Belmont City Council
April 26, 2022

CITY OF BELMONT

CITY COUNCIL AND BELMONT FIRE

PROTECTION DISTRICT BOARD OF

DIRECTORS
www.belmont.gov

1. ITEMS OF BUSINESS
Persons wishing to orally address the Council on the items of business listed below will be given an opportunity to do so before or during the Councils consideration of the item.

2. CLOSED SESSION

CITY COUNCIL SPECIAL MEETING AGENDA
Tuesday, April 26, 2022
5:45 PM
City Council Chambers
City Hall, One Twin Pines Lane, Belmont, California
TELECONFERENCE PARTICIPANTS:
26 North Washington Avenue, Ventnor City, NJ 08406
(Councilmember Lieberman)
The meeting will be broadcast live to Belmont residents on Comcast Cable Channel 27, streamed live via the City’s website at www.Belmont.gov. The public may also attend the meeting in the City Council Chambers and address the council from the chambers. Social distancing must be practiced, and seating will be limited.

PUBLIC COMMENT:

To maximize time for live public comment, we encourage members of the public to provide comments by joining the City Council meeting via Zoom: For web, visit https://belmontgov.zoom.us/ select “Join” and enter Meeting ID: 95745673035. Use the Raise Hand feature to request to speak. You may rename your profile if you wish to remain anonymous.

For dial-in comments, call *67 1-(669) 900-6833 (your phone number will appear on the live broadcast if *67 is not dialed prior to the phone number), enter Meeting ID: 95745673035, and press *9 to request to speak. All public comments are subject to a 3-minute time limit unless otherwise determined by the Mayor.

If you wish to submit written public comment, you may send an email to [email protected] before the council considers the item. Please indicate the agenda item topic or agenda item number you wish to comment on in your email’s subject line. Any public comment regarding agenda items that are received from the publication of the agenda through the meeting date will be made part of the meeting record, but will not be read during the Council meeting.
1 Conference with Conference With Labor Negotiators (Govt Code Section 54957.6 ) Agency designated representatives: Afshin Oskoui, City Manager; Employee organization: MidManagement Confidential Employees Association (MMCEA)

3. STUDY SESSION
A. Housing Element 2023-2030 Review of Draft Housing Opportunity Sites, Fair Housing Assessment, and Draft Programs
Recommendation: Receive presentation introducing the General Plan Housing Element 2023- 2030 fair housing analysis, draft housing opportunity sites, and draft programs, and provide feedback to staff
Attachment(s):

Staff Report
Belmont Fair Housing Assessment Draft w Appendices
Proposed New Programs (Draft).

Presentation

4. ADJOURNMENT
If you need assistance to participate in this meeting, please contact the City Clerk at (650) 595-7413. The speech and hearing-impaired may call (650) 637-2999 for TDD services. Notification in advance of the meeting will enable the City to make reasonable arrangements to ensure accessibility to this meeting.

Meeting information can also be accessed via the internet at: www.belmont.gov. All staf reports will be posted to the web in advance of the meeting, and any writings or documents provided to a majority of the City Council/District Board or Commission regarding any item on this agenda will be made available for public inspection in the City Clerk's Of ice, One Twin Pines Lane, during normal business hours and at the Council Chambers at City Hall, Second Floor, during the meeting. 2

Housing Element Review

STAFF REPORT
Meeting Date: April 26, 2022
Agency: City of Belmont
Staff Contact: Jennifer Rose, Housing and Economic Development Manager, [email protected] Agenda Title: Housing Element 2023-2030 Review of Draft Housing Opportunity Sites, Fair Housing Assessment, and Draft Programs
Agenda Action: For Information Only
Recommendation
Receive presentation introducing the General Plan Housing Element 2023-2030 fair housing analysis, draft housing opportunity sites, and draft programs, and provide feedback to staff.

Strategic Focus Area
Economic Development and Housing
Background
The Housing Element is part of Belmont’s General Plan and identifies policies and programs to meet the housing needs of the City’s current and future residents. State law (Government Code Sections 65580- 65589.8) requires that every city and county in California adopt a Housing Element, approximately every eight years. In addition, the State Department of Housing and Community Development (HCD) reviews and certifies that each jurisdiction’s Housing Element meets all the requirements of the law. Belmont’s previous Housing Element was adopted in 2015 and the new document must be adopted by January 2023.

The approval process is expected to take several months, including public comment periods and review by HCD.

This Housing Element update process has been more challenging than previous cycles, in part because Belmont’s Regional Housing Needs Allocation (RHNA) is significantly higher this cycle at 1,785 units, compared to 468 units in the previous cycle. Additionally, there are new requirements to include information and analysis on the City’s efforts to affirmatively further fair housing (described below).

Consistent with past cycles, the City has collaborated with other jurisdictions in San Mateo County through the 21 Elements initiative, with additional support from a Housing Element Project Manager (Diana Elrod Consulting) and an environmental consulting firm preparing a Programmatic Environmental Impact Report (Rincon Consultants, Inc.).

The purpose of this study session is to:

1. Introduce the fair housing assessment that is new to the Housing Element this cycle;

2. Review the draft housing opportunity sites;

3. Review a summary of new housing programs.

No action is required of the City Council at this time. 3

Housing Element
Fair Housing Assessment and Outreach
The Housing Element has traditionally analyzed housing needs in the Belmont community, assessed what government or other constraints may potentially inhibit the development of new housing units, and provided a plan showing how the City’s land use regulations could accommodate its local fair share of regional housing growth. The Housing Element has always included programs and policies that support development of new housing units across all income levels. With the adoption of recent fair housing legislation, housing elements must also include policies and programs to affirmatively further fair housing (AFFH), which is defined as:
“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.”
Through a collaborative effort with 21 Elements and Root Policy—a community planning and housing research firm—Belmont has completed a comprehensive Fair Housing Assessment, following guidance from the State of California. The assessment looks at fair housing enforcement and outreach, identifies areas of integration and segregation, examines differences in access to opportunity, identifies disparate housing needs, and identifies the primary factors contributing to fair housing challenges and ways in which Belmont can address those challenges.

The City has engaged in several community outreach efforts aimed at affirmatively furthering fair housing, including a citywide housing resident mailer (sent to all residential units), hosting a bilingual community survey on the City website, hosting a local fair housing community workshop, and participating in regional stakeholder listening sessions through 21 Elements.

An overview of Belmont’s fair housing assessment and community engagement activities is included in the attached presentation (Attachment A). A full copy of the draft fair housing assessment and all required appendices are provided as Attachment B, including:

Belmont Fair Housing Assessment
Appendix 1: San Mateo County Resident Survey Findings
Appendix 2: Access to Education
Appendix 3: AFFH Segregation Report
Appendix 4: State Fair Housing Laws
Appendix 5: Map and Data Packet
Draft Housing Opportunity Sites Overview and Strategy
The Association of Bay Area Governments (ABAG) determines how much each Bay Area city is responsible for based on their size and how they are expected to grow by 2050, according to Plan Bay Area. Cities do not need to ensure these homes are built, but they do need to put in place the proper zoning and address constraints so the private sector can build the housing. The table below shows Belmont’s income-specific RHNA allocation for both the current RHNA-5 planning cycle and the proposed RHNA6 cycle, and shows the significant increase in RHNA for the proposed planning cycle:
4

Housing Element
RHNA-5 Current RHNA-6 Proposed
Very Low Income: 116 units Very Low Income: 488 units
Low Income: 63 units Low Income: 281 units
Moderate Income: 67 units Moderate Income: 283 units
Above Mod Income: 222 units Above Mod Income: 733 units Total: 468 units Total: 1,785 units
A summary of Belmont’s progress towards meeting its RHNA allocations is provided in the attached presentation (Attachment A). As of December 2021, the City of Belmont issued building permits for 508 new housing units, exceeding the total RHNA-5 allocation; however, the City still needs additional moderate and extremely low housing units to meet the income allocations.

For the Housing Element update, the City has identified 144 urban infill properties that provide opportunity for future development of housing units. The general selection criteria includes sites that do not currently contain residential units, sites that include large surface parking areas or are otherwise underutilized, properties subject to regular tenant turnover, and where possible, clusters of properties that could be consolidated to accommodate large scale development. Many of the identified properties are located along the El Camino Real and Old County Road corridors, with proximity to transit and other community services.

The site selection strategy also includes a sufficient number of sites to provide a buffer of units above the required RHNA allocation 1,785 units to ensure the City is able to demonstrate “no net loss” of housing units when development occurs that may not be consistent with the densities envisioned in the Housing Element (i.e. when an opportunity site is developed with lower density market rate units rather than affordable units, the City still has sufficient capacity to meet the RHNA-6 allocation).

Future development capacity was modeled using actual Belmont project densities for projects under construction or in the entitlement pipeline, and staff assumed that lot consolidation by private developers would continue to occur given the strong legislative incentives that exist for new housing development.

The draft housing opportunity sites strategy also assumes that Belmont will pursue the following zoning and General Plan amendments that enable housing development to occur at the densities envisioned:
• Rezone identified Service Commercial (SC) sites along Old County Road to Corridor Mixed Use (CMU) to allow for residential development.
• Increase maximum building height in the Corridor Mixed Use (CMU) and Village Station Core (VSC) zones to allow structures up to 65 feet in height (47 of 144 draft housing opportunity sites). • Increase the maximum floor area ratio and eliminate the maximum density metric in the Corridor Mixed Use (CMU) zone.

It should be noted that no zoning amendments are contemplated for single family residential properties; those sites continue to be available for residential development consistent with current zoning and State regulations. The draft housing opportunity sites may be viewed on an interactive map available on the City’s website: www.belmont.gov/housinglement.
5

Housing Element
Draft Opportunity Sites - East
Draft Housing Opportunity Sites – West (Existing Single Family Vacant Lots) 6

Housing Element
Housing Element Draft Program Highlights
The Housing Element must include programs and policies that support development of new housing units across all income levels and that will affirmatively further fair housing. Programs have historically been aimed at reducing or eliminating governmental constraints (fees, zoning restrictions, development review process), and encouraging new development activity (accessory dwelling unit resources, funding for housing service providers, developer support).

A summary of new draft programs proposed for the housing element has been provided as Attachment C.

Programs have been expanded to focus on addressing units at risk of losing affordability and addressing housing displacement. The programs include opportunities for the City to identify funding available for lower income housing development, and connecting with service providers to support a variety of community resident needs. The program list also includes the zoning modifications (Height, FAR, Density) contemplated to achieve the RHNA goal with the identified opportunity sites.

Next Steps
Staff will be holding a study session with the Belmont Planning Commission on May 3, 2022. After that meeting, comments and feedback received form both the City Council and Planning Commission will be incorporated into the draft Housing Element. Staff anticipates releasing a full draft of the Housing Element and the CEQA Programmatic Environmental Impact Report in late May, which will be followed by a 45- day public review period. Once the public review period closes, and at the direction of the City Council, the draft Housing Element will be routed to the State Department of Housing and Community Development (HCD) for review. The review and revised process with HCD is expected to take several months based on recent experiences by other jurisdictions in the state. Once HCD had approved the draft housing element, both the Belmont Planning Commission and City Council will be asked to hold public hearings to review and approve the General Plan Amendment and related CEQA action.

The Housing Element must be adopted by the City and certified by HCD prior to January 31, 2023.

Alternatives

1. Provide alternative direction on the Housing Element update process.

Attachments
A. Presentation
B. Belmont Fair Housing Assessment and Appendices
C. Summary of New Housing Element Programs (Draft)
Fiscal Impact
☒ No Fiscal Impact
☐ Funding Source Confirmed:

Source: Purpose: Public Outreach:

Staff Statutory/Contractual Requirement Other*
*In addition to posting the regular agenda, notification of this meeting was sent to all parties or stakeholders who have requested to be notified of Housing Element activity in the City of Belmont. 7 City of Belmont Fair Housing Assessment
What is AFFH?
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 Development (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. Taken together, these actions should address significant disparities in housing needs and in access to opportunity, replacing segregated living patterns with truly integrated and balanced living patterns, transforming 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).)” Source: California Department of Housing and Community Development Guidance, 2021, page 14. 1 California Department of Housing and Community Development Guidance, 2021, page 9. 8 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 2 History of segregation in the region. The
United States’ oldest cities 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 redlining and
discriminatory mortgage approvals—as well as
“structural inequities” in society, and “selfsegregation” (i.e., preferences to live near similar people).

Researcher Richard Rothstein’s 2017 book The Color
of Law: A Forgotten History of How Our Government
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 local 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 Historical 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 many 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 housing— housing segregated into less desirable areas, next to highways, and concentrated in public housing and urban renewal developments.

The segregating 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 thenpresident of the California Real Estate Association set up an office in East Palo Alto to scare White families 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 Palo Alto— initially established with “whites only” neighborhoods—became 82% African American. The FHA 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.
9 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 3 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 intentions 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 appears on the following page.

As shown in the timeline, exclusive zoning practices were common in the early 1900s. Courts struck down only the most discriminatory, 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 versus 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/ 10 Major Public and Legal Actions that Influence Fair Access to Housing 11 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.

The figures that are referenced throughout this section are contained in the Map and Data appendix. This supplemental appendix includes data tables comparing the jurisdiction to the county and broader region, in addition to HCD maps created to support the AFFH.

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.

Section V. Contributing Factors and Fair Housing Action Plan identifies the primary factors contributing to fair housing challenges and the plan for taking meaning actions to improve access to housing and economic opportunity.

Appendices.

1. Resident survey results—findings from a survey of Belmont residents on their experience finding and remaining in housing, with comparisons to the experience of county residents overall.

2. Access to education supplement—findings from a countywide analysis of access to education and educational outcomes by protected class.

3. UC Merced Study of Segregation in Belmont.

4. State Fair Housing Laws and Regulations—summary of key state laws and regulations related to mitigating housing discrimination and expanding housing choice.

5. Complete maps and data packet referenced within this document. 12 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 6 Primary Findings
 Nine fair housing inquiries and two fair housing complaints were made by Belmont residents between 2017 and 2021. A complaint filed in 2017 concerned refusal to rent based on national origin; it was settled through a conciliation agreement. A 2021 complaint based on disability for failure to make reasonable accommodations was withdrawn by the complainant.
 Fifty-four percent of Belmont’s population is non-Hispanic White; 28% is Asian; 12% is Hispanic/Latinx; and 4% is other races or mixed race. Belmont has proportionately fewer residents of color than in the county and the Bay area overall, although the city’s Asian population is on par with the proportion in the county and Bay area.

Belmont has grown more racially and ethnically diverse since 2000 and 2010, largely due to growth in Asian residents (mostly occurring between 2010 and 2020) and a slight increase in Hispanic/Latinx residents (mostly between 2000 and 2010).  Racial and ethnic minority populations in Belmont—with the exception of Asians— are more likely to be living in poverty and be housing cost burdened compared to the non-Hispanic White population. They are also slightly more likely to be denied a mortgage loan. On the positive side, residents living Belmont—regardless of race and ethnicity—have strong access to highly resourced neighborhoods.  Although Asian residents are less likely than other residents to experience cost burden, they have relatively high rates of overcrowding, suggesting that some are doubling up to afford to live in Belmont.
 One-third of non-Hispanic White households are cost burdened, with 15% severely cost burdened. Only 2.8% live in overcrowded conditions.  One-quarter of Asian households are cost burdened with only 9% severely burdened. Yet 7.8% live in overcrowded conditions.
 Hispanic/Latinx households have much higher rates of cost burden—48% are burdened and 25% severely burdened—and one in ten lives in an overcrowded household.
 Nearly all of Belmont’s households earning 30% of AMI and less are cost burdened, with the vast majority severely burdened (Figure IV-10). The level of burden diminishes greatly once households earn 80% of AMI and more.
 Geospatially, low-income households are mostly likely to live in the block groups that abut Highway 101 and in the southwest portion of the city (Figure II-27). The mapping completed by the state to support this study suggests that these moderatelyconcentrated neighborhoods offer similar access to jobs, good schools, and positive environmental outcomes as other parts of Belmont.
13 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 7  According to TCAC’s educational opportunity map, all census tracts in Belmont have schools with the most positive educational outcomes. Belmont offers a strong educational environment citywide. Students with disabilities in the Belmont-Redwood Shores district score comparatively well compared to students with disabilities in other districts.
 Yet educational outcomes still vary for some racial and ethnic groups, and the school district could improve proficiency gaps and differences in suspension rates.

Specifically,
 82% of students in Belmont’s district meet or exceed English language arts and literacy standards; 79% exceed math standards; both are higher than the county overall. These outcomes are much lower for some students of color: 44% of Black or African American students and 64% of Hispanic students meet or exceed state English language arts standards, and 37% of Black or African American students and 52% of Hispanic students meet or exceed state math standards.
 Suspensions are disproportionately high for Hispanic students. Hispanic students make up 34% of the high school district student body yet account for 66% of suspensions.
 Belmont’s largest challenge lies in housing affordability. In two census tracts, between 40% and 60% of renter households face housing burden. Yet compared to surrounding cities, Belmont offers better rental affordability, according to the HCD Location Affordability Index (Figure IV-29). Just 18% of Belmont’s owner-occupied units are priced below $1 million, compared to 44% for the county and 65% for the Bay area overall.
 Belmont has not kept up with demand for new housing. The number of homes in Belmont increased by 4% from 2010 to 2020, much improved from the prior decade where housing units increased by just 1%. Yet production still lags demand, especially for affordable housing. Between 2010 and 2020, Belmont built more single family than multifamily homes, and the city’s share of housing stock comprised of single family detached homes is above that of other jurisdictions in the region. 5  Nine-percent of Belmont’s residents have a disability, which is on par with the county and the Bay area overall.
5 Housing Needs Data Report completed by ABAG/MTC staff and Baird + Driskell Planning. 14 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 8 Fair Housing Challenges and Contributing Factors
Belmont is characterized by high-resourced neighborhoods, where residents have good access to employment opportunities and strong educational outcomes, and live in environmentally healthy areas. This is true even for the neighborhoods where low-income households are concentrated. Compared to the region overall and jurisdictions within the region, Belmont does better than most in not concentrating low-income households in lower resourced neighborhoods.

Belmont struggles, however, to provide the housing and affordability needed by low- and moderate-income households. As such, Belmont households who cannot afford housing are significantly cost burdened and some live overcrowded conditions.

The fair housing issues—and the factors contributing to those issues—identified by the research conducted for this AFFH include:

Fair housing issue: Few residents file fair housing complaints, indicating a potential lack of awareness about fair housing rights.

Contributing factors:
 Lack of access to information about fair housing rights.  Limited knowledge of fair housing by residents.

Fair housing issue: Other than Asian residents, Belmont has proportionately fewer residents of color than in the county and the Bay area overall.

Contributing factors:
 Lack of affordable housing
 Low housing production
Fair housing issue: Persons of color are highly cost burdened (Hispanic/Latinx households and/or live in overcrowded housing conditions (Asian households). Households with incomes of less than 80% AMI face very high levels of burden, with 93% of extremely low-income households facing burden and 79% of 31-50% AMI households facing burden.

Contributing factors:
 Lack of affordable housing
 Lower incomes of persons of color
 Low housing production
Fair housing issues in education:

Access to quality educational institutions is strong, but students of color still face disparities in subject proficiency within schools. 15 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 9  Overall, 82% of students in Belmont’s district meet or exceed English language arts and literacy standards and 79% exceed math standards. For Black or African American students, however, 44% are English language arts and literacy proficient and 37% are math proficient. For Hispanic students, 64% are English language arts and literacy proficient and 52% are math proficient.

Hispanic students face high rates of suspension compared to their representation among student bodies.

Contributing factors: The reason for these disparities is unclear, and should be examined.

The gaps suggest that Black and African American and Hispanic students need greater support to succeed, and that schools in the Belmont area need to focus more closely on efforts to close proficiency gaps and ensure equity in education. 16 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 10 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.

Fair housing legal cases and inquiries. California fair housing law extends beyond the protections 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 significant 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 Society 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). Of these, two—or

3.5% of complaints -- were in Belmont. Countywide, most complaints cited disability status as the bias (56%), followed by race (19%), and familial status (14%). In Belmont, the 6 https://www.dfeh.ca.gov/aboutdfeh/
7 https://www.dfeh.ca.gov/complaintprocess/
17 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 11 complaints alleged discrimination in the terms and conditions for renting and discriminatory refusal to rent.

Countywide, no cause determination was found in 27 complaints followed by successful conciliation or settlement with 22 complaints. In Belmont, one of the complaints was withdrawn by the complainant and the other was successfully settled. Fair housing inquiries in 2020 were primarily submitted from the City of San Mateo, Redwood City, Daly City, and Menlo Park.

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 2020. 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 have 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 housing 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
8 https://nationalfairhousing.org/2021/07/29/annual-fair-housing-report-shows-increase-in-housing-harassment/ 18 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 12 Compliance with state law. Belmont is compliant with the follow state laws that promote fair and affordable housing. The city has not been alleged or found in violation of the following:
 Housing Accountability Act (Gov Code. Section 65589.5) requiring adoption of a Housing Element and compliance with RHNA allocations;
19 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 13  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).

Housing specific policies enacted locally. Belmont has implemented the following housing policies that promote housing production, funding for housing affordability, and neighborhood stabilization. The city also streamlines the processing of applications for Accessory Dwelling Units (ADUs).

Housing Tools Leveraged in Belmont
Form based codes. Zoning that focuses
on how a structure conforms to
surrounding structures. Allows more
flexibility in structure type and use.

Mixed use zoning. Zoning that allows
commercial, retail, community and
residential uses to occupy the same site.

Can facilitate affordable housing by
leveraging uses and encouraging onsite
services.

Inclusionary housing. Requires that
residential developments of 25 units or
more provide 15% of units at affordable
rents to low-income households.

In-lieu fees for inclusionary housing.

Allows developers to pay a fee in-lieu of
constructing affordable units required
under the inclusionary housing
ordinance.

