workbook

Figure IV-4: Distribution of Home Value for Owner Occupied Units, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 51

Figure IV-5: Zillow Home Value Index, 2001-2020
Source: ABAG Housing Needs Data Workbook

Figure IV-6: Distribution of Contract Rents for Renter Occupied Units, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 52

Figure IV-7: Median Contract Rent, 2009-2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 53
Cost burden and severe cost burden.

Figure IV-8: Overpayment (Cost Burden) by Jurisdiction, 2019 Source: ABAG Housing Needs Data Workbook

Figure IV-9: Overpayment (Cost Burden) by Tenure, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 54

Figure IV-10: Overpayment (Cost Burden) by Area Median Income (AMI), City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

Figure IV-11: Overpayment (Cost Burden) by Race and Ethnicity, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 55

Figure IV-12: Overpayment (Cost Burden) by Family Size, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 56

Figure IV-13: Overpayment (Cost Burden) for Renter Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 57

Figure IV-14: Overpayment (Cost Burden) for Owner Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 58
Overcrowding.

Figure IV-15: Occupants per Room by Jurisdiction, 2019
Source: ABAG Housing Needs Data Workbook

Figure IV-16: Occupants per Room by Tenure, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 59

Figure IV-17: Overcrowding by Race and Ethnicity, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook
Note: Overcrowding is indicated by more than 1 person per room.

Figure IV-18: Occupants per Room by AMI, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 60

Figure IV-19: Overcrowded Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 61
Substandard housing.

Figure IV-20: Percent of Units Lacking Complete Kitchen and Plumbing Facilities, City of San Mateo, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 62
Homelessness.

Figure IV-21: Homelessness by Household Type and Shelter Status, San Mateo County, 2019 Source: ABAG Housing Needs Data Workbook

Figure IV-22: Share of General and Homeless Populations by Race, San Mateo County, 2019 Source: ABAG Housing Needs Data Workbook

Sheltered - Emergency Shelter 0 68 198
Sheltered - Transitional Housing 0 271 74
Unsheltered 1 62 838
People in
Households
Solely
Children
People in
Households
Without
Children
People in
Households
with Adults
and Children

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 63

Figure IV-23: Share of General and Homeless Populations by Ethnicity, San Mateo County, 2019 Source: ABAG Housing Needs Data Workbook

Figure IV-24: Characteristics of the Population Experiencing Homelessness, San Mateo County, 2019 Source: ABAG Housing Needs Data Workbook

Sheltered - Emergency Shelter 46 0 70 31 10
Sheltered - Transitional Housing 46 3 46 4 14
Unsheltered 20 0 189 34 103
C hronic
Substance Abuse HIV/AIDS
Severely
Mentally Ill Veterans
Victims of Domestic
Violence
ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 64
Displacement.

Figure IV-25: Location of Population One Year Ago, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

Figure IV-26: Tenure by Year Moved to Current Residence, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 65

Figure IV-27: Assisted Units at Risk of Conversion, City of San Mateo, 2019 Source: ABAG Housing Needs Data Workbook

San Mateo 630 0 72 0 702
San Mateo County 4,656 191 359 58 5,264
Bay Area 110,177 3,375 1,854 1,053 116,459
Low Moderate H igh V e r y H igh
Total Assisted
Units in Database
ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 66

Figure IV-28: Census Tracts Vulnerable to Displacement
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 67

Figure IV-29: Location Affordability Index by Census Tract Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 68

Figure IV-30: Share of Renter Occupied Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 69

Figure IV-31: Special Flood Hazard Areas, 2000
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET , PAGE 70
Other considerations.

Figure IV-32: Mortgage Applications by Race and Ethnicity, City of San Mateo, 2018-2019 Source: ABAG Housing Needs Data Workbook

Figure IV-33: Mortgage Application Denial Rate by Race and Ethnicity, City of San Mateo, 2018-2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH SECTION V, PAGE 1
APPENDIX D | Attachment 3 – Access to Educational Opportunities This section examines the extent to which members of protected classes and those in poverty experience disparities in access to opportunity as measured by access to education . This section dr aws from data provided by the San Mateo Office of Education, the California Department of Education, and U.S. Census American Community Surveys (ACS). This section discusses the following topics:
 Changes in school enrollment during COVID-19 by race and ethnicity, and by groups with extenuating circumstances;1
 Achievement gaps by race and ethnicity and for groups with extenuating circumstances as measured by test scores, California State University or University of California admissions standards, and college-going rates;
 Barriers to success measured by chronic absenteeism, dropout rates, and suspension rates.

After describing this section’s primary findings, we describe the county’s school districts before launching into data measuring achievement gaps and barriers to success.

Primary Findings
Student racial and ethnic diversity is modestly increasing. Student bodies in San Mateo County have become increasingly racially and ethnically diverse.
 Hispanic students make up the largest ethnic group in the coun ty’s schools, representing 38% of students in the 2020 -2021 academic school year. This a slight increase from the 2010 -2011 school year, where Hispanic students made up 37% of the population.  There has been a large increase in Asian students, with 17% iden tifying as such in 2020 -2021, an increase of 5 percentage points from 2010-2011.
 Students identifying as White (26%) have decreased by 3 percentage points since 2010-2011.

Free and reduced lunch-qualifying students and English language learners are concentrated in a handful of schools. Overall, 29% of public school students in San Mateo County qualify for reduced or free lunch.  The rate of reduced lunch qualification was highest in Ravenswood City Elementary School District, where 83% of students qualify f or reduced lunch. Also in Ravenswood City Elementary, 30% of students are experiencing homelessness. This is a large outlier in the county, where overall just 2% are experiencing homelessness.
 Countywide, 20% of public school students are English learners. Again, this rate is highest at Ravenswood City Elementary, where 53% of students are English learners. La Honda- Pescadero

1 The term “extenuating circumstances” i s used in this section to capture students who se socioeconomic situations and/ or disability may make standard educational environments challenging.

ROOT POLICY RESEARCH SECTION V, PAGE 2
Unified School District, Jefferson Union High School, and Redwood City Elementary also have high rates of English learners, representing more than a third of students.

Enrollment is dropping. Public school enrollment re duced substantially in some areas during the pandemic. Total enrollment decreased by 3% between 2019 -2020 and 2020-2021 in San Mateo County , which was the largest decrease of the decade.
 Portola Valley and La Honda-Pescadero school districts had the largest enrollment decreases during COVID-19, with a 11% and 10% decline in enrollments, respectively.  Decreased enrollment was especially common among Pacific Islander students. Between 2019-2021, enrollment among Pacific Island er students decreased by 6% (from 1,581 students in 2019 -20 to 1,484 students in 2020-21), substantially higher than the 3% countywide average.  Enrollment among migrant students decreased drastically by 16% over the same period (from 332 students to 279 students).

Learning proficiency is improving yet disparities exist . Across all racial and ethnic groups, the rate at which students met or exceeded English and mathematics testing standards has increased since the 2014- 2015 school year. Students with extenuating circumstances (i.e., disability, facing homelessness, learning English) tend to score lower on English and mathematics tests than the overall student body.  Proficiency gaps are especially pronou nced among English learning students in Portola Valley Elementary, Woodside Elementary, Menlo Park City Elementary, and Brisbane Elementary , where students with extenuating circumstances met or exceeded mathematics test standards at a rate at least 50 percentage points below the overall test rate in each district.  Students with disabilities in San Carlos Elementary and Las Lomitas Elementary school districts scored far below the overall student body: I n these districts, students with disabilities met or exc eeded mathematics test standards at 54 percentage points below the overall test rate.

Many students meet admissions standards for CSU or UC schools.  Among the high school districts in San Mateo County, Sequoia Union had the highest rate of graduates who met such admission standards, at 69%. On the other end of the spectrum, Cabrillo Unified and South San Francisco Unified had the lowest rates at 41%.  Jefferson Union High School District had the most drastic increase in the share of graduates meeting CSU or UC standards: just 21% of students met these standards in 2016 -2017 compared to 48% of students in 2019 -2020. La Honda -Pescadero Unified School D istrict experienced a 10 percentage point increase in this success rate over the same period.

Most school districts in the county have a college-going rate at 70% or higher—yet there are wide gaps by race and ethnicity.

ROOT POLICY RESEARCH SECTION V, PAGE 3
 In every district, White students have a higher college -going rate than Hispanic students, but the largest gaps are in South San Francisco United, where 91% of White students go to college compared to just 68% of Hispanic students—a 23 percentage point gap.

Students with extenuating circumstances are highly concentrated in a few schools and move schools often due to housing instability.
 Students with extenuating circumstances may need additional resources —e.g., onsite health care, free meals, tutoring—to be successful in school. When these students are concentrated into a few schools, th e schools bear an unequal responsibility for providing needed resources. K -12 school funding in California has long been inadequate, and, although policymakers have recently allocated additional resources to schools with high proportions of low income children under a “concentration grant” system, funding gaps remain.
 The highest concentration of high needs students is found in Ravenswood City Elementary , where 30% of all students are experiencing homelessness and 83% qualify for free and reduced lunch.  Currently, students whose families have been evicted do not ha ve protections allowing them to remain in their current school district. This can result in frequent changes in schools for low income children, raising their vulnerability to falling behind in school.

Absenteeism, dropout rates, and discipline rates are highest for students of color, students with disabilities, and students with other extenuating circumstances. While 10% of students were chronically absent during the 2018-2019 school year, chronic absenteeism rates were higher in districts with a large number of students experiencing economic and housing precarity.  For instance, Ravenswood Elementary, which has a 30% rate of homelessness among students, had one of the higher rates of chronic absenteeism at 16%.
 Pacific Islander students (26%), Black/African American students (18%), and Hispanic students (15%) had notably higher rates of chronic absenteeism than the overall student population (10%).  In most districts, chronic absenteeism is higher among students with disabilities. In fact, only Bayshore Elementary’s students with disabilities had a lower rate of chronic absenteeism than the overall student body.

Dropout rates vary across the county:
 Dropout rates were highest in Sequoia Union High School District (10%) and South San Francisco Unified (9%).
 In all school districts in the county, dropout rates are higher for boys than for girls.  Pacific Islander, Black/African American , and Hispanic students in the county often had higher dropout rates than those in other racial and ethnic groups  Students with disabilities, students experiencing homelessness, foster youth, and students learning English had higher dropout rates than the overall population.

Discipline rates also vary by area and race and ethnicity.

ROOT POLICY RESEARCH SECTION V, PAGE 4
 In many school districts across San Mateo Cou nty, Hispanic students are disciplined at disproportionately higher rates compared to their peers.  In most districts, Black/African American and Pacific Islander students are also overrepresented in terms of suspension rates, but these rates are slight compared to those of Hispanic students.  Asian and Filipino students were underrepresented in terms of suspension rates. White students were also underrepresented in discipline rates in most districts except for La Honda-Pescadero.

The demographics of faculty and staff are fairly similar to that of students.  There is a slightly larger share of White and Black/African American staff than students, meaning that Black/African American and White student groups are more likely to interact with same- race staff and faculty than other racial groups.
 Asian students are less likely to interact with a same-race staff of faculty member: 17% of the student body is Asian compared to just 8% of staff and faculty.

Background
This section describes the school districts in San Mateo County, including their geographic boundaries and a brief history of the school districts’ formation. This section also includes details on how districts’ enrollments and student demographic have changed over time.

San Mateo County School District s. There are three unified school districts in San Mateo C ounty which include both elementary and high schools. These are Cabrillo Unified School District, La Honda-Pescadero Unified School District, and South San Francisco Unified School District.

In addition to the unified school districts, there are three high schoo l districts, which include: Jefferson Union High School District , San Mateo Union High School District , and Sequoia Union High School District. The elementary schools covering these high schoo ls’ district boundaries areas are described below:
 In the Jefferson Union High School District geographic boundary, elementary school districts are the Bayshore Elementary School District, Brisbane School District, Jefferson Elementary School District, and Pacifica School District.
 Within the San Mateo Union High School District geographic boundary , elementary school districts include San Mateo -Foster City School District, Hillsborough City School District, Burlingame School District, San Bruno Park School District, and Millbrae School District.  Within the Sequoia Union High School District geographic boundary , the elementary schools include Belmont-Redwood Shores School District, San Carlos School District, Redwood City School District, Ravenswood City School District, Menlo Park City School District, Woodside Elementary School District, Las Lomitas Elementary School District, and Portola Valley School District.

ROOT POLICY RESEARCH SECTION V, PAGE 5
Geographic boundaries of school districts. Figure V-1 illustrates the geographic boundaries of the unified s chool districts as well as the three high school districts. Municipal boundaries are overlayed on the map.

Figure V -1.

Unified School Districts and High School Districts in San Mateo County

Source: San Mateo County Office of Education.

ROOT POLICY RESEARCH SECTION V, PAGE 6

As illustrated in the map, Cabrillo Unified School District covers Half Moon Bay and some unincorporated areas of San Mateo County . South San Francisco Unified cover s South San Francisco and a small portion of Daly City. La Honda- Pescadero Unified School District covers unincorporated areas of San Mateo County.

The other high school districts , J efferson Union, San Mateo Union, and Sequoia Union, cover the remaining jurisdictions. J efferson Union cover s Brisbane, Colma, Daly City, and Pacifica. San Mateo Un ion covers Burlingame, Hillsborough, Millbrae, San Bruno, San Mateo City, and Foster City. Sequoia Union covers Atherton, Belmont, Redwood City, East Palo Alto, Menlo Park, San Carlos, Portola Valley, and Woodside.

The county’s elementary school districts cover the same areas as the three high school districts.

Their geographic boundaries are illustrated in the map below.

ROOT POLICY RESEARCH SECTION V, PAGE 7
Figure V -2.

Elem entary School Districts in San Mateo County

Source: San Mateo County Office of Education.

ROOT POLICY RESEARCH SECTION V, PAGE 8
Because the elementary sc hool districts are much smaller, many jurisdictions have several elementary schools. The table blow shows each jurisdiction and their associated elementary school.

Figure V -3.

School Districts in San Mateo County’s J urisdictions

Source: San Mateo County Office of Education.

A brief history of district form ation. San Mateo County’s numerous school districts were formed over a century ago, when the county was more rural and scattered: communities needed elementary schools close to home, and only a few stud ents were attending high school.

As young people began going to high school, individual districts often found they had too few students and resources to support their own high schools, so se parate high school districts, J urisdiction
Atherton Sequoia Union Menlo Park City ; Las Lomitas Elementary; Redwood City
Belmont Sequoia Union Belmont-Redwood Shores
Brisbane J efferson Union Brisbane; Bayshore Elementary B urlingam e San Mateo Union B urlingam e
Colma J efferson Union J efferson Elementary
Daly City J efferson Union; South San Francisco Unifie J efferson Elementary East Palo Alto Sequoia Union Ravenswood City
Foster City San Mateo Union San Mateo-Foster City
Half Moon Bay Cabrillo Unified (none, included in Cabrillo Unified) Hillsborough San Mateo Union Hillsborough City
Menlo Park Sequoia Union Menlo Park City; Las Lomitas Elementary; Ravenswood City
Millbrae San Mateo Union Millbrae
Pacifica J efferson Union Pacifica
Portola Valley Sequoia Union Portola Valley
Redwood City Sequoia Union Redwood City
San Bruno San Mateo Union San Bruno Park
San Carlos Sequoia Union San Carlos; Redwood City
San Mateo San Mateo Union San Mateo-Foster City
South San Francisco South San Francisco Unified (none, included in South San Francisco Unified) Woodside Sequoia Union Woodside Elementary; Portola Valley; Las Lomitas; Redwood City
Unified or High School District Elementary School District(s) ROOT POLICY RESEARCH SECTION V, PAGE 9
covering the territories of two or more elementary districts, were established to meet the communities’ needs. 2
Once California’s population grew and San Mateo County became more urbanized, “a jigsaw puzzle of overlapping districts evolved haphazardly.” Since 1920, the state has been pushin g elementary districts to unify with the high school districts that serve their communities, citing improved educational quality and equity of opportunity. However, there has been limited success and local voters in San Mateo County have consistently resisted unification. 3 Early efforts at unification were more successful in the rural communities along the coast —for example, voters approved the new Cabrillo Unified district for the area around Half Moon Bay and the La Honda -Pescadero Unified district in a 1964 election. Unification was not supported by many suburban communities edging the Bay. The county ’ s school district committee proposed to split each of the three high sch ool districts and feeder schools into two or three smaller unified district s, but the State Board of Education rejected variations of those plans three times . The Board argued that the county committee’s proposals would create districts with widely varying property tax bases and could contribute to racial segregation. The State Board in stead devised a plan that would create a single unified district within each of the existing high school district boundaries. Voters turned down the state plans in all three districts in J une 1966 , and rejected a similar proposal again in 1972. In 1973, th e Mid -Peninsula Task Force for Integrated Education petitioned the county committees to unify the elementary districts of Menl o Park, Las Lomitas, Portola Valley, Ravenswood and a portion of Sequoia Union High School District across county lines with Palo Alto Unified. Their goal was racial integration , but the county committee did not support the effort.
4 Efforts against unifi cation have persisted, leaving the county with several elementary school districts which feed into a high school, rather than a uni fied district. As a result, some elementary school districts have faced waning budgets and administrative hurtles. For instance, Brisbane and Bayshore elementary school districts, at the northern end of the county , serve a little more than 1,000 students a nd long have struggled with tight budgets. To rectify their budgetary concerns, the districts now share both a superintendent and a chief business officer. They also participate in a special education collaborative with the J efferson elementary and high sc hool districts.

According to the county’ s superintendent of schools Anne Campbell, o ther districts may find themselves pooli ng their resources in the future : local identification may be strong, she says, but

2 Watson, Aleta. “ How Did We End Up With 54 School Districts in San Mateo and Santa Clara Counties?” Silico n Valley Community Foundation, 2012. https://www.siliconvalleycf.org/sites/default/files/report -edu.pd f 3 Ibid.
4 Ibid.