Density bonuses. Allows residential
development with 5 or more dwelling
units providing low, very low, senior, or
moderate-income housing additional
density. Conforms to State Density Bonus
law.

Commercial linkage fees. An impact
fee for new commercial and residential
developments that typically levies a per
square foot fee that goes toward funding
to develop or preserve affordable
housing.

According to Belmont staff, the city is considering implementing programs that would prevent displacement; program options could include foreclosure avoidance programs, an eviction protection ordinance, or funding for fair housing legal services.

According to the California Department of Housing and Community Development AFFH Data Viewer (HCD data viewer), Belmont does not have any public housing buildings (Figure 20 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 14 I-6). HCD’s map of voucher holder locations show Belmont as having three census tracts where less than 5% of units have voucher holders and two census tracts with no data (Figure I-7). This is a similar pattern in neighboring jurisdictions and in much of central San Mateo County. Voucher concentrations occur in and around Daly City, South San Francisco, and East Palo Alto.
21 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 15 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.”
Source: California Department of Housing and Community Development Guidance, 2021, page 31.

Race and ethnicity. Compared to San Mateo County, Belmont has a larger proportion of residents who identify as White, non-Hispanic and a smaller proportion of Hispanic residents. As shown in Figure II-1, 54% of Belmont residents are White, nonHispanic versus 39% of the county and the Bay area overall. Twelve percent of Belmont residents are Hispanic, compared to 24% in the county and the Bay area overall 9 Belmont has become increasingly racially and ethnically diverse since 2000, largely due to growth in the Asian population (93% increase) and Hispanic population (57% increase) and a decline in the White, non-Hispanic population (17% decline).

Older residents are less diverse with 71% of the population older than 65 years identifying as White, compared to 58% of the population for children less than 18 years old.

By race and ethnic group in Belmont, poverty is highest for American Indian Alaskan Natives (41% live below the poverty line) and Black or African Americans (17%). These residents make up a very small proportion of the city (American Indian Alaskan Native residents totaled 61 in 2019 according to 5-year ACS data; Black or African Americans 9 In Belmont, the share of the population that identifies as American Indian or Alaska Native is less than 1% (61 residents); Black/African American is 1% (275 residents); and Other Races is 4% (1,192 residents). 22 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 16 totaled 275), and, as such, reflect a small number of people living in poverty (approximately 25 American Indian Alaskan Natives and show 47 Black or African Americans).

When examined by income range, the data show a mixed picture with American Indian Alaskan Natives also showing a large proportion of high-income households—exhibiting a split income distribution with no middle-income households.10 Households with the most even income distribution include Black or African American and Hispanic or Latinx.

Geospatially, Belmont is split between census tracts with a sizeable gap of White majority residents, as shown in the White Majority Census Tracts map in Figure II-7. The census tracts identified as “Predominant[ly]” White majority are located in the far northwest and far southeast parts of the city, with the latter directly adjacent to White majority tracts that comprise most of San Carlos. Compared to northern jurisdictions in the county, Belmont has more of its area identified as White majority tracts; compared to southern jurisdictions of San Carlos, Woodside, and Menlo Park, Belmont has far fewer White majority tracts.

The city has no Asian majority tracts, no Hispanic majority tracts, and only Census tract with less than an even balance of racial groups; the far northwestern city Census tract is mostly comprised of Asian and White residents (Figure II-10).11 Belmont’s diversity index is low to moderate. The census tract with the highest diversity abuts Highway 101. Data do not show this tract as having concentrations of vouchers or public housing, yet this tract does contain several apartment complexes.

Dissimilarity and isolation indices.

The Dissimilarity Index, or DI, is a common tool that measures segregation in a community.

The DI is 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 between 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 isolation tend to indicate higher levels of segregation. 10 A large margin of error due to the limited sampling of AIAN residents by the Census is also a factor. 11 Redlining maps, otherwise known as Home Owners’ Loan Corporation (HOLC) maps, are not available for San Mateo County.
23 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 17 The attached Segregation Report conducted for the City of Belmont by the UC Merced Urban Policy Lan and ABAG/MTC provides a detailed discussion of the extent to which various groups are segregated from one another. The following summaries the key findings.

Segregation in the City of Belmont
• 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 Belmont, 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 Belmont the highest level of racial segregation is between Black and white residents. However, local jurisdiction staff should note that this dissimilarity index value is not a reliable data point due to small population size.
• According to the Theil’s H-Index, neighborhood racial segregation in Belmont declined between 2010 and 2020. Neighborhood income segregation increased between 2010 and 2015.
• Above Moderate-income residents are the most segregated compared to other income groups in Belmont. 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 increased between 2010 and 2015. In 2015, the income segregation in Belmont between lower-income residents and other residents was higher than the average value for Bay Area jurisdictions. 24 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 18 Segregation Between the City of Belmont and Other Jurisdictions in the Bay Area Region
• Belmont has a higher share of white residents than other jurisdictions in the Bay Area as a whole, a lower share of Latinx residents, a lower share of Black residents, and a higher share of Asian/Pacific Islander residents.
• Regarding income groups, Belmont has a lower share of very low-income residents than other jurisdictions in the Bay Area as a whole, a lower share of low-income residents, a similar share of moderate-income residents, and a higher share of above moderate-income residents.

Disability status. The share of the population living with at least one disability is 9% in Belmont compared to 8% in San Mateo County. There are two census tracts in the city that have a 10% to 20% share of the population living with a disability; both are located in the central part of Belmont (Figure II-14). Geographic concentrations of people living with a disability may indicate a need for increased access to services, amenities, and transportation that support this population.

Familial status. A majority of households in Belmont are made up of married-couples, and the city has a higher proportion of married-couple households (62%) than the county (55%) or Bay area overall (51%). Belmont has a similar share of single-person households as the county and Bay area but a smaller share of single parent households—10% in Belmont versus 15% in the county and Bay area—as well as “other” household types, such as roommates (Figure II-17).

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.

Belmont is slowly aging (Figure II-15), yet the city does not have a disproportionately higher share of single-person households. This could change in the future—leading to increased demand for in-home care and supportive services—if Belmont’s older adults remain in their homes as they age.

Belmont’s owner-occupied homes are mostly 3-to-4-bedroom units, while rental units are mostly 1 to 2 bedroom (Figure II-20). This distribution aligns well with the distribution of household types. Compared to the county, the Belmont has a smaller proportion of large (5 persons+) households (Figure II-16), which is partially due to the city’s limited number rental units with 5 and more bedrooms and, to a lesser extent, 3 to 4 bedrooms.

In its analysis of housing needs for Belmont, the Association of Bay Area Governments (ABAG) reports that between 2010 and 2020, the number of single-family units in Belmont 25 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 19 increased more than multi-family units: 33 new single family detached homes were built compared to 18 attached homes, and 18 multifamily units. Belmont’s share of single-family homes is about average for jurisdictions in the county, although there is a large variance in shares. For example, single family homes comprise 58% of Belmont’s housing stock compared to 44% of the City of San Mateo’s, 56% of San Bruno’s, 68% of San Carlos’, and 98% of Atherton’s.

Household income. Compared to San Mateo County, Belmont has:
 A much higher proportion of high-income households: 59% of households in Belmont earn 100% of the AMI and more, versus 49% in the county;  A similar share of very low-income households earning 50% AMI and less (22% in Belmont, versus 24% in the county); and
 A much smaller share of middle-income households earning between 50% and 100% AMI (18% in Belmont, versus 26% in the county).

The city also has fewer 0-30% AMI households than the Bay area overall as a proportion of total households, fewer middle-income households, and more high-income households.

Belmont’s neighborhoods are all moderately high to very high income, with no significantly concentrated low-income households (Figures II-26 and 27) or poverty concentrations (Figure II-28). Compared to other jurisdictions, Belmont exhibits more variance in income distributions than others in the county when examined spatially by census block group (Figure II-27).
26 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 20 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 particular 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 perpetuate these areas of high opportunity and exclusion.12
12 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 27 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 21 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% or more (majority-minority) or, for non-urban areas, 20%, AND a poverty rate of 40% or more; OR  A census tract that has a non-white population of 50% or more (majority-minority) AND the poverty rate is three times the average tract poverty rate for the County, whichever is lower.

Source: California Department of Housing and Community Development Guidance, 2021.

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 housing 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.

For this study, the poverty threshold used was three times the average tract poverty rate for the County—or 19.1%. In addition to R/ECAPs that meet the HUD threshold, this study includes edge or emerging R/ECAPs which hit two thirds of the HUD defined threshold for poverty—emerging R/ECAPs in San Mateo County have two times the average tract poverty rate for the county (12.8%).

In 2010 there were three Census Tracts that qualify as R/ECAPs (19.4% poverty rate) in the county and 11 that qualify as edge R/ECAPs (13% poverty rate). None of the 2010 R/ECAPs or edge R/ECAPs were located in Belmont.

In 2019 there were two Census Tracts that qualify as R/ECAPs (19.1% poverty rate) in the county and 14 that qualify as edge R/ECAPs (12.8% poverty rate). None of the 2019 R/ECAPs or edge R/ECAPs were located in Belmont.

RCAAs. HCD’s definition of a Racially Concentrated Area of Affluence is:
 A census tract that has a percentage of total white population that is 1.25 times higher than the average percentage of total white population in the given COG region, and a median income that was 2 times higher than the COG AMI.

As of this writing, ABAG has not provided this data.
28 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 22 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).”
Source: California Department of Housing and Community Development Guidance, 2021, page 34.

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. 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.

The TCAC opportunity maps are useful in comparing how well the sites identified for affordable housing and mixed income development compare with opportunity access in a jurisdiction overall and against other neighborhoods. The overall goal of that analysis is to create a better balance in the siting of affordable housing among opportunity areas.

Education. TCAC’s education score is based on math proficiency, reading proficiency, high school graduation rates, and the student poverty rate. According to TCAC’s educational opportunity map, all Census Tracts in Belmont have schools with the most positive educational outcomes. Belmont offers a strong educational environment citywide.

The City of Belmont is served by the Sequoia Union High School District and the BelmontRedwood Shores Elementary School District. Sequoia Union is fed by eight elementary school districts in the county.

Enrollment. Overall enrollment in the high school district increased 18% between the 2010-2011 and 2020-2021 school years. Enrollment fluctuated significantly among 29 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 23 Elementary School Districts, however, with the Belmont-Redwood Shore district far outpacing any other district for enrollment increases.

Between the 2010-2011 and 2019-2020 school years, elementary school enrollment in the Belmont-Redwood Shores district increased by 35%, or more than 1,000 students.

Enrollment dropped slightly between the 2020 and 2021 school years (down 162 students), for an overall enrollment change of 30%.

Student diversity. The Belmont-Redwood Shores elementary student body has grown more racially and ethnically diverse since 2011, mostly due to an increase in Asian students (24% in 2010-2011 versus 32% in 2020-2021) and multi-race students (4% to 14%). The proportion of White students has declined (55% to 34%), while the proportion of Hispanic students has increased slightly (15% versus 12%).

Compared to the county overall, the Belmont-Redwood Shores Elementary School District has:
 A much larger share of Asian students (32% versus 17%),  A lower share of Filipino students (3% versus 8%),
 A much lower share of Hispanic students (12% versus 38%),  A slightly larger share of White students (34% versus 26%), and  A much larger share of mixed-race students (14% versus 8%).

Enrollment of special student groups in Belmont-Redwood Shores has been relatively stable over time: 7% of students qualify for free and reduced lunch (FRL) and 10% are English language learners (versus 6% and 8%, respectively, in 2011). Even during the early part of the pandemic, when the county experienced a loss of English Language Learners (decline of 5%), the Belmont-Redwood Shores school district was able to retain these students.

Many schools have sought to increase their diversity of teaching and administrative staff to improve inclusivity for students of color. The gap in student and staff/teacher Hispanic representation is higher in the Sequoia Union High School District than in other districts in the county.

Extenuating circumstances. The Belmont-Redwood Shores district reports no students who are in foster care, are experiencing homelessness, or are members of migrant families as of the 2020-2021 school year. Chronic absenteeism is low for students in the Belmont-Redwood Shores district overall, yet varies by student group, with Hispanic and Pacific Islander students having relatively high rates (12% and 17%, respectively), and students with disabilities at 10%.

Test proficiency. Students in the Belmont-Redwood Shores district score well on state tests: 82% meet or exceed English language arts and literacy standards; 79% exceed math standards. This compares to 62% (English) and 52% (math) for the county overall.

However, this varies considerably by race and ethnicity, with 44% of Black or African 30 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 24 American students and 64% of Hispanic students meeting or exceeding state English language arts standards and 37% of Black or African American students and 52% of Hispanic students meeting or exceeding state math standards.

Students with disabilities in the Belmont-Redwood Shores district do well compared to other districts: 43% of students with disabilities are proficient in math (only Hillsborough Elementary is higher at 48%), and 45% are proficient in English (Woodside is higher at 56%; Hillsborough at 47%).

College readiness. 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.

Drop-out rates and suspensions. Although San Mateo Union High School has relatively low drop-out rates—4% of students—compared to other districts in the county, drop-out rates among Pacific Islander (20%), Hispanic (16%), and Black (12%) students are significantly higher.

Suspensions are disproportionately high for Hispanic students. Hispanic students make up 34% of the high school district student body, yet account for 66% of suspensions.

Employment. The professional and managerial services industry provides the largest number of jobs Belmont, and has increased the number of jobs faster than any other industry except for the information industry (Figure III-3). The health and educational services industry is the second largest provider of jobs in the city, with all other industries providing a much smaller share.

Belmont has a much lower job to household ratio when compared to the county, at .62 which is a decline from .75 in 2002. This compares to 1.59 for the county and 1.47 for the Bay area. The city is a net provider of workers to the region, mostly of high-wage workers, based on the jobs to workers ratio by wage (Figure III-5). The city also has a slightly lower unemployment rate than the county and Bay area.

TCAC’s economic opportunity score is comprised of poverty, adult educational attainment, employment, job proximity, and median home value. Except for the census tract abutting Highway 101 and the south-central tract that borders San Carlos, Belmont’s neighborhoods are classified as having the most positive economic outcomes.

HUD’s job proximity, which measures how close residents in a census tract can access jobs in the regional employment market (with larger employment centers weighted more heavily) shows that Belmont’s proximity to jobs is about average—better than cities located further south but not as strong as cities to the north, who are closer to the City of San Francisco.
31 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 25 Transportation. [TCAC’s transportation opportunity score and maps were not available at the time of this report] SamTrans provides bus services in San Mateo County including Redi-Wheels paratransit service. The San Mateo County Transit District acts as the administrative body for transit and transportation programs in the county including SamTrans and the Caltrain commuter rail.

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. That 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 Belmont and the county overall13.

Although Belmont was not mentioned specifically in the plan, its residents with public transportation needs have not grown significantly. In the county overall, the primary gaps in transportation access include:
 The “spatial gaps” in public transportation continue to be a challenge. Lack of connectivity can make it very difficult for populations reliant on public transit to access the region.
 Transit and paratransit systems lack service in the evenings, late at night, and on weekends—which can limit users’ access to jobs, education, and entertainment.  The areas in the region that are aging the fastest are suburban and rural in nature— and can be difficult to serve by fixed-route transit.
 Fees to access transit and paratransit are too high for many low- and fixed-income households.
 Funding needs are growing faster than revenues.

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.”14 TRACS highlights that improving accessibility requires engagement for the community because there are no “watch-dog” systems in place to hold agencies accountable.
13 https://mtc.ca.gov/sites/default/files/MTC_Coordinated_Plan.pdf 14 https://wid.org/transportation-accessibility/
32 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 26 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.15 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.16
Environment. TCAC’s opportunity areas environmental scores are based on the CalEnviroScreen 4.0 indicators which include pollution metrics such as ozone, PM2.5, diesel PM, drinking water, pesticides, toxic release, traffic, cleanup sites, groundwater threats, hazardous waste, impaired water bodies, and solid waste sites.

Belmont scores moderately-high on environmental outcomes. The one census tract that abuts Highway 101 has the lowest environmental score in the city; this is also the tract with the most housing type diversity.

All of Belmont’s neighborhoods have the strongest measures in the California Healthy Places Index (HPI) (Figure III-11) developed by the Public Health Alliance of Southern California (PHASC). The HPI includes 25 community characteristics in eight categories including economic, social, education, transportation, neighborhood, housing, clean environment, and healthcare.17 This index suggests that all parts of Belmont provide residents access to healthy environments as measured by the index.

Disparities in access to opportunity. Because Belmont is classified as a high resource area throughout and lacks racial and ethnic concentrations, the distribution of residents among high resource areas matches the city’s racial and ethnic distribution (Figure III-12).
15 https://www.samtrans.com/Planning/Planning_and_Research/Mobility_Plan_for_Older_Adults_and_People_with_Disabilit ies.html
16 https://mtc.ca.gov/planning/transportation/access-equity-mobility/clipperr-startsm 17 https://healthyplacesindex.org/about/
33 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 27 TCAC’s composite opportunity score for Belmont shows all but one census tract as “highest resource.” The census tract bordering Highway 101 is classified as “high resource.” The Social Vulnerability Index (SVI) provided by the Centers for Disease Control (CDC)— ranks census tracts based on their ability to respond to a disaster—includes four themes of socioeconomic status, household composition, race or ethnicity, and housing and transportation. Belmont is rated as having low to moderately-low vulnerability to disasters (Figure III-15).

Belmont does not have any disadvantaged communities as defined under SB 535, “disadvantaged communities are defined as the top 25% scoring areas from CalEnviroScreen along with other areas with high amounts of pollution and low populations.”18
Disability
“Disability types include hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty.” Source: California Department of Housing and Community Development Guidance, 2021, page 36.

Disparities specific to the population living with a disability. Nine percent of the population in Belmont is living with at least one disability, compared to 8% in the county. The most common disabilities in the city are ambulatory (3.6%), independent living (3.2%), and cognitive (3.2%).

For the population 65 and over the share of the population with an ambulatory or independent living difficulty increases significantly—to 14.4% of the senior population.

Should Belmont’s seniors choose to age in place, demand for in-home care, paratransit, and accessibility improvements will increase.
18 https://oehha.ca.gov/calenviroscreen/sb535
34 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 28 Access to Opportunity
Regional Access
Belmont San Mateo County
Jobs to Household Ratio 0.62 1.59
Unemployment Rate 5% 6%
LEP Population 4% 7%
Share of Population by Race in Resource Areas
Employment by Disability Status
High/Highest Resource Area 0% 28% 1% 54% 4% 12%
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%
95%
3%
5%
No Disability
With A Disability
Employed Unemployed
35 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 29 SECTION I. 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 relevant 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.”
Source: California Department of Housing and Community Development Guidance, 2021, page 39.

Housing needs. According to ABAG, the population of Belmont increased by 6.7% from 2000 to 2020, which is below the growth rate of the Bay area. However, the city’s population growth has generally been in line with the county.

ABAG also reports that the number of homes in Belmont increased, 0.6% from 2010 to 2020, below the growth rate for San Mateo County and the broader region.

A total of 240 building permits were issued in Belmont between 2015 and 2020. Of those:
 138, or 57.5% were for above moderate-income units,
 36, or 15% were for moderate income units,
 44, of 18.3% for low-income units, and
 22, or 9%, for very low-income units.
 Altogether, more than one-fourth of the permits were for low and very low-income units.

The majority of the housing inventory in Belmont was built between 1940 and 1960 (5,131 units), followed by 1960 to 1979 (3,358 units). The number of units built after 2010 (470) is three times the number built between 2000 and 2009 (just 149)—a positive trend for addressing housing gaps.
36 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 30 Belmont has a much larger share of owner-occupied units priced between $1 million and $2 million than the county—65% of units in the city fall within this price range compared to 37% in the county. Just 18% of Belmont’s owner-occupied units are priced below $1 million, compared to 44% for the county and 65% for the Bay area overall.

According to the Zillow home value index, home prices have experienced remarkable growth in the city and county since 2011, with Belmont’s prices accelerating faster than the county or Bay area overall (Figure IV-5).

Rents in Belmont have increased at nearly exactly the same pace as in the county and remain about 30% higher than rents in the Bay area overall. Belmont’s rental distribution is more closely aligned with the county’s overall but much less affordable than the Bay area overall (Figure IV-6). In Belmont, 65% of rental units are priced at $2,000 or more, compared to the 59% in the county, and 42% for the Bay area overall.

Cost burden and severe cost burden. Belmont households face lower rates of cost burden—spending more than 30% of gross income on housing costs—than in the county and Bay area overall19. Seventeen percent of Belmont households are cost burdened (versus 20% in the county) and 13% are severely cost burdened (versus 17% in the county), spending more than 50% of their gross income on housing costs. Cost burdened households have less money to spend on other essentials like groceries, transportation, education, healthcare, and childcare. Extremely cost burdened households are considered at risk for homelessness.

Cost burden is much higher for renters: in Belmont, 47% of renters are cost burdened compared with 21% of owners. Nearly all of Belmont’s households earning 30% of AMI and less are burdened, with the vast majority severely burdened (Figure IV-10). The level of burden diminishes greatly once households earn 80% of AMI and more.

Cost burden varies across residents of different races and ethnicities. Black or African American households, mixed race households, and Hispanic households are most likely to face severe cost burden, with rates ranging from 25% to 30%. Asian households are the least likely to be severely-burdened, at 9%.

Belmont’s large family households—considered households with five or more persons— are less likely to be cost burdened than other households. There are no highly concentrated areas of cost burden in Belmont (Figures IV-13 and IV-14).

Overcrowding. The vast majority of households (95%) in Belmont are not overcrowded—indicated by more than one occupant per room. Renter households make up the vast majority of the city’s households living in overcrowded conditions. 19 Cost burden and severe cost burden are HUD and industry standard metrics. 37 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 31 By race and ethnicity, the city’s Hispanic and mixed-race households are the most likely to be living in overcrowded conditions. Belmont has no geographic concentrations of overcrowded households.