ROOT POLICY RESEARCH SECTION V, PAGE 10
financial reality is hard to ignore: “As we mov e forward in time, I think it’s going to be interesting to see what school districts are going to do, especially as budgets g et more bleak. ”5 E nrollm ent changes . Total public school enrollment in the county has decreased slightly, by just 1%, from the 2010- 2011 academic year to 2020- 2021. Figure V-4 illustrates enrollment changes by district.

Bayshore Elementary, Ravenswood Ci ty, and Portola Valley school districts experienced the largest enrollment decreases ( by at least 30% ) between 2010- 11 and 2020- 21. School districts with the largest increases in enrollments were Burlingame (22%) and B elmont -Redwood Shores (30%) .

5 Ibid.

ROOT POLICY RESEARCH SECTION V, PAGE 11
Figure V -4 .

Enrollment changes by district, 201 0 -1 1 to 2020 -20 21

Source: California Department of Education and Root Policy Research

Howe ver, it is important to note that many of these enrollment decreases were driven by the pandemic . In fact, t otal enrollment in these public schools decreased by 3% between 2019 -2020 School District
Unified School Districts
Cabrillo Unified 3,352 2,934 -12%
La Honda- Pescadero 341 275 -19%
South San Francisco 9,312 8,182 -12%
High & Elementary School Districts
J efferson Union Hig h School 4,960 4,705 -5%
Bayshore Elementary 543 361 -34%
Brisbane Elementary 545 474 -13%
J efferson Elementary 6,998 6,653 -5%
Pacifica 3,164 3,006 -5%
San Mateo Union Hig h School 8,406 9,760 16%
Burlingame Elementary 2,771 3,387 22%
Hillsborough City Elementary 1,512 1,268 -16%
Millbrae Elementary 2,222 2,238 1%
San Bruno Park Elementary 2,599 2,275 -12%
San Mateo-Foster City 10,904 10,969 1%
Sequoia Union High School 8,765 10,327 18%
Belmont-Redwood Shores 3,206 4,152 30%
Las Lomitas Elementary 1,336 1,116 -16%
Menlo Park City Elementary 2,629 2,781 6%
Portola Valley Elementary 711 491 -31%
Ravenswood City Elementary 4,285 2,993 -30%
Redwood City Elementary 9,119 8,086 -11%
San Carlos Elementary 3,212 3,265 2%
Woodside Elementary 453 369 -19%
Total Enrollment 91,345 90,067 -1%
2010-2011
Enrollment
2020-2021
Enrollment Percent Change
ROOT POLICY RESEARCH SECTION V, PAGE 12
and 2020- 2021 in San Mateo County : the larg est decrease of the decade. As shown in Figure V -5, enrollments actually increased steadily from 2010 -2011 to 2017- 2018, then began decreasing afterwards.

Figure V -5.

P ublic School Enrollment C hanges, 201 0 -20 11 t o 2 0 2 0-20 21

Note: These data exclude enrollments in SBE Everest Public High School District, which in 2015 combined with the Sequoia Union High School District .

Source: California Department of Education and Root Policy Research

Portola Valley and La Honda -Pescadero school districts had the largest enrollmen t decreases duri ng COVID -19, with a 11% and 10% decline in enrollments, respectively. The only school district with increasing enrollment s between the 2019-2020 to 2020- 2021 school years was Sequoia Union High School District, with a modest 1% increase in enrollments.

ROOT POLICY RESEARCH SECTION V, PAGE 13
Fi gure V -6 .

Enrollment changes by district during COVID -19, 20 19 -20 to 2020 -21

Source: California Department of Education and Root Policy Resear ch.

Declining enrollments in public schools have been common across the state and country during the COVID -19 pandemic , and enrollment declines in San Mateo County are on par with those School District
Unified School Districts
Cabrillo Unified 3,136 2,934 -6%
La Honda- Pescadero 306 275 -10%
South San Francisco 8,438 8,182 -3%
High & Elementary School Districts
J efferson Union Hig h School 4,811 4,705 -2%
Bayshore Elementary 381 361 -5%
Brisbane Elementary 476 474 0%
J efferson Elementary 6,687 6,653 -1%
Pacifica 3,110 3,006 -3%
San Mateo Union Hig h School 9,885 9,760 -1%
Burlingame Elementary 3,534 3,387 -4%
Hillsborough City Elementary 1,290 1,268 -2%
Millbrae Elementary 2,349 2,238 -5%
San Bruno Park Elementary 2,454 2,275 -7%
San Mateo-Foster City 11,576 10,969 -5%
Sequoia Union High School 10,238 10,327 1%
Belmont-Redwood Shores 4,314 4,152 -4%
Las Lomitas Elementary 1,208 1,116 -8%
Menlo Park City Elementary 2,922 2,781 -5%
Portola Valley Elementary 551 491 -11%
Ravenswood City Elementary 3,269 2,993 -8%
Redwood City Elementary 8,530 8,086 -5%
San Carlos Elementary 3,405 3,265 -4%
Woodside Elementary 376 369 -2%
Total Enrollment 93,246 90,067 -3%
2019-2020
Enrollment
2020-2021
Enrollment Percent Change
ROOT POLICY RESEARCH SECTION V, PAGE 14
across the state . According to a study conducted by the Public Policy Institute of California, public K–12 enrollm ent declined by 3% in California from the 2019- 2020 school year to the 2020- 2021 school year. 6
As funding is tied directly to the number of enrolled pupils, schools in San Mateo County could suffer fiscal consequences with continued declines. By law, districts are “held harmless” for declines for one year —that is, scho ol budgets for 2020–2021 were unaffected, but continued enrollment declines could mean cuts in future years. 7 Reductions in enrollments, and consequently funding, could also worsen economic inequality in the long -term by r educing students’ resources and ac cess to opportunities.

Dem ographics : race & e thnicity . Over the last decade, S an Mateo County’s school districts have diversified in terms of students’ race and ethnicity. Hispanic students make up the largest ethnic group in the county’s schools: 38% of s tudents identified as Hispanic in the 2020 - 2021 academic school year. This is just a one percentage point increase from 2010 -2011. Many other students are White (26%) , though this has decreased by 3 percentage points since 2010 - 2011, The largest increase w as in Asian students, with 17% identifying as such in 2020 -2021, an increase of 5 percentage points from 2010 -2011. Other students identify as Filipino (8%), or bi- or multi -racial (8%). A small and decreasing percentage of students identify as Black/Afric an American (1%) and Pacific Islander (2%).

6 Lafortune, J ulien & Prunty , Emmanuel. “ Dig ging into Enrollment Drops at California Public Schools.” Public Policy Institute of California. May 14, 2021. https://www.ppic.org/blog/digging -into -enrollment -drops -at-california -public -schools/ 7 Ibid.

ROOT POLICY RESEARCH SECTION V, PAGE 15
Figure V -7 .

Changes in R ace and
E thnicity, 20 1 0 -20 11 t o 2 0 2 0-
20 21
Note: These data exclude enrollments in SBE
Everest Public High School District, which
in 2015 combined with the Sequoia Union
High School District .

Source: California Department of Education and
Root Policy Research
:

Figure V-8 shows the racial and ethnic distribution of students enrolle d in public schools by jurisdiction in 2020- 2021 .
 Portola Valley Elementary School District (66%) and Wo odside Elementary School District (64%) had the highest share of White students, making them among the least racially and ethnically diverse districts in the county.
 Ravenswood City Elementary School D istrict and Redwood City Elementary School District ha d the highest share of Hispanic students, at 84% and 70%, respectively.  Ravenswood City also ha d the highest proportion of Pacific Islander students (7%) and Black/African American students (5%) compared to other districts .  Millbrae Elementary (46%), Hillsborough Elementary (32%), and Belmont -Redwood Shores Elementary (32%) ha d the highest share of Asian students .  J efferson Elementary School District and J efferson Union High School District ha d the highest portion of Filipino students, at 25% and 29% respe ctively.

ROOT POLICY RESEARCH SECTION V, PAGE 16
Figure V -8 .

Student body by R ace and E thnicity, 20 20 -20 21

Note: In almost all schoo l districts, less than 1% of students were Native American , so they are not included in this table.

Source: California Department of Education and Root Policy R esearch Enrollment changes due to COVID -19 varied by race and ethnicity. For instance, between 2 019- 2021, enrollment among Pacific Islander students decreased by 6% (from 1,581 students in 2019- 20 to 1,484 students in 2020- 21). This is substantially higher than the 3% countywide average.

Enrollments among Filipino and Hispanic students decreased by 4 % while enrollment among Black/African American students decreased by 2%. On the other end of the spectrum, t here was School District
Unified School Districts
Cabrillo Unified 1% 0% 1% 52% 0% 40% 5%
La Honda- Pescadero 0% 0% 1% 63% 0% 35% 1%
South San Francisco 14% 1% 23% 48% 2% 6% 6%
High & Elementary School Districts
J efferson Union Hig h School 15% 1% 29% 31% 1% 14% 7%
Bayshore Elementary 19% 3% 21% 41% 4% 3% 8%
Brisbane Elementary 20% 1% 12% 28% 0% 24% 11%
J efferson Elementary 19% 2% 25% 36% 1% 11% 5%
Pacifica 8% 1% 9% 26% 0% 39% 16%
San Mateo Union Hig h School 23% 1% 5% 32% 2% 28% 10%
Burlingame Elementary 27% 0% 3% 16% 0% 41% 9%
Hillsborough Elementary 32% 0% 2% 5% 0% 48% 12%
Millbrae Elementary 46% 1% 6% 20% 2% 16% 8%
San Bruno Park Elementary 16% 1% 10% 41% 5% 15% 1%
San Mateo-Foster City 26% 1% 3% 37% 2% 21% 9%
Sequoia Union High School 9% 2% 1% 45% 2% 35% 5%
Belmont-Redwood Shores 32% 1% 3% 12% 1% 34% 14%
Las Lomitas Elementary 18% 1% 1% 13% 0% 53% 14%
Menlo Park City Elementary 13% 1% 1% 17% 1% 55% 11%
Portola Valley Elementary 6% 0% 0% 14% 0% 66% 13%
Ravenswood City Elementary 0% 5% 0% 84% 7% 1% 2%
Redwood City Elementary 4% 1% 1% 70% 1% 19% 4%
San Carlos Elementary 18% 1% 1% 14% 0% 49% 13%
Woodside Elementary 4% 2% 0% 16% 1% 64% 11%
Total 17% 1% 8% 38% 2% 26% 8%
White Asian
Two or
more racesHispanicFilipinoBlack
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 17
a 3% increase in enrollment among White st udents (from 22,308 students to 23 ,055 students) between 2019- 20 and 2020- 21. Similarly, there was a 1% increase in enrollment among Asian students and a 4% increase among students of two or more races.

Figure V -9 .

E nrollm ent C hanges by R ace and E thnicity , San Mateo County, 2 0 19-20 to 20 20 - 21 Source: California Department of Education and Root Policy Research While many of their families may have simply moved out of San Mateo County during the pandemic, i t is pos sible that Black/African American , Filipino, Hispanic, and Pacific Islander students are otherwise slipping through the cracks of the education system during this period.

Demographics: students with e xtenuating c ircumstances. Several students in the count y’ s public schools are facing additional hurtles to educational ease. Many are English learners, qualify for reduced lunch, are foster children, are experiencing homelessness, have a disability, or are migrants. Students in these groups often have hindrances to excelling in school because of detr imental circumstances beyond their con trol. These include financial and social hardships as well as problems within students' families.

Qualification for free and reduced lunch is often used as a proxy for extenuating circumstances .

Qualifications are det ermined based on household size and in come. For instance, in the 2020- 2021 academic year, students from a household of three making less than $40,182 annually qualified for reduced price meals, and t hose making less than $28,236 in a household of three qua lified for free meals. 8

8 “Income Eligibility Scales for Sc hool Year 2020- 2021. ” California Department of Education.

ROOT POLICY RESEARCH SECTION V, PAGE 18
Free and reduced lunch disparities. Overall, 29% of public school students in San Mateo County qualify for reduced or free lunch. This rate was substantially lower in districts like Hillsborough Elementary, San Carlos Elementary , Portola Valley Elementary, Las Lomi tas Elementary, Belmont -Redwood Shores, and Menlo Park City Elementary, where each had less than 10% of students qualify for free or reduced lunch.

The rate of reduced lunch qualification was far higher in Ravenswood Cit y Elementary School District , where 83% of students qualify for reduced lunch.

Disparities in homelessness. In Ravenswood City Elementary , 30% of students are experiencing homelessness. This is an outlier in the county, where overall just 2 % are experienc ing homelessness. The school district has received media attention due to its astronomically high rate of students experiencing homelessness. Some have noted that rates of homelessness have increased due to escalating costs of living in an a rea surrounded by affluence .9 Others have highlighted that ”Having a roof over your head, having a safe place to sleep and study, is fundamental to absolutely everything," and have not ed that students who experience homelessness have higher dropout rates and are more lik ely to experience homelessness as adults. 10
School m oves related to evictions. Currently, students who se families have been evicted do not have protections allowing them to remain in their current school district. This means that precarious housing also mea ns precarious schooling for many of the county’s students. Frequent moves by students are closely related to lower educational proficiency.

In the City of San Francisco , a 2010 ordinance protects some students from being evicted during the school year; ho wever, it only relates to owner/relative move- in evictions. 11 Children in families who are evicted for other reasons may need to move schools or districts when their housing is lost.

English language learners. Countywide , 20% of public school students are English learners.

Again, this rate is highest at Ravenswood City Elementary, where 53% of students are English learners. La Honda -Pescadero Unified School District, J efferson Un ion High School, and Redwood City Elementary also have high rates of English le arners , representing more than a third of students .

9 Bartley, Kaitlyn. “ Homelessness: The shadow that hangs over students in this Bay Area school district.” The Mercury News.

December 2018.
10 J ones, Carolyn. “California schools see big jump in homel ess students.” P alo Alto Online. October 2020. 11 https://sfrb.org/new -amendment -prohibiting -owner -move -evictions -minor -children -during -school -year ROOT POLICY RESEARCH SECTION V, PAGE 19
Less than one percent of students in San Mateo County public school districts are foster youth or mig rant s. Cabrillo Unified School District had the highest rate of migrant students at 3%. La Honda -Pescad ero had the highest rate of foster children at 2%.

School districts without large low income populations also tend to serve very few English language learners. For instance, in Hillsborough Elementary where 0% of students qualify for reduced lunch, only 1 % of students are English language learners .

ROOT POLICY RESEARCH SECTION V, PAGE 20
Figure V -10.

Student s w ith E xtenuating C ircumstances , 20 20 -20 21

Source: California Department of Education and Root Policy Research The overall share of students in these groups has not changed drastically o ver time. As shown in Figure V-11, there have been slight decreases in the share of students who are English learners and the share of students who qualify for reduced lunc h from 2016- 2017 to 2020- 2021. A round School District
Unified School Districts
Cabrillo Unified 20% 37% 0% 2% 3%
La Honda- Pescadero 38% 38% 2% 1% 1%
South San Francisco 21% 34% 0% 1% 1%
High & Elementary School Districts
J efferson Union Hig h School 36% 44% 0% 0% 0%
Bayshore Elementary 30% 57% 0% 0% 0%
Brisbane Elementary 16% 19% 0% 0% 0%
J efferson Elementary 14% 27% 0% 1% 0%
Pacifica 9% 18% 0% 1% 0%
San Mateo Union Hig h School 10% 21% 0% 0% 0%
Burlingame Elementary 13% 11% 0% 0% 0%
Hillsborough Elementary 1% 0% 0% 0% 0%
Millbrae Elementary 19% 25% 0% 0% 0%
San Bruno Park Elementary 29% 18% 0% 0% 0%
San Mateo-Foster City 26% 28% 0% 2% 0%
Sequoia Union High School 15% 30% 0% 0% 0%
Belmont-Redwood Shores 10% 7% 0% 0% 0%
Las Lomitas Elementary 7% 6% 0% 0% 0%
Menlo Park City Elementary 6% 7% 0% 0% 0%
Portola Valley Elementary 4% 5% 0% 0% 0%
Ravenswood City Elementary 53% 83% 0% 30% 0%
Redwood City Elementary 38% 56% 0% 2% 1%
San Carlos Elementary 5% 6% 0% 0% 0%
Woodside Elementary 8% 10% 0% 0% 0%
Total 20% 29% <1% 2% <1%
M igr a n t
Reduced
Lunch
Eng lish
Learners
Foster
Children Homeless
ROOT POLICY RESEARCH SECTION V, PAGE 21
2% of students in the county are homeless and this has not changed between 2016- 2017 and 2020- 2021. Foster youth and migrant students are not shown in the figure, as both have hovered at less than 1% from year to year.

Figure V -11.

Changes in rates of English
Leaners, Reduced Lunch,
and Homelessness, 2 0 16-
20 17 t o 2 0 2 0-20 21
Note: These data exclude enrollments in SBE
Everest Public High School District, which
in 2015 combined with the Sequoia Union
High School District .

Source: California Department of Education and
Root Policy Research
:

Dur ing COVID -19, enrollments decreased by 3% between 2019 -2020 and 2020- 2021 school years, as families withdrew or did not reenroll their children from public schools . Enrollment among migrant students decreased much more drastically, by 16% (from 332 stud ents to 279 students).

Similarly, enrollment among students who qualify for reduced lunch declined at a higher rate (10%) than the overall student population. Foster children and English learners also experienced enrollment decreases at a rate higher than the tot al population , with 7% and 10% decreases in enrollment, respectively .

ROOT POLICY RESEARCH SECTION V, PAGE 22
Figure V -12.

E nrollm ent C hanges by E xtenuating C ircumstance, San Mateo County, 201 9 - 2020 to 20 20 -20 21

Source: California Department of Education and Root Policy Resear ch

Achievement Gaps
This section details achievement gaps within school districts. Gaps are measured by test scores, meeting California State University or University of California admissions standards , and college - going rates .

Test scores. Figure V-13 indicates the per cent of students who met or exceeded English and mathematics testing standards set by the California State Assessment of Student Performance and Progress . Overall, 62% of students in the county met or exceeded English testing standards an d 52% met or excee ded mathematics testing standards.

Of all the districts with high schools, San Mateo Union High School District had the highest student pass rates : 70% of their students met or exceeded standards in English testing and 50% met or exceeded standards in math ematics testing.

Among elementary school districts, Portola Valley Elementary School District and Woodside Elementary School District had the highest rate s of success in English, with 87% and 88% of students meeting or exceeding English testing standards, respectively. Woodside Elementary School District and Hillsborough Elementary School District had the highest rates of success in mathematics, with 84% and 85% meeting math testing standards, respectively.

In every school district, girl s scored higher on English tests than boys. Overall, girls m et or exceeded English testing at a rate of 67% while boys met or exceeded English testing at a rate of 57%. The largest gender gap was in Brisbane Elementary School District, where 72% of girls m et or exceeded Eng lish testing standards and just 56% of boys did: a gap of 16 percentage points.

ROOT POLICY RESEARCH SECTION V, PAGE 23
Gender gaps in mathematics were less pronounced , but largest gender gaps were in Cabrillo Unified School District and in La Honda Pescadero Unified School District. In Cabrillo Unified, girls passed mathematics at a rate 7% higher than boys, while in La Honda -Pescadero, boys passed at a rate 6% higher than girls.

Figure V -14.

Students who M et or E xceeded T esting S tandards, by G ender and D istrict, 201 8 - 20 19

Source: Californi a Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research The gender gap in test scores has started to close in recent years, as indicated in Figure V -15. In 2014- 2015 there was a 11 p ercentage point gap in girls’ and boys’ English testing pass rates, and by 2018- 2019 this was just a 10 percentage point gap. The figure also indicates that there have been steady gains in the share of students meeting or exceeding testing standards in the county.

District
Unified School Districts
Cabrillo Unified 48% 41% 55% 34% 31% 38%
La Honda- Pescadero 43% 36% 49% 31% 34% 28%
South San Francisco 52% 45% 60% 44% 42% 45%
High & Elementary School Districts
J efferson Union Hig h School 57% 52% 63% 37% 38% 35%
Bayshore Elementary 27% 24% 31% 27% 27% 28%
Brisbane Elementary 64% 56% 72% 54% 56% 53%
J efferson Elementary 48% 43% 54% 37% 39% 35%
Pacifica 60% 55% 65% 57% 57% 57%
San Mateo Union Hig h School 70% 66% 76% 50% 50% 50%
Burlingame Elementary 80% 75% 84% 78% 78% 78%
Hillsborough Elementary 85% 81% 89% 85% 86% 84%
Millbrae Elementary 63% 57% 70% 58% 58% 58%
San Bruno Park Elementary 50% 47% 53% 41% 43% 38%
San Mateo-Foster City 62% 58% 67% 56% 56% 56%
Sequoia Union High School 68% 64% 72% 50% 50% 50%
Belmont-Redwood Shores 82% 78% 86% 79% 78% 80%
Las Lomitas Elementary 86% 84% 88% 82% 84% 80%
Menlo Park City Elementary 84% 81% 87% 83% 82% 83%
Portola Valley Elementary 87% 83% 91% 83% 84% 82%
Ravenswood City Elementary 22% 20% 23% 15% 16% 13%
Redwood City Elementary 54% 49% 59% 46% 46% 46%
San Carlos Elementary 80% 77% 83% 75% 76% 74%
Woodside Elementary 88% 85% 91% 84% 85% 83%
Total 62% 57% 67% 52% 52% 52%
English Language Arts/Literacy Mathem atics
Total Boys Girls Total Boys Girls
ROOT POLICY RESEARCH SECTION V, PAGE 24
Figure V -15.

Students who M et or E xceeded T esting S tandards, by G ender, 2 0 14-20 15 t o 2 0 18- 20 19

Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Very large gaps in test scores by race and ethnicity exist among students in some areas . Figure V-16 illustrates the rate at which students of various racial and ethnic groups met or exceeded English testing standards .

For the past five years in San Mateo County, Asi an, White, and Filipino stu dents have met or exceeded English testing standards at rates higher than the overall student population. Hispanic, Black/African American , and Pacific Islander students, on the other hand, have been underserved in this realm and have consistently scored l ower than the overall student body .

However, across all groups, the rate at which students met or exceed English testing standards has increased since the 2014 -2015 school year. Hispanic students have made the largest percentage point gain: 34% met standa rds in 2014- 2015 and 40% met standards in 2019- 19, an increase of six percentage points.

ROOT POLICY RESEARCH SECTION V, PAGE 25
Figure V -16.

Students who M et or E xceeded E nglish T esting S tandards, by R ace and E thnicity , 2 0 14-20 15 t o 2 0 18-20 19

Source: California Department of Education, Cali fornia Assessment of Student Performance and Progress, and Root Policy Research A similar narrative holds in Math testing standards, where scores have improved among each racial and ethnic group from 2014- 201 5 to 2018 -2019. Again, White and Asian students meet or exceed math testing standards at rates higher than the overall population while Hispanic, Pacific Islander, and Black/African American students score d lower .

White and Hispanic students have seen the biggest increases in rates of mathematics succe ss:
both have experienced a five percentage point increase in the percent of students who met or exceeded math testing standards.

ROOT POLICY RESEARCH SECTION V, PAGE 26
Figure V -17.

Students who M et or E xceeded mathematics testing standards, by R ace and E thnicity , 2 0 14-20 15 t o 2 0 18-20 19

Sou rce: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Figure V-18 illustrates the rates at which students of various racial and ethnic groups met or exceeded mathematics testing standar ds by district .

The re were several districts in which the gaps between the overall test pas s rate s and a specific racial groups’ pa ss rate s were especially wide. For instance, in San Carlos Elementary School District, 75% of the total student body met or exceeded math testing standards, but only 11% of Black/African American students met or exceeded math testing standards — a gap of 64 percentage points.

Other school districts with wide gaps between B lack/A frican American and overall math testing success w ere Las Lomitas Elementary (46 percentage point gap), Menlo Park City Elementary (43 percentage point gap), and Belmont -Redwood Shores (42 percentage point gap).

Some school districts also had similar gaps in Pacific Islander students’ math passing rates and overall passing rates. For instance, in Menlo Park City Elementary School District, 83% of the student body met or exceeded mathematics testing standards but just 35% of Pacific Islander students passed or exceeded mathematics testing standards —a gap o f 48 percentage points.

Millbrae Elementary School District also had a 47 percentage poin t gap between Pacific Islander students ’ and total s tudents’ math test rates.

ROOT POLICY RESEARCH SECTION V, PAGE 27
Figure V -18.

Students who M et or E xceeded M athem atics T esting S tandards, by R ace/ E thnici ty and D istrict, 20 1 8 -20 19

Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Although racial gaps in English testing were less pronounced, San Carlos Elementary School Distric t also had a wide gap between the total student body and Black/African American students.

Namely, 80% of the student body met or exceeded English testing stan dards, but only 19% of Black/African American students met or exceeded testing standards —a 61 perc entage point gap.

School District
Unified School Districts
Cabrillo Unified 34% 65% (no data) 38% 16% (no data) 54% La Honda- Pescadero 31% (no data) (no data) (no data) 20% (no data) 46% South San Francisco 44% 75% 19% 60% 29% 33% 46%
High & Elementary School Districts
J efferson Union Hig h School 37% 75% (no data) 36% 17% (no data) 42% Bayshore Elementary 27% 44% (no data) 38% 17% 14% (no data) Brisbane Elementary 54% 67% (no data) 65% 38% (no data) 60% J efferson Elementary 37% 61% 15% 42% 23% 20% 30%
Pacifica 57% 74% 38% 48% 38% (no data) 66%
San Mateo Union Hig h School 50% 84% (no data) 46% 22% 20% 63% Burlingame Elementary 78% 92% 53% 66% 50% (no data) 81% Hillsborough Elementary 85% 92% (no data) (no data) 76% (no data) 82% Millbrae Elementary 58% 75% 31% 63% 27% 11% 51%
San Bruno Park Elementary 41% 69% 23% 64% 25% 27% 50%
San Mateo-Foster City 56% 87% 30% 61% 23% 27% 69%
Sequoia Union High School 50% 81% 18% 53% 22% 11% 76%
Belmont-Redwood Shores 79% 92% 37% 77% 52% 43% 79%
Las Lomitas Elementary 82% 93% 36% (no data) 44% (no data) 87% Menlo Park City Elementary 83% 94% 40% (no data) 55% 35% 88% Portola Valley Elementary 83% 89% (no data) (no data) 56% (no data) 89% Ravenswood City Elementary 15% (no data) 9% (no data) 15% 11% (no data) Redwood City Elementary 46% 92% 22% 76% 34% 44% 75%
San Carlos Elementary 75% 91% 11% 85% 51% (no data) 78% Woodside Elementary 84% 92% (no data) (no data) 52% (no data) 89% Total 52% 82% 18% 50% 27% 21% 71%
Overall WhiteAsian Black Filipino Hispanic
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 28
Las Lomitas Elementary had a 41 percentage point gap between overall English testing success and Black/African American English testing succ ess.

O ther districts had large gaps between the total student body’s English test scores and Paci fic Islander students’ test scores. Namely, in Menlo Park City Elementary School District 84% of students met or exceeded English testing standards, but only 40% of Pacific Islander students — a 44 percentage point gap.

ROOT POLICY RESEARCH SECTION V, PAGE 29
Figure V -19.

Students who M et or E xce eded English T esting S tandards, by R ace/ E thnicity and D istrict, 201 8 -20 19

Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research Students with extenuating circumstances across all districts met or exceeded testing standards at lower rates . However, some districts had especially wide disparities between overall test scores an d test scores of students with extenuating circumstances.

School District
Unified School Districts
Cabrillo Unified 48% 78% (no data) 54% 28% (no data) 71% La Honda- Pescadero 43% (no data) (no data) (no data) 27% (no data) 61% South San Francisco 52% 76% 36% 66% 38% 44% 56%
High & Elementary School Districts
J efferson Union Hig h School 57% 81% (no data) 60% 43% (no data) 59% Bayshore Elementary 27% 49% (no data) 33% 20% 14% (no data) Brisbane Elementary 64% 63% (no data) 75% 51% (no data) 79% J efferson Elementary 48% 62% 28% 59% 34% 33% 43%
Pacifica 60% 65% 32% 52% 45% (no data) 68%
San Mateo Union Hig h School 70% 88% 55% 79% 50% 34% 81% Burlingame Elementary 80% 88% 61% 73% 55% (no data) 83% Hillsborough Elementary 85% 89% (no data) (no data) 77% (no data) 83% Millbrae Elementary 63% 74% 46% 68% 42% 23% 61%
San Bruno Park Elementary 50% 72% 39% 76% 36% 31% 56%
San Mateo-Foster City 62% 85% 41% 68% 34% 37% 77%
Sequoia Union High School 68% 87% 44% 92% 47% 31% 88%
Belmont-Redwood Shores 82% 91% 44% 81% 64% 61% 83%
Las Lomitas Elementary 86% 91% 45% (no data) 65% (no data) 89% Menlo Park City Elementary 84% 92% 60% (no data) 62% 40% 88% Portola Valley Elementary 87% 92% (no data) (no data) 58% (no data) 93% Ravenswood City Elementary 22% (no data) 24% (no data) 21% 18% (no data) Redwood City Elementary 54% 91% 35% 73% 43% 47% 83%
San Carlos Elementary 80% 90% 19% 76% 60% (no data) 83% Woodside Elementary 88% 92% (no data) (no data) 58% (no data) 92% Total 62% 82% 34% 64% 40% 31% 79%
Overall WhiteAsian Black Filipino Hispanic
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 30
For example, English learning students in Portola Va lley Elementary, Woodside Elementary, Menlo Park City Elementary, and Brisbane Elementary each met or exceeded mathematics test standards at a rat e at least 50 percentage points below the overall test rate in each district.

English learning students in Las Lomitas Elementary ( 54%) had the highest mathematics pass rates, followed by those in Belmont -Redwood Shores (42%) and Burlingame Elementary ( 40% ).

Students with disabilities scored especially high on mathematics tests in Hillsborough Elementary, where 4 8% met or exceeded standards. Others in B elmont -Redwood Shores (43%) and Woodside Elementary (41%) had high pass rates as well. S tudents with disabilities in San Carlos Elementary and Las Lomitas Elementary school districts scored far below the overall stu dent body : in these districts, students with disabilities met or exceeded mathematics test standards at 5 4 percentage points belo w the overall test rate .

In J efferson Elementary and Ravenswood Elementary students experiencing homelessness passed math test s at a rate similar to their housed peers . In other districts, however, students experiencing homelessness often scored substanti ally lower . School districts with the widest math testing gaps between the overall student body and students experiencing homel essness were San Mateo -Foster City and Millbrae Elementary, with a 41 percentage point gap and 42 percentage point gap, respectiv ely.

ROOT POLICY RESEARCH SECTION V, PAGE 31
Figure V -20 .

Students who M et or E xceeded M ath T esting S tandards, by S pecial C ase and D istrict, 20 1 8 -20 19

Source: Cali fornia Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research

School District
Unified School Districts
Cabrillo Unified 34% 4% 5% 4% 9%
La Honda- Pescadero 31% 4% (no data) (no data) 2%
South San Francisco 44% 20% 25% 4% 18%
High & Elementary School Districts
J efferson Union Hig h School 37% 5% (no data) (no data) 6% Bayshore Elementary 27% 11% (no data) (no data) 9%
Brisbane Elementary 54% 4% (no data) (no data) 12%
J efferson Elementary 37% 15% 36% (no data) 11%
Pacifica 57% 22% (no data) (no data) 17%
San Mateo Union Hig h School 50% 10% (no data) (no data) 13% Burlingame Elementary 78% 40% (no data) (no data) 29%
Hillsborough Elementary 85% (no data) (no data) (no data) 48% Millbrae Elementary 58% 26% 16% (no data) 25%
San Bruno Park Elementary 41% 12% (no data) (no data) 9% San Mateo-Foster City 56% 11% 15% (no data) 14%
Sequoia Union High School 50% 3% 33% (no data) 9%
Belmont-Redwood Shores 79% 42% (no data) (no data) 43%
Las Lomitas Elementary 82% 54% (no data) (no data) 28%
Menlo Park City Elementary 83% 31% (no data) (no data) 38% Portola Valley Elementary 83% 14% (no data) (no data) 39% Ravenswood City Elementary 15% 5% 11% (no data) 2%
Redwood City Elementary 46% 14% (no data) 29% 14%
San Carlos Elementary 75% 24% (no data) (no data) 21%
Woodside Elementary 84% 27% (no data) (no data) 41%
Eng lish
Learners
Experiencing
homelessness M igr a n t
With
DisabilitiesOverall
ROOT POLICY RESEARCH SECTION V, PAGE 32
Students with extenuating circumstances also consistently scored lower in English testing than the overall student body .

For instance, E nglish learning students in San Mateo Union High School District, Hillsborough Elementary School District, Sequoia Union High School District , Menlo Park City Elementary School District, and Portola Valley Elementary School District met or exceeded English test standards at a rate at least 60 percentage points below the overall test rate in each district.

Hills borough Elementary had the largest gap at 85 percentage points. Las Lomitas Elementary had the highest success rate among English learners, where 50% m et or exceeded English testing standards.

However, s tudents with disabilities in Las Lomitas Elementary a nd San Carlos Elementary school districts met or exceeded English test standards at rate 55 and 51 percentage points below the overall test rate , respe ctively . These were the largest gaps in the county. Students with disabilities at Woodside Elementary did the best on English testing, where 56% passed or exceeded standards.

Among students experiencing homelessness, those at Sequoia Union High School wer e most likely to meet English testing standards, with 42% meeting or exceeding standards. The s chool dist rict with the widest gap between overall English test scores and scores among students experiencing homelessness was Cabrillo Unified with a 34 percent age point gap.

J ust three districts reported English testing scores among mig rant students. Redwood City Elementary had the highest pass rate at 34 % and C abrillo Unified had the lowest at 16%.

ROOT POLICY RESEARCH SECTION V, PAGE 33
Figure V -21.

Students who M et or E xceeded English T esting St a ndards, by S pecial C ase and D istrict, 20 1 8 -20 19

Source: California Department of Education, California Assessment of Student Performance and Progress, and Root Policy Research

School District
Unified School Districts
Cabrillo Unified 48% 9% 14% 16% 12%
La Honda- Pescadero 43% 9% (no data) (no data) 9%
South San Francisco 52% 21% 35% 20% 18%
High & Elementary School Districts
J efferson Union Hig h School 57% 3% (no data) (no data) 19% Bayshore Elementary 27% 3% (no data) (no data) 4%
Brisbane Elementary 64% 21% (no data) (no data) 16%
J efferson Elementary 48% 16% 30% (no data) 15%
Pacifica 60% 12% (no data) (no data) 15%
San Mateo Union Hig h School 70% 11% (no data) (no data) 27% Burlingame Elementary 80% 33% (no data) (no data) 33%
Hillsborough Elementary 85% (no data) (no data) (no data) 47% Millbrae Elementary 63% 19% 34% (no data) 23%
San Bruno Park Elementary 50% 14% (no data) (no data) 12% San Mateo-Foster City 62% 9% 33% (no data) 15%
Sequoia Union High School 68% 8% 42% (no data) 27%
Belmont-Redwood Shores 82% 31% (no data) (no data) 45%
Las Lomitas Elementary 86% 51% (no data) (no data) 31%
Menlo Park City Elementary 84% 21% (no data) (no data) 42% Portola Valley Elementary 87% 17% (no data) (no data) 37% Ravenswood City Elementary 22% 6% 16% (no data) 5%
Redwood City Elementary 54% 13% (no data) 34% 16%
San Carlos Elementary 80% 29% (no data) (no data) 28%
Woodside Elementary 88% 18% (no data) (no data) 56%
Eng lish
Learners
Experiencing
homelessness M igr a n t
With
DisabilitiesOverall
ROOT POLICY RESEARCH SECTION V, PAGE 34
Students who met university requirements. Many high schoolers in the county met admission standards for a University of California (UC) or California State University (CSU ) school. Figure V -22 illustrates the percentage of cohort graduates who met admission requirements for a CSU or UC school according to California Department of Education data.