Substandard housing. Renter households are also more likely to have substandard kitchen and plumbing facilities compared to owner households. Of the city’s renters, 6.6% 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.

Homelessness. In 2019, 1,512 people were experiencing homelessness in the county, 40% of people were 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.

People who identified as American Indian or Alaskan Native (6% homeless, but represent less than 1% of the general 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. People struggling with chronic substance abuse (112 people), severe mental illness (305), and domestic violence (127) represent a substantial share of the homeless population in 2019.

Retaining assisted units. According to HUD, Belmont has 84 low-income affordable units at risk of converting to market rate units. These units make up about 2% of units atrisk of converting to market in the county overall, and less than 1% of housing units in Belmont. The risk of these units converting to market rate is low, however, according to the California Housing Partnership. Low risk is defined as units that are at risk of converting in a longer timeframe (10+ years) and which are currently owned by a large and/or stable nonprofit, mission-driven developer.

According to the Urban Displacement Project, three census tract in the city—two abutting Highway 101 and one abutting Highway 280—are at risk of “becoming exclusive.” This is based on a large rise in the home value and rent change in the census tracts between 2012 and 2018. These tracts also have high shares of renter households.

Preservation and anti-displacement efforts should focus on the units located near Highway 101 to facilitate access to services, transit, employment, and quality education, both within the city and surrounding areas.

Access to mortgage loans. Disparities by race and ethnicity are also prevalent for home mortgage applications, particularly in denial rates. Hispanic mortgage loan applicants (29% denial rate), American Indian or Alaska Native households (33%), and Black or African American households (27%) had the highest denial rates for 38 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 32 mortgage loan applications in 2018 and 2019. These compare to 14% for Asian applicants and 20% for White applicants.

Disproportionate Housing Needs
Cost Burden. Belmont, 2019
Area Median Income (AMI)
Overcrowding, Belmont, 2019
Occupants per Room by Tenure
Substandard Housing, Belmont, 2019
Incomplete Kitchen and Plumbing Facilities by Tenure
Homelessness, San Mateo County, 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, Belmont, 2019
Assisted Units at High or Very
High Risk of Displacement Belmont San Mateo County
Number of Units 0 417
% of Assisted Units 0% 8%
7%
21%
44%
73%
90%
17%
39%
39%
23%
10%
76%
39%
17%
4%
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

0.6%

0.0%

6.6%

0.3%
Kitchen
Plumbing
Owner Renter

4.7%

4.8%

0.8%

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

0.6%

0.0%

6.6%

0.3%
Kitchen
Plumbing
Owner Renter

1.5+ Occupants
per Room
1-1.5 Occupants
per Room
39 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 33 Site Inventory Analysis
AB 686 requires an analysis of sites identified to meet RHNA obligations for their ability to affirmatively further fair housing. The City is working with ABAG and Root Policy to conduct a mapping exercise of the proposed sites inventory so that a more detailed analysis can be conducted. The analysis will be placed here and will consist of:
 Map of identified sites by lower income, moderate income, and above moderate income units;
 Identification of sites within or proximity to R/ECAPs and edge R/ECAPs and/or low income/poverty concentrations;
 Proportion of low and very low-income units located in that area, as well as concentrations of Housing Choice Vouchers,
 How the distribution of lower, moderate, and above moderate-income units—and the share located in low, moderate, and high resourced areas—will change with proposed site inventory development;
 Proximity to:
 High proficiency K-12 education institutions;
 High-resourced areas/positive economic outcome areas;  Low social vulnerability;
 Good jobs proximity;
 Access to transportation;
 Healthy places; and
 Flood hazards.

Contributing Factors and Fair Housing
Action Plan
Based on the findings above, attached is a matrix linking data analysis to contributing factors and recommended Fair Housing Action Plan for the City. 40 AFFH Action Matrix Template
Actions
Fair Housing
Issues Contributing Factors
Fair Housing
Category
Type of
Action
Responsible
Party Objectives Quantified Objectives Timeline
Action Area 1. Enhancing housing mobility strategies: consist of removing barriers to housing in areas of opportunity and strategically enhancing access.

Action 1.1: Improve access to fair
housing information.

Lack of fair housing
complaints filed
Lack of access to
information about fair
housing rights. Limited
knowledge of fair
housing by residents.

Outreach and
capacity
enforcement
Human
resources
City of
Belmont
Provide an easy way for residents
and property owners to find
information on fair housing laws,
rights, and responses (filing a
complaint, ensure property
owners do not violate fair housing
laws).

Conduct a best practices review of
other jurisdictions' websites.

Update Belmont's website to
contain fair housing resources and
information on how to file
complaints.

Complete best
practice review by
fall 2022; complete
website update by
year end 2022
Action 1.2: Adjust the city's Below
Market Rate (inclusionary) program to
allow a smaller unit contribution
(<15%), larger density bonuses,
and/or increased city support in
exchange for affordable units that
address the needs of residents with
extremely low and very low incomes
who face very high rates of cost
burden in the city.

Very high rates of
cost burden for
<50% AMI
households and
Black and Hispanic
households; high
rates of
overcrowding
among Asian
households
Lack of affordable
housing citywide; low
housing production
Disproportionate
housing needs
Land use
resources
City of
Belmont
Expand the variety of housing
units produced under the
inclusionary housing program.

Perform a feasibility analysis to
redesign the program to allow a
menu of options (e.g., 8% of units
for extremely low income or 15%
for low income or 30% for
moderate income).

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

Action 1.3: Design a regional
forgivable loan program for
homeowners to construct an ADU that
is held affordable for extremely lowincome households for 15 years.

Market through HEART.

Very high rates of
cost burden for
<50% AMI
households and
Black and Hispanic
households; high
rates of
overcrowding
among Asian
households
Lack of affordable
housing citywide; low
housing production
Disproportionate
housing needs
Land use
resources
21 Elements/
HEART
Increase opportunities for lowerincome households to find housing that is affordable.

Design a regional loan forgiveness
program.

Begin design in
Summer 2025 and
complete by winter
2026.

Actions
Fair Housing
Issues Contributing Factors
Fair Housing
Category
Type of
Action
Responsible
Party Objectives Quantified Objectives Timeline
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.

Action 2.1: Add more city supported
housing with affordability restrictions.

Affirmatively market the housing to
households with disproportionate
housing needs including Black and
Hispanic households
Very high rates of
cost burden for
<50% AMI
households and
Black and Hispanic
households; high
rates of
overcrowding
among Asian
households
Lack of affordable
housing citywide; low
housing production
Disproportionate
housing needs
Financial
resources
City of
Belmont
Increase development of
accessible units beyond minimum
requirements
Modify developer agreements
when appropriate; update
inclusionary policy.
2026
41 ROOT POLICY RESEARCH BELMONT JURISDICTIONAL SUMMARY, PAGE 35 Actions
Fair Housing
Issues Contributing Factors
Fair Housing
Category
Type of
Action
Responsible
Party Objectives Quantified Objectives Timeline
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 improving assets in areas of lower opportunity and concentrated poverty.

Action 3.3: Engage the school district
in conversations and a plan to
address proficiency gaps in education
for Black and Hispanic students
Differences in
proficiency gaps;
higher suspension
rates for Hispanic
students
Unclear; could be
related to support and
resources
Disparities in
educational
outcomes
Human
resources
City of
Belmont to
lead
Work with school district leaders
to engage in conversations about
the nexus between healthy
housing and educational
attainment; develop a plan to
reach Hispanic households with
students about housing resources
that currently are available to
them.

Meet with school district leaders to
create a workshop in which local
housing and service providers with
cultural competency assist 100
Spanish-speaking families access
services to improve their housing
condition.

Meet with school
district in early 2027;
develop workshop by
the end of 2027.

Actions
Fair Housing
Issues Contributing Factors
Fair Housing
Category
Type of
Action
Responsible
Party Objectives Quantified Objectives Timeline
Action Area 4. Protecting existing residents from displacement: strategies that protects residents in areas of lower or moderate opportunity and concentrated poverty and preserves housing choices and affordability.

Action 4.1: Develop a plan to
preserve the city's affordable units
that will expire in the next decade to
keep them affordable long term.

Very high rates of
cost burden for
<50% AMI
households and
Black and Hispanic
households; high
rates of
overcrowding
among Asian
households
Lack of affordable
housing citywide; low
housing production
Disproportionate
housing needs
Human
resources
City of
Belmont
Work with property owners of
existing assisted housing
developments for lower-income
households and partner with
nonprofits to determine methods
to extend affordability covenants
to preserve affordable units,
including assistance from the
City.

Conduct best practices research
on other jurisdictions' programs
and prepare recommendations to
City Council.

Conduct best
practices work in
2025; bring
recommendations to
Council in the
beginning of 2026;
implement program
by mid-2026.

Action 4.2: Partner with Project
Sentinel to perform fair housing
training for landlords and tenants.

Focus enforcement efforts on racebased discrimination and reasonable accommodations.

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

Lack of accessible
affordable units; housing
discrimination
Disproportionate
housing needs;
housing
discrimination
Human
resources
Project
Sentinel
Increase awareness of fair
housing laws and tenants' rights
to reduce unlawful discrimination
and displacement.

Request Project Sentinel to
provide training every two years in
the Spring, targeting 50 landlords
each training. Determine whether
this can be accomplished within
existing Countywide contracts, or
if the City needs to provide
additional funding.

Ongoing
42 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 1 AFFH Appendix.

Community Engagement
This section reports the findings from the resident survey conducted of San Mateo County residents to support the AFFH analysis of Housing Elements. It explores residents’ housing, affordability, and neighborhood challenges and experiences with displacement and housing discrimination. The survey also asks about residents’ access to economic opportunity, captured through residents’ reported challenges with transportation, employment, and K-12 education. The survey was offered in both English and Spanish.

The resident survey was available online, in both Spanish and English, in a format accessible to screen readers, and promoted through jurisdictional communications and social media and through partner networks. A total of 2,382 residents participated.

The survey instrument included questions about residents’ current housing situation, housing, neighborhood and affordability challenges, healthy neighborhood indicators, access to opportunity, and experience with displacement and housing discrimination.

Explanation of terms. Throughout this section, several terms are used that require explanation.
 “Precariously housed” includes residents who are currently homeless or living in transitional or temporary/emergency housing, as well as residents who live with friends or family but are not themselves on the lease or property title. These residents may (or may not) make financial contributions to pay housing costs or contribute to the household in exchange for housing (e.g., childcare, healthcare services).  “Disability” indicates that the respondent or a member of the respondent’s household has a disability of some type—physical, mental, intellectual, developmental.  “Single parent” are respondents living with their children only or with their children and other adults but not a spouse/partner.
 “Tenure” in the housing industry means rentership or ownership.  “Large households” are considered those with five or more persons residing in a respective household.
 “Seriously Looked for Housing” includes touring or searching for homes or apartments, putting in applications or pursuing mortgage financing. 43 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 2 Sampling note. The survey respondents do not represent a random sample of the county or jurisdictions’ population. A true random sample is a sample in which each individual in the population has an equal chance of being selected for the survey. The selfselected nature of the survey prevents the collection of a true random sample. Important insights and themes can still be gained from the survey results, however, with an understanding of the differences among resident groups and between jurisdictions and the county overall. Overall, the data provide a rich source of information about the county’s households and their experience with housing choice and access to opportunity in the communities where they live.

Jurisdiction-level data are reported for cities with 50 responses or more. Response by jurisdiction and demographics are shown in the figure below. Overall, the survey received a very strong response from typically underrepresented residents including: people of color, renters, precariously housed residents, very low income households, households with children, large households, single parents, and residents with disabilities. 44 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 3 Figure 1.

Resident Survey Sample Sizes by Jurisdictions and Selected Characteristics Note: Numbers do not aggregate either due to multiple responses or that respondents chose not to provide a response to all demographic and socioeconomic questions.

Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Belmont
Total Responses 2,382 89 82 173 130 53 148 63 59 55 84 163 99 175 832 Race/Ethnicity
African American 134 5 7 4 9 8 10 6 4 4 5 14 4 17 15
Hispanic 397 7 9 14 26 27 13 8 1 8 12 59 13 31 149
Asian 500 18 9 26 43 6 32 6 8 13 14 11 19 23 249
Other Race 149 7 10 6 8 3 14 3 3 3 3 9 7 13 47
Non-Hispanic White 757 41 35 89 27 4 44 27 27 15 35 54 36 58 195 Tenure
Homeowner 1,088 39 51 96 39 9 89 26 46 18 42 37 48 58 409 Renter 1,029 40 30 65 67 36 43 28 7 33 38 105 41 88 324 Precariously Housed 309 10 8 12 26 12 17 14 5 7 13 23 16 29 87 Income
Less than $25,000 282 14 11 12 21 15 12 11 5 6 7 40 11 29 61 $25,000-$49,999 265 13 9 10 22 9 8 6 3 6 7 28 5 20 97
$50,000-$99,999 517 10 14 38 43 10 26 11 3 10 17 37 22 40 206 Above $100,000 721 38 24 69 16 8 64 12 30 14 32 31 40 40 251 Household Characteristics
Children under 18 840 38 24 53 50 26 44 17 18 20 29 61 37 64 287 Large households 284 5 7 11 20 18 8 3 5 7 8 20 13 15 133 Single Parent 240 14 8 15 19 11 12 9 3 7 7 30 9 21 49
Disability 711 28 25 41 38 22 40 22 13 17 29 62 34 65 210 Older Adults (age 65+) 736 25 27 66 37 11 54 25 25 18 33 44 32 37 248 San
Mateo
South San
Francisco
Half
Moon
Bay Hillsborough Milbrae Pacifica
Redwood
City
San
Bruno
Foster
County Brisbane Burlingame City
Daly
City
East
Palo
Alto
45 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 4 Primary Findings
The survey data present a unique picture of the housing choices, challenges, needs, and access to economic opportunity of San Mateo County residents.

Top level findings from residents’ perspectives and experiences:
 The limited supply of housing that accommodates voucher holders presents several challenges. Specifically,
 Eight out of 10 voucher holders represented by the survey find a landlord that accepts a housing voucher to be “difficult” or “very difficult.”  According to the survey data, vouchers not being enough to cover the places residents want to live is a top impediment for residents who want to move in San Mateo County, as well as for African American, Asian, and Hispanic residents, households with children under 18, single parents, older adults, households with a member experiencing a disability, and several jurisdictions.
 Low income is a barrier to accessing housing. The impacts are highest for precariously housed respondents. large households, Hispanic households, and residents in Daly City and Redwood City.
 Nearly 4 in 10 respondents who looked for housing experienced denial of housing. African American/Black respondents, single parent households, precariously housed respondents, and households with income below $50,000 reported the highest denial rates.
 1 in 5 residents have been displaced from their home in the past five years. One of the main reasons cited for displacement was the rent increased more than I could pay. African American households, single parents, households that make less than $25,000, and precariously housed respondents reported the highest rates of displacement.
 For households with children that were displaced in the past five years, 60% of children in those households have changed schools. The most common outcomes identified by households with children who have changed schools include school is more challenging, they feel less safe at the new school, and they are in a worse school.
 Nearly 1 in 5 residents reported they have experienced discrimination in the past five years. African American, single parent, and precariously housed respondents reported the highest rates of discrimination. The most common actions 46 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 5 in response to discrimination cited by survey respondents were Nothing/I wasn’t sure what to do and Moved/found another place to live.
 Of respondents reporting a disability, about 25% report that their current housing situation does not meet their accessibility needs. The three top greatest housing needs identified by respondents included installation of grab bars in bathroom or bench in shower, supportive services to help maintain housing, and ramps.
 On average, respondents are fairly satisfied with their transportation situation. Groups with the highest proportion of respondents somewhat or not at all satisfied with their transportation options included African American, single parents, precariously housed, and Brisbane respondents.

There are some housing, affordability, and neighborhood challenges unique to specific resident groups. These include:
 Would like to move but can’t afford it—Most likely to be a challenge for Daly City, East Palo Alto, and Redwood City respondents, as well as Hispanic, renter, precariously housed, households making less than $50,000, and large household respondents.
 My house or apartment isn’t big enough for my family—Most likely to be a challenge for East Palo Alto respondents, as well as Hispanic households, large and single parent households, and households with children under 18.  I’m often late on my rent payments—Most likely to be a challenge for East Palo Alto and renter respondents, as well as households that make less than $25,000.  I can’t keep up with my utility payments—Most likely to be a challenge for East Palo Alto, Daly City, and San Mateo respondents, as well as African American and Hispanic respondents, single parent households, households with children under 18, and households that make less than $50,000.
 Bus/rail does not go where I need to go or does not operate during the times I need— Most likely to be a challenge for African American, precariously housed, single parent households, Brisbane and Pacifica respondents.  Schools in my neighborhood are poor quality—Most likely to be a challenge for East Palo Alto, Redwood City, San Bruno and South San Francisco respondents, as well as Hispanic respondents and households with children under 18. 47 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 6 Resident Survey Findings
Of survey respondents who reported their race or ethnicity, nearly 40% of survey respondents identified as non-Hispanic White, followed by Asian (26%), Hispanic (20%), African American (7%), and Other Minority (8%) residents (Figure 2). Overall, 45% of the survey respondents were homeowners, followed by 42% of renter respondents. Thirteen percent of respondents reported they are precariously housed (Figure 3). Four in ten respondents reported having household income greater than $100,000. Nearly 30% of respondents reported a household income between $50,000-99,999, followed by 15% of respondents who made between $25,000-49,999 and 16% of respondents making less than $25,000 (Figure 4).

The survey analysis also included selected demographic characteristics of respondents, including those with children under the age of 18 residing in their household, adults over the age of 65, respondents whose household includes a member experiencing a disability, those who live in large households, and single parents. Thirty five percent of respondents indicated they had children in their household, while 31% indicated they were older adults.

Thirty percent of respondents indicated they or a member of their household experienced a disability, 12% of respondents reported having large households, and 10% were single parents (Figure 5).
48 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 7 Figure 2.

Survey Respondents
by Race/Ethnicity
Note:
n=1,937; 535 respondents did not
indicate their race or ethnicity.

Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

Figure 3.

Survey Respondents
by Tenure
Note:
n=2,426.

Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

Figure 4.

Survey Respondents
by Income
Note:
n=1,785.

Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.
49 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 8 Figure 5.

Survey Respondents
by Selected
Household
Characteristics
Note:

Denominator is total responses to the
survey (n=2,382)
Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.

Housing, Neighborhood and Affordability Challenges
Housing challenges: overall. Survey respondents were asked to select the housing challenges they currently experience from a list of 34 different housing, neighborhood, and affordability challenges. Figures 6a through 8c present the top 10 housing and neighborhood challenges and top 5 affordability challenges experienced by jurisdiction, race/ethnicity, tenure, income, and selected household characteristics.

These responses allow a way to compare the jurisdictions to the county for housing challenges for which other types of data do not exist. In this analysis, “above the county”—shaded in light red or pink—is defined as the proportion of responses that is 25% higher than the overall county proportion. “Below the county”—shown in light blue—occurs when the proportion of responses is 25% lower than the overall county proportion.

As shown in Figure 6a, residents in Redwood City and East Palo Alto experience several housing challenges at a higher rate than the county overall. Conversely, Foster City and Hillsborough residents experience nearly all identified housing challenges at a lower rate than the county.

Notable trends in housing, neighborhood, and affordability challenges by geographic area include:
 Residents in Daly City, East Palo Alto, and Redwood City are less likely to move due to the lack of available affordable housing options.
 East Palo Alto, Redwood City, and San Mateo residents report living in housing that is too small for their families.
 Millbrae, Belmont, and Redwood City residents report being more reticent to request a repair to their unit in fear that their landlord will raise their rent or evict them.  Nearly 1 in 5 Pacifica survey respondents report that their home or apartment is in bad condition.
50 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 9  Brisbane and East Palo Alto residents are more likely to experience a landlord refusing to make repairs to their unit.
 Residents in Daly City and Millbrae are more likely to report that they don’t feel safe in their neighborhood or building.
 Half Moon Bay and East Palo Alto respondents expressed the greatest need for assistance in taking care of themselves or their home.

When compared to the county overall, the most common areas where respondents’ needs were higher than the county overall were:
 Overall, half of the jurisdictions’ respondents reported I need help taking care of myself/my home and can’t find or afford to hire someone at a higher rate than the county.  Over 40% of jurisdictions’ respondents reported a higher rate than the county for the following housing challenge: My home/apartment is in bad condition.  Nearly 40% of jurisdictions’ respondents reported a higher rate than the county for the following housing challenges: My landlord refuses to make repairs despite my requests and I don’t feel safe in my neighborhood/building.
51 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 10 Figure 6a.

Top 10 Housing Challenges Experienced by Jurisdiction
Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,159 82 73 158 118 49 135 59 50 53 79 151 93 163 738
31% 27% 12% 20% 51% 41% 16% 25% 4% 32% 28% 43% 30% 38% 35% 20% 22% 11% 14% 24% 35% 10% 12% 4% 21% 11% 26% 20% 26% 21% 14% 21% 10% 13% 17% 14% 9% 10% 2% 23% 15% 20% 11% 15% 13% 11% 15% 14% 9% 15% 12% 3% 7% 0% 11% 18% 14% 5% 15% 10%
6% 6% 14% 3% 5% 12% 4% 5% 2% 2% 9% 9% 5% 10% 5%
6% 6% 5% 4% 8% 4% 5% 8% 6% 6% 3% 8% 4% 7% 5%
6% 7% 5% 5% 13% 8% 0% 7% 6% 11% 10% 8% 3% 6% 3%
5% 2% 7% 7% 7% 10% 2% 14% 2% 8% 9% 3% 4% 8% 4%
5% 10% 5% 4% 3% 16% 2% 3% 4% 6% 9% 11% 6% 4% 3%
4% 2% 5% 1% 3% 8% 11% 3% 2% 4% 5% 3% 3% 4% 2%
42% 37% 48% 50% 20% 33% 55% 44% 76% 36% 47% 28% 45% 35% 46% I have bed bugs/insects or rodent
infestation
The HOA in my neighborhood won't
let me make changes to my house
or property
None of the above
I need help taking care of
myself/my home and can't find or
afford to hire someone
South San
Milbrae Francisco
Housing or Neighborhood
Condition
Valid cases
Half
Moon
Bay Hillsborough Pacifica
Redwood
City
San
Bruno
San
County Brisbane Burlingame Mateo
I would like to move but I can't
afford anything that is
available/income too low
My house or apartment isn't big
enough for my family
Daly
City
East
Palo
Alto
Foster
City
I live too far from family/
friends/my community
I don't feel safe in my building/
neighborhood
I worry that if I request a repair it
will result in a rent increase or
eviction
My home/apartment is in bad
condition
My landlord refuses to make repairs
despite my requests
Belmont
52 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 11 The following two figures segment the answers by:
 Housing affordability challenges only; and
 Neighborhood challenges only.