Of the high school districts in San Mateo County, Sequoia Union had the highes t rate of graduates who met such admission standards, at 69% . On the other end of the spectrum, Cabrillo Unified and South San Francisco Unified had the lowest r ates at 41%.

Figure V -22 .

Students Meeting
California University
Adm ission
S t a n d a r d s , 2 0 19 -
2020

Source:

California Department of Education
and Root Policy Research.

Cabrillo Unified and South San Francisco Unified have experienced a decrease in the s hare of graduates meeting CSU or UC admission standards in recent years. For instance, in 2016 -2017, 57% of South San Francisco Unified graduates met these standards, but this decreased by 16 percentage points by 2019 -2020. Cabrillo Unified experienced a l ess drastic decrease over the same period, but the rate still shrunk by two percentage points.

J efferson Union High School District had the most drastic increase in the s hare of graduates meeting CSU or UC standards: just 21% of students met these standar ds in 2016- 2017 compared to 48% of students in 2019- 2020. La Honda -Pescadero Unified School District experienced a 10 percentage point increase in this success rate over t he same period.

Sequoia Union and San Mateo Union experienced more modest increases, but remain the districts with the highest rates of students meeting CSU and UC standards.

ROOT POLICY RESEARCH SECTION V, PAGE 35
Figure V -23 .

Students Meeting
University
Adm ission
Standards, 2 0 16-2 0 17
and 201 9 -20 20

Source:

California Department of Education
and Root Policy Research.

Rat es at which students met CSU or UC admissions standards varied substantially by race and ethnicity in 2019 -2020. In all high school districts in San Mateo County, White and Asian students meet CSU and UC admissions standards at higher rates than the overal l student population.

The largest gap is in South San Francisco Unified, where just 41% of students meet CSU or UC admissions standards, but 73% of Asian students meet those standards —a 32 percentage point gap.

On the other end of the spectrum, Black/Afr ican American students typically m et CSU or UC admissions standards at lower -than -average rates. The largest gap was in San Mateo Union, where just 29% of Black/African American students met CSU or UC standards compared to 68% of students in the district o verall.

Filipino students typical ly met admissions standards at rates similar to the overall student body.

For instance, i n J efferson Union, San Mateo Union, and South San Francisco Unified, Filipino students are slightly more likely to have meet CSU and UC standards than the overall stud ent population. In Sequoia Union, they are slightly less likely to have met admission standards than the overall student population.

ROOT POLICY RESEARCH SECTION V, PAGE 36
In La Honda -Pescadero, Hispanic student s are slightly more likely to have met CSU or UC standards than the overall student body. However, in all other school districts, Hispanic students are less likely to have met CSU and UC standards than the overall student body. The largest disparity is in San Mateo Union, where just 46% of Hispanic studen ts meet the university admissions standards compared to 68% of students overall.

Finally, Pacific Islander students in J efferson Union were slightly more likely to have met California university admissions standards compared to the overall student b ody, b ut in Sequoia Union and San Mateo Union they were substantially less likely.

ROOT POLICY RESEARCH SECTION V, PAGE 37
Figure V -24 .

Students Meeting University Admission Standards, by R ace and E thnicity, 20 1 9 - 2020

Source: California Department of Education and Root Policy Research As exp ected, students with extenuating circumstances were less likely to meet CSU or UC admissions standards than students in the county overall. In all school districts where data are available, students with disabilities, students experiencing homelessness, En glish learners, foster youth, and migrant students met CSU or UC admission standards at lower rates than the overall student population .

ROOT POLICY RESEARCH SECTION V, PAGE 38
English learners in Sequoia Union and San Mateo Regional met CSU or UC admission standards at higher rates than their peers in other school districts . However, compared to the overall student body within their own school districts, they had a larger gap than other districts. Namely , in Sequoia Union, 69% of students met admissions standards compared to just 32% of student s lear ning English — a 37 percentage point gap.

Similarly, students with disabilities in Sequoia Union had the highest rate of meeting admissions standards (31%) compared to peers with disabilities in other districts, but also had the largest gap ( 38 perce ntage points) compared to the district’s overall student body.

Migrant students met admission standards at the lowest rate in South San Francisco Unified (27%) and at the highest rate in Sequoia Union (45%). However, in Cabrillo Unified, their rates were only e ight percentage points lower than that of the overall student body, the smallest gap in the county.

Approximately 36% of students experiencing homelessness in Sequoia Union met CSU or UC admission stand ards, which was higher than rates in San Mateo Union (21%) and J efferson Union (21%).

J ust San Mateo Union and Sequoia Union had enough foster youth to report their rate of meeting CSU or UC admission standards. In Sequoia Union, 29% met admissions standa rds and 22% in San Mateo Union met admissions standar ds .

ROOT POLICY RESEARCH SECTION V, PAGE 39
Figure V -25 .

Students Meeting
University
Adm ission
Standards , 2 0 19-
2020

Source:

California Department of Education
and Root Policy Research.

Notes; La -Honda Pescadero Unified
is excluded from these data as they
do not report admission standards
data for these special groups, likely
due to small sample size.

College -going rates. The college -going ra te is defined as the percentage of public high school students who completed high school in a given year and subsequently enrolled in any public or pri vate postsecondary institution (in -state or out- of-state) in the United States within 12 or 16 months of completing high school.

Most school districts in the county have a college -going rate at 70% or higher. San Mateo Union had the hi ghest college -going r ate at 77%. La Honda- Pescadero Sc hool District is the notable exception, with just 32% of graduates attending college within 12 or 16 months.

ROOT POLICY RESEARCH SECTION V, PAGE 40
Figure V -26 .

College -G oing
Ra tes, 2 0 17-2 0 18

Source:

California Department of Education
and Root Policy Research.

As shown in Figure V -27, La Honda -Pescadero School District previously had the highest college - going rate of all the county’s high school districts, with an 80% colleg e-going rate in 2014- 2015 and a 93% college -going rate in 2015- 2016. The district ex perienced a rapid decline in college - going rates, starting in 2016- 2017. However, La Honda -Pescadero has especially small sample sizes. For in stance, the district had just 26 twelfth -graders in the 2017- 2018 school year, meaning that just a couple students going to college (or not) drastically alters the college -going rate in La Honda -Pescadero. All other high school districts in the county have maintained relatively consist ent college -going rates.

ROOT POLICY RESEARCH SECTION V, PAGE 41
Figure V -27 .

College -G oing
R a t e s , 2 0 14-20 15 to
2 0 17-20 18

Source:

California Department of Education
and Root Policy Research.

Within each of the high school districts, college -going rates vary by race and ethnicity.  In every district, White students have a higher college- going rate than Hispanic students , but the largest gaps are in South San Francisco United, where 91% of White students go to college compared to just 68% of Hi spanic students, a 23 percentage point gap. J efferson Union has the smallest gap between the two g roups : 77% of White students go to college compared to 71% of Hispanic students .
 Am ong Black/African American students, those at San Mateo Union have the hig hest college -going rate at 82%. Those at J efferson Union have the lowest at just 53%, which is 24 percentage points lower than that o f White students and 34 percentage points lower than that of Asian students.
 Overall, Asian students have among the highes t college -going-rates in the county. The rate is especially high in South San Francisco Unified, where 92% go to college. The rate is lowest in Sequoia Union High School District, where 84% go to college.

ROOT POLICY RESEARCH SECTION V, PAGE 42
 Filipino students also have generally high rates of college -going. The highest college -going rate among Filipino students is in Sequoia Union (86%) and the lowest is in South San Franc isco Unified (73%).
 College -going rates for Pacific Islander students vary substantially by district. For instance, in Sequ oia Union 54% go to college, but in South San Francisco Unified 92% go to college.

Figure V -28 .

College -going R ates by R ace and E thn icity, 20 1 7-18

Note: Cabrillo Unified and La Honda - Pescadero Unified are not included here because they do not report the data, likely due to small sample sizes.

Source: California Department of Education and Root Policy Research

ROOT POLICY RESEARCH SECTION V, PAGE 43
College -going rates a re lower for students with disabilities and those learning Englis h compared to the overall student population across the county .
 For instance, the largest gap between overall college- going rates and English learners’ college -going rates is in South San Fr ancisco Unified, where just 52% of English learning students go t o college as opposed to 74% of the overall student population — a 22 percentage point gap. Among English learners, San Mateo Union High School District had the highest college- going rate, wher e 63% of English learners go to college.  Among students with dis abilities, South San Francisco Unified also had the largest gap, where 59% of students with disabilities went to college compared to 74% of the overall student population — a 15 percentage po int gap. J efferson Union, on the other hand, had a relatively hig h college -going rate among students with disabilities that was not very different from the district’s overall college- going r ate : 71% went to college which is just five percentage points lowe r than the district’s overall student population.

Figure V -29 .

C ollege -going R ates
for English L earners
and S tudents with
D isabilities , 2 0 17-
20 18

Note:

Cabrillo Unified and La Honda -
Pescadero Unified are not included
here because they do not report the
data, likely due to small sample sizes.

Source:

California Department of Education
and Root Policy Research.

Gaps in college enrollment by race, ethnicity, disability status, or English learning have stark financial consequences for students in the long -term. Figure V -30 illustrates median annual earnings by educational attainment. College degrees are especially important in San Mateo County: those with a bachelor’s degree in the county earn 115% more than those with a high school diploma. This gap i s wider in San Mateo County than in other parts of California and nationwide . The differences between high -school gr aduate earnings and bachelor 's degree earnings are around 100% in California and 76% in the US overall .

Figure V -30 .

ROOT POLICY RESEARCH SECTION V, PAGE 44
Median A nnual E arnings by E ducational A ttainm ent, 201 9

Source: 5 -year 2019 American Community Surveys Data.

Unfortunately, the gap between high school graduate s’ and college graduates ’ earnings have been increasing in San Mateo County. As illustrated in Figure V -31, median ea rnings for high school graduates increased by just 15% over the last decade (from $31,816 to $36,747) while ear nings for college graduates increased by 29% over the same period (from $61,485 to $79,080) .

ROOT POLICY RESEARCH SECTION V, PAGE 45
Figure V -31.

Median A nnual Ea rnings by E ducational A ttainm ent in San Mateo County , 2 0 10 t o 2 0 19

Source: 5 -year American Community Surveys Data.

Because income disparities between college graduates and high school graduates have been increasing, it is increasingly important that school districts in San Ma teo County address differences in college- going rates stratified by race, ethnicity, and extenu ating circumstances.

B arriers to Success
Many students are unable to achieve academic success because of barriers in home and school.

This section explores the a vailable indicators of barriers to success, including chronic absenteeism and dropout rates. It also describes inequities in discipline rates by race and

ROOT POLICY RESEARCH SECTION V, PAGE 46
ethnicity, which has been linked both to discrimination by education professionals as well as a major barrier to students’ future success.

Chronic absenteeism . Academic studies have found that if a student is chronically absent, it reduces their math and reading achievement outcomes, educational engagement, and social engagement. 12 Chronic absenteeism also has spillover effects and negatively impacts students who themselves are not chronically absent. For instance, one study found that students suffer academically from having chronically absent classmates —as exhibited across both reading and math testing ou tcomes. 13
Students are considered chronically absent if th ey were absent for 10% or more of the days during a school year . Note, however, s tudents are exempt from chronic absenteeism calculations if they receive instruction through a home or hospital instru ctional setting , are attending community college full- tim e, or were not expected to attend more than 31 days.

In the county overall, 10% of students were chronically absent during the 2018- 2019 school year .14 This is a slight increase from the 2016 -2017 sc hool year, where just 9% of students overall were chroni cally absent.

Chronic absenteeism rates were higher in districts with a large number of students experiencing economic and housing precarity. For instance, Ravenswood Elementary, which has a 30% rate of homelessness among students, had one of the higher rates of chronic absenteeism at 16%. La Honda -Pescadero and Sequoia Union high school dis tricts also had high rate s of chronically absent students at 16% and 17%, respectively.

When disaggregating by race and ethnicity, just 3% of Asian students were chronically absent, and 7% of White and Filipino students were chronically absent. On the other end of the spectrum, Pacific Islander students (26%) , Black/African American students (18%), and Hispanic stu dents (15%) had notably higher rates of chronic absenteeism than the overall student population (10%).

Chronic absenteeism among Pacific Islande r students has increased in recent years, as illustrated in Figure V -32.

12 Gottfried, Michael A. "Chronic absenteeism and its effects on students’ academic and socioemotional outcomes." J ournal of Education for Students Placed at Risk (J ESPAR) 19.2 (2014): 53- 75. 13 Gottfried, Michael A. "Chronic absenteeism in the classroom context: Effects on achievement." Urban Education 54.1 (2019): 3- 34.
14 Because of the phys ical school closures during the COVID -19 pandemic, the California Department of Education determined that 2019– 2020 absenteeism data are not valid, therefore, we present data from the 2018 -2019 school year.

ROOT POLICY RESEARCH SECTION V, PAGE 47
Figure V -32 .

Chronic
A bsenteeism by
R a ce/ E thnicity,
2 0 16-20 17 t o 2 0 18 -
2 0 19
Source: California Department of
Education and Root Policy
Research

Chronic absenteeism among Pacific Islander students was especially pronounced in San Mateo - Foster City school district where there was a 26 percent age point gap between chronic absenteeism rates for Pacific Islander students ( 32%) and the overall student b ody (6%). Other districts had similarly large gaps, including San Bruno Park Elementary (20 percentage points) and South San Francisco Unified (18 percentage points) .

Some districts had larger gaps in absenteeism rates between Black/African American students and the overall population. For instance, in San Carlos Elementary, 4% of the overall student body is chronically absent compared to 27% of Bla ck/African American students — a 23 percentage point gap. J efferson Elementary school district had a 17 percentage point gap between their overall chronic absenteeism rate (12%) and their chronic absenteeism rate among Black/African American students ( 28%).

Among White student s, Bayshore Elementary School District was a major outlier, where 46% of White students were chronically absent compared to just 12% of the total student population.

However, it is important to note that this represents a very small sa mple of White student s: just 3% of students at Bayshore Elementary are White, one of lowest in the c ounty.

ROOT POLICY RESEARCH SECTION V, PAGE 48
Figure V -33 .

Chronic A bsenteeism by D istrict and R ace/ E thnicity, 20 1 8 -2 0 19

Source: California Department of Education and Root Policy Research In most districts, chronic absenteeism is higher among students with disabilitie s. In fact, only Bayshore Elementary’s students with disabilities had a lower rate of chronic absenteeism than the overall student body. In all other districts , students with dis abilities were more likely to be chronically absent than the overall student population . This was particularly true in Sequoia Union High School District, J efferson Union High School District, and San Mateo Union High School District
Unified School Districts
Cabrillo Unified 10% 5% (no data) 5% 11% (no data) 10%
La Honda- Pescadero 16% (no data) (no data) (no data) 14% (no data) 18% South San Francisco 13% 4% 16% 7% 17% 31% 12%
High & Elementary School Districts
J efferson Union Hig h School 15% 8% 22% 11% 22% 18% 15% Bayshore Elementary 12% 5% 12% 0% 18% 19% 46%
Brisbane Elementary 12% 3% (no data) 12% 17% (no data) 17% J efferson Elementary 12% 5% 28% 6% 13% 25% 23%
Pacifica 7% 4% 12% 6% 9% 21% 7%
San Mateo Union Hig h School 10% 3% 18% 4% 17% 21% 9%
Burlingame Elementary 5% 2% 15% 5% 10% 20% 5%
Hillsborough Elementary 4% 1% (no data) 4% 4% (no data) 6% Millbrae Elementary 10% 3% 6% 17% 16% 26% 14%
San Bruno Park Elementary 12% 5% 10% 4% 14% 32% 9%
San Mateo-Foster City 6% 2% 9% 2% 10% 32% 4%
Sequoia Union High School 17% 6% 23% 8% 23% 33% 10%
Belmont-Redwood Shores 5% 3% 8% 5% 12% 17% 5%
Las Lomitas Elementary 4% 2% 0% (no data) 7% (no data) 3% Menlo Park City Elementary 3% 1% 8% 7% 5% 14% 3%
Portola Valley Elementary 4% 0% (no data) (no data) 6% (no data) 3% Ravenswood City Elementary 16% 0% 20% (no data) 15% 24% 21% Redwood City Elementary 10% 2% 19% 3% 12% 18% 4%
San Carlos Elementary 4% 2% 27% 8% 7% (no data) 3%
Woodside Elementary 8% 0% 0% (no data) 12% (no data) 7% Total 10% 3% 18% 7% 15% 26% 7%
Total Asian Black Filipino Hispanic
Pacific
Islander White
ROOT POLICY RESEARCH SECTION V, PAGE 49
School District, which had gaps bet wee n the overall absenteeism rate and the absenteeism rate among students with disabilities of 13, 12, and 11 percentage points, respectively.

Rates of chronic absenteeism were also higher among English learners than the general population in most district s ( with the exception of Ravenswood City Elementary and J efferson Elementary). Woodside Elementary and Sequoia Union High School districts both had 14 percentage point gaps between absenteeism rates of English learners and the overall student body.

In eve ry school district where the data are available, foster youth had higher rates of chronic absenteeism than the overall population. This was especially true in Sequoia Union High School District, where 63% of foster youth were chronically absent compared to just 17% of the overall student body.

Similarly, in almost all districts with available data, students experiencing homelessness had higher rates of chronic absenteeism than the overall student body. The chronic absenteeism rate among students experienci ng homelessness was highest in B urlingam e Elementary at 64%.

Migrant students were chronically absent at rates similar to or lower than the total student body in all districts with reported data .

ROOT POLICY RESEARCH SECTION V, PAGE 50
Figure V -34 .

Chronic A bsenteeism by D istrict and E xtenuatin g C ircumstance , 2 0 18-2 0 19

Source: California Department of Education and Root Policy Research

Dropout rates. As previously indicated, workers without a high school degree have the lowest annual earnings compared to others at higher levels of educational a ttainment. In addition to the economic and housing precarity associated with low earnings, l ow earnings also often lead to increased incentives to participate in criminal activity. In fact, one study suggest that School District
Unified School Districts
Cabrillo Unified 10% 12% 23% 9% (no data) 18%
La Honda- Pescadero 16% 16% (no data) (no data) (no data) 22% South San Francisco 13% 14% 47% 13% 49% 18%
High & Elementary School Districts
J efferson Union Hig h School 15% 27% 33% (no data) 36% 28% Bayshore Elementary 12% 19% (no data) (no data) (no data) 11% Brisbane Elementary 12% 18% (no data) (no data) (no data) 18% J efferson Elementary 12% 10% 21% (no data) 24% 16%
Pacifica 7% 11% (no data) (no data) (no data) 14%
San Mateo Union Hig h School 10% 21% 50% (no data) 53% 21% Burlingame Elementary 5% 8% 64% (no data) (no data) 12% Hillsborough Elementary 4% 6% (no data) (no data) (no data) 8% Millbrae Elementary 10% 12% 5% (no data) (no data) 12%
San Bruno Park Elementary 12% 12% (no data) (no data) 18% 20% San Mateo-Foster City 6% 8% 15% (no data) 17% 13%
Sequoia Union High School 17% 31% 52% 16% 63% 29%
Belmont-Redwood Shores 5% 11% (no data) (no data) (no data) 10% Las Lomitas Elementary 4% 6% (no data) (no data) (no data) 5% Menlo Park City Elementary 3% 5% (no data) (no data) (no data) 9% Portola Valley Elementary 4% 3% (no data) (no data) (no data) 9% Ravenswood City Elementary 16% 16% 19% 17% 23% 21%
Redwood City Elementary 10% 12% 30% 6% 32% 16%
San Carlos Elementary 4% 8% 23% (no data) (no data) 11% Woodside Elementary 8% 22% (no data) (no data) (no data) 10% Total
Eng lish
Learners
Experiencing
homelessness M igr a n t
With
Disabilities
Foster
Youth
ROOT POLICY RESEARCH SECTION V, PAGE 51
high school dropouts are 3.5 times more lik ely than high school graduates to be imprisoned at some point during their lifetime. 15 Another study found tha t raising the high school completion rate by one percent for all men ages 20 through 60 would save the US $1.4 billion annually in crime related co sts. 16 Dropping out of high school also has adverse health costs: for instance, research has shown that high school dropouts are more likely to smoke and have a marijuana disorder in adulthood. 17 For these reasons, reducing high school dropout rates in San M ateo County is pivotal to the health and economic prosperity of the community.

In this report, dropout rate s shown for high school districts with available data and are defined as the percentage of cohort students who did not graduate with a regular high s chool diploma, did not complete high school, and are not still enrolled as a "fifth year senior".

In the 20 19- 2020 academic year, dropout rates were highest in Sequoia Union High School District, where 10% of students dropped out. This is similar to South San Francisco Unified, where 9% of students dropped out. In both these districts, and in Cabrillo Unified, dropout rates have increased since 2016- 2017.

Dropout rates have decreased by one percentage point over the same period in San Mateo Union High Scho ol District, from 5% to 4%. J efferson Union had the lowest dropout rate in the county at just 3%, which aft er slightly higher rates in 2017 -18 and 2018- 19, is the same as its 2016- 2017 rate.

15 Monrad, Maggie. "High School Dropout: A Quick S tats Fact Sheet." National High Schoo l Center (2007). 16 U.S. Department of J ustice, Bureau of J ustice Statistics. (2002). Correctional populations in the United States, 1998 (NCJ - 192929). Washington: U.S. Government Printing Office.
17 Gonzalez, J ennifer M. Reingle, et al. "The long -term effec ts of school dropout and GED attainment on substance use disorders." Drug and alcohol dependence 158 (2016): 60- 66.

ROOT POLICY RESEARCH SECTION V, PAGE 52
Figure V -35 .

Dropout R ates by
D istrict, 20 1 6 -20 17 t o
2 0 19-20 20
Note: La Honda -Pescadero Unified
School District is excluded
from these data .

Source: California Department of
Educatio n and Root Policy
Research

In all school districts in the county, dropout rates are higher for boys than for girls. J efferson Union h ad the smallest gender gap, where 3% of girls dropped out and 4% of boys dropped out.

Sequoia Union had the widest gender gap, where 13% of boys dropped out compared to just 7% of girls.

Figure V -36 .

Dropout R ates by
G ender, 201 9 -20 20
Note: La Honda -Pesca dero Unified
School District is excluded
from these data.

Source: California Department of
Educa tion and Root Policy
Research

Pacific Islander , Black/African American , and Hispanic students in the county often had hig her dropout rates than those in ot her racial and ethnic groups.

ROOT POLICY RESEARCH SECTION V, PAGE 53
 In Sequoia Union High School District, dropout rates were highest among Pacific Islander students, where 20% dropped out in the 2019- 2020 academic year. Dropout rates were also especially high among Hispanic and Black/African American students in Sequoia Union, at 16% and 12% respectively.
 In districts with lower dropout rates , for instance, J efferson Union, the highest dropout rates still found am ong Black/African American (7%) and Hispanic students ( 6%).  Notably, however, in South San Francisco Unified, White students were mo re likely to drop out than any other racial or ethnic group. In fact, 12% of White students dropped out compared to 11% of Hispanic students, 5% of Filipino students, and 3% of Asian students.

Data for Black/Afr ican American and Pacific Islander students were not available for South San Francisco Unified due to small sample sizes.

Figure V -37 .

Dropout R ates by R a c e , 2 0 19-20 20

ROOT POLICY RESEARCH SECTION V, PAGE 54
Source: California Department of Education and Root Policy Research In all school di stricts in the county, students with disabilities, students experiencing homelessness, foster youth, and students learning English had higher dropout rates than the overall population.
 Among students with disabilities, the highest dropout rate wa s in Sequ oia Union, where 24% dropped ou t. The gap between overall dropout rates and dropout rates among students with disabilities was wide in Sequoia Union at 14 percentage points.  Cabrillo Unified, on the other hand, had less than a one percentage poin t gap bet ween the dropout rate of overall students (6%) and students with disabilities (6%).  Among students learning English, Sequoia Union had the highest dropout rate at 27%, while J efferson Union had the lowest dropout rate at 8%.  Sequoia Union also h ad the hi ghest rate of dropout among students experiencing homelessness at 29% while J efferson Union, again, had the lowest at 15%.  Foster Youth in Sequoia Union had an exceptionally high dropout rate a t 40%. San Mateo Union is the only other district in the county which reported these data in 2019 -2020, and found only 18% of foster youth dropped out.
 Migrant students at South San Francisco Unified actually dropped out at a rate slightly lower than the general student body : just 8% of migrant students dr opped out compared to 9% of the overall student body. However, those in Cabrillo Unified were 11 percentage points more likely than the total student body to dropout.

ROOT POLICY RESEARCH SECTION V, PAGE 55
Figure V -38 .

Dropout R ates by E xtenuating C ircumstance, 201 9 -20 20

Source: California Department of Education and Root Policy Research

Disproportionate discipline rates . Strict discipline policies may stigmatize suspended students and expose them to the criminal justice system at a young age, setting them up for limited economic and social success down the line . Research has found that suspensions not only negatively affect the suspended students, b ut also their peers. Students in schools with higher

ROOT POLICY RESEARCH SECTION V, PAGE 56
suspension rates are more likely to drop out or school and less likely to attend a four -yea r college. 18
Other a cademic studies have found that students from African American and Latino families are more likely than their White peers to receive expulsion or out of school suspension as consequences for the same or similar problem behavior. 19 This m eans that Black/African American and Hispanic students suffer more of the economic and social consequences than their White peers for the same behaviors.

Luckily, i n every high school district in San Mateo County, suspension rates have decreased since 2011- 2012. La Honda -Pescadero School District experienced the largest decrease: it was the district with the highest suspension rate in 2011 -2012 at 10% , but now has the lowest suspension rate at just 1% in 2019- 2020. San Mateo Union also experienced a rapid d ecrease in suspension rates over the same period, with a rate of 9% in 2011 -2012 to a rate of 3% in 2019- 2020.

18 Bacher -Hicks, Andrew, Stephen B. Billings, and David J . Deming. The school to prison pipeline: Long -run impacts of school suspensions on a dult crime. No. w26257. National Bureau of Economic Research, 2019. 19 Skiba, Russell J ., et al. "Race is not neutral: A national investigation of African American and Latino disproportionality in school discipline." Sch ool Psychology Review 40.1 (2011): 85- 107.

ROOT POLICY RESEARCH SECTION V, PAGE 57
Figure V -39 .

Suspension R a t e s , 2 0 11-20 1 2 to 201 9-20 20

Source: California Department of Education and Root Policy Research In many school dist ricts across San Mateo County, H ispanic students are disciplined at disproportionately higher rates compared to their peers. Figure V-40 compares each racial/ethnic gr o u p’s share of suspensions to the ir share of the overall student populatio n.  In all dist ricts except for La Honda -Pescadero, Hispanic students make up a larger share of suspensions than their overall share of the student body. For instance, in San Mateo Union, 34% of students are Hispanic, but 66% of suspended students are Hispanic, making a 32 percentage point overre presentation gap.
 In most districts, Black and Pacific Islander students are also overrepresented in terms of suspension rates, but these rates are slight compared to those of Hispanic students. For instance, in Sequoia Union, ju st 2% of the student body identified as Pacific Islander but 8% of suspended students were Pacific Islander.
 Asian and Filipino students were
underrepresented in terms of suspension rates. For
example, in J efferson Union High School District, 31% of stude nts identified as Filipino but just 10% of suspended students were Filipino, a 21 percentage point gap. In San Mateo

ROOT POLICY RESEARCH SECTION V, PAGE 58
Union High School, 22% of students identified as Asian but just 5% of suspended students were Asian, a 17 percentage point gap.
 White stud ents were also underrepresented in discipline rates in most districts except for La Honda- Pescadero, where they were overrepresented by 30 percentage points. They were substantially underrepresented in Cabrillo Unified (with a gap of 21 percent age points) and Sequoia Union ( 18 percentage points).

ROOT POLICY RESEARCH SECTION V, PAGE 59
Figure V -40 .

Suspension R ates by R ace and E thnicity, 20 1 9 -20 20

Notes: the percentage of suspensions and shares of racial groups do not sum to 100% because we exclude students with no reported race, with more tha n one reported race, where districts did not report racial/ethnic data due to small sample sizes. Gaps of 15 percentage points or more are highlighted.

Source: California Department of Education and Root Policy Research

School District
Asian Students
Share of Student Body 1% 14% 22% 9% 13%
Share of Suspensions 1% 7% 5% 1% 3%
Gap 0% -7% -17% -8% -10%
Black Students
Share of Student Body 1% 1% 3% 1%
Share of Suspensions 5% 1% 6% 2%
Gap 4% 0% 3% 1%
Filipino Students
Share of Student Body 1% 31% 6% 2% 23%
Share of Suspensions 0% 10% 2% 0% 9%
Gap -1% -21% -4% -2% -14%
Hispanic Students
Share of Student Body 52% 32% 61% 34% 41% 48%
Share of Suspensions 79% 46% 33% 66% 62% 69%
Gap 27% 14% -28% 32% 21% 21%
Pacific Islander Students
Share of Student Body 1% 2% 2% 2%
Share of Suspensions 4% 4% 8% 3%
Gap 3% 2% 6% 1%
White Students
Share of Student Body 40% 14% 37% 26% 38% 7%
Share of Suspensions 19% 16% 67% 14% 20% 7%
Gap -21% 2% 30% -12% -18% 0%
Cabrillo
Unified
J efferson
Union
H igh
La Honda-
Pescadero
San
Mateo
Union
H igh
Sequoia
Union
H igh
South San
Francisco
Unified
ROOT POLICY RESEARCH SECTION V, PAGE 60
Staff demographics. Diversity of sc hool staff has been shown to improve outcomes for students of color. For instance, one recent stu dy found that students are less likely to be removed from school as punishment when they and their teachers are the same race. This effect is driven almost ent irely by black students, especially black boys, who are markedly less likely to be subjected to e xclusionary discipline when taught by black teachers. There is little evidence of any benefit for white students of being matched with white teachers. 20 Other r esearch in California has found that , when students have a teacher of their race, they are more likely to attend class, therefore reducing chronic absenteeism. 21 Even more studies have found that having a teacher of a student’s own race substantially improv es their math and reading achievement. 22

In San Mateo County, the demographics of faculty and staff are fairly similar to that of its students. Figure V -41 illustrates the share of the county’s faculty and staff who are Asian, Black/African American , Hispa nic, Filipino, Pacific Islander, and White , and compares those shares to the racial/ethnic bre akdown of the county’s student body.

There is a slightly larger share of White and Black/African American staff than students, meaning that Black/African America n and White student groups are more likely to interact with same -race staff and faculty than o ther racial groups. Asian students are less likely to interact with a same - race staff of faculty member: 17% of the student body is Asian compared to just 8% of s taff and faculty.

20 Lindsay, Constance A., and Cassandra MD Hart. "Teacher race and school discipline: Are students suspended less often when they have a teacher of the same race?." Education Next 17.1 (2017): 72 -79. 21 Gottfried, Michael, J . J acob Kirksey, and Tina L. Fletcher. "Do High School Students With a Same -Race Teacher Attend Class More Often?." Educational Evaluation and Policy Analysis (2021): 01623737211032241. 22 Dee, T. S. (2004). Teachers, race, and student achievement in a randomized experiment. Review of economics and statistics, 86(1), 195- 210.

ROOT POLICY RESEARCH SECTION V, PAGE 61
Figure V -41.

Staff and S tudent
D emographics,
2020 -20 21
Notes: Percentages do not always
sum to 100% because we do
not show shares of staff
with no reported race, with
more than one reported
race, or Native American
staff.

Source: Calif ornia Department of
Education and Root Policy
Research

Since 2011- 2012, the county’s school districts have diversified in that there has been a 13 percentage point decrease in the share of White faculty and staff and a 10 percentage point increase in H ispanic faculty and staff. However, there has been a slight decrease (by two percentage point s) in the share of faculty and staff who identify as Black/African American . There has been a two percentage point increase in the share of Asian and Filipino facu lty and staff, and a one percent increase in the share of Pacific Islander faculty and staff.

ROOT POLICY RESEARCH SECTION V, PAGE 62
Figure V -4 2.

Faculty and S taff D emographics, 2 0 11-201 2 to 2020 -20 21

Notes: Percentages do not always sum to 100% because we do not show shares of staff with n o reported race, with more than one reported race, or Native American staff.

Source: California Department of Education and Root Policy Research Figure V-43 illustra tes faculty and staff racial and ethnic diversity for the 2020 -2021 school year by distric t.
 Portola Valley has the least diverse faculty and staff in the county, with 59% identifying as White.
 Ravenswood Elementary has the most diverse faculty and staff : the district has the highest share of Pacific Islander (5%) , Black/African American (12% ) and Hispanic ( 72%) faculty and staff .
 South San Francisco Unified School District has the highest share of Asian faculty and staff at 14% .
 Brisbane Elementary and J efferson Elementary have the highest shares of Filipino faculty and staff at 28%.

ROOT POLICY RESEARCH SECTION V, PAGE 63
Figure V -4 3.

Faculty and S taff R ace /E thnicity, by D istrict, 2020 -20 21

Notes: Percentages do not always sum to 100% because we do not show shares of staff with no reported race, with more than one rep orted race, or Native American staff.

Source: California De partment of Education and Root Policy Research School District
Unified School Districts
Cabrillo Unified 0% 1% 1% 46% 0% 51%
La Honda- Pescadero 0% 5% 5% 39% 0% 51%
South San Francisco 14% 3% 16% 34% 2% 28%
High & Elementary School Districts
J efferson Union Hig h School 3% 3% 13% 26% 1% 43%
Bayshore Elementary 13% 4% 17% 61% 0% 4%
Brisbane Elementary 7% 0% 28% 20% 4% 42%
J efferson Elementary 13% 3% 28% 25% 0% 29%
Pacifica 7% 2% 8% 23% 2% 54%
San Mateo Union Hig h School 11% 5% 6% 34% 3% 40%
Burlingame Elementary 8% 5% 11% 27% 3% 45%
Hillsborough Elementary 2% 1% 7% 20% 1% 55%
Millbrae Elementary 13% 3% 9% 25% 0% 48%
San Bruno Park Elementary 4% 2% 13% 26% 4% 48%
San Mateo-Foster City 13% 2% 7% 33% 3% 37%
Sequoia Union High School 2% 12% 2% 54% 4% 26%
Belmont-Redwood Shores 13% 2% 3% 39% 0% 42%
Las Lomitas Elementary 7% 7% 0% 42% 0% 42%
Menlo Park City Elementary 3% 1% 3% 28% 1% 40%
Portola Valley Elementary 4% 4% 0% 33% 0% 59%
Ravenswood City Elementary 2% 12% 1% 72% 5% 3%
Redwood City Elementary 4% 5% 2% 65% 1% 21%
San Carlos Elementary 8% 6% 3% 37% 1% 42%
Woodside Elementary 12% 8% 0% 30% 0% 49%
Total 8% 5% 8% 40% 2% 35%
WhiteAsian Black Filipino Hispanic
Pacific
Islander
ROOT POLICY RESEARCH SECTION V, PAGE 64
Figure V-44 illustra tes the ga p between faculty/staff representation and the student body. For instance, at San Bruno Park Elementary, 15% of the students are White while 48% of the faculty/staff are White, le aving a 33 percentage point gap.

If schools are striving for a distribution of faculty/staff that reflects the racial and ethnic distribution of their student body, the closer to a 0 percentage point gap, the better. Schools like San Bruno Park Elementar y fall short of meeting this goal, in that there is a large overr epresentation of White faculty/staff compared to the student body. Many other districts have a large overrepresentation of White faculty/staff, including Millbrae Elementary ( 32 percentage po int gap), J efferson Union High School District ( 29 percentage poi nt gap), and South San Francisco Unified School District (22 percentage points). There are just a few school districts where the share of White students is higher than the share of White facu lty, particularly Woodside Elementary and Menlo Park City Element ary, both with a 15 percentage point gap.