Housing affordability challenges. As shown in Figure 6b, residents in San Mateo, Daly City, East Palo Alto, and Pacifica experience affordability challenges at a higher rate than the county overall. Conversely, Belmont, Hillsborough, Burlingame, and South San Francisco residents experience affordability challenges at a lower rate than the county.

The most significant geographic variations occur in:
 San Mateo city residents experience all five affordability challenges at a greater rate than the county overall. In addition to being less likely to pay utility bills or rent on time, San Mateo residents are more than twice as likely than the average county respondent to have bad credit or a history of eviction/foreclosure that impacts their ability to rent.  East Palo Alto, San Mateo, and Daly City residents are most likely to experience difficulty paying utility bills.
 Residents in East Palo Alto and Redwood City are most likely to be late on their rent payments.
 Millbrae residents experience the greatest difficultly paying their property taxes among jurisdictions in San Mateo County.
 Respondents from Brisbane, Half Moon Bay, and Pacifica are also more likely to have trouble keeping up with property taxes.
 Daly City, City of San Mateo, and Redwood City respondents are more likely to have bad credit or an eviction history impacting their ability to rent.

Overall, over a third of jurisdictions’ respondents experienced the following affordability challenges at a higher rate than the county: I can’t keep up with my property taxes and I have bad credit/history of evictions/foreclosure and cannot find a place to rent. .
53 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 12 Figure 6b.

Top 5 Affordability Challenges Experienced by Jurisdiction Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,130 83 73 157 115 51 134 58 50 50 77 147 93 160 728
10% 6% 5% 6% 15% 16% 5% 12% 4% 12% 8% 12% 9% 15% 9%
8% 6% 5% 6% 10% 20% 3% 7% 2% 8% 4% 12% 4% 11% 7%
6% 2% 10% 4% 3% 2% 8% 10% 0% 16% 10% 3% 5% 9% 5%
4% 1% 4% 2% 13% 6% 0% 0% 2% 0% 5% 8% 4% 10% 2%
4% 2% 7% 3% 3% 2% 7% 3% 4% 4% 5% 3% 3% 6% 2%
73% 73% 68% 80% 65% 59% 78% 66% 88% 64% 71% 70% 77% 63% 80% I have bad credit/history of
evictions/foreclosure and cannot
find a place to rent
I have Section 8 and I am worried my
landlord will raise my rent higher
than my voucher payment
None of the above
Affordability Challenges
San
Mateo
Foster
Belmont City
South San
Francisco
Valid cases
I can't keep up with my utilities
I'm often late on my rent payments
I can't keep up with my property
taxes
Half
Moon
Bay Hillsborough Milbrae Pacifica
Redwood
City
San
County Brisbane Burlingame Bruno
Daly
City
East
Palo
Alto
54 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 13 Neighborhood challenges. As shown in Figure 6c, residents in East Palo Alto, Brisbane, Daly City, and Pacifica experience neighborhood challenges at a higher rate than the county.

Burlingame and Foster City both experience neighborhood challenges at a lower rate than the county.

Hillsborough and Belmont residents report divergent experiences related to neighborhood challenges — respondents identified more challenges around neighborhood infrastructure and access to transit but fewer challenges around school quality and job opportunities.

There are a handful of jurisdictions who experience specific neighborhood challenges at a disproportionate rate compared to the county.  For instance, East Palo Alto and Belmont residents experience neighborhood infrastructure issues (e.g., bad sidewalks, no lighting) more acutely than county residents overall.  Brisbane residents experience transportation challenges in their neighborhoods.  East Palo Alto, Redwood City, San Bruno, and South San Francisco experience challenges with school quality in their neighborhoods.
 Residents in Brisbane, Hillsborough, Pacifica, Belmont, and Half Moon Bay report the highest rates of difficulty accessing public transit.
 Daly City, Millbrae, San Mateo, and East Palo Alto residents were more likely to identify the lack of job opportunities available in their neighborhoods.

Over a third of jurisdictions’ respondents experienced the following neighborhood challenges at a higher rate than the county: I can’t get to public transit/bus/light rail easily or safely. 55 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 14 Figure 6c.

Top 5 Neighborhood Challenges Experienced by Jurisdiction Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,079 80 72 153 116 48 130 56 53 46 75 145 91 151 712
17% 31% 18% 13% 25% 40% 4% 18% 23% 20% 15% 21% 14% 12% 16% 15% 6% 18% 3% 17% 25% 4% 14% 2% 7% 13% 20% 20% 15% 20%
15% 14% 24% 8% 14% 15% 21% 18% 9% 15% 24% 17% 14% 17% 10% 14% 19% 29% 7% 9% 10% 14% 18% 25% 17% 21% 12% 13% 15% 10% 12% 9% 8% 7% 20% 17% 8% 14% 0% 20% 13% 11% 11% 18% 12%
50% 41% 28% 69% 45% 33% 62% 46% 57% 50% 52% 41% 52% 52% 55% Bus/rail does not go where I need to
go or does not operate during the
times I need
I can't get to public transit/bus/light
rail easily or safely
There are not enough job
opportunities in the area
None of the above
San
Mateo
Foster
Belmont City
South San
Neighborhood Challenges Francisco
Valid cases
My neighborhood does not have good
sidewalks, walking areas, and/or
lighting
Schools in my neighborhood are poor
quality
Half
Moon
Bay Hillsborough Milbrae Pacifica
Redwood
City
San
County Brisbane Burlingame Bruno
Daly
City
East
Palo
Alto
56 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 15 Differences in needs by race and ethnicity and housing tenure. As shown in Figure 7a, and compared to the county overall:
 African American, Hispanic, and Other Race respondents, as well as Renters and those who are precariously housed experience several housing challenges at a higher rate than the county overall.
 Conversely, non-Hispanic White residents and homeowners are less likely to experience housing challenges.

Specifically,
 Black or African American residents are more than three times as likely to have a landlord not make a repair to their unit after a request compared to county residents overall.

Renters, Hispanic, Other Race, and Precariously housed residents are also more likely to experience this challenge.
 African American, Asian, Hispanic, Renter, and Precariously Housed households are more likely to experience bed bugs or rodent infestation in their homes.  African American, Other Race, Renter, and Precariously Housed households are also more likely to live further away from family, friends, and their community.  African Americans are three times more likely than the average county respondent to be told by their HOA they cannot make changes to their house or property. Asian households are twice as likely to experience this challenge.
 Renter, Hispanic, and Other Race respondents are more likely to worry that if they request a repair it will result in a rent increase or eviction and to report that their homes are in bad condition.
57 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 16 Figure 7a.

Top 10 Housing Challenges Experienced by Race/Ethnicity and Tenure Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,159 132 489 392 144 734 986 974 301
31% 30% 32% 50% 31% 20% 7% 48% 56%
20% 16% 21% 35% 22% 11% 12% 29% 18%
14% 17% 13% 23% 19% 11% 2% 28% 13%
11% 12% 9% 16% 17% 10% 6% 17% 10%
6% 20% 7% 10% 10% 5% 2% 13% 10%
6% 15% 6% 6% 13% 6% 5% 8% 9%
6% 13% 6% 6% 9% 5% 4% 8% 7%
5% 14% 7% 5% 6% 5% 5% 6% 11%
5% 14% 8% 7% 5% 4% 4% 9% 9%
4% 14% 8% 4% 3% 3% 5% 3% 7%
42% 18% 37% 24% 38% 58% 68% 21% 13%
I need help taking care of myself/my home and can't find or afford to hire someone
I have bed bugs/insects or rodent infestation
The HOA in my neighborhood won't let me make changes
to my house or property
County
African
American Asian Hispanic
I would like to move but I can't afford anything that is available/income too low
Homeowner Renter
Precariously
Housing or Neighborhood Condition Housed
Valid cases
Other
Race
Non-Hispanic
White
None of the above
My house or apartment isn't big enough for my family
I worry that if I request a repair it will result in a rent increase or eviction
My home/apartment is in bad condition
My landlord refuses to make repairs despite my requests I live too far from family/ friends/my community
I don't feel safe in my building/ neighborhood
58 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 17 The above trends are similar for the most acute housing affordability challenges. As shown in Figure 7b, African American and Hispanic households, as well as renters and those precariously housed, experience affordability challenges at a higher rate than the county overall. Non-Hispanic White residents and homeowners experience these same challenges at a lower rate than the county.
 African American residents experience all five affordability challenges at a greater rate than the county overall.
 In addition to being more likely to not pay utility bills or rent on time, African American residents are more than four times as likely than the average county respondent to have a Section 8 voucher and worry that their landlord will raise their rent more than the voucher payment.
 Along with African American residents, Hispanic households, renters, and precariously housed households are most likely to experience difficulty paying utility bills, as well as have bad credit or eviction/foreclosure history impacting their ability to find a place to rent.  These groups, with the exception of those precariously housed, are also more likely to be late on their rent payments.
59 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 18 Figure 7b.

Top 5 Affordability Challenges Experienced by Race/Ethnicity and Tenure Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,130 132 487 391 146 739 983 953 293
10% 22% 11% 17% 14% 5% 5% 15% 15%
8% 13% 6% 12% 12% 4% 1% 15% 8%
6% 16% 8% 4% 5% 7% 9% 5% 14%
4% 5% 3% 8% 4% 2% 1% 6% 11%
4% 18% 5% 6% 7% 2% 2% 7% 8%
None of the above 73% 32% 70% 63% 64% 83% 84% 61% 54%
County
African
American Asian Hispanic
Valid cases
I can't keep up with my utilities
I'm often late on my rent payments
I can't keep up with my property taxes
I have bad credit/history of evictions/foreclosure
and cannot find a place to rent
I have Section 8 and I am worried my landlord will
raise my rent higher than my voucher payment
Non-Hispanic
White Homeowner Renter
Precariously
Affordability Challenges Housed
Other
Race
60 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 19 As shown in Figure 7c, African American and precariously housed residents experience neighborhood challenges at a higher rate than the county. These two groups experience neighborhood issues related to transportation more acutely than county residents overall. In addition to Other Race respondents, they are also more likely to identify the lack of job opportunities in their respective neighborhoods.

Additionally, Hispanic residents are more likely to live in neighborhoods with poor performing schools than the average county respondent. Homeowners are also more likely to report that they cannot access public transit easily or safely.
61 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 20 Figure 7c.

Top 5 Neighborhood Challenges Experienced by Race/Ethnicity and Tenure Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,079 133 486 389 146 737 975 918 284
17% 14% 17% 19% 16% 18% 18% 15% 18%
15% 13% 18% 20% 17% 13% 18% 13% 13%
15% 33% 16% 13% 17% 17% 17% 14% 24%
14% 24% 15% 11% 16% 16% 18% 11% 19%
12% 22% 14% 12% 19% 9% 9% 15% 20%
50% 23% 46% 48% 45% 53% 49% 51% 36%
My neighborhood does not have good sidewalks, walking
areas, and/or lighting
County
African
American Asian Hispanic Homeowner Renter
Precariously
Neighborhood Challenges Housed
Valid cases
Other
Race
NonHispanic
White
Schools in my neighborhood are poor quality
Bus/rail does not go where I need to go or does not
operate during the times I need
I can't get to public transit/bus/light rail easily or safely There are not enough job opportunities in the area
None of the above
62 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 21 Differences in needs by household status. As shown in Figure 8a, single parents, households making less than $50,000, households with children under 18 and households with a member experiencing a disability are more likely to experience housing challenges.

Conversely, households making more than $100,000 experience nearly all specified housing challenges at a lower rate than the county.

Single parents experience all ten housing challenges at a greater rate than the county overall.

Households making less than $25,000 also experience every challenge at a higher rate, with the exception of I worry that if I request a repair it will result in a rent increase or eviction.

Households making less than $50,000, single parents, and households with children under 18 are more likely to experience the following challenges:
 My house or apartment isn’t big enough for my family;  My house or apartment is in bad condition;
 My landlord refuses to make repairs despite my request;  I live too far from family/friends/my community;
 I don’t feel safe in my building/neighborhood;
 I need help taking care of myself/my home and can’t find or afford to hire someone; and  I have bed bugs/insects or rodent infestation.

Households with a member experiencing a disability are also more likely to experience landlords refusing their requests to make repairs, living further away from family/friends/community, and not being able to find or afford someone to help take care of themselves or their homes. These households are also more likely to experience bed bugs, insects, or rodent infestation, as well as HOA restrictions impacting their ability to make changes to their home or property.

Additionally, large households have the highest proportion of respondents among the selected groups that would like to move but can’t afford anything that is available. 63 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 22 Figure 8a.

Top 10 Housing Challenges Experienced by Income and Household Characteristics Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,159 280 260 505 701 827 278 240 701 709
31% 47% 48% 37% 16% 35% 51% 40% 36% 25%
20% 25% 25% 23% 16% 34% 43% 32% 20% 13%
14% 16% 18% 19% 9% 19% 19% 28% 16% 11%
11% 15% 20% 12% 6% 15% 17% 17% 12% 9%
6% 13% 13% 8% 2% 9% 8% 14% 10% 6%
6% 9% 9% 6% 5% 10% 5% 10% 8% 6%
6% 9% 9% 6% 3% 8% 4% 10% 7% 5%
5% 9% 9% 5% 3% 7% 6% 12% 11% 6%
5% 10% 9% 5% 3% 9% 4% 15% 9% 6%
4% 7% 3% 4% 3% 7% 4% 11% 6% 5%
42% 21% 21% 37% 61% 28% 26% 12% 32% 49%
Above
Housing or Neighborhood Condition $100,000
Valid cases
I would like to move but I can't afford anything
that is available/income too low
My house or apartment isn't big enough for my
family
I worry that if I request a repair it will result in a
rent increase or eviction
My home/apartment is in bad condition
My landlord refuses to make repairs despite my
requests
None of the above
County
Less than
$25,000
$25,000-
$49,999
$50,000-
$99,999
I live too far from family/ friends/my community
I don't feel safe in my building/ neighborhood
I need help taking care of myself/my home and
can't find or afford to hire someone
I have bed bugs/insects or rodent infestation
The HOA in my neighborhood won't let me make
changes to my house or property
Children
under 18
Large
Households
Single
Parent Disability
Adults
(age 65+)
64 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 23 As shown in Figure 8b, households making less than $50,000, as well as large households, single parents, households with children under 18, and households with a member experience a disability, experience the most acute affordability challenges at a higher rate than the county overall. Households making more than $50,000 and adults over the age of 65 are less likely to experience affordability challenges.

Households making between $25,000-$50,000, single parents, and households with children under 18 experience all five affordability challenges at a greater rate than the average county respondent.

Of households experiencing major affordability issues, single parent households are most acutely impacted. These households are more than three times as likely to have a Section 8 voucher and fear their landlord will raise the rent impacting the viability of their voucher, more than twice as likely to miss utility payments and have bad credit/eviction or foreclosure history impacting their ability to rent, and twice as likely to have trouble keeping up with their property taxes.
65 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 24 Figure 8b.

Top 5 Affordability Challenges Experienced by Income and Household Characteristics Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
2,130 276 260 509 703 830 279 239 699 716
10% 16% 16% 12% 3% 16% 14% 23% 15% 8%
8% 19% 16% 6% 1% 11% 12% 15% 11% 4%
6% 7% 9% 8% 5% 9% 4% 12% 8% 7%
4% 8% 7% 4% 1% 5% 6% 10% 6% 3%
4% 11% 6% 4% 1% 7% 3% 14% 8% 5%
73% 46% 56% 72% 90% 59% 70% 32% 59% 75%
Adults
(age 65+)
I have Section 8 and I am worried my landlord
will raise my rent higher than my voucher
payment
None of the above
County
Less than
$25,000
$25,000-
$49,999
$50,000-
Affordability Challenges $99,999
Valid cases
I can't keep up with my utilities
I'm often late on my rent payments
I can't keep up with my property taxes
I have bad credit/history of
evictions/foreclosure and cannot find a place
to rent
Above
$100,000
Children
under 18
Large
Households
Single
Parent Disability
66 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 25 As shown in Figure 8c, households with children under 18, as well as single parents, households with a member experiencing a disability, and households making less than $25,000 are more likely to experience neighborhood challenges. These households are most likely to report that the bus/rail does not go where I need to go or does not operate during the times I need. In addition to households that make between $25,000-$100,000, these groups are more likely to identify the lack of job opportunities in their respective neighborhoods.

Households with children under 18 are more likely to live in neighborhoods with poor quality schools. Large households are more likely to report issues with neighborhood infrastructure (e.g., bad sidewalks, poor lighting) and households with a member experiencing a disability are more likely to report they cannot access public transit easily or safely. 67 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 26 Figure 8c.

Top 5 Neighborhood Challenges Experienced by Income and Household Characteristics Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. 25% Above County average
25% Below County average
County
2,079 273 259 503 709 824 277 234 692 714
17% 17% 15% 18% 17% 19% 22% 16% 19% 14%
15% 17% 14% 11% 19% 24% 19% 17% 14% 9%
15% 19% 16% 15% 16% 19% 11% 28% 19% 16%
14% 15% 12% 14% 14% 15% 12% 15% 19% 17%
12% 21% 17% 16% 6% 17% 12% 19% 15% 11%
50% 40% 45% 51% 53% 38% 48% 31% 41% 53%
Above
Neighborhood Challenges $100,000
Valid cases
My neighborhood does not have good
sidewalks, walking areas, and/or lighting
Schools in my neighborhood are poor
quality
There are not enough job opportunities
in the area
None of the above
Less than
$25,000
$25,000-
$49,999
$50,000-
$99,999
Bus/rail does not go where I need to go or
does not operate during the times I need
I can't get to public transit/bus/light rail
easily or safely
Children
under 18
Large
Households
Single
Parent Disability
Adults
(age 65+)
68 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 27 Experience Finding Housing
This section explores residents’ experience seeking a place to rent or buy in the county and the extent to which displacement—having to move when they do not want to move—is prevalent.

For those respondents who seriously looked for housing in the past five years, this section also examines the extent to which respondents were denied housing to rent or buy and the reasons why they were denied.

Recent experience seeking housing to rent. Figure 9 presents the proportion of respondents who seriously looked to rent housing for the county, jurisdictions, and selected respondent characteristics, as well as the reasons for denial.

Over half of county respondents (56%) have seriously looked for housing in the past five years.

The most common reasons for denial included:
 Landlord not returning the respondent’s call (26%),
 Landlord told me the unit was available over the phone but when I showed up in person, it was no longer available (22%), and
 Landlord told me it would cost more because of my service or emotional support animal (14%).

Jurisdictions with the highest percentage of respondents who seriously looked for housing include Millbrae (74%), San Mateo (73%), and Redwood City (72%). While all three jurisdictions reported that landlord not returning the respondent’s call was one of their main reasons for denial, 18% of Redwood City respondents identified landlord told me they do not accept Section 8 vouchers as a main reason for denial.

Among respondents by race/ethnicity, 80% of African American respondents reported that they had seriously looked for housing in the past five years while the lowest percentage of respondents who reported seriously looking for housing were non-Hispanic White (46%). The main reasons for denial experienced by African American respondents included landlord told me the unit was available over the phone but when I showed up in person, it was no longer available (39%), landlord told me it would cost more because of my service or emotional support animal (34%), and landlord told me I couldn’t have a service or emotional support animal (28%).

Among respondents by tenure, renters (75%) and precariously housed (74%) respondents reported the highest rates of seriously looking for housing.

Among respondents by income, households making less than $25,000 (71%) had the highest rate. The main reasons for denial reported by these households were landlord told me I couldn’t have a service or emotional support animal (36%) and landlord told me it would cost more because of my service or emotional support animal (30%).
69 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 28 Single parents (79%) and households with children under 18 (66%) also reported the highest percentage of those who seriously looked for housing in the past five years among the selected household characteristics respondent groups. In addition to sharing the top two reasons for denial with the county, 25% of single parent household respondents also reported they were denied housing because the landlord told me I can’t have a service or emotional support animal. 70 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 29 Figure 9. If you looked seriously for housing to rent in San Mateo County in the past five years, were you ever denied housing?
Note: The "Percent Seriously Looked for Housing" column includes all respondents, not just those who indicated they rent.

Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Jurisdiction
County 56% 26% 22% 14% 45% 928
Belmont 62% 33% 27% 31% 49
Brisbane 59% 41% 22% 26% 27
Burlingame 48% 19% 23% 54% 57
Daly City 63% 33% 16% 16% 44% 61
East Palo Alto 58% 35% 30% 26% 23
Foster City 50% 12% 16% 14% 55% 51
Half Moon Bay 68% 17% 17% 48% 29
Hillsborough 42% 14% 29% 14% 57% 14
Milbrae 74% 25% 46% 36% 28
Pacifica 51% 16% 26% 16% 55% 31
Redwood City 72% 31% 18% 40% 99
San Bruno 57% 22% 22% 39% 36
San Mateo 73% 30% 34% 39% 98
South San Francisco 47% 24% 13% 56% 248
Race/Ethnicity
African American 80% 39% 34% 28% 15% 101
Asian 56% 19% 29% 40% 199
Hispanic 63% 32% 22% 41% 230
Other Race 70% 29% 22% 45% 91
Non-Hispanic White 46% 29% 20% 48% 263
Tenure
Homeowner 36% 25% 15% 54% 183
Renter 75% 29% 22% 43% 641
Precariously Housed 74% 23% 32% 26% 188
Income
Less than $25,000 71% 30% 36% 29% 182
$25,000-$49,999 60% 39% 32% 27% 149
$50,000-$99,999 58% 24% 20% 45% 251
Above $100,000 48% 19% 14% 64% 216
Household Characteristics
Children under 18 66% 30% 29% 33% 447
Large Households 60% 33% 19% 18% 44% 139
Single Parent 79% 25% 35% 25% 19% 173
Disability 63% 24% 24% 34% 386
Older Adults (age 65+) 48% 20% 29% 39% 282
n
Overall
Percent
Seriously
Looked for
Housing
Reason for Denial
Landlord did not
return calls
and/or emails
asking about a
unit
Landlord said unit
was available over
phone, but when I
showed up in
person, it was no
longer available
Landlord told me it
would cost me
more for my
service or
emotional animal
Landlord told me
I can't have a
service or
emotional
support animal
Landlord told
me it would cost
me more to rent
because I have
children
Landlord told me
they don't rent to
families with
children
Landlord told
me they do not
accept Section 8
vouchers
Landlord told me they
couldn't make
changes to the
apartment/ home for
my disability
None
of the
Above
71 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 30 Recent experience seeking housing to buy. Figure 10 presents the proportion of respondents who seriously looked to buy housing in the county, by jurisdiction, and selected respondent characteristics, as well as the reasons for denial. As noted above, 56% of county respondents have seriously looked for housing in the past five years.

The most common reasons for denial included:
 Real estate agent told me I would need to show I was prequalified with a bank (29%) and  A bank would not give me a loan to buy a home (23%).

For the jurisdictions with the highest percentage of respondents who seriously looked for housing (Millbrae, San Mateo and Redwood City), all three cities shared the same top two reasons for denial as the county. Additionally, 21% of Millbrae respondents reported that the real estate agent would not make a disability accommodation when I asked.

For African American respondents who looked to buy housing in the last five years, the most common reason for denial was the real estate agent would not make a disability accommodation when I asked (47%).

African Americans, along with Other Races, also most commonly reported that they needed a loan prequalification before real estate agents would work with them. While between 43-54% of respondents from other racial/ethnic groups reported they did not experience any reason for denial when seriously looking to buy housing over the past five years, 12% of African American respondents reported similarly.

Among respondents by income, the main reasons for denial for households making less than $25,000 were the real estate agent told me I would need to show I was prequalified with a bank (32%) and real estate agent only showed me or only suggested homes in neighborhoods where most people were of my same race or ethnicity (26%).

Among the selected housing characteristics category, single parent households and households with children under 18 reported shared the same top two reasons for denial as the county. Additionally, 36% of single parent household respondents reported that the real estate agent would not make a disability accommodation when I asked, as well as 25% of respondents over the age of 65.

Residents in Redwood City, Millbrae, and South San Francisco, as well as large households, also reported that a bank or other lender charged me a high interest rate on my home loan as a reason for denial. 72 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 31 Figure 10. If you looked seriously for housing to buy in San Mateo County in the past five years, were you ever denied housing?
Note: The "Percent Seriously Looked for Housing" column includes all respondents, not just those who indicated they buy.

Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. n
Jurisdiction
County 56% 29% 23% 50% 870
Belmont 62% 21% 15% 35% 48
Brisbane 59% 36% 30% 42% 33
Burlingame 48% 22% 14% 61% 51
Daly City 63% 19% 27% 56% 52
East Palo Alto 58% 24% 33% 48% 21
Foster City 50% 25% 20% 49% 51
Half Moon Bay 68% 35% 23% 23% 50% 26
Hillsborough 42% 18% 23% 59% 22
Milbrae 74% 25% 29% 21% 21% 54% 28
Pacifica 51% 35% 35% 42% 31
Redwood City 72% 30% 22% 27% 50% 64
San Bruno 57% 14% 21% 62% 42
San Mateo 73% 40% 32% 38% 82
South San Francisco 47% 26% 18% 16% 57% 251
Race/Ethnicity
African American 80% 40% 38% 47% 12% 89
Asian 56% 30% 25% 43% 223
Hispanic 63% 29% 28% 49% 174
Other Race 70% 36% 21% 21% 50% 90
Non-Hispanic White 46% 29% 23% 54% 250
Tenure
Homeowner 36% 29% 17% 54% 332
Renter 75% 32% 27% 46% 467
Precariously Housed 74% 36% 36% 30% 30% 154
Income
Less than $25,000 71% 32% 25% 26% 41% 131
$25,000-$49,999 60% 42% 40% 29% 106
$50,000-$99,999 58% 35% 30% 38% 216
Above $100,000 48% 22% 13% 10% 64% 296
Household Characteristics
Children under 18 66% 33% 28% 40% 443
Large Households 60% 33% 25% 25% 49% 126
Single Parent 79% 38% 43% 36% 24% 143
Disability 63% 35% 26% 38% 330
Older Adults (age 65+) 48% 35% 29% 25% 38% 252
Percent
Seriously
Looked for
Housing
Reason for Denial
The real estate agent told
me I would need to show I
was prequalified with a
bank
A bank or other
lender would not
give me a loan to
buy a home
The real estate agent would
not make a disability
accommodation when I
asked
Only showed homes in
neighborhoods where
most people were same
race/ethnicity
A bank or other lender
charged me a high
interest rate on my
home loan
None of
the
Above
73 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 32 Denied housing to rent or buy. Figure 11 presents the proportion of those who looked and were denied housing to rent or buy for the county, jurisdictions, and selected respondent characteristics, as well as reason for denial. As shown, nearly 4 in 10 county respondents who looked for housing experienced denial of housing. African American/Black respondents, precariously housed respondents, households with income below $50,000, and single parent respondents have denial rates of 60% or higher. African American (79%) and single parent (74%) respondents report the highest rates of denial.

Among the reasons for denial:
 Income too low was a major reason for denial for all groups except homeowners and households with incomes above $100,000. Additionally, all jurisdictions report this as a common reason for being denied housing with the exception of Foster City, Hillsborough, and San Bruno.  Haven’t established a credit history or no credit history was also a common reason of denial for most groups. The impacts are higher for Asian, Hispanic and African American households, along with renter and precariously housed respondents, households with income below $50,000, and single parent households, households with children under 18, households with a member experiencing a disability, and several jurisdictions.
 Another top denial reason among certain groups is the landlord didn’t accept the type of income I earn (social security or disability benefit or child support). Source of income was the most common reason for denial among African American households (28%). Other groups with denial rates of 25% or higher for this specific issue include precariously housed respondents, single parent households, and households with a member experiencing a disability, as well as Foster City and San Bruno residents.
 Bad credit is another barrier for accessing housing, particularly for Hispanic and Other Race households, households with income between $50,000-$100,000, and large households. This also impacts East Palo Alto, San Mateo, Daly City, Redwood City, Burlingame, and South San Francisco residents at a higher rate.
74 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 33 Figure 11. If you looked seriously for housing to rent or buy in San Mateo County in the past five years, were you ever denied housing?
Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Jurisdiction
County 39% 1154 18% 44% 19% 21% 449
Belmont 52% 50 38% 27% 27% 26
Brisbane 42% 38 25% 19% 31% 16
Burlingame 30% 71 24% 29% 21
Daly City 49% 73 28% 53% 28% 19% 36
East Palo Alto 55% 29 38% 44% 25% 16
Foster City 30% 63 25% 40% 30% 19
Half Moon Bay 41% 34 29% 29% 14
Hillsborough 23% 22 40% 5
Milbrae 36% 33 67% 25% 33% 25% 12
Pacifica 38% 39 47% 27% 33% 15
Redwood City 41% 105 28% 63% 26% 26% 43
San Bruno 25% 51 31% 31% 38% 13
San Mateo 48% 112 30% 38% 28% 53
South San Francisco 30% 331 19% 58% 28% 17% 98
Race/Ethnicity
African American 79% 107 25% 25% 25% 28% 27% 85
Asian 42% 281 38% 28% 21% 21% 117
Hispanic 49% 253 28% 60% 26% 26% 125
Other Race 43% 105 22% 49% 24% 45
Non-Hispanic White 31% 351 40% 19% 23% 25% 108
Tenure
Homeowner 26% 348 24% 22% 23% 91
Renter 45% 687 48% 20% 24% 310
Precariously Housed 61% 208 42% 22% 25% 126
Income
Less than $25,000 64% 199 47% 31% 29% 127
$25,000-$49,999 65% 158 48% 21% 20% 20% 103
$50,000-$99,999 38% 302 21% 51% 24% 114
Above $100,000 18% 346 27% 16% 20% 16% 64
Household Characteristics
Children under 18 51% 558 42% 26% 19% 283
Large Households 43% 171 27% 64% 41% 74
Single Parent 74% 189 41% 27% 25% 138
Disability 54% 446 39% 21% 25% 239
Older Adults (age 65+) 44% 350 35% 22% 21% 153
Percent
Denied
Housing Total n
Bad
Credit
Eviction
history
Income
too low
I had/
have
COVID Foreclosure n
Reason for Denial
Too many
people in my
household
Other
renter/
applicant
willing to pay
more for rent
Haven't
established a
credit
history/no
credit history
Don't have a
regular/
steady job/
consistent
work history
Landlord didn't
accept the type of
income I earn
(social security or
disability)
Lack of
stable
housing
record
Real or
perceived
sexual
orientation or
gender
identity
75 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 34 Experience using housing vouchers. It is “difficult” or “very difficult” for eight out of 10 voucher holders to find a landlord that accepts a housing voucher (Figure 13).

As shown in Figure 12, this is related to the amount of the voucher and current rents and the lack of supply (inability to find a unit in the allotted amount of time). Over half of voucher holders (53%) who experienced difficulty indicated the voucher is not enough to cover the rent for places I want to live and almost half of voucher holders (49%) who experienced difficulty indicated there is not enough time to find a place to live before the voucher expires.

Other significant difficulties using vouchers identified by respondents included landlords have policies of not renting to voucher holders (46%) and can’t find information about landlords that accept Section 8 (36%).

Among respondents by race/ethnicity, African American respondents had the greatest proportion of those with a housing choice voucher (60%). Of those respondents, 76% found it difficult to find a landlord that accepts a housing voucher. While 13% of Hispanic respondents have a housing voucher, 85% have found it difficult to use the voucher. Fourteen percent of Asian respondents have housing vouchers— nearly three quarters of these respondents reported that the voucher is not enough to cover the rent for the places I want to live.

Other groups of respondents with higher proportions of voucher utilization include single parent households (43%), precariously housed respondents (30%), and households with income below $25,000 (29%). For each of the aforementioned groups, more than 75% of their respective respondents reported difficulty in utilizing the housing choice voucher. The voucher is not enough to cover the rent for places I want to live was one of the main reasons cited for not using the voucher. 76 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 35 Figure 12.

Why is it difficult to
use a housing
voucher?
Source:

Root Policy Research from the 2021-
2022 21 Elements AFFH Resident
Survey.
77 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 36 Figure 13. How difficult is it to find a landlord that accepts a housing voucher? Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Jurisdiction
County 12% 18% 55% 27% 250 53% 49% 46% 36% 6% 203
Belmont 16% 14% 64% 21% 81 45% 64% 36% 27% 9% 11
Brisbane 22% 20% 73% 7% 15 50% 50% 42% 33% 0% 12
Burlingame 8% 0% 75% 25% 12 50% 50% 25% 8% 0% 12
Daly City 12% 14% 50% 36% 14 83% 25% 42% 17% 25% 12
East Palo Alto 14% 29% 57% 14% 7 20% 20% 40% 60% 0% 5
Foster City 12% 18% 47% 35% 17 47% 40% 27% 33% 7% 15
Half Moon Bay 19% 22% 56% 22% 9 71% 29% 29% 43% 14% 7
Hillsborough 8% 25% 75% 0% 4 67% 67% 33% 0% 0% 3
Milbrae 22% 50% 20% 30% 10 60% 40% 20% 40% 0% 5
Pacifica 11% 13% 50% 38% 8 86% 43% 43% 43% 0% 7
Redwood City 16% 13% 61% 26% 23 40% 50% 70% 45% 5% 20
San Bruno 12% 9% 64% 27% 11 40% 60% 50% 10% 10% 10
San Mateo 24% 24% 50% 26% 38 43% 54% 43% 39% 7% 28
South San Francisco 4% 11% 33% 56% 27 63% 50% 71% 63% 8% 24 Race/Ethnicity
African American 60% 24% 60% 16% 82 55% 52% 40% 31% 6% 62 Asian 14% 23% 63% 14% 71 73% 44% 31% 31% 0% 55
Hispanic 13% 15% 40% 45% 53 58% 42% 51% 49% 11% 45
Other Race 19% 29% 50% 21% 28 55% 45% 65% 35% 5% 20
Non-Hispanic White 8% 14% 61% 25% 64 43% 61% 57% 38% 4% 56 Tenure
Homeowner 8% 23% 59% 18% 78 58% 49% 42% 31% 0% 59
Renter 18% 19% 52% 30% 165 55% 52% 48% 43% 6% 134
Precariously Housed 30% 14% 66% 20% 86 57% 54% 35% 26% 7% 74 Income
Less than $25,000 29% 17% 58% 25% 84 47% 41% 47% 37% 10% 70 $25,000-$49,999 18% 17% 52% 31% 48 63% 55% 63% 40% 5% 40 $50,000-$99,999 12% 23% 52% 26% 62 55% 55% 51% 37% 2% 49 Above $100,000 5% 20% 57% 23% 35 43% 61% 29% 32% 4% 28
Household Characteristics
Children under 18 21% 20% 60% 20% 179 59% 51% 44% 35% 1% 143 Large Households 7% 20% 45% 35% 20 63% 56% 63% 56% 6% 16 Single Parent 43% 17% 58% 24% 103 62% 52% 38% 33% 2% 85 Disability 22% 18% 58% 24% 158 57% 52% 42% 29% 5% 129
Older Adults (age 65+) 17% 18% 63% 19% 123 56% 53% 44% 34% 3% 102 Not enough time to find
a place to live before the
voucher expires
Landlords have
policies of not
renting to voucher
holders
Can't find information
about landlords that
accept Section 8 Other n
Voucher is not enough
to cover the rent for
places I want to live
Percent
with a
Housing
Voucher
Not
difficult
Somewhat
difficult
Very
difficult n
78 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 37 Displacement. Figure 14 presents the proportion of residents who experienced displacement in the past five years, as well as the reason for displacement.  Overall, 21% of survey respondents experienced displacement in the past five years. Among all survey respondents, the main reason for displacement was rent increased more than I could pay (29%).
 Respondents who are precariously housed have higher rates of recent displacement than homeowners or renters; this suggests that when displaced a unit these housing-insecure tenants are more likely to couch surf or experience homelessness for some period of time before securing a new place to live.
 Among respondents by race/ethnicity, African American respondents reported the highest rate of displacement (59%). The primary reason reported by African American respondents for their displacement was housing was unsafe (e.g., domestic assault, harassment).

Twenty eight percent also reported that they were forced out for no reason.  Asian households, as well as homeowners, households that make less than $25,000, single parent households, households that include a member experiencing a disability, and Millbrae, Brisbane and Pacifica residents are also more likely than other respondents to have been displaced due to an unsafe housing situation (e.g., domestic assault, harassment).  Additionally, Asian, precariously housed respondents, households making less than $25,000, single parent households, and Hillsborough residents are more likely than other respondents to have been displaced and not given a reason.

For respondents that had experienced displacements, they were asked to identify which city they moved from and which city they moved to. The most common moves to and from cities included:
 Moved within South San Francisco (28 respondents)
 Moved from outside San Mateo County to San Mateo (10 respondents)  Moved from San Bruno to South San Francisco (9 respondents)  Moved from Daly City to South San Francisco (9 respondents)  Moved within Burlingame (8 respondents)
79 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 38 Figure 14. Displacement Experience and Reasons for Displacement Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Jurisdiction
County 21% 2066 29% 19% 18% 417
Belmont 26% 80 25% 25% 30% 20
Brisbane 24% 67 25% 31% 25% 16
Burlingame 22% 152 24% 30% 18% 33
Daly City 25% 115 35% 27% 31% 26
East Palo Alto 32% 50 20% 20% 20% 15
Foster City 11% 130 21% 21% 21% 43% 14
Half Moon Bay 31% 51 31% 25% 16
Hillsborough 12% 52 33% 33% 33% 33% 33% 6
Milbrae 27% 44 42% 33% 25% 25% 12
Pacifica 21% 75 31% 31% 31% 16
Redwood City 29% 146 31% 21% 42
San Bruno 25% 89 33% 29% 24% 21
San Mateo 37% 153 35% 31% 20% 54
South San Francisco 12% 712 42% 15% 16% 81
Race/Ethnicity
African American 59% 134 29% 30% 28% 79
Asian 22% 500 31% 22% 22% 109
Hispanic 29% 397 33% 22% 18% 115
Other Race 28% 149 54% 20% 24% 41
Non-Hispanic White 14% 757 27% 20% 31% 102
Tenure
Homeowner 8% 975 27% 25% 31% 75
Renter 34% 905 32% 18% 22% 292
Precariously Housed 48% 280 23% 24% 23% 132
Income
Less than $25,000 45% 282 28% 20% 20% 20% 127
$25,000-$49,999 30% 265 31% 19% 18% 78
$50,000-$99,999 22% 517 32% 22% 18% 115
Above $100,000 8% 721 27% 20% 23% 60
Household Characteristics
Children under 18 30% 840 27% 20% 19% 249
Large Households 20% 284 32% 19% 18% 57
Single Parent 55% 240 24% 24% 20% 131
Disability 34% 711 26% 20% 20% 20% 241
Older Adults (age 65+) 22% 736 23% 22% 22% 162
Because of
apartment
rules
Landlord
refused to
renew my
lease
Reason for Displacement
Percent
Displaced Total n
Rent
increased
more than I
could pay
Personal/
relationship
reasons
Landlord was
selling the
home/
apartment
Landlord
wanted to
move back
in/move in
family
Landlord
wanted to
rent to
someone
else
Housing was
unsafe (e.g.,
domestic
assault,
harassment)
Forced
out for
no
reason
Health/m
edical
reasons
I was
behind
on rent
Poor
condition of
property
Utilities were
too
expensive/
shut off
Natural
disaster/
flooding/
fire n
80 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 39 Children changing schools after displacement. Overall, for households with children that were displaced in the past five years, 60% of children in those households have changed schools. The most common outcomes reported among these respondents included school is more challenging (28%), they feel less safe at the new school (25%), and they are in a worse school (24%) (Figure 15).

Among respondents by race/ethnicity, non-Hispanic White households (44%) were the only subgroup to report that being displaced resulted in their children being in better schools. Of African American households that were displaced and have children, 87% reported that their children changed schools.

Of these respondents, 32% reported that their children feel safer at the new school but also have fewer activities.

Among respondents by tenure, precariously housed (78%) and homeowner (74%) households had the highest proportion of children who changed schools. The most common outcomes for precariously housed households included School is less challenging/they are bored (35%) and their children feel less safe at school (34%). For homeowner households, 39% reported that school is more challenging, followed by 31% who reported that their children feel less safe at school.

Among respondents by selected household characteristics, older adult (77%), single parent (74%), households with a member experiencing a disability (70%), and households with children under 18 (67%) all reported high proportions of children who changed schools. The most common outcomes for these respondents included School is more challenging and they feel less safe at the new school. 81 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 40 Figure 15. Children Changing Schools and Outcomes, Displaced Households Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey.

Jurisdiction
County 60% 306 28% 24% 25% 183
Belmont 45% 20 33% 44% 33% 9
Brisbane 81% 16 38% 31% 31% 13
Burlingame 55% 22 33% 33% 33% 12
Daly City 41% 17 43% 29% 29% 29% 7
East Palo Alto 54% 13 43% 57% 29% 7
Foster City 62% 13 50% 8
Half Moon Bay 58% 12 43% 29% 29% 43% 7
Hillsborough 60% 5 67% 3
Milbrae 82% 11 33% 44% 44% 33% 9
Pacifica 91% 11 50% 10
Redwood City 52% 23 25% 33% 25% 12
San Bruno 67% 18 33% 33% 33% 12
San Mateo 66% 35 32% 32% 22
South San Francisco 36% 56 26% 26% 26% 19
Race/Ethnicity
African American 87% 69 30% 30% 32% 32% 60
Asian 73% 91 27% 32% 32% 27% 66
Hispanic 49% 91 23% 30% 23% 25% 44
Other Race 65% 31 40% 30% 25% 25% 20
Non-Hispanic White 60% 60 28% 31% 44% 28% 36
Tenure
Homeowner 74% 66 39% 29% 31% 49
Renter 58% 213 25% 30% 25% 122
Precariously Housed 78% 104 35% 34% 30% 80
Income
Less than $25,000 65% 92 22% 32% 35% 60
$25,000-$49,999 66% 56 25% 28% 28% 25% 36
$50,000-$99,999 55% 85 30% 28% 23% 47
Above $100,000 59% 44 35% 31% 38% 26
Household Characteristics
Children under 18 67% 237 32% 23% 25% 158
Large Households 45% 44 32% 26% 32% 19
Single Parent 74% 124 32% 28% 29% 92
Disability 70% 188 26% 28% 30% 132
Older Adults (age 65+) 77% 117 35% 29% 29% 89
n
Percent of
Children that
Changed
Schools
School change outcomes
Total n
School is less
challenging/
they are bored
School is more
challenging
They are
in a better
school
They are
in a worse
school
They feel
less safe at
the new
school
They feel
safer at
the new
school
They have
fewer
activities
They have
more
activities
Things are
about the
same
82 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 41 Experience with housing discrimination. Overall, 19% of survey respondents felt they were discriminated against when they looked for housing in the area.1 As shown in Figure 16, African American respondents (62%), single parent households (44%) and precariously housed respondents (39%) are most likely to say they experienced housing discrimination. Residents with income above $100,000 and homeowners are least likely (11%).

Respondents who believed they experienced discrimination when looking for housing in the county reported when the discrimination occurred. Nearly half of respondents (45%) reported that the discrimination they experienced occurred between 2 and 5 years ago. Twenty eight percent of respondents reported that the discrimination occurred in the past year, 20% reported more than 5 years ago and 7% of respondents did not remember when the discrimination occurred.

How discrimination was addressed. Respondents who believed they experienced discrimination when looking for housing in the county were asked to describe the actions they took in response to the discrimination. Overall, the most common responses to discrimination experienced by survey respondents were Nothing/I wasn’t sure what to do (42%), Moved/found another place to live (30%), and Nothing/I was afraid of being evicted or harassed (20%).

Among top responses for actions taken in response to experienced discrimination, every group reported Nothing/I wasn’t sure what to do with the exception of African American and single parent households, as well as Brisbane and Hillsborough residents. Similarly, survey respondents from Foster City and Pacifica were the only groups not to include Moved/found another place to live among their top responses. African American and Asian households, as well as single parent households, were more likely than other groups to contact either a housing authority, local fair housing organization, or the California Department of Housing or Civil Rights to report their discrimination incident.

Reasons for discrimination. Respondents who believed they experienced discrimination when looking for housing in the county provided the reasons why they thought they were discriminated against. Note that the basis offered by residents is not necessarily protected by federal, state, or local fair housing law, as respondents could provide open-ended and multiple reasons why they thought they experienced discrimination.

Examples of how respondents described why they felt discriminated against, which they provided as open-ended responses to the survey, include:
1 Note that this question applies to all respondents, not just those who seriously looked for housing in the past five years. 83 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 42 Appearance/Characteristics
 “Because of my race and ethnicity”
 “[We] were given a subprime loan for home purchase for being Latinx, low-income and primarily Spanish-speaking; refinance last year was lower than expected.”  “It was clear my disability is the reason”
 “I have a child and a couple places told me they wouldn’t rent to me due to my son.”  “The agent asked if I was a tech worker. When I said no, the agent said the place was just rented, even though it was on the listing as active.”
 “I was approved for the unit and when they met my partner, who is Black, they said [the unit] was rented.”
Source of Income/Credit
 “Income was through SSDI [social security disability insurance]”  “The landlord wanted an excellent credit score…”
 “We were not able to provide all the requirement to rent, like SSN [social security number], income proof, employment, and we don’t make enough income…”
 “They wanted someone with income from employment not due to disability.”  “I was discriminated against because of my race and the fact that I had Section 8 at the time. Being African American and having Section 8 made a lot of people feel like I wouldn’t take care of their property.”
 “I am currently being discriminated against due to my need with rental help and because two of us in our household have a need for an emotional support animal.” Immigration status
 Mi hermana llamo a los departamentos donde yo vivo y la manager le dijo que no había disponible pero no era verdad también le dijo que hablara inglés y le pidió seguro social pensando que no tenia y le dijo que tenía que ganar una cierta cantidad de dinero para poder rentar. (My sister called the apartments where I live and the manager told her that there was no one available but it was not true.

She also told her to speak English and asked for social security thinking that she did not have it and told her that she had to earn a certain amount of money to be able to rent). 84 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 43 Figure 16. Percent of respondents who felt they were discriminated against and how was it addressed Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. n
Jurisdiction
County 19% 28% 45% 20% 7% 357 42% 30% 20% 359
Belmont 21% 19% 56% 19% 6% 16 38% 38% 50% 16
Brisbane 22% 29% 36% 29% 7% 14 64% 21% 21% 14
Burlingame 14% 25% 50% 20% 5% 20 35% 25% 20% 20% 20
Daly City 15% 20% 40% 33% 7% 15 56% 25% 25% 16
East Palo Alto 29% 23% 54% 15% 8% 13 38% 38% 23% 23% 13 Foster City 18% 15% 40% 45% 0% 20 38% 24% 24% 21
Half Moon Bay 26% 27% 55% 9% 9% 11 27% 36% 36% 11
Hillsborough 15% 14% 71% 0% 14% 7 29% 57% 7
Milbrae 29% 36% 50% 7% 7% 14 31% 23% 38% 23% 13
Pacifica 21% 29% 36% 36% 0% 14 50% 21% 29% 21% 21% 14
Redwood City 24% 34% 34% 19% 13% 32 47% 26% 21% 21% 34
San Bruno 12% 30% 60% 0% 10% 10 50% 30% 30% 30% 10
San Mateo 30% 35% 45% 15% 5% 40 53% 26% 26% 38
South San Francisco 13% 30% 40% 23% 6% 82 59% 27% 83
Race/Ethnicity
African American 62% 16% 59% 25% 0% 83 36% 29% 27% 26% 27% 24% 84 Asian 16% 24% 50% 20% 6% 82 28% 25% 29% 29% 24% 24% 83
Hispanic 27% 25% 42% 24% 8% 107 52% 27% 107
Other Race 30% 28% 47% 14% 12% 43 47% 30% 26% 43
Non-Hispanic White 12% 38% 41% 14% 7% 91 44% 27% 18% 91 Tenure
Homeowner 11% 26% 46% 20% 7% 95 32% 29% 22% 96
Renter 28% 26% 47% 20% 6% 232 42% 32% 23% 232
Precariously Housed 39% 21% 54% 20% 4% 98 24% 28% 35% 26% 100 Income
Less than $25,000 36% 29% 51% 11% 9% 100 39% 30% 25% 102 $25,000-$49,999 24% 31% 41% 22% 6% 64 42% 36% 25% 22% 64 $50,000-$99,999 19% 27% 45% 25% 3% 97 44% 29% 18% 97
Above $100,000 11% 28% 45% 21% 7% 76 45% 22% 16% 16% 76 Household Characteristics
Children under 18 26% 21% 57% 15% 6% 216 36% 31% 26% 218 Large Households 19% 26% 52% 9% 13% 54 65% 24% 15% 55
Single Parent 44% 13% 65% 17% 5% 106 33% 32% 27% 26% 26% 107 Disability 33% 27% 48% 21% 4% 215 33% 30% 22% 219
Older Adults (age 65+) 20% 20% 51% 20% 8% 144 24% 34% 24% 24% 146 Called/
emailed
California
Department
of Housing/
Civil Rights
Called/ emailed
City office,
County office,
or human rights
department/
agency
Filed a
complaint Other
Percent who felt
they were
discriminated
against
In the
past
year
2 to 5
years
ago
More
than 5
years
ago n
Nothing/ I
wasn't
sure what
to do
Moved/
found
another
place to
live
Nothing/ I
was afraid of
being
evicted/
harassed
Called/
emailed
housing
authority
Called/ emailed
local fair
housing
organization
Don't
remember
85 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 44 Experience of persons with disabilities. Overall, 35% of respondents’ households include a member experiencing a disability. Of these households, 26% said their housing does not meet their accessibility needs; 74% report that their current housing situation meets their needs. The three top greatest housing needs expressed by respondents included grab bars in bathroom or bench in shower (34%), supportive services to help maintain housing (33%), and ramps (26%). Other needs expressed by a substantial proportion of groups included wider doorways, reserved accessible parking spot by the entrance, and more private space in the facility in which I live.

Of respondents by jurisdiction, East Palo Alto (64%) has the lowest proportion of respondents with disabilities whose current housing situation meets their needs. Of these respondents, 63% indicated they needed supportive services to help maintain housing.

The highest proportion of respondents by group reporting that they or a member of their household experiences a disability were African American (71%), households making less than $25,000 (59%), single parent households (58%), and precariously housed respondents (56%). 86 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 45 Figure 17. Respondents experiencing a disability and their top three greatest housing needs Source: Root Policy Research from the 2021-2022 21 Elements AFFH Resident Survey. n
Jurisdiction
County 35% 74% 711 34% 33% 26% 171
Belmont 35% 89% 28 67% 67% 3
Brisbane 37% 72% 25 29% 29% 29% 29% 7
Burlingame 27% 80% 41 63% 50% 50% 8
Daly City 34% 68% 38 36% 36% 45% 36% 11
East Palo Alto 44% 64% 22 63% 8
Foster City 31% 83% 40 29% 29% 7
Half Moon Bay 45% 68% 22 29% 29% 7
Hillsborough 26% 100% 13 n/a
Milbrae 40% 82% 17 25% 25% 25% 25% 25% 25% 4
Pacifica 39% 93% 29 100% 2
Redwood City 42% 68% 62 33% 28% 28% 33% 18
San Bruno 40% 82% 34 50% 33% 33% 6
San Mateo 43% 72% 65 41% 47% 41% 17
South San Francisco 30% 68% 210 35% 28% 32% 57
Race/Ethnicity
African American 71% 87% 95 40% 40% 33% 15
Asian 31% 77% 157 29% 34% 26% 26% 35
Hispanic 41% 70% 162 37% 54% 35% 46
Other Race 38% 71% 56 63% 50% 44% 16
Non-Hispanic White 32% 77% 241 33% 27% 21% 52
Tenure
Homeowner 29% 82% 280 35% 37% 37% 43
Renter 39% 73% 347 41% 40% 27% 88
Precariously Housed 56% 71% 154 37% 26% 33% 43
Income
Less than $25,000 59% 71% 167 42% 27% 23% 48
$25,000-$49,999 40% 67% 107 45% 45% 45% 31
$50,000-$99,999 35% 77% 180 43% 26% 24% 42
Above $100,000 23% 82% 167 52% 34% 41% 29
Household Characteristics
Children under 18 35% 78% 293 40% 29% 32% 63
Large Households 35% 70% 99 41% 45% 34% 29
Single Parent 58% 81% 139 48% 28% 41% 29
Older Adults (age 65+) 46% 76% 337 44% 29% 30% 79
Ramps
Percent of
respondents
with a
disability
Current
housing
situation
meeting
needs Total n
Grab bars in
bathroom or
bench in
shower
Supportive
services to
help
maintain
housing
Alarm to
notify if a
non-verbal
child leaves
the home
Fire
alarm/doorbell
made accessible
for person with
hearing
disability/deaf
Better
navigation
for person
who is blind
Wider
doorways
Reserved
accessible
parking spot
by entrance
More
private
space in
the facility
in which I
live
Service or
emotional
support
animal
allowed
Would like to
live alone
(not with a
roommate)
Fewer
restrictions/
more freedom
87 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 46 Transportation. Over 80% of respondents indicated the type of transportation used most often is driving a personal vehicle. This share was relatively similar across the majority of jurisdictions and was the number one type of transportation used across all jurisdictions and demographic characteristics.

The groups with the lowest proportion of those who primarily drive included African American (40%), households making less than $25,000 (53%), single parents (57%), and precariously housed (57%) respondents.

As shown in Figure 18, on average respondents are fairly satisfied with their transportation situation.

Those groups somewhat or not at all satisfied with their transportation options include African American (58%), Brisbane (51%), single parents (45%) and precariously housed (44%) respondents. 88 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 47 Figure 18.

Are you satisfied
with your current
transportation
options?
Source:

Root Policy Research from the
2021-2022 21 Elements AFFH
Resident Survey.

Jurisdiction
County 29% 45% 20% 6% 1,903
Belmont 21% 42% 27% 10% 78
Brisbane 17% 33% 38% 13% 64
Burlingame 32% 45% 21% 1% 139
Daly City 19% 52% 20% 8% 109
East Palo Alto 31% 36% 24% 9% 45
Foster City 29% 43% 20% 9% 115
Half Moon Bay 30% 35% 26% 9% 46
Hillsborough 50% 34% 14% 2% 44
Milbrae 30% 45% 13% 13% 40
Pacifica 28% 42% 15% 15% 65
Redwood City 30% 36% 27% 8% 142
San Bruno 23% 54% 19% 4% 81
San Mateo 29% 52% 14% 4% 134
South San Francisco 34% 48% 15% 3% 666
Race/Ethnicity
African American 22% 21% 48% 10% 134
Asian 23% 49% 24% 4% 500
Hispanic 29% 43% 22% 7% 397
Other Race 29% 41% 21% 9% 149
Non-Hispanic White 32% 45% 17% 5% 757
Tenure
Homeowner 31% 45% 18% 6% 905
Renter 27% 44% 23% 6% 834
Precariously Housed 20% 36% 35% 9% 254
Income
Less than $25,000 22% 39% 29% 10% 282
$25,000-$49,999 25% 42% 26% 8% 265
$50,000-$99,999 28% 52% 16% 4% 517
Above $100,000 34% 44% 18% 4% 721
Household Characteristics
Children under 18 25% 43% 25% 6% 840
Large Households 29% 50% 18% 4% 284
Single Parent 20% 36% 38% 7% 240
Disability 25% 40% 27% 8% 658
Older Adults (age 65+) 30% 43% 21% 6% 736
Entirely
satisfied
Mostly
satisfied
Somewhat
unsatisfied
Not at all
satisfied n
89 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 48 Solutions offered by residents. Respondents were asked a series of questions about how to improve their situations related to housing, employment, health, education and neighborhood.

Improve housing security. When asked what could improve a respondent’s housing security, the top answers among respondents by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics were none of the above and help me with a downpayment/purchase.

The highest proportion of respondents among groups that selected None of the above includes:
 Hillsborough residents, 71%
 Owners, 65%
 Income greater than $100,000, 54%
 Foster City residents, 53%
 White, 51%
 Burlingame residents, 50%
The highest proportion of respondents among groups that selected Help me with a downpayment or purchase includes:
 Renters, 44%
 Large households, 42%
 Daly City residents, 41%
 Hispanic, 39%
 Precariously housed, 39%
 City of San Mateo residents, 37%
Other solutions to improve housing security identified by several different groups included Help me with the housing search, help me pay rent each month, and find a landlord who accepts Section 8. The highest proportion of respondents among groups that selected these solutions includes:

Help me with the housing search
 Precariously housed, 39%
 Income less than $25,000, 34%
 Income between $25,000-$50,000, 29%
90 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 49  Half Moon Bay residents, 27%
Help me pay rent each month
 Income less than $25,000, 35%
 Single parent, 31%
Find a landlord who accepts Section 8
 Black or African American, 37%
Improve neighborhood situation. When asked what could improve a respondent’s neighborhood situation, nearly every respondent group by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics identified Better lighting. Other solutions flagged by multiple respondent groups to improve their neighborhood situations includes Improve street crossings and none of the above.

The highest proportion of respondents among groups that selected Better lighting includes:
 East Palo Alto residents, 45%
 Millbrae residents, 45%
 Other race, 42%
 Daly City residents, 41%
 Hispanic, 40%
 Income between $25,000-$50,000, 40%
 Income between $50,000-$100,000, 40%
The highest proportion of respondents among groups that selected Improve street crossings includes:
 City of San Mateo residents, 34%
 Single parent, 31%
The highest proportion of respondents among groups that selected None of the above includes:
 Foster City residents, 37%
 Hillsborough residents, 36%
 Burlingame residents, 28%
91 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 50 Additionally, 42% of Millbrae respondents chose Reduce crime, 40% of Brisbane respondents chose More stores to meet my needs, and Belmont (34%) and Half Moon Bay (33%) respondents chose Build more sidewalks.

Improve health situation. When asked what could improve a respondent’s health situation, the majority of respondent groups by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics selected Make it easier to exercise, More healthy food and None of the above.

The highest proportion of respondents among groups that selected Make it easier to exercise includes:
 Redwood City residents, 48%
 Hispanic, 42%
 South San Francisco residents, 41%
 City of San Mateo residents, 41%
 Asian, 41%
 Renters, 40%
The highest proportion of respondents among groups that selected More healthy food includes:
 East Palo Alto residents, 48%
 Precariously Housed, 47%
 Single parent, 41%
 Daly City residents, 40%
 Income less than $25,000, 38%
 Black or African American, 37%
 Large Households, 37%
The highest proportion of respondents among groups that selected None of the above includes residents from:
 Hillsborough residents, 48%
 Burlingame residents, 47%
 Foster City residents, 42%
 White, 41%
 Owners, 39%
92 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 51 Additionally, African American (34%) and San Bruno (29%) respondents identified Better access to mental health care as a solution to help improve their health situations.

Improve job situation. When asked what could improve a respondent’s employment situation, the majority of respondent groups by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics selected Increase wages and None of the above.

The highest proportion of respondents among groups that selected Increase wages includes:
 Renters, 52%
 Single parents, 50%
 Hispanic, 49%
 Households with children, 49%
 Daly City residents, 49%
 Income between $50,000-$100,000, 49%
 Large households, 48%
The highest proportion of respondents among groups that selected None of the above includes:
 Hillsborough residents, 76%
 Owners, 58%
 White, 57%
 Over 65+, 53%
 Income greater than $100,000, 53%
 Foster City residents, 53%
Additionally, 29% of households with income less than $25K identified Find a job near my apartment or house as a solution to help improve their situation.

Improve education situation. When asked what could improve a respondent’s education situation for their children, the majority of respondent groups by jurisdiction, race/ethnicity, tenure, income, and other selected housing characteristics selected None of the above, Have more activities, and Stop bullying/crime/drug use at school.

The highest proportion of respondents among groups that selected None of the above includes:
 Burlingame residents, 55%
93 ROOT POLICY RESEARCH RESIDENT SURVEY APPENDIX, PAGE 52  White, 52%
 Over 65+, 51%
 Hillsborough residents, 49%
 Foster City residents, 46%
 Brisbane residents, 45%
The highest proportion of respondents among groups that selected Have more activities includes:
 Single parent, 45%
 Households with children, 41%
 Large households, 41%
 Other race, 37%
 Daly City residents, 34%
 Hispanic, 34%
The highest proportion of respondents among groups that selected Stop bullying/crime/drug use at school includes:
 East Palo Alto residents, 38%
 Precariously housed, 31%
 Other race, 30%
 Redwood City residents, 29%
 Hispanic, 29%
 San Mateo residents, 28%
Additionally, 29% of Millbrae respondents identified Have better teachers at their schools as a means to improve the education situation in their respective households. 94 ROOT POLICY RESEARCH SECTION V, PAGE 1
Disparate 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 draws 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 county’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% identifying 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.
1 The term “extenuating circumstances” is used in this section to capture students whose socioeconomic situations and/or disability may make standard educational environments challenging. 95 ROOT POLICY RESEARCH SECTION V, PAGE 2
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 for 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 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 reduced 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 Islander 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 pronounced 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: In these districts, students with disabilities met or exceeded mathematics test standards at 54 percentage points below the overall test rate.
96 ROOT POLICY RESEARCH SECTION V, PAGE 3
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 District 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.
 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, the 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 have 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%. 97 ROOT POLICY RESEARCH SECTION V, PAGE 4
 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.  In many school districts across San Mateo County, 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. 98 ROOT POLICY RESEARCH SECTION V, PAGE 5
San Mateo County School Districts. There are three unified school districts in San Mateo County 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 school 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 schools’ 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.

Geographic boundaries of school districts. Figure V-1 illustrates the geographic boundaries of the unified school districts as well as the three high school districts. Municipal boundaries are overlayed on the map. 99 ROOT POLICY RESEARCH SECTION V, PAGE 6
Figure V-1.

Unified School Districts and High School Districts in San Mateo County Source: San Mateo County Office of Education.

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 covers South San 100
ROOT POLICY RESEARCH SECTION V, PAGE 7
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, Jefferson Union, San Mateo Union, and Sequoia Union, cover the remaining jurisdictions. Jefferson Union covers Brisbane, Colma, Daly City, and Pacifica. San Mateo Union 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. 101
ROOT POLICY RESEARCH SECTION V, PAGE 8
Figure V-2.

Elementary School Districts in San Mateo County
Source: San Mateo County Office of Education.
102
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Because the elementary school 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 Jurisdictions
Source: San Mateo County Office of Education.

A brief history of district formation. 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 students 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 Jurisdiction
Atherton Sequoia Union Menlo Park City ; Las Lomitas Elementary; Redwood City
Belmont Sequoia Union Belmont-Redwood Shores
Brisbane Jefferson Union Brisbane; Bayshore Elementary
Burlingame San Mateo Union Burlingame
Colma Jefferson Union Jefferson Elementary
Daly City Jefferson Union; South San Francisco Unifie Jefferson 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 Jefferson 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) 103
ROOT POLICY RESEARCH SECTION V, PAGE 10
separate high school districts, 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 pushing 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 school districts and feeder schools into two or three smaller unified districts, 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 instead 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 June 1966, and rejected a similar proposal again in 1972.

In 1973, the Mid-Peninsula Task Force for Integrated Education petitioned the county committees to unify the elementary districts of Menlo 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 unification have persisted, leaving the county with several elementary school districts which feed into a high school, rather than a unified 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 and 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 Jefferson elementary and high school districts.

According to the county’s superintendent of schools Anne Campbell, other districts may find themselves pooling their resources in the future: local identification may be strong, 2 Watson, Aleta. “How Did We End Up With 54 School Districts in San Mateo and Santa Clara Counties?” Silicon Valley Community Foundation, 2012. https://www.siliconvalleycf.org/sites/default/files/report-edu.pdf 3 Ibid.
4 Ibid.
104
ROOT POLICY RESEARCH SECTION V, PAGE 11
she says, but financial reality is hard to ignore: “As we move forward in time, I think it’s going to be interesting to see what school districts are going to do, especially as budgets get more bleak.”5
Enrollment 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 City, 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 BelmontRedwood Shores (30%). 5 Ibid.
105
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Figure V-4.