Across most school districts, the share of Asian students is larger than the share of Asian faculty/staff . This suggests that Asian students are les s likely than their peers to interact with a same -race teacher or staff member. The largest disparity is in Millbrae Elementary, where just 13% of the faculty identify as Asian compared to 46% of the student body, a 33 percentage point gap.

In many school districts, there is a dearth of Hispanic faculty and staff. For instance, in La Honda - Pescadero, 63% of students are Hispanic compared to 39% of faculty, a 24 percentage point gap.

In other districts, however, there is a larger share of Hispanic faculty/s taff than students. In Las Lomitas Elementary, for instance, 13% of students are Hispanic and 42% of faculty/staff are Hispanic. Recall that Las Lomitas Elementary commonly has high -perform ing English language learnings students. This may be partly due to the district ’s large portion of Hispanic faculty/staff.

Though d istrict wide there are approximately the same portions of Filipino students as there are faculty/staff, J efferson Union High School stands out as a district where Filipino students are less likely to interact with a same -race teacher or staff member. In J e fferson Union, 29% of students are Filipino compared to just 13% of faculty/staff.

In all districts, there only very small gaps in the share of students that identify as Pacific Islander and the share of faculty/staff that identify as Pacific Islander. All in all, they are represented in approximately equal proportions.

ROOT POLICY RESEARCH SECTION V, PAGE 65
Figure V -4 4 .

Difference B etween S taff and S tudent P opulation s , by D istrict, 20 20 -20 21

Notes: The figure shows percentage point gaps in student representation versus faculty/staff representation (calculated as the share of faculty/staff minus the share of students).

Source: California Department of Education an d Root Policy Research

School District
Unified School Districts
Cabrillo Unified -1% 1% 0% -6% 0% 11%
La Honda- Pescadero 0% 5% 4% -24% 0% 16%
South San Francisco 0% 2% -7% -14% 0% 22%
High & Elementary School Districts
J efferson Union Hig h School -12% 2% -16% -5% 0% 29%
Bayshore Elementary -6% 1% -4% 20% -4% 1%
Brisbane Elementary -13% -1% 16% -8% 4% 18%
J efferson Elementary -6% 1% 3% -11% -1% 18%
Pacifica -1% 1% -1% -3% 2% 15%
San Mateo Union Hig h School -12% 4% 1% 2% 1% 12%
Burlingame Elementary -19% 5% 8% 11% 3% 4%
Hillsborough Elementary -30% 1% 5% 15% 1% 7%
Millbrae Elementary -33% 2% 3% 5% -2% 32%
San Bruno Park Elementary -12% 1% 3% -15% -1% 33%
San Mateo-Foster City -13% 1% 4% -4% 1% 16%
Sequoia Union High School -7% 10% 1% 9% 2% -9%
Belmont-Redwood Shores -19% 1% 0% 27% -1% 8%
Las Lomitas Elementary -11% 6% -1% 29% 0% -11%
Menlo Park City Elementary -10% 0% 2% 11% 0% -15%
Portola Valley Elementary -2% 4% 0% 19% 0% -7%
Ravenswood City Elementary 2% 7% 1% -12% -2% 2%
Redwood City Elementary 0% 4% 1% -5% 0% 2%
San Carlos Elementary -10% 5% 2% 23% 1% -7%
Woodside Elementary 8% 6% 0% 14% -1% -15%
Total -9% 4% 0% 2% 0% 9%
Asian Black Filipino Hispanic
Pacific
Islander White

1 APPENDIX D | Attachment 4 – UC Merced Segregation Report UC Merced Urban Policy Lab and ABAG/MTC Staff
Version of Record: March 06, 15:56:53

2 0.1 Table of content

0.1 Table of content .................................................................................................... 2

0.2 List of figures ....................................................................................................... 3

0.3 List of tables ........................................................................................................ 3 1 Introduction .............................................................................................................. 4

1.1 Purpose of this Report ............................................................................................. 4

1.2 Defining Segregation ............................................................................................... 5

1.3 Segregation Patterns in the Bay Area ........................................................................... 5

1.4 Segregation and Land Use ......................................................................................... 6 2 Racial Segregation in City of San Mateo ............................................................................ 8

2.1 Neighborhood Level Racial Segregation (within City of San Mateo) ....................................... 8

2.2 Regional Racial Segregation (between San Mateo and other jurisdictions) ............................. 16 3 Income Segregation in City of San Mateo ......................................................................... 21

3.1 Neighborhood Level Income Segregation (within San Mateo) .............................................. 21

3.2 Regional Income Segregation (between San Mateo and other jurisdictions) ............................ 27 4 Appendix 1: Summary of Findings ................................................................................. 31

4.1 Segregation in City of San Mateo ............................................................................... 31

4.2 Segregation Between City of San Mateo and Other jurisdictions in the Bay Area Region ............. 31 5 Appendix 2: Segregation Data ...................................................................................... 33 6 References .............................................................................................................. 37

3 0.2 List of figures
Figure 1: Racial Dot Map of San Mateo (2020) ............................................................................. 9 Figure 2: Racial Isolation Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2020) ...... 11 Figure 3: Racial Dissimilarity Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2020) . 14 Figure 4: Theil’s H Index Values for Racial Segregation in San Mateo Compared to Other Bay Area Jurisdictions (2020) ........................................................................................................... 15 Figure 5: Racial Dot Map of San Mateo and Surrounding Areas (2020) ................................................ 16 Figure 6: Racial Demographics of San Mateo Compared to All Bay Area Jurisdictions (2020) .................... 18 Figure 7: Comparing the Share of People of Color in San Mateo and Vicinity to the Bay Area (2020) ........... 19 Figure 8: Income Dot Map of San Mateo (2015) .......................................................................... 22 Figure 9: Income Group Isolation Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2015) ................................................................................................................................... 24 Figure 10: Income Group Dissimilarity Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2015) ........................................................................................................................... 26 Figure 11: Income Group Theil’s H Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2015) ........................................................................................................................... 27 Figure 12: Income Dot Map of San Mateo and Surrounding Areas (2015) ............................................. 28 Figure 13: Income Demographics of San Mateo Compared to Other Bay Area Jurisdictions (2015) .............. 29

0.3 List of tables
Table 1: Racial Isolation Index Values for Segregation within San Mateo ............................................ 10 Table 2: Racial Dissimilarity Index Values for Segregation within San Mateo ....................................... 13 Table 3: Theil’s H Index Values for Racial Segregation within San Mateo ........................................... 15 Table 4: Population by Racial Group, San Mateo and the Region ..................................................... 17 Table 5: Regional Racial Segregation Measures .......................................................................... 20 Table 6: Income Group Isolation Index Values for Segregation within San Mateo .................................. 23 Table 7: Income Group Dissimilarity Index Values for Segregation within San Mateo .............................. 25 Table 8: Theil’s H Index Values for Income Segregation within San Mateo .......................................... 26 Table 9: Population by Income Group, San Mateo and the Region .................................................... 28 Table 10: Regional Income Segregation Measures ....................................................................... 30 Table 11: Neighborhood Racial Segregation Levels in San Mateo ..................................................... 33 Table 12: Neighborhood Income Segregation Levels in San Mateo .................................................... 34 Table 13: Regional Racial Segregation Measures ......................................................................... 35 Table 14: Regional Income Segregation Measures ....................................................................... 35 Table 15: Population by Racial Group, San Mateo and the Region .................................................... 36 Table 16: Population by Income Group, San Mateo and the Region .................................................. 36

4 1 INTRODUCTION
The requirement to Affirmatively Further Fair Housing (AFFH) is derived from The Fair Housing Act of 1968, which prohibited discrimination concerning the sale, rental, and financing of housing based on race, color, religion, national origin, or sex—and was later amended to include familial status and disability.1 The 2015 U.S. Department of Housing and Urban Development (HUD) Rule to Affirmatively Further Fair Housing and California Assembly Bill 686 (2018) mandate that each jurisdiction takes meaningful action to address significant disparities in housing needs and access to opportunity. 23 AB 686 requires that jurisdictions incorporate AFFH into their Housing Elements, which includes inclusive community participation, an assessment of fair housing, a site inventory reflective of AFFH, and the development of goals, policies, and programs to meaningfully address local fair housing issues. ABAG and UC Merced have prepared this report to assist Bay Area jurisdictions with the Assessment of Fai r Housing section of the Housing Element.

Assessment of Fair Housing Components
The Assessment of Fair Housing includes five components, which are discussed in detail on pages 22-43 of HCD’s AFFH Guidance Memo:

A: Summary of fair housing enforcement and outreach capacity B: Integration and segregation patterns, and trends related to people with protected characteristics
C: Racially or ethnically concentrated areas of poverty D: Disparities in access to opportunity
E: Disproportionate housing needs, including displacement risk

1.1 Purpose of this Report
This report describes racial and income segregation in Bay Area jurisdictions. Local jurisdiction staff can use the information in this report to help fulfill a portion of the second component of the Assessment of Fair Housing, which requires analysis of integration and segregation patterns and trends related to people with protected characteristics and lower incomes. Jurisdictions will still need to perform a similar analysis for familial status and populations with disability.

This report provides segregation measures for both the local jurisdiction and the region using several indices. For segregation between neighborhoods within a city (intra-city segregation), this report includes isolation indices, dissimilarity indices, and Theil’s-H index. The isolation index measures

1 https://www.justice.gov/crt/fair-housing-act-2
2 HCD AFFH Guidance Memo
3 The 2015 HUD rule was reversed in 2020 and partially reinstated in 2021.

5 segregation for a single group, while the dissimilarity index measures segregation between two groups.

The Theil’s H-Index can be used to measure segregation between all racial or income groups across the city at once. HCD’s AFFH guidelines require local jurisdictions to include isolation indices and dissimilarity indices in the Housing Element. Theil’s H index is provided in addition to these required measures. For segregation between cities within the Bay Area (inter-city segregation), this report includes dissimilarity indices at the regional level as required by HCD’s AFFH guidelines. HCD’s AFFH guidelines also require jurisdictions to compare conditions at the local level to the rest of the region; and this report presents the difference in the racial and income composition of a jurisdiction relative to the region as a whole to satisfy the comparison requirement.

1.2 Defining Segregation
Segregation is the separation of different demographic groups into different geographic locations or communities, meaning that groups are unevenly distributed across geographic space. This report examines two spatial forms of segregation: neighborhood level segregation within a local jurisdiction and city level segregation between jurisdictions in the Bay Area.

Neighborhood level segregation (within a jurisdiction, or intra-city): Segregation of race and income groups can occur from neighborhood to neighborhood within a city. For example, if a local jurisdiction has a population that is 20% Latinx, but some neighborhoods are 80% Latinx while others have nearly no Latinx residents, that jurisdiction would have segregated neighborhoods.

City level segregation (between jurisdictions in a region, or inter-city): Race and income divides also occur between jurisdictions in a region. A region could be very diverse with equal numbers of white, Asian, Black, and Latinx residents, but the region could also be highly segregated with each city comprised solely of one racial group.

There are many factors that have contributed to the generation and maintenance of segregation.

Historically, racial segregation stemmed from explicit discrimination against people of color, such as restrictive covenants, redlining, and discrimination in mortgage lending. This history includes many overtly discriminatory policies made by federal, state, and local governments ( Rothstein 2017).

Segregation patterns are also affected by policies that appear race-neutral, such as land use decisions and the regulation of housing development.

Segregation has resulted in vastly unequal access to public goods such as quality schools, n eighborhood services and amenities, parks and playgrounds, clean air and water, and public safety (Trounstine 2015). This generational lack of access for many communities, particularly people of color and lower income residents, has often resulted in poor life outcomes, including lower educational attainment, higher morbidity rates, and higher mortality rates (Chetty and Hendren 2018, Ananat 2011, Burch 2014, Cutler and Glaeser 1997, Sampson 2012, Sharkey 2013).

1.3 Segregation Patterns in the Bay Area
Across the San Francisco Bay Area, white residents and above moderate-income residents are significantly more segregated from other racial and income groups (see Appendix 2). The highest levels of racial segregation occur between the Black and white populations. The analysis completed for this report indicates that the amount of racial segregation both within Bay Area cities and across jurisdictions in the region has decreased since the year 2000. This finding is consistent with recent research from the Othering and Belonging Institute at UC Berkeley, which concluded that “[a]lthough 7

6 of the 9 Bay Area counties were more segregated in 2020 than they were in either 1980 or 1990, racial residential segregation in the region appears to have peaked around the year 2000 and has generally declined since.”4 However, compared to cities in other parts of California, Bay Area jurisdictions have more neighborhood level segregation between residents from different racial groups. Additionally, there is also more racial segregation between Bay Area cities compared to other regions in the state.

1.4 Segregation and Land Use
It is difficult to address segregation patterns without an analysis of both historical and existing land use policies that impact segregation patterns. Land use regulations influence what kind of housing is built in a city or neighborhood (Lens and Monkkonen 2016, Pendall 2000). These land use regulations in turn impact demographics: they can be used to affect the number of houses in a community, the number o f people who live in the community, the wealth of the people who live in the community, and where within the community they reside (Trounstine 2018). Given disparities in wealth by race and ethnicity, the ability to afford housing in different neighborhoods, as influenced by land use regulations, is highly differentiated across racial and ethnic groups (Bayer, McMillan, and Reuben 2004). 5 ABAG/MTC plans to issue a separate report detailing the existing land use policies that influence segregation patterns i n the Bay Area.

4 For more information, see https://belonging.berkeley.edu/most-segregated-cities-bay-area-2020. 5 Using a household-weighted median of Bay Area county median household incomes, regional values were $61,050 for Black residents, $122,174 for Asian/Pacific Islander residents, $121,794 for white residents, and $76,306 for Latinx residents. For the source data, see U.S. Census Bureau, American Community Survey 5-Year Data (2015- 2019), Table B19013B, Table B19013D, B19013H, and B19013I.

7 Definition of Terms - Geographies
Neighborhood: In this report, “neighborhoods” are approximated by tracts.6 Tracts are statistical geographic units defined by the U.S. Census Bureau for the purposes of disseminating data. In the Bay Area, tracts contain on average 4,500 residents. Nearly all Bay Area jurisdictions contain at least two census tracts, with larger jurisdictions containing dozens of tracts.

Jurisdiction: Jurisdiction is used to refer to the 109 cities, towns, and unincorporated county areas that are members of ABAG. Though not all ABAG jurisdictions are cities, this report also uses the term “city” interchangeably with “jurisdiction” in some places.

Region: The region is the nine-county San Francisco Bay Area, which is comprised of Alameda County, Contra Costa County, Marin County, Napa County, San Francisco County, San Mateo County, Santa Clara County, Solano County, and Sonoma County.

6 Throughout this report, neighborhood level segregation measures are calculated using census tract data.

However, the racial dot maps in Figure 1 and Figure 5 use data from census blocks, while the income group dot maps in Figure 8 and Figure 12 use data from census block groups. These maps use data derived from a smaller geographic scale to better show spatial differences in where different groups live. Census block groups are subdivisions of census tracts, and census blocks are subdivisions of block groups. In the Bay Area, block groups contain on average 1,500 people, while census blocks contain on average 95 people.

8 2 RACIAL SEGREGATION IN CITY OF SAN MATEO
Definition of Terms - Racial/Ethnic Groups
The U.S. Census Bureau classifies racial groups (e.g. white or Black/African American) separately from Hispanic/Latino ethnicity.7 This report combines U.S. Census Bureau definitions for race and ethnicity into the following racial groups:

White: Non-Hispanic white
Latinx: Hispanic or Latino of any race8
Black: Non-Hispanic Black/African American
Asian/Pacific Islander: Non-Hispanic Asian or Non-Hispanic Pacific Islander People of Color: All who are not non-Hispanic white (including people who identify as “some other race” or “two or more races”)9

2.1 Neighborhood Level Racial Segregation ( within City of San Mateo) Racial dot maps are useful for visualizing how multiple racial groups are distributed within a specific geography. The racial dot map of San Mateo in Figure 1 below offers a visual representation of the spatial distribution of racial groups within the jurisdiction. Generally, when the distribution of dots does not suggest patterns or clustering, segregation measures tend to be lower. Conversely, when clusters of certain groups are apparent on a racial dot map, segregation measures may be higher.

7 More information about the Census Bureau’s definitions of racial groups is available here:
https://www.census.gov/topics/population/race/about.html. 8 The term Hispanic has historically been used to describe people from numerous Central American, South American, and Caribbean countries. In recent years, the term Latino or Latinx has become preferred. This report generally uses Latinx to refer to this racial/ethnic group. 9 Given the uncertainty in the data for population size estimates for racial and ethnic groups not included in the Latinx, Black, or Asian/Pacific Islander categories, this report only analyzes these racial groups in the aggregate People of Color category.

9 Figure 1: Racial Dot Map of San Mateo (2020)
Universe: Population. Source: U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002.

Note: The plot shows the racial distribution at the census block level for City of San Mateo and vicinity. Dots in each census block are randomly placed and should not be construed as actual placement of people.

There are many ways to quantitatively measure segregation. Each measure captures a different aspect of the ways in which groups are divided within a community. One way to measure segregation is by using an isolation index:
• The isolation index compares each neighborhood’s composition to the jurisdiction’s demographics as a whole.
• This index ranges from 0 to 1. Higher values indicate that a particular group is more isolated from other groups.
• Isolation indices indicate the potential for contact between different groups. The index can be interpreted as the experience of the average member of that group. For example, if the isolation index is .65 for Latinx residents in a city, then the average Latinx resident in that city lives in a neighborhood that is 65% Latinx.

Within City of San Mateo the most isolated racial group is white residents. San Mateo’s isolation index of 0.428 for white residents means that the average white resident lives in a neighborhood that is

42.8% white. Other racial groups are less isolated, meaning they may be more likely to encounter other racial groups in their neighborhoods. The isolation index values for all racial groups in San Mateo for the years 2000, 2010, and 2020 can be found in Table 1 below. Among all racial groups in this jurisdiction, the white population’s isolation index has changed the most over time, becoming less segregated from other racial groups between 2000 and 2020.