Enrollment changes by district, 2010-11 to 2020-2021
Source: California Department of Education and Root Policy Research However, it is important to note that many of these enrollment decreases were driven by the pandemic. In fact, total enrollment in these public schools decreased by 3% between 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
Jefferson Union High School 4,960 4,705 -5%
Bayshore Elementary 543 361 -34%
Brisbane Elementary 545 474 -13%
Jefferson Elementary 6,998 6,653 -5%
Pacifica 3,164 3,006 -5%
San Mateo Union High 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
106
ROOT POLICY RESEARCH SECTION V, PAGE 13
2019-2020 and 2020-2021 in San Mateo County: the largest 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.

Public School Enrollment Changes, 2010-2011 to 2020-2021 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 enrollment decreases during COVID-19, with a 11% and 10% decline in enrollments, respectively. The only school district with increasing enrollments between the 2019-2020 to 2020-2021 school years was Sequoia Union High School District, with a modest 1% increase in enrollments.
107
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Figure V-6.

Enrollment changes by district during COVID-19, 2019-20 to 2020-21 Source: California Department of Education and Root Policy Research.

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 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
Jefferson Union High School 4,811 4,705 -2%
Bayshore Elementary 381 361 -5%
Brisbane Elementary 476 474 0%
Jefferson Elementary 6,687 6,653 -1%
Pacifica 3,110 3,006 -3%
San Mateo Union High 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
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with those across the state. According to a study conducted by the Public Policy Institute of California, public K–12 enrollment 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, school 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 reducing students’ resources and access to opportunities.

Demographics: race & ethnicity. Over the last decade, San 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 students 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 was 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/African American (1%) and Pacific Islander (2%). 6 Lafortune, Julien & Prunty, Emmanuel. “Digging 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-publicschools/ 7 Ibid.
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Figure V-7.

Changes in Race and
Ethnicity, 2010-2011 to 2020-
2021
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 enrolled in public schools by jurisdiction in 2020-2021.
 Portola Valley Elementary School District (66%) and Woodside 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 District and Redwood City Elementary School District had the highest share of Hispanic students, at 84% and 70%, respectively.  Ravenswood City also had 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%) had the highest share of Asian students.  Jefferson Elementary School District and Jefferson Union High School District had the highest portion of Filipino students, at 25% and 29% respectively. 110
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Figure V-8.

Student body by Race and Ethnicity, 2020-2021
Note: In almost all school 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 Research Enrollment changes due to COVID-19 varied by race and ethnicity. For instance, between 2019-2021, 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 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
Jefferson Union High 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%
Jefferson Elementary 19% 2% 25% 36% 1% 11% 5%
Pacifica 8% 1% 9% 26% 0% 39% 16%
San Mateo Union High 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%
Asian White
Two or
Black Filipino Hispanic more races
Pacific
Islander
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end of the spectrum, there was a 3% increase in enrollment among White students (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.

Enrollment Changes by Race and Ethnicity, San Mateo County, 2019-20 to 2020-21
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, it is possible 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 extenuating circumstances. Several students in the county’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 detrimental circumstances beyond their control. 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 determined based on household size and income. For instance, in the 2020-2021 academic year, students from a household of three making less 112
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than $40,182 annually qualified for reduced price meals, and those making less than $28,236 in a household of three qualified for free meals.8 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 Lomitas 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 City 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 experiencing 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 area 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 noted that students who experience homelessness have higher dropout rates and are more likely to experience homelessness as adults.10
School moves related to evictions. Currently, students whose families have been evicted do not have protections allowing them to remain in their current school district.

This means that precarious housing also means 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; however, 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, Jefferson Union High 8 “Income Eligibility Scales for School Year 2020-2021.” California Department of Education. 9 Bartley, Kaitlyn. “Homelessness: The shadow that hangs over students in this Bay Area school district.” The Mercury News. December 2018.
10 Jones, Carolyn. “California schools see big jump in homeless students.” Palo Alto Online. October 2020. 11 https://sfrb.org/new-amendment-prohibiting-owner-move-evictions-minor-children-during-school-year 113
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School, and Redwood City Elementary also have high rates of English learners, representing more than a third of students.

Less than one percent of students in San Mateo County public school districts are foster youth or migrants. Cabrillo Unified School District had the highest rate of migrant students at 3%. La Honda-Pescadero 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. 114
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Figure V-10.

Students with Extenuating Circumstances, 2020-2021
Source: California Department of Education and Root Policy Research The overall share of students in these groups has not changed drastically over 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 lunch from 2016-2017 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
Jefferson Union High School 36% 44% 0% 0% 0%
Bayshore Elementary 30% 57% 0% 0% 0%
Brisbane Elementary 16% 19% 0% 0% 0%
Jefferson Elementary 14% 27% 0% 1% 0%
Pacifica 9% 18% 0% 1% 0%
San Mateo Union High 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%
Migrant
Reduced
Lunch
English
Learners
Foster
Children Homeless
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to 2020-2021. Around 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, 2016-
2017 to 2020-2021
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
:

During 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 students 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 total population, with 7% and 10% decreases in enrollment, respectively. 116
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Figure V-12.

Enrollment Changes by Extenuating Circumstance, San Mateo County, 2019-2020 to 2020-2021
Source: California Department of Education and Root Policy Research 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 percent 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 and 52% met or exceeded 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 mathematics testing.

Among elementary school districts, Portola Valley Elementary School District and Woodside Elementary School District had the highest rates 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, girls scored higher on English tests than boys. Overall, girls met 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% 117
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of girls met or exceeded English testing standards and just 56% of boys did: a gap of 16 percentage points.

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 Met or Exceeded Testing Standards, by Gender and District, 2018-2019
Source: California 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 V15. In 2014-2015 there was a 11 percentage point gap in girls’ and boys’ English testing pass 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
Jefferson Union High School 57% 52% 63% 37% 38% 35%
Bayshore Elementary 27% 24% 31% 27% 27% 28%
Brisbane Elementary 64% 56% 72% 54% 56% 53%
Jefferson Elementary 48% 43% 54% 37% 39% 35%
Pacifica 60% 55% 65% 57% 57% 57%
San Mateo Union High 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 Mathematics
Total Boys Girls Total Boys Girls
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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.

Figure V-15.

Students who Met or Exceeded Testing Standards, by Gender, 2014-2015 to 2018-2019
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, Asian, White, and Filipino students 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 lower 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 standards in 2014-2015 and 40% met standards in 2019-19, an increase of six percentage points.
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Figure V-16.

Students who Met or Exceeded English Testing Standards, by Race and Ethnicity, 2014-2015 to 2018-2019
Source: California Department of Education, California 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 2014-2015 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 scored lower.

White and Hispanic students have seen the biggest increases in rates of mathematics success: both have experienced a five percentage point increase in the percent of students who met or exceeded math testing standards.
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Figure V-17.

Students who Met or Exceeded mathematics testing standards, by Race and Ethnicity, 2014-2015 to 2018-2019
Source: 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 standards by district.

There were several districts in which the gaps between the overall test pass rates and a specific racial groups’ pass rates 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 Black/African American and overall math testing success were 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 of 48 121
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percentage points. Millbrae Elementary School District also had a 47 percentage point gap between Pacific Islander students’ and total students’ math test rates.

Figure V-18.

Students who Met or Exceeded Mathematics Testing Standards, by Race/Ethnicity and District, 2018-2019
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 District also had a wide gap between the total student body and Black/African American 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
Jefferson Union High 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% Jefferson Elementary 37% 61% 15% 42% 23% 20% 30%
Pacifica 57% 74% 38% 48% 38% (no data) 66%
San Mateo Union High 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 Asian Black Filipino Hispanic White
Pacific
Islander
122
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students. Namely, 80% of the student body met or exceeded English testing standards, but only 19% of Black/African American students met or exceeded testing standards—a 61 percentage point gap. Las Lomitas Elementary had a 41 percentage point gap between overall English testing success and Black/African American English testing success.

Other districts had large gaps between the total student body’s English test scores and Pacific 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.
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Figure V-19.

Students who Met or Exceeded English Testing Standards, by Race/Ethnicity and District, 2018-2019
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 and 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
Jefferson Union High 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% Jefferson Elementary 48% 62% 28% 59% 34% 33% 43%
Pacifica 60% 65% 32% 52% 45% (no data) 68%
San Mateo Union High 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 Asian Black Filipino Hispanic White
Pacific
Islander
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For example, English learning students in Portola Valley Elementary, Woodside Elementary, Menlo Park City Elementary, and Brisbane Elementary each met or exceeded mathematics test standards at a rate 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 48% met or exceeded standards. Others in Belmont-Redwood Shores (43%) and Woodside Elementary (41%) had high pass rates as well. Students with disabilities in San Carlos Elementary and Las Lomitas Elementary school districts scored far below the overall student body: in these districts, students with disabilities met or exceeded mathematics test standards at 54 percentage points below the overall test rate.

In Jefferson Elementary and Ravenswood Elementary students experiencing homelessness passed math tests at a rate similar to their housed peers. In other districts, however, students experiencing homelessness often scored substantially lower. School districts with the widest math testing gaps between the overall student body and students experiencing homelessness were San Mateo-Foster City and Millbrae Elementary, with a 41 percentage point gap and 42 percentage point gap, respectively.
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Figure V-20.

Students who Met or Exceeded Math Testing Standards, by Special Case and District, 2018-2019
Source: California 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
Jefferson Union High 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%
Jefferson Elementary 37% 15% 36% (no data) 11%
Pacifica 57% 22% (no data) (no data) 17%
San Mateo Union High 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%
English
Learners
Experiencing
homelessness Migrant
With
Overall Disabilities
126
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Students with extenuating circumstances also consistently scored lower in English testing than the overall student body.

For instance, English 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. Hillsborough Elementary had the largest gap at 85 percentage points. Las Lomitas Elementary had the highest success rate among English learners, where 50% met or exceeded English testing standards.

However, students with disabilities in Las Lomitas Elementary and San Carlos Elementary school districts met or exceeded English test standards at rate 55 and 51 percentage points below the overall test rate, respectively. 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 were most likely to meet English testing standards, with 42% meeting or exceeding standards.

The school district with the widest gap between overall English test scores and scores among students experiencing homelessness was Cabrillo Unified with a 34 percentage point gap.

Just three districts reported English testing scores among migrant students. Redwood City Elementary had the highest pass rate at 34% and Cabrillo Unified had the lowest at 16%. 127
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Figure V-21.

Students who Met or Exceeded English Testing Standards, by Special Case and District, 2018-2019
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
Jefferson Union High 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%
Jefferson Elementary 48% 16% 30% (no data) 15%
Pacifica 60% 12% (no data) (no data) 15%
San Mateo Union High 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%
English
Learners
Experiencing
homelessness Migrant
With
Overall Disabilities
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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 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%.

Figure V-22.

Students Meeting
California University
Admission
Standards, 2019-
2020
Source:

California Department of Education
and Root Policy Research.

Cabrillo Unified and South San Francisco Unified have experienced a decrease in the share 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 less drastic decrease over the same period, but the rate still shrunk by two percentage points.

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 District experienced a 10 percentage point increase in this success rate over the 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. 129
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Figure V-23.

Students Meeting
University
Admission
Standards, 2016-
2017 and 2019-2020
Source:

California Department of Education
and Root Policy Research.

Rates 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 overall 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/African American students typically met 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 overall.

Filipino students typically met admissions standards at rates similar to the overall student body. For instance, in Jefferson 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 student population. In Sequoia Union, they are slightly less likely to have met admission standards than the overall student population. 130
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In La Honda-Pescadero, Hispanic students 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 students meet the university admissions standards compared to 68% of students overall.

Finally, Pacific Islander students in Jefferson Union were slightly more likely to have met California university admissions standards compared to the overall student body, but in Sequoia Union and San Mateo Union they were substantially less likely. 131
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Figure V-24.

Students Meeting University Admission Standards, by Race and Ethnicity, 2019-2020
Source: California Department of Education and Root Policy Research As expected, 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, English learners, foster youth, and migrant students met CSU or UC admission standards at lower rates than the overall student population.
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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 students learning 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 percentage 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 eight 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 standards, which was higher than rates in San Mateo Union (21%) and Jefferson Union (21%).

Just 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 standards and 22% in San Mateo Union met admissions standards.
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Figure V-25.

Students Meeting
University
Admission
Standards, 2019-
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 rate 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 private 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 highest college-going rate at 77%. La Honda-Pescadero School District is the notable exception, with just 32% of graduates attending college within 12 or 16 months. 134
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Figure V-26.

College-Going
Rates, 2017-2018
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% college-going rate in 2014-2015 and a 93% college-going rate in 2015-2016. The district experienced a rapid decline in college-going rates, starting in 2016-2017. However, La Honda-Pescadero has especially small sample sizes. For instance, 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 consistent college-going rates. 135
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Figure V-27.

College-Going
Rates, 2014-2015 to
2017-2018
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 Hispanic students, a 23 percentage point gap. Jefferson Union has the smallest gap between the two groups: 77% of White students go to college compared to 71% of Hispanic students.  Among Black/African American students, those at San Mateo Union have the highest college-going rate at 82%. Those at Jefferson Union have the lowest at just 53%, which is 24 percentage points lower than that of White students and 34 percentage points lower than that of Asian students.
 Overall, Asian students have among the highest 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. 136
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 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 Francisco Unified (73%).
 College-going rates for Pacific Islander students vary substantially by district. For instance, in Sequoia Union 54% go to college, but in South San Francisco Unified 92% go to college.

Figure V-28.

College-going Rates by Race and Ethnicity, 2017-18
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.
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Source: California Department of Education and Root Policy Research College-going rates are lower for students with disabilities and those learning English 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 Francisco Unified, where just 52% of English learning students go to 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, where 63% of English learners go to college.
 Among students with disabilities, 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 point gap. Jefferson Union, on the other hand, had a relatively high college-going rate among students with disabilities that was not very different from the district’s overall college-going rate: 71% went to college which is just five percentage points lower than the district’s overall student population.

Figure V-29.

College-going Rates
for English Learners
and Students with
Disabilities, 2017-
2018
Note:

Cabrillo Unified and La HondaPescadero 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 is wider in San Mateo County than in other parts of California 138
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and nationwide. The differences between high-school graduate earnings and bachelor's degree earnings are around 100% in California and 76% in the US overall.

Figure V-30.

Median Annual Earnings by Educational Attainment, 2019
Source: 5-year 2019 American Community Surveys Data.

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

Median Annual Earnings by Educational Attainment in San Mateo County, 2010 to 2019
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 Mateo County address differences in college-going rates stratified by race, ethnicity, and extenuating circumstances.

Barriers to Success
Many students are unable to achieve academic success because of barriers in home and school. This section explores the available indicators of barriers to success, including chronic absenteeism and dropout rates. It also describes inequities in discipline rates by 140
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race and 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 outcomes.13 Students are considered chronically absent if they were absent for 10% or more of the days during a school year. Note, however, students are exempt from chronic absenteeism calculations if they receive instruction through a home or hospital instructional setting, are attending community college full-time, 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 school year, where just 9% of students overall were chronically 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 districts also had high rates 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 students (15%) had notably higher rates of chronic absenteeism than the overall student population (10%). Chronic absenteeism among Pacific Islander 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." Journal of Education for Students Placed at Risk (JESPAR) 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 physical 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.
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Figure V-32.

Chronic
Absenteeism by
Race/Ethnicity,
2016-2017 to 2018-
2019
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 percentage point gap between chronic absenteeism rates for Pacific Islander students (32%) and the overall student body (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 Black/African American students— a 23 percentage point gap. Jefferson 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 students, 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 sample of White students: just 3% of students at Bayshore Elementary are White, one of lowest in the county.
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Figure V-33.

Chronic Absenteeism by District and Race/Ethnicity, 2018-2019 Source: California Department of Education and Root Policy Research 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. In all other districts, students with disabilities were more likely to be chronically absent than the overall student population. This was particularly true in Sequoia Union High School District, Jefferson Union High School District, and San Mateo Union High School District, which had gaps between the overall 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
Jefferson Union High 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% Jefferson Elementary 12% 5% 28% 6% 13% 25% 23%
Pacifica 7% 4% 12% 6% 9% 21% 7%
San Mateo Union High 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
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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 districts (with the exception of Ravenswood City Elementary and Jefferson 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 every 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 experiencing homelessness was highest in Burlingame 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.
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Figure V-34.

Chronic Absenteeism by District and Extenuating Circumstance, 2018-2019 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 attainment. In addition to the economic and housing precarity associated with low earnings, low earnings also often lead to increased incentives to participate in criminal activity. In fact, one study 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
Jefferson Union High 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% Jefferson Elementary 12% 10% 21% (no data) 24% 16%
Pacifica 7% 11% (no data) (no data) (no data) 14%
San Mateo Union High 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
English
Learners
Experiencing
homelessness Migrant
With
Disabilities
Foster
Youth
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suggest that high school dropouts are 3.5 times more likely than high school graduates to be imprisoned at some point during their lifetime.15 Another study found that 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 costs.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 Mateo County is pivotal to the health and economic prosperity of the community.

In this report, dropout rates 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 school diploma, did not complete high school, and are not still enrolled as a "fifth year senior".

In the 2019-2020 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 School District, from 5% to 4%. Jefferson Union had the lowest dropout rate in the county at just 3%, which after 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 Stats Fact Sheet." National High School Center (2007). 16 U.S. Department of Justice, Bureau of Justice Statistics. (2002). Correctional populations in the United States, 1998 (NCJ-192929). Washington: U.S. Government Printing Office. 17 Gonzalez, Jennifer M. Reingle, et al. "The long-term effects of school dropout and GED attainment on substance use disorders." Drug and alcohol dependence 158 (2016): 60-66. 146
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Figure V-35.

Dropout Rates by
District, 2016-2017 to
2019-2020
Note: La Honda-Pescadero Unified
School District is excluded
from these data.

Source: California Department of
Education and Root Policy
Research
In all school districts in the county, dropout rates are higher for boys than for girls.

Jefferson Union had 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 Rates by
Gender, 2019-2020
Note: La Honda-Pescadero Unified
School District is excluded
from these data.

Source: California Department of
Education and Root Policy
Research
Pacific Islander, Black/African American, and Hispanic students in the county often had higher dropout rates than those in other racial and ethnic groups. 147
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 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, Jefferson Union, the highest dropout rates still found among Black/African American (7%) and Hispanic students (6%).
 Notably, however, in South San Francisco Unified, White students were more 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/African American and Pacific Islander students were not available for South San Francisco Unified due to small sample sizes. 148
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Figure V-37.

Dropout Rates by Race, 2019-2020
Source: California Department of Education and Root Policy Research In all school districts 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 was in Sequoia Union, where 24% dropped out. 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 point gap between the dropout rate of overall students (6%) and students with disabilities (6%).
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 Among students learning English, Sequoia Union had the highest dropout rate at 27%, while Jefferson Union had the lowest dropout rate at 8%.  Sequoia Union also had the highest rate of dropout among students experiencing homelessness at 29% while Jefferson 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 dropped 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. 150
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Figure V-38.

Dropout Rates by Extenuating Circumstance, 2019-2020
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, but also their peers. 151
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Students in schools with higher suspension rates are more likely to drop out or school and less likely to attend a four-year college.18
Other academic 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 means that Black/African American and Hispanic students suffer more of the economic and social consequences than their White peers for the same behaviors.

Luckily, in 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 decrease 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 adult 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." School Psychology Review 40.1 (2011): 85-107. 152
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Figure V-39.

Suspension Rates, 2011-2012 to 2019-2020
Source: California Department of Education and Root Policy Research In many school districts across San Mateo County, Hispanic students are disciplined at disproportionately higher rates compared to their peers. Figure V-40 compares each racial/ethnic group’s share of suspensions to their share of the overall student population.  In all districts 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 overrepresentation 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, just 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 Jefferson Union High School District, 31% of students identified as Filipino but just 10% of suspended students were Filipino, a 21 percentage point 153
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gap. In San Mateo Union High School, 22% of students identified as Asian but just 5% of suspended students were Asian, a 17 percentage point gap.  White students 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 percentage points) and Sequoia Union (18 percentage points). 154
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Figure V-40.

Suspension Rates by Race and Ethnicity, 2019-2020
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 than 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
Jefferson
Union
High
La HondaPescadero
San
Mateo
Union
High
Sequoia
Union
High
South San
Francisco
Unified
155
ROOT POLICY RESEARCH SECTION V, PAGE 62
Staff demographics. Diversity of school staff has been shown to improve outcomes for students of color. For instance, one recent study 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 entirely by black students, especially black boys, who are markedly less likely to be subjected to exclusionary discipline when taught by black teachers. There is little evidence of any benefit for white students of being matched with white teachers.20 Other research 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 improves 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, Hispanic, Filipino, Pacific Islander, and White, and compares those shares to the racial/ethnic breakdown 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 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.
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. Jacob 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.
156
ROOT POLICY RESEARCH SECTION V, PAGE 63
Figure V-41.

Staff and Student
Demographics,
2020-2021
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: California 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 Hispanic faculty and staff. However, there has been a slight decrease (by two percentage points) 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 faculty and staff, and a one percent increase in the share of Pacific Islander faculty and staff.
157
ROOT POLICY RESEARCH SECTION V, PAGE 64
Figure V-42.

Faculty and Staff Demographics, 2011-2012 to 2020-2021
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: California Department of Education and Root Policy Research Figure V-43 illustrates faculty and staff racial and ethnic diversity for the 2020-2021 school year by district.
 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 Jefferson Elementary have the highest shares of Filipino faculty and staff at 28%.
158
ROOT POLICY RESEARCH SECTION V, PAGE 65
Figure V-43.