10 The “Bay Area Average” column in this table provides the average isolation index value across Bay Area jurisdictions for different racial groups in 2020. 10 The data in this column can be used as a comparison to provide context for the levels of segregation experienced by racial groups in this jurisdiction. For example, Table 1 indicates the average isolation index value for white residents across all Bay Area jurisdictions is 0.491, meaning that in the average Bay Area jurisdiction a white resident lives in a neighborhood that is 49.1% white.

Table 1: Racial Isolation Index Values for Segregation within San Mateo San Mateo Bay Area
Average
Race 2000 2010 2020 2020
Asian/Pacific Islander 0.180 0.220 0.293 0.245
Black/African American 0.050 0.031 0.021 0.053
Latinx 0.313 0.354 0.333 0.251
White 0.627 0.527 0.428 0.491
Universe: Population.

Source: IPUMS National Historical Geographic Information System (NHGIS). U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002. Data from 2010 is from U.S. Census Bureau, Census 2010, Table P4. Data for 2000 is standardized to 2010 census tract geographies and is from U.S. Census Bureau, Census 2000, Table P004.

Figure 2 below shows how racial isolation index values in San Mateo compare to values in othe r Bay Area jurisdictions. In this chart, each dot represents a Bay Area jurisdiction. For each racial group, the spread of dots represents the range of isolation index values among Bay Area jurisdictions.

Additionally, the black line within each racial group notes the isolation index value for that group in City of San Mateo, and each dashed red line represents the Bay Area average for the isolation index for that group. Local staff can use this chart to contextualize how segregation levels for racial group s in their jurisdiction compare to other jurisdictions in the region.

10 This average only includes the 104 jurisdictions that have more than one census tract, which is true for all comparisons of Bay Area jurisdictions’ segregation measures in this report. The segregation measures in this report are calculated by comparing the demographics of a jurisdiction’s census tracts to the jurisdiction’s demographics, and such calculations cannot be made for the five jurisdictions with only one census tract (Brisbane, Calistoga, Portola Valley, Rio Vista, and Yountville).

11 Figure 2: Racial Isolation Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

Another way to measure segregation is by using a dissimilarity index:
• This index measures how evenly any two groups are distributed across neighborhoods relative to their representation in a city overall. The dissimilarity index at the jurisdiction level can be interpreted as the share of one group that would have to move neighborhoods to create perfect integration for these two groups.
• The dissimilarity index ranges from 0 to 1. Higher values indicate that groups are more unevenly distributed (e.g. they tend to live in different neighborhoods).

12 Dissimilarity Index Guidance for Cities with Small Racial Group Populations The analysis conducted for this report suggests that dissimilarity index values are unreliable for a population group if that group represents approximately less than 5% of the jurisdiction’s total population.

HCD’s AFFH guidance requires the Housing Element to include the dissimilarity index values for racial groups, but also offers flexibility in emphasizing the importance of various measures. ABAG/MTC recommends that when cities have population groups that are less than 5% of the jurisdiction’s population (see Table 4), jurisdiction staff use the isolation index or Thiel’s H-Index to gain a more accurate understanding of their jurisdiction’s neighborhood-level segregation patterns (intra-city segregation).

If a jurisdiction has a very small population of a racial group, this indicates that segregation between the jurisdiction and the region (inter-city segregation) is likely to be an important feature of the jurisdiction’s segregation patterns.

In City of San Mateo, the Black/African American group is 1.6 percent of the population - so staff should be aware of this small population size when evaluating dissimilarity index values involving this group.

Table 2 below provides the dissimilarity index values indicating the level of segregation in San Mateo between white residents and residents who are Black, Latinx, or Asian/Pacific Islander. The table also provides the dissimilarity index between white residents and all residents of color in the jurisdiction, and all dissimilarity index values are shown across three time periods (2000, 2010, and 2020).

In San Mateo the highest segregation is between Latinx and white residents (see Table 2). San Mateo’s Latinx /white dissimilarity index of 0.345 means that 34.5% of Latinx (or white) residents would need to move to a different neighborhood to create perfect integration between Latinx residents and white residents.

The “Bay Area Average” column in this table provides the average dissimilarity index values for th ese racial group pairings across Bay Area jurisdictions in 2020. The data in this column can be used as a comparison to provide context for the levels of segregation between communities of color are from white residents in this jurisdiction.

13 For example, Table 2 indicates that the average Latinx/white dissimilarity index for a Bay Area jurisdiction is 0.207, so on average 20.7% of Latinx (or white residents) in a Bay Area jurisdiction would need to move to a different neighborhood within the jurisdiction to create perfect integration between Latinx and white residents in that jurisdiction.

Table 2: Racial Dissimilarity Index Values for Segre gation within San Mateo San Mateo Bay Area
Average
Race 2000 2010 2020 2020
Asian/Pacific Islander vs. White 0.218 0.202 0.168 0.185 Black/African American vs. White 0.417* 0.350* 0.307* 0.244 Latinx vs. White 0.389 0.363 0.345 0.207
People of Color vs. White 0.288 0.267 0.228 0.168
Universe: Population.

Note: If a number is marked with an asterisk (*), it indicates that the index is based on a racial group making up less than 5 percent of the jurisdiction population, leading to unreliable numbers.

Figure 3 below shows how dissimilarity index values in City of San Mateo compare to values in other Bay Area jurisdictions. In this chart, each dot represents a Bay Area jurisdiction. For each racial group pairing, the spread of dots represents the range of dissimilarity index values among Bay Area jurisdictions. Additionally, the black line within each racial group pairing notes the dissimilarity index value in San Mateo, and each dashed red line represents the Bay Area average for the dissimilarity index for that pairing. Similar to Figure 2, local staff can use this chart to contextualize how segregation levels between white residents and communities of color in their jurisdiction compare to the rest of the region. However, staff should be mindful of whether a racial group in their jurisdiction has a small population (approximately less than 5% of the jurisdiction’s population), as the dissimilarity index value is less reliable for small populations.

14 Figure 3: Racial Dissimilarity Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

Note: The analysis conducted for this report suggests that dissimilarity index values are unreliable for a population group if that group represents approximately less than 5% of the jurisdiction’s total population. ABAG/MTC recommends that when cities have population groups that are less than 5% of the jurisdiction’s population (see Table 4), jurisdiction staff could focus on the isolation index or Thiel’s H-Index to gain a more accurate understanding of neighborhood-level racial segregation in their jurisdiction.

The Theil’s H Index can be used to measure segregation between all groups within a jurisdiction:
• This index measures how diverse each neighborhood is compared to the diversity of the whole city. Neighborhoods are weighted by their size, so that larger neighborhoods play a mo re significant role in determining the total measure of segregation. • The index ranges from 0 to 1. A Theil’s H Index value of 0 would mean all neighborhoods within a city have the same demographics as the whole city. A value of 1 would mean each group live s exclusively in their own, separate neighborhood.
• For jurisdictions with a high degree of diversity (multiple racial groups comprise more than 10% of the population), Theil’s H offers the clearest summary of overall segregation.

The Theil’s H Index values for neighborhood racial segregation in San Mateo for the years 2000, 2010, and 2020 can be found in Table 3 below. The “Bay Area Average” column in the table provides the average Theil’s H Index across Bay Area jurisdictions in 2020. Between 2010 and 2020, the Theil’s H Index for racial segregation in San Mateo declined, suggesting that there is now less neighborhood level racial segregation within the jurisdiction. In 2020, the Theil’s H Index for racial segregation in San

15 Mateo was higher than the average value for Bay Area jurisdictions, indicating that neighborhood level racial segregation in San Mateo is more than in the average Bay Area city.

Table 3: Theil’s H Index Values for Racial Segregation within San Mateo San Mateo Bay Area
Average
Index 2000 2010 2020 2020
Theil's H Multi-racial 0.089 0.071 0.053 0.042
Universe: Population.

Figure 4 below shows how Theil’s H index values for racial segregation in San Mateo compare to values in other Bay Area jurisdictions in 2020. In this chart, each dot represents a Bay Area jurisdiction.

Additionally, the black line notes the Theil’s H index value for neighborhood racial segregati on in San Mateo, and the dashed red line represents the average Theil’s H index value across Bay Area jurisdictions. Local staff can use this chart to compare how neighborhood racial segregation levels in their jurisdiction compare to other jurisdictions in the region.

Figure 4: Theil’s H Index Values for Racial Segregation in San Mateo Compared to Other Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

16 2.2 Regional Racial Segregation ( between San Mateo and other jurisdictions)
At the regional level, segregation is measured between cities instead of between neighborhoods. Racial dot maps are not only useful for examining neighborhood racial segregation within a jurisdiction, but these maps can also be used to explore the racial demographic differences betwee n different jurisdictions in the region. Figure 5 below presents a racial dot map showing the spatial distribution of racial groups in San Mateo as well as in nearby Bay Area cities.

Figure 5: Racial Dot Map of San Mateo and Surrounding Areas (2020) Universe: Population.

Source: U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002.

To understand how each city contributes to the total segregation of the Bay Area, one can look at the difference in the racial composition of a jurisdiction compared to the racial composition of the region as a whole. The racial demographics in San Mateo for the years 2000, 2010, and 2020 can be found in Table 4 below. The table also provides the racial composition of the nine-county Bay Area. As of 2020, San Mateo has a higher share of white residents than the Bay Area as a whole, a higher share of Latinx residents, a lower share of Black residents, and a lower share of Asian/Pacific Islander residents.

17 Table 4: Population by Racial Group, San Mateo and t he Region San Mateo Bay Area
Race 2000 2010 2020 2020
Asian/Pacific Islander 14.9% 20.7% 27.8% 28.2%
Black/African American 2.5% 2.2% 1.6% 5.6%
Latinx 20.5% 26.6% 25.7% 24.4%
Other or Multiple Races 5.6% 4.1% 6.5% 5.9%
White 56.5% 46.5% 38.3% 35.8%
Universe: Population.

Figure 6 below compares the racial demographics in San Mateo to those of all 109 Bay Area jurisdictions.11 In this chart, each dot represents a Bay Area jurisdiction. For each racial group, the spread of dots represents the range of that group’s representation among Bay Area jurisdictions.

Additionally, the black line within each racial group notes the percentage of the population of City of San Mateo represented by that group and how that percentage ranks among all 109 jurisdictions. Local staff can use this chart to compare the representation of different racial groups in their jurisdiction to those groups’ representation in other jurisdictions in the region, which can indicate the extent of segregation between this jurisdiction and the region.

11 While comparisons of segregation measures are made only using the 104 jurisdictions with more than one census tract, this comparison of jurisdiction level demographic data can be made using all 109 jurisdictions.

18 Figure 6: Racial Demographics of San Mateo Compared to All Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

Source U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002.

The map in Figure 7 below also illustrates regional racial segregation between San Mateo and other jurisdictions. This map demonstrates how the percentage of people of color in San Mateo and surrounding jurisdictions compares to the Bay Area as a whole:
• Jurisdictions shaded orange have a share of people of color that is less than the Bay Area as a whole, and the degree of difference is greater than five percentage points. • Jurisdictions shaded white have a share of people of color comparable to the regional percentage of people of color (within five percentage points). • Jurisdictions shaded grey have a share of people of color that is more than five percentage points greater than the regional percentage of people of color.

19 Figure 7: Comparing the Share of People of Color in San Mateo and Vicinity to the Bay Area (2020)
Universe: Population.

Source: U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002.

Note: People of color refer to persons not identifying as non-Hispanic white. The nine-county Bay Area is the reference region for this map.

Segregation between jurisdictions in the region can also be analyzed by calculating regional values for the segregation indices discussed previously. Table 5 presents dissimilarity index, isolation index, and Theil’s H index values for racial segregation for the entire nine-county Bay Area in 2010 and 2020. In the previous section of this report focused on neighborhood level racial segregation, these indices were calculated by comparing the racial demographics of the census tracts within a jurisdiction to the demographics of the jurisdiction as a whole. In Table 5, these measures are calculated by comparing the racial demographics of local jurisdictions to the region’s racial makeup. For example, looking at the 2020 data, Table 5 shows the white isolation index value for the region is 0.429, meaning that on average white Bay Area residents live in a jurisdiction that is 42.9% white in 2020. An example of regional dissimilarity index values in Table 5 is the Black/white dissimilarity index value of 0.459, which means that across the region 45.9% of Black (or white) residents would need to move to a different jurisdiction to evenly distribute Black and white residents across Bay Area jurisdictions. The dissimilarity index values in Table 5 reflect recommendations made in HCD’s AFFH guidance for calculating dissimilarity at the region level.12 The regional value for the Theil’s H index measures how

12 For more information on HCD’s recommendations regarding data considerations for analyzing integration and segregation patterns, see page 31 of the AFFH Guidance Memo.

20 diverse each Bay Area jurisdiction is compared to the racial diversity of the whole region. A Theil’s H Index value of 0 would mean all jurisdictions within the Bay Area have the same racial demographics as the entire region, while a value of 1 would mean each racial group lives exclusively in their own separate jurisdiction. The regional Theil’s H index value for racial segregation dec reased slightly between 2010 and 2020, meaning that racial groups in the Bay Area are now slightly less separated by the borders between jurisdictions.

Table 5: Regional Racial Segregation Measures
Index Group 2010 2020
Isolation Index Regional Level
Asian/Pacific Islander 0.317 0.378
Black/African American 0.144 0.118
Latinx 0.283 0.291
White 0.496 0.429
People of Color 0.629 0.682
Dissimilarity Index Regional Level
Asian/Pacific Islander vs. White 0.384 0.369
Black/African American vs. White 0.475 0.459
Latinx vs. White 0.301 0.297
People of Color vs. White 0.296 0.293
Theil's H Multi-racial All Racial Groups 0.103 0.097
Universe: Population.

21 3 INCOME SEGREGATION IN CITY OF SAN MATEO
Definition of Terms - Income Groups
When analyzing segregation by income, this report uses income group designations consistent with the Regional Housing Needs Allocation and the Housing Element:

Very low-income: individuals earning less than 50% of Area Median Income (AMI)
Low-income: individuals earning 50%-80% of AMI
Moderate-income: individuals earning 80%-120% of AMI
Above moderate-income: individuals earning 120% or more of AMI Additionally, this report uses the term “lower-income” to refer to all people who earn less than 80% of AMI, which includes both low-income and very low-income individuals.

The income groups described above are based on U.S. Department of Housing and Urban Development (HUD) calculations for AMI. HUD calculates the AMI for different metropolitan areas, and the nine county Bay Area includes the following metropolitan areas: Napa Metro Area (Napa County), Oakland-Fremont Metro Area (Alameda and Contra Costa Counties), San Francisco Metro Area (Marin, San Francisco, and San Mateo Counties), San Jose-Sunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo- Fairfield Metro Area (Solano County).

The income categories used in this report are based on the AMI for the HUD metro area where this jurisdiction is located.

3.1 Neighborhood Level Income Segregation ( within San Mateo) Income segregation can be measured using similar indices as racial segregation. Income dot maps, similar to the racial dot maps shown in Figures 1 and 5, are useful for visualizing segregation between multiple income groups at the same time. The income dot map of San Mateo in Figure 8 below offers a visual representation of the spatial distribution of income groups within the jurisdiction. As with the racial dot maps, when the dots show lack of a pattern or clustering, income segregation measures tend to be lower, and conversely, when clusters are apparent, the segregation measures may be higher as well.

22 Figure 8: Income Dot Map of San Mateo (2015)
Universe: Population.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate- Income Summary Data.

Note: The plot shows the income group distribution at the census block group level for City of San Mateo and vicinity. Dots in each block group are randomly placed and should not be construed as actual placement of individuals.

The isolation index values for all income groups in San Mateo for the years 2010 and 2015 can be found in Table 6 below.13 Above Moderate-income residents are the most isolated income group in San Mateo.

San Mateo’s isolation index of 0.420 for these residents means that the average Above Moderate - income resident in San Mateo lives in a neighborhood that is 42.0% A bove Moderate-income. Among all income groups, the Very Low-income population’s isolation index has changed the most over time, becoming more segregated from other income groups between 2010 and 2015.

Similar to the tables presented earlier for neighborhood racial segregation, the “Bay Area Average” column in Table 6 provides the average isolation index value across Bay Area jurisdictions for different income groups in 2015. The data in this column can be used as a comparison to provide context for the levels of segregation experienced by income groups in this jurisdiction. For example, Table 6 indicates the average isolation index value for very low-income residents across Bay Area jurisdictions is 0.269,

13 This report presents data for income segregation for the years 2010 and 2015, which is different than the time periods used for racial segregation. This deviation stems from the data source recommended for income segregation calculations in HCD’s AFFH Guidelines. This data source most recently updated with data from the 2011-2015 American Community Survey 5-year estimates. For more information on HCD’s recommendations for calculating income segregation, see page 32 of HCD’s AFFH Guidelines.

23 meaning that in the average Bay Area jurisdiction a very low-income resident lives in a neighborhood that is 26.9% very low-income.

Table 6: Income Group Isolation Index Values for Segregation within San Mateo San Mateo Bay Area
Average
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 0.263 0.361 0.269
Low-Income (50%-80% AMI) 0.203 0.179 0.145
Moderate-Income (80%-120% AMI) 0.228 0.212 0.183
Above Moderate-Income (>120% AMI) 0.465 0.420 0.507
Universe: Population.

Source: Data for 2015 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011- 2015 Low- and Moderate-Income Summary Data. Data for 2010 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2006-2010 Low- and Moderate-Income Summary Data.

Figure 9 below shows how income group isolation index values in San Mateo compare to values in other Bay Area jurisdictions. In this chart, each dot represents a Bay Area jurisdiction. For each in come group, the spread of dots represents the range of isolation index values among Bay Area jurisdictions.

Additionally, the black line within each income group notes the isolation index value for that group in San Mateo, and each dashed red line represents the Bay Area average for the isolation index for that group. Local staff can use this chart to contextualize how segregation levels for income groups in their jurisdiction compare to the rest of the region.

24 Figure 9: Income Group Isolation Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate- Income Summary Data.

Table 7 below provides the dissimilarity index values indicating the level of segregation in San Mateo between residents who are lower-income (earning less than 80% of AMI) and those who are not lower- income (earning above 80% of AMI). This data aligns with the requirements described in HCD’s AFFH Guidance Memo for identifying dissimilarity for lower-income households.14 Segregation in San Mateo between lower-income residents and residents who are not lower-income has not substantively changed between 2010 and 2015. Additionally, Table 7 shows dissimilarity index values for the level of segregation in Albany between residents who are very low-income (earning less than 50% of AMI) and those who are above moderate-income (earning above 120% of AMI). This supplementary data point provides additional nuance to an analysis of income segregation, as this index value indicates the extent to which a jurisdiction’s lowest and highest income residents live in separate neighborhoods.

Similar to other tables in this report, the “Bay Area Average” column shows the average di ssimilarity index values for these income group pairings across Bay Area jurisdictions in 2015. For example, Table 7 indicates that the average dissimilarity index between lower-income residents and other residents in a Bay Area jurisdiction is 0.198, so on average 19.8% of lower-income residents in a Bay Area jurisdiction would need to move to a different neighborhood within the jurisdiction to create perfect income group integration in that jurisdiction.

14 For more information, see page 32 of HCD’s AFFH Guidance Memo.

25 In 2015, the income segregation in San Mateo between lower-income residents and other residents was higher than the average value for Bay Area jurisdictions (See Table 7). This means that the lower- income residents are more segregated from other residents within San Mateo compared to other Jurisdictions in the region.

Table 7: Income Group Dissim ilarity Index Values for Segregation within San Mateo
San Mateo Bay Area
Average
Income Group 2010 2015 2015
Below 80% AMI vs. Above 80% AMI 0.241 0.247 0.198
Below 50% AMI vs. Above 120% AMI 0.325 0.378 0.253
Universe: Population.

Figure 10 below shows how dissimilarity index values for income segregation in San Mateo compar e to values in other Bay Area jurisdictions. In this chart, each dot represents a Bay Area jurisdiction. For each income group pairing, the spread of dots represents the range of dissimilarity index values among Bay Area jurisdictions. Additionally, the black line within each income group pairing notes the dissimilarity index value in San Mateo, and each dashed red line represents the Bay Area average for the dissimilarity index for that pairing. Local staff can use this chart to contextualize how segregati on levels between lower-income residents and wealthier residents in their jurisdiction compared to the rest of the region.

26 Figure 10: Income Group Dissimilarity Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate- Income Summary Data.

The Theil’s H Index values for neighborhood income group segregation in San Mate o for the years 2010 and 2015 can be found in Table 8 below. The “Bay Area Average” column in this table provides the average Theil’s H Index value across Bay Area jurisdictions for different income groups in 2015. By 2015, the Theil’s H Index value for income segregation in San Mateo was about the same amount as it had been in 2010. In 2015, the Theil’s H Index value for income group segregation in San Mateo w as higher than the average value for Bay Area jurisdictions, indicating there is more neighborhood level income segregation in San Mateo than in the average Bay Area city.

Table 8: Theil’s H Index Values for Income Segregation within San Mateo San Mateo Bay Area
Average
Index 2010 2015 2015
Theil's H Multi-income 0.059 0.066 0.043
Universe: Population.

27 Figure 11 below shows how Theil’s H index values for income group segregation in San Mateo compare to values in other Bay Area jurisdictions in 2015. In this chart, each dot represents a Bay Area jurisdiction. Additionally, the black line notes the Theil’s H index value for income group segregation in San Mateo, and the dashed red line represents the average Theil’s H index value across Bay Area jurisdictions. Local staff can use this chart to compare how neighborhood income group segregation levels in their jurisdiction compare to other jurisdictions in the region.

Figure 11: Income Group Theil’s H Index Values for San Mateo Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate- Income Summary Data.

3.2 Regional Income Segregation ( between San Mateo and other jurisdictions)
At the regional level, segregation is measured between jurisdictions instead of between neighborhoods.

Income dot maps are not only useful for examining neighborhood income segregation within a jurisdiction, but these maps can also be used to explore income demographic d ifferences between jurisdictions in the region. Figure 12 below presents an income dot map showing the spatial distribution of income groups in San Mateo as well as in nearby Bay Area jurisdictions.

28 Figure 12: Income Dot Map of San Mateo and Surrounding Areas (2015) Universe: Population.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate- Income Summary Data.

When looking at income segregation between jurisdictions in the Bay Area, one can examine how San Mateo differs from the region. The income demographics in San Mateo for the years 2010 and 2015 can be found in Table 9 below. The table also provides the income composition of the nine-county Bay Area in 2015. As of that year, San Mateo had a higher share of very low-income residents than the Bay Area as a whole, a higher share of low-income residents, a higher share of moderate-income residents, and a lower share of above moderate-income residents.

Table 9: Population by Income Group, San Mateo and the Region San Mateo Bay Area
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 21.73% 30.26% 28.7%
Low-Income (50%-80% AMI) 18.48% 16.78% 14.3%
Moderate-Income (80%-120% AMI) 19.77% 19.51% 17.6%
Above Moderate-Income (>120% AMI) 40.01% 33.45% 39.4%

29 Universe: Population.

Source: Data for 2015 is from Housing U.S. Department of and Urban Development, American Community Survey 5-Year 2011- 2015 Low- and Moderate-Income Summary Data. Data for 2010 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2006-2010 Low- and Moderate-Income Summary Data.

Figure 13 below compares the income demographics in San Mateo to other Bay A rea jurisdictions.15 Like the chart in Figure 3, each dot represents a Bay Area jurisdiction. For each income group, the spread of dots represents the range of that group’s representation among Bay Area jurisdictions. The smallest range is among jurisdictions’ moderate-income populations, while Bay Area jurisdictions vary the most in the share of their population that is above moderate-income. Additionally, the black lines within each income group note the percentage of San Mateo population represented by that group and how that percentage ranks among other jurisdictions. Local staff can use this chart to compare the representation of different income groups in their jurisdiction to those groups’ representation in other jurisdictions in the region, which can indicate the extent of segregation between this jurisdiction and the region.

Figure 13: Income Demographics of San Mateo Compared to Other Bay Area Jurisdictions (2015)
Universe: Bay Area Jurisdictions.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate- Income Summary Data.

15 While comparisons of segregation measures are made only using the 104 jurisdictions with more than one census tract, this comparison of jurisdiction level demographic data can be made using all 109 jurisdictions.

30 Income segregation between jurisdictions in the region can also be analyzed by calculating regional values for the segregation indices discussed previously. Similar to the regional racial segregation measures shown in Table 5, Table 10 presents dissimilarity index, isolation index, and Theil’s H index values for income segregation for the entire nine-county Bay Area in 2010 and 2015. In the previous section of this report focused on neighborhood level income segregation, segregation indices were calculated by comparing the income demographics of the census tracts within a juris diction to the demographics of the jurisdiction as a whole. In Table 10, these measures are calculated by comparing the income demographics of local jurisdictions to the region’s income group makeup. For example, looking at 2015 data, Table 10 shows the regional isolation index value for very low-income residents is

0.315 for 2015, meaning that on average very low-income Bay Area residents live in a jurisdiction that is 31.5% very low-income. The regional dissimilarity index for lower-income residents and other residents is 0.194 in 2015, which means that across the region 19.4% of lower -income residents would need to move to a different jurisdiction to create perfect income group integration in the Bay Area as a whole. The regional value for the Theil’s H index measures how diverse each Bay Area jurisdiction is compared to the income group diversity of the whole region. A Theil’s H Index value of 0 would mean all jurisdictions within the Bay Area have the same income demographics as the entire region, while a value of 1 would mean each income group lives exclusively in their own separate jurisdiction. The regional Theil’s H index value for income segregation decreased slightly between 2010 and 2015, meaning that income groups in the Bay Area are now slightly less separated by the borders between jurisdictions.

Table 10: Regional Income Segregation Measures
Index Group 2010 2015
Isolation Index Regional Level
Very Low-Income (<50% AMI) 0.277 0.315
Low-Income (50%-80% AMI) 0.157 0.154
Moderate-Income (80%-120% AMI) 0.185 0.180
Above Moderate-Income (>120% AMI) 0.467 0.435
Dissimilarity Index Regional Level
Below 80% AMI vs. Above 80% AMI 0.186 0.194
Below 50% AMI vs. Above 120% AMI 0.238 0.248
Theil's H Multi-income All Income Groups 0.034 0.032
Universe: Population.

31 4 APPENDIX 1: SUMMARY OF FIND INGS

4.1 Segregation in City of San Mateo
• The isolation index measures the segregation of a single group, and the dissimilarity index measures segregation between two different groups. The Theil’s H-Index can be used to measure segregation between all racial or income groups across the city at once. • As of 2020, white residents are the most segregated compared to other racial groups in San Mateo, as measured by the isolation index. White residents live in neighborhoods where they are less likely to come into contact with other racial groups. • Among all racial groups, the white population’s isolation index value has changed the most over time, becoming less segregated from other racial groups between 2000 and 2020. • According to the dissimilarity index, within San Mateo the highest level of racial segregation is between Latinx and white residents.16
• According to the Theil’s H-Index, neighborhood racial segregation in San Mateo declined between 2010 and 2020. Neighborhood income segregation stayed about the same be tween 2010 and 2015.
• Above Moderate-income residents are the most segregated compared to other income groups in San Mateo. Above Moderate-income residents live in neighborhoods where they are less likely to encounter residents of other income groups.
• Among all income groups, the Very Low-income population’s segregation measure has changed the most over time, becoming more segregated from other income groups between 2010 and 2015.
• According to the dissimilarity index, segregation between lower-income residents and residents who are not lower-income has not substantively changed between 2010 and 2015. In 2015, the income segregation in San Mateo between lower-income residents and other residents was higher than the average value for Bay Area jurisdictions.

4.2 Segregation Between City of San Mateo and Other jurisdictions in the Bay Area Region
• San Mateo has a higher share of white residents than other jurisdictions in the Bay Area as a whole, a higher share of Latinx residents, a lower share of Black residents, and a lower share of Asian/Pacific Islander residents.

16 The analysis conducted for this report suggests that dissimilarity index values are unreliable for a population group if that group represents approximately less than 5% of the jurisdiction’s total population. ABAG/MTC recommends that when cities have population groups that are less than 5% of the jurisdiction’s population (see Table 15 in Appendix 2), jurisdiction staff could focus on the isolation index or Thiel’s H-Index to gain a more accurate understanding of neighborhood-level racial segregation in their jurisdiction.

32 • Regarding income groups, San Mateo has a higher share of very low-income residents than other jurisdictions in the Bay Area as a whole, a higher share of low-income residents, a higher share of moderate-income residents, and a lower share of above moderate-income residents.

33 5 APPENDIX 2: SEGREGATION DATA
Appendix 2 combines tabular data presented throughout this report into a more condensed format. This data compilation is intended to enable local jurisdiction staff and their consultants to easily reference this data and re-use the data in the Housing Element or other relevant documents/analyses.

Table 11 in this appendix combines data from Table 1, Table 2, and Table 3 in the body of the report.

Table 12 in this appendix combines data from Table 6, Table 7, and Table 8 in the body of the report.

Table 13 represents a duplication of Table 5 in the body of the report; Table 14 represents a duplication of Table 10 in the body of the report; Table 15 in this appendix represents a duplication of Table 4 in the body of the report, while Table 16 represents a duplication of Table 9 in the body of the report.

Table 11: Neighborhood Racial Segregation Levels in San Mateo San Mateo Bay Area
Average
Index Race 2000 2010 2020 2020
Isolation
Asian/Pacific Islander 0.180 0.220 0.293 0.245
Black/African American 0.050 0.031 0.021 0.053
Latinx 0.313 0.354 0.333 0.251
White 0.627 0.527 0.428 0.491
Dissimilarity
Asian/Pacific Islander vs. White 0.218 0.202 0.168 0.185 Black/African American vs. White 0.417* 0.350* 0.307* 0.244 Latinx vs. White 0.389 0.363 0.345 0.207
People of Color vs. White 0.288 0.267 0.228 0.168
Theil's H Multi-racial All 0.089 0.071 0.053 0.042
Universe: Population.

Source: IPUMS National Historical Geographic Information System (NHGIS). U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002. Data from 2010 is from U.S. Census Bureau, 2010 Census of Population and Housing, Table P4. Data for 2000 is standardized to 2010 census tract geographies and is from U.S. Census Bureau, Census 2000, Table P004.

Note: If a number is marked with an asterisk (*), it indicates that the index is based on a racial group making up less than 5 percent of the jurisdiction population, leading to unreliable numbers.

34 Table 12: Neighborhood Income Segregation Levels in San Mateo San Mateo Bay Area
Average
Index Income Group 2010 2015 2015
Isolation
Very Low-Income (<50% AMI) 0.263 0.361 0.269
Low-Income (50%-80% AMI) 0.203 0.179 0.145
Moderate-Income (80%-120% AMI) 0.228 0.212 0.183
Above Moderate-Income (>120% AMI) 0.465 0.420 0.507
Dissimilarity
Below 80% AMI vs. Above 80% AMI 0.241 0.247 0.198
Below 50% AMI vs. Above 120% AMI 0.325 0.378 0.253
Theil's H Multi-racial All 0.059 0.066 0.043
Universe: Population.

Source: Income data for 2015 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and Moderate-Income Summary Data. Data for 2010 is from U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2006-2010 Low- and Moderate-Income Summary Data.

35 Table 13: Regional Racial Segregation Measures
Index Group 2010 2020
Isolation Index Regional Level
Asian/Pacific Islander 0.317 0.378
Black/African American 0.144 0.118
Latinx 0.283 0.291
White 0.496 0.429
People of Color 0.629 0.682
Dissimilarity Index Regional Level
Asian/Pacific Islander vs. White 0.384 0.369
Black/African American vs. White 0.475 0.459
Latinx vs. White 0.301 0.297
People of Color vs. White 0.296 0.293
Theil's H Multi-racial All Racial Groups 0.103 0.097
Universe: Population.

Table 14: Regional Income Segregation Measures
Index Group 2010 2015
Isolation Index Regional Level
Very Low-Income (<50% AMI) 0.277 0.315
Low-Income (50%-80% AMI) 0.157 0.154
Moderate-Income (80%-120% AMI) 0.185 0.180
Above Moderate-Income (>120% AMI) 0.467 0.435
Dissimilarity Index Regional Level
Below 80% AMI vs. Above 80% AMI 0.186 0.194
Below 50% AMI vs. Above 120% AMI 0.238 0.248
Theil's H Multi-income All Income Groups 0.034 0.032
Universe: Population.

36 Table 15: Population by Racial Group, San Mateo and the Region San Mateo Bay Area
Race 2000 2010 2020 2020
Asian/Pacific Islander 14.93% 20.67% 27.84% 35.8%
Black/African American 2.46% 2.16% 1.61% 5.6%
Latinx 20.52% 26.56% 25.74% 28.2%
Other or Multiple Races 5.58% 4.08% 6.46% 24.4%
White 56.51% 46.54% 38.35% 5.9%
Universe: Population.

Source: IPUMS National Historical Geographic Information System (NHGIS). U.S. Census Bureau, 2020 Census State Redistricting Data (Public Law 94-171) Summary File, 2020 Census of Population and Housing, Table P002. Data from 2010 is from U.S. Census Bureau, 2010 Census of Population and Housing, Table P4. Data for 2000 is standardized to 2010 census tract geographies and is from U.S. Census Bureau, Census 2000, Table P004.

Table 16: Population by Income Group, San Mateo and the Region San Mateo Bay Area
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 21.73% 30.26% 28.7%
Low-Income (50%-80% AMI) 18.48% 16.78% 14.3%
Moderate-Income (80%-120% AMI) 19.77% 19.51% 17.6%
Above Moderate-Income (>120% AMI) 40.01% 33.45% 39.4%
Universe: Population.

37 6 REFERENCES
Ananat, Elizabeth Oltmans. 2011. “The wrong side(s) of the tracks: The causal effects of racial segregation on urban poverty and inequality,” American Economic Journal: Applied Economics 3: 34-

66. Bayer, Patrick, Robert McMillan, and Kim S. Rueben. 2004. “What Drives Racial Segregation? New Evidence using Census Microdata,” Journal of Urban Economics 56(3): 514-535.

Burch, Traci. 2014. “The Old Jim Crow: Racial Residential Segregation and Imprisonment,” Law and Policy 36(3): 223-255.

Chetty, Raj and Nathanial Hendren. 2018. “The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects,” The Quarterly Journal of Economics 133(3):1107-1162 Cutler, David M., and Edward L. Glaeser. 1997. “Are ghettos good or bad?,” The Quarterly Journal of Economics 112(3): 827-72.

Lens, Michael and Paavo Monkkonen. 2016. “Do Strict Land Use Regulations Make Metropolitan Areas More Segregated by Income?,” Journal of the American Planning Association 82(1): 6–21.

Pendall, Rolf. 2000. “Local Land-Use Regulation and the Chain of Exclusion,” Journal of the American Planning Association 66(2): 125-142.

Rothstein, Richard. 2017. The Color of Law: A Forgotten History of how our Government Segregated America. New York: Liveright Publishing.

Sampson, Robert J. 2012. Great American city: Chicago and the enduring neighborhood effect.

Chicago: University of Chicago Press.

Sharkey, Patrick. 2013. Stuck in place: Urban neighborhoods and the end of progress toward racial equality. Chicago: University of Chicago Press.

Trounstine, Jessica. 2015. “Segregation and Inequality in Public Goods,” American Journal of Political Science 60(3): 709-725.

Trounstine, Jessica. 2018. Segregation by Design: Local Politics and Inequality in American Cities. New York: Cambridge University Press.

Page 1 of 3 ends

San Mateo City CouncilJanuary 24, 2023

San Mateo City Council
January 24, 2023