Faculty and Staff Race/Ethnicity, by District, 2020-2021 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: California Department 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
Jefferson Union High School 3% 3% 13% 26% 1% 43%
Bayshore Elementary 13% 4% 17% 61% 0% 4%
Brisbane Elementary 7% 0% 28% 20% 4% 42%
Jefferson Elementary 13% 3% 28% 25% 0% 29%
Pacifica 7% 2% 8% 23% 2% 54%
San Mateo Union High 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%
Asian Black Filipino Hispanic White
Pacific
Islander
159
ROOT POLICY RESEARCH SECTION V, PAGE 66
Figure V-44 illustrates the gap 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, leaving 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 Elementary fall short of meeting this goal, in that there is a large overrepresentation 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 point gap), Jefferson Union High School District (29 percentage point 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 faculty, particularly Woodside Elementary and Menlo Park City Elementary, 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 less 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/staff 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-performing English language learnings students. This may be partly due to the district’s large portion of Hispanic faculty/staff.

Though district wide there are approximately the same portions of Filipino students as there are faculty/staff, Jefferson 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 Jefferson 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.
160
ROOT POLICY RESEARCH SECTION V, PAGE 67
Figure V-44.

Difference Between Staff and Student Populations, by District, 2020-2021 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 and 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
Jefferson Union High School -12% 2% -16% -5% 0% 29%
Bayshore Elementary -6% 1% -4% 20% -4% 1%
Brisbane Elementary -13% -1% 16% -8% 4% 18%
Jefferson Elementary -6% 1% 3% -11% -1% 18%
Pacifica -1% 1% -1% -3% 2% 15%
San Mateo Union High 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
161
1 AFFH SEGREGATION REPORT: BELMONT
UC Merced Urban Policy Lab and ABAG/MTC Staff
Version of Record: March 06, 15:54:31
162
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 Belmont............................................................................... 8

2.1 Neighborhood Level Racial Segregation (within City of Belmont) ......................................... 8

2.2 Regional Racial Segregation (between Belmont and other jurisdictions)................................16 3 Income Segregation in City of Belmont ........................................................................... 21

3.1 Neighborhood Level Income Segregation (within Belmont) ................................................21

3.2 Regional Income Segregation (between Belmont and other jurisdictions)...............................27 4 Appendix 1: Summary of Findings ................................................................................. 31

4.1 Segregation in City of Belmont .................................................................................31

4.2 Segregation Between City of Belmont and Other jurisdictions in the Bay Area Region ...............31 5 Appendix 2: Segregation Data ...................................................................................... 33 6 References.............................................................................................................. 37 163
3 0.2 List of figures
Figure 1: Racial Dot Map of Belmont (2020) ............................................................................... 9 Figure 2: Racial Isolation Index Values for Belmont Compared to Other Bay Area Jurisdictions (2020) ........11 Figure 3: Racial Dissimilarity Index Values for Belmont Compared to Other Bay Area Jurisdictions (2020)....14 Figure 4: Theil’s H Index Values for Racial Segregation in Belmont Compared to Other Bay Area Jurisdictions (2020) ...........................................................................................................................15 Figure 5: Racial Dot Map of Belmont and Surrounding Areas (2020) ..................................................16 Figure 6: Racial Demographics of Belmont Compared to All Bay Area Jurisdictions (2020).......................18 Figure 7: Comparing the Share of People of Color in Belmont and Vicinity to the Bay Area (2020) .............19 Figure 8: Income Dot Map of Belmont (2015).............................................................................22 Figure 9: Income Group Isolation Index Values for Belmont Compared to Other Bay Area Jurisdictions (2015) ...................................................................................................................................24 Figure 10: Income Group Dissimilarity Index Values for Belmont Compared to Other Bay Area Jurisdictions (2015) ...........................................................................................................................26 Figure 11: Income Group Theil’s H Index Values for Belmont Compared to Other Bay Area Jurisdictions (2015) ...................................................................................................................................27 Figure 12: Income Dot Map of Belmont and Surrounding Areas (2015) ...............................................28 Figure 13: Income Demographics of Belmont Compared to Other Bay Area Jurisdictions (2015) ................29

0.3 List of tables
Table 1: Racial Isolation Index Values for Segregation within Belmont ..............................................10 Table 2: Racial Dissimilarity Index Values for Segregation within Belmont..........................................13 Table 3: Theil’s H Index Values for Racial Segregation within Belmont..............................................15 Table 4: Population by Racial Group, Belmont and the Region........................................................17 Table 5: Regional Racial Segregation Measures ..........................................................................20 Table 6: Income Group Isolation Index Values for Segregation within Belmont ....................................23 Table 7: Income Group Dissimilarity Index Values for Segregation within Belmont................................25 Table 8: Theil’s H Index Values for Income Segregation within Belmont ............................................26 Table 9: Population by Income Group, Belmont and the Region ......................................................28 Table 10: Regional Income Segregation Measures .......................................................................30 Table 11: Neighborhood Racial Segregation Levels in Belmont........................................................33 Table 12: Neighborhood Income Segregation Levels in Belmont ......................................................34 Table 13: Regional Racial Segregation Measures.........................................................................35 Table 14: Regional Income Segregation Measures .......................................................................35 Table 15: Population by Racial Group, Belmont and the Region ......................................................36 Table 16: Population by Income Group, Belmont and the Region.....................................................36 164
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 Fair 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. 165
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, neighborhood 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 7 166
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 of 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 in 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. 167
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. 168
8 2 RACIAL SEGREGATION IN CITY OF BELMONT
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 Belmont) Racial dot maps are useful for visualizing how multiple racial groups are distributed within a specific geography. The racial dot map of Belmont 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.
169
9 Figure 1: Racial Dot Map of Belmont (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 Belmont 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 Belmont the most isolated racial group is white residents. Belmont’s isolation index of

0.485 for white residents means that the average white resident lives in a neighborhood that is 48.5% 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 Belmont 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.
170
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 Belmont Belmont Bay Area
Average
Race 2000 2010 2020 2020
Asian/Pacific Islander 0.166 0.215 0.302 0.245
Black/African American 0.016 0.018 0.014 0.053
Latinx 0.092 0.124 0.130 0.251
White 0.712 0.624 0.485 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 Belmont compare to values in other 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 Belmont, 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 groups 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).
171
11 Figure 2: Racial Isolation Index Values for Belmont Compared to Other Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

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.

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). 172
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 Belmont, the Black/African American group is 1.2 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 Belmont 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 Belmont the highest segregation is between Black and white residents (see Table 2). Belmont’s Black /white dissimilarity index of 0.165 means that 16.5% of Black (or white) residents would need to move to a different neighborhood to create perfect integration between Black residents and white residents.

However, local jurisdiction staff should note that this dissimilarity index value is not a reliable data point due to small population size. See callout box above for more information.

The “Bay Area Average” column in this table provides the average dissimilarity index values for these 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.
173
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 Segregation within Belmont Belmont Bay Area
Average
Race 2000 2010 2020 2020
Asian/Pacific Islander vs. White 0.161 0.156 0.088 0.185 Black/African American vs. White 0.151* 0.175* 0.165* 0.244 Latinx vs. White 0.151 0.142 0.119 0.207
People of Color vs. White 0.141 0.139 0.087 0.168
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.

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 Belmont 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 Belmont, 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.
174
14 Figure 3: Racial Dissimilarity Index Values for Belmont Compared to Other Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

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.

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 more 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 lives 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 Belmont 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 Belmont 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 Belmont was 175
15 lower than the average value for Bay Area jurisdictions, indicating that neighborhood level racial segregation in Belmont is less than in the average Bay Area city.

Table 3: Theil’s H Index Values for Racial Segregation within Belmont Belmont Bay Area
Average
Index 2000 2010 2020 2020
Theil's H Multi-racial 0.014 0.015 0.007 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, 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 4 below shows how Theil’s H index values for racial segregation in Belmont 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 segregation in Belmont, 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 Belmont Compared to Other Bay Area Jurisdictions (2020)
Universe: Bay Area Jurisdictions.

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. 176
16 2.2 Regional Racial Segregation (between Belmont 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 between different jurisdictions in the region. Figure 5 below presents a racial dot map showing the spatial distribution of racial groups in Belmont as well as in nearby Bay Area cities.

Figure 5: Racial Dot Map of Belmont 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.

Note: The plot shows the racial distribution at the census block level for City of Belmont and vicinity. Dots in each census block are randomly placed and should not be construed as actual placement of people.

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 Belmont 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, Belmont has a higher share of white residents than the Bay Area as a whole, a lower share of Latinx residents, a lower share of Black residents, and a higher share of Asian/Pacific Islander residents. 177
17 Table 4: Population by Racial Group, Belmont and the Region Belmont Bay Area
Race 2000 2010 2020 2020
Asian/Pacific Islander 15.3% 20.4% 29.9% 28.2%
Black/African American 1.5% 1.6% 1.2% 5.6%
Latinx 8.3% 11.5% 12.4% 24.4%
Other or Multiple Races 4.4% 5.2% 8.6% 5.9%
White 70.4% 61.3% 47.9% 35.8%
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 6 below compares the racial demographics in Belmont 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 Belmont 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. 178
18 Figure 6: Racial Demographics of Belmont 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 Belmont and other jurisdictions. This map demonstrates how the percentage of people of color in Belmont 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. 179
19 Figure 7: Comparing the Share of People of Color in Belmont 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. 180
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 decreased 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.

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. 181
21 3 INCOME SEGREGATION IN CITY OF BELMONT
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 VallejoFairfield 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 Belmont) 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 Belmont 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.
182
22 Figure 8: Income Dot Map of Belmont (2015)
Universe: Population.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

Note: The plot shows the income group distribution at the census block group level for City of Belmont 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 Belmont 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 Belmont.

Belmont’s isolation index of 0.527 for these residents means that the average Above Moderate-income resident in Belmont lives in a neighborhood that is 52.7% Above 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. 183
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 Belmont Belmont Bay Area
Average
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 0.173 0.243 0.269
Low-Income (50%-80% AMI) 0.146 0.125 0.145
Moderate-Income (80%-120% AMI) 0.213 0.182 0.183
Above Moderate-Income (>120% AMI) 0.513 0.527 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 Belmont compare to values in other Bay Area jurisdictions. In this chart, each dot represents a Bay Area jurisdiction. For each income 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 Belmont, 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.
184
24 Figure 9: Income Group Isolation Index Values for Belmont 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 ModerateIncome Summary Data.

Table 7 below provides the dissimilarity index values indicating the level of segregation in Belmont between residents who are lower-income (earning less than 80% of AMI) and those who are not lowerincome (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 Belmont between lower-income residents and residents who are not lower-income increased 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 dissimilarity 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. 185
25 In 2015, the income segregation in Belmont between lower-income residents and other residents was higher than the average value for Bay Area jurisdictions (See Table 7). This means that the lowerincome residents are more segregated from other residents within Belmont compared to other Jurisdictions in the region.

Table 7: Income Group Dissimilarity Index Values for Segregation within Belmont
Belmont Bay Area
Average
Income Group 2010 2015 2015
Below 80% AMI vs. Above 80% AMI 0.188 0.244 0.198
Below 50% AMI vs. Above 120% AMI 0.209 0.237 0.253
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 10 below shows how dissimilarity index values for income segregation in Belmont compare 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 Belmont, 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 segregation levels between lower-income residents and wealthier residents in their jurisdiction compared to the rest of the region.
186
26 Figure 10: Income Group Dissimilarity Index Values for Belmont 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 ModerateIncome Summary Data.

The Theil’s H Index values for neighborhood income group segregation in Belmont 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 Belmont was more than it had been in 2010.

In 2015, the Theil’s H Index value for income group segregation in Belmont was lower than the average value for Bay Area jurisdictions, indicating there is less neighborhood level income segregation in Belmont than in the average Bay Area city.

Table 8: Theil’s H Index Values for Income Segregation within Belmont Belmont Bay Area
Average
Index 2010 2015 2015
Theil's H Multi-income 0.018 0.033 0.043
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. 187
27 Figure 11 below shows how Theil’s H index values for income group segregation in Belmont 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 Belmont, 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 Belmont 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 ModerateIncome Summary Data.

3.2 Regional Income Segregation (between Belmont 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 differences between jurisdictions in the region. Figure 12 below presents an income dot map showing the spatial distribution of income groups in Belmont as well as in nearby Bay Area jurisdictions. 188
28 Figure 12: Income Dot Map of Belmont and Surrounding Areas (2015) Universe: Population.

Source: U.S. Department of Housing and Urban Development, American Community Survey 5-Year 2011-2015 Low- and ModerateIncome Summary Data.

Note: The plot shows the income group distribution at the census block group level for City of Belmont and vicinity. Dots in each block group are randomly placed and should not be construed as actual placement of individuals.

When looking at income segregation between jurisdictions in the Bay Area, one can examine how Belmont differs from the region. The income demographics in Belmont 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, Belmont had a lower share of very low-income residents than the Bay Area as a whole, a lower share of low-income residents, a similar share of moderate-income residents, and a higher share of above moderate-income residents.

Table 9: Population by Income Group, Belmont and the Region Belmont Bay Area
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 15.7% 21.99% 28.7%
Low-Income (50%-80% AMI) 13.56% 10.7% 14.3%
Moderate-Income (80%-120% AMI) 21.12% 17.12% 17.6%
Above Moderate-Income (>120% AMI) 49.62% 50.19% 39.4%
189
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 Belmont to other Bay Area 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 Belmont 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 Belmont 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 ModerateIncome 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. 190
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 jurisdiction 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.

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. 191
31 4 APPENDIX 1: SUMMARY OF FINDINGS

4.1 Segregation in City of Belmont
• 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 Belmont, 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 Belmont the highest level of racial segregation is between Black and white residents.16 However, local jurisdiction staff should note that this dissimilarity index value is not a reliable data point due to small population size. • According to the Theil’s H-Index, neighborhood racial segregation in Belmont declined between 2010 and 2020. Neighborhood income segregation increased between 2010 and 2015. • Above Moderate-income residents are the most segregated compared to other income groups in Belmont. 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 increased between 2010 and 2015. In 2015, the income segregation in Belmont between lower-income residents and other residents was higher than the average value for Bay Area jurisdictions.

4.2 Segregation Between City of Belmont and Other jurisdictions in the Bay Area Region
• Belmont has a higher share of white residents than other jurisdictions in the Bay Area as a whole, a lower share of Latinx residents, a lower share of Black residents, and a higher 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. 192
32 • Regarding income groups, Belmont has a lower share of very low-income residents than other jurisdictions in the Bay Area as a whole, a lower share of low-income residents, a similar share of moderate-income residents, and a higher share of above moderate-income residents. 193
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 Belmont Belmont Bay Area
Average
Index Race 2000 2010 2020 2020
Isolation
Asian/Pacific Islander 0.166 0.215 0.302 0.245
Black/African American 0.016 0.018 0.014 0.053
Latinx 0.092 0.124 0.130 0.251
White 0.712 0.624 0.485 0.491
Dissimilarity
Asian/Pacific Islander vs. White 0.161 0.156 0.088 0.185 Black/African American vs. White 0.151* 0.175* 0.165* 0.244 Latinx vs. White 0.151 0.142 0.119 0.207
People of Color vs. White 0.141 0.139 0.087 0.168
Theil's H Multi-racial All 0.014 0.015 0.007 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. 194
34 Table 12: Neighborhood Income Segregation Levels in Belmont Belmont Bay Area
Average
Index Income Group 2010 2015 2015
Isolation
Very Low-Income (<50% AMI) 0.173 0.243 0.269
Low-Income (50%-80% AMI) 0.146 0.125 0.145
Moderate-Income (80%-120% AMI) 0.213 0.182 0.183
Above Moderate-Income (>120% AMI) 0.513 0.527 0.507
Dissimilarity
Below 80% AMI vs. Above 80% AMI 0.188 0.244 0.198
Below 50% AMI vs. Above 120% AMI 0.209 0.237 0.253
Theil's H Multi-racial All 0.018 0.033 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. 195
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.

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.

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.

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. 196
36 Table 15: Population by Racial Group, Belmont and the Region Belmont Bay Area
Race 2000 2010 2020 2020
Asian/Pacific Islander 15.3% 20.44% 29.86% 35.8%
Black/African American 1.55% 1.56% 1.23% 5.6%
Latinx 8.32% 11.52% 12.43% 28.2%
Other or Multiple Races 4.4% 5.21% 8.58% 24.4%
White 70.44% 61.28% 47.9% 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, Belmont and the Region Belmont Bay Area
Income Group 2010 2015 2015
Very Low-Income (<50% AMI) 15.7% 21.99% 28.7%
Low-Income (50%-80% AMI) 13.56% 10.7% 14.3%
Moderate-Income (80%-120% AMI) 21.12% 17.12% 17.6%
Above Moderate-Income (>120% AMI) 49.62% 50.19% 39.4%
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. 197
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.
198
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State Fair Housing Laws and Regulations
This appendix summarizes key state laws and regulations related to mitigating housing discrimination and expanding housing choice.

California Fair Employment and Housing Act (FEHA) (Part 2.8 (commencing with Section 12900) of Division 3 of Title 2) is the state fair housing law that prohibits those engaged in the housing business—landlords, real estate agents, home sellers, builders, mortgage lenders, and others—from discriminating against tenants or homeowners.

California law protects individuals from illegal discrimination by housing providers based on:
 Race, color
 Ancestry, national origin
 Citizenship, immigration status
 Primary language
 Age
 Religion
 Disability, mental or physical
 Sex, gender
 Gender identity, gender expression
 Marital status
 Familial status
 Source of income
 Military or veteran status
Government Code section 65008. Covers actions of a city, county, city and county, or other local government agency, and makes those actions null and void if the action denies an individual or group of individuals the enjoyment of residence, landownership, tenancy, or other land use in the state because of membership in a protected class, the method of financing, and/or the intended occupancy.
 For example, a violation under Government Code section 65008 may occur if a jurisdiction applied more scrutiny to reviewing and approving an affordable 199
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development as compared to market-rate developments, or multifamily housing as compared to single family homes.
 Government Code section 65008, subdivision (e), authorizes preferential treatment of affordable housing
Government Code section 8899.50 requires all public agencies to administer programs and activities relating to housing and community development in a manner to affirmatively further fair housing and avoid any action that is materially inconsistent with its obligation to affirmatively further fair housing.

Government Code section 11135 et seq. requires full and equal access to all programs and activities operated, administered, or funded with financial assistance from the state, regardless of one’s membership or perceived membership in a protected class.

Density Bonus Law (Gov. Code, section 65915) requires California jurisdictions to adopt ordinances that specify how density bonuses will be offered to incentivize affordable housing. The state law contains the minimum specifications for density bonuses.

Housing Accountability Act (Gov. Code, section 65589.5) prohibits local agencies from disapproving housing developments, including farmworker housing and emergency shelters, or requiring conditions that make such housing infeasible except under certain conditions specified in the law.

No-Net-Loss Law (Gov. Code, section 65863) is meant to ensure that development opportunities remain available throughout a jurisdiction’s regional housing need allocation (RHNA) period, especially for low and moderate income households. It prohibits jurisdictions from lowering residential densities without substantial evidence.

Least Cost Zoning Law (Gov. Code, section 65913.1) requires jurisdictions to designate and zone sufficient vacant land for residential use with sufficient standards in relation to growth projections.

Excessive subdivision standards (Gov. Code, section 65913.2) prohibits jurisdictions from imposing design criteria that make residential development infeasible.

Limits on growth controls (Gov. Code, section 65302.8) describes how flood plains are used in comprehensive planning and zoning.

Housing Element Law (Gov. Code, section 65583, esp. subds. (c) (5), (c) (10) governs state-required housing elements.
200
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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/
Legal Aid
Society of San
Mateo County
San Mateo County
330 Twin Dolphin
Drive, Suite 123,
Redwood City, CA
94065
(650) 558-0915 https://www.legalaidsmc.org/h
ousing-resources
Community
Legal 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
Service Area Address Phone Website
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%
2017-2021 Total
2017 2018 2019 2020 2021 Cases % of Total
201
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Figure I-3.

HCD Fair Housing Inquiries by
City, January 2013-March 2021
Note:

Atherton, Brisbane, Colma, Hillsborough, Millbrae, and
Portola Valley had no inquiries during this time.

Source:

California Department of Housing and Community
Development
202
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Figure I-4.

FHEO Inquiries by City to HCD, Belmont, 2013-2021
Source: California Department of Housing and Community Development AFFH Data Viewer 203
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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
Belmont 2 0 1 0 0 0 0 6 9
Brisbane 0 0 0 0 0 0 0 0 0
Burlingame 3 0 2 0 0 0 0 1 6
Colma 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
Disability Race Total
Familial
Status
National
Origin Religion Sex Color
None
Cited
204
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Figure I-6.

Public Housing Buildings, San Mateo County
Source: California Department of Housing and Community Development AFFH Data Viewer 205
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Figure I-7.

Housing Choice Vouchers by Census Tract
Source: California Department of Housing and Community Development AFFH Data Viewer 206
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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, Belmont, 2000-2019
Source: ABAG Housing Needs Data Workbook
207
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Figure II-3.

Senior and Youth Population by Race, Belmont, 2000-2019 Source: ABAG Housing Needs Data Workbook
Figure II-4.

Area Median Income by Race and Ethnicity, Belmont, 2019 Source: ABAG Housing Needs Data Workbook
208
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Figure II-5.

Poverty Rate by Race and Ethnicity, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
209
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Figure II-6.
% Non-White Population by Census Block Groups, 2018
Source: California Department of Housing and Community Development AFFH Data Viewer 210
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Figure II-7.

White Majority Census Tracts
Source: California Department of Housing and Community Development AFFH Data Viewer 211
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 212
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Figure II-9.

Hispanic Majority Census Tracts
Source: California Department of Housing and Community Development AFFH Data Viewer 213
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Figure II-10.

Neighborhood Segregation by Census Tract, 2019
Source: California Department of Housing and Community Development AFFH Data Viewer 214
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Figure II-11.

Diversity Index by Block Group, 2010
Source: California Department of Housing and Community Development AFFH Data Viewer 215
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Figure II-12.

Diversity Index by Block Group, 2018
Source: California Department of Housing and Community Development AFFH Data Viewer 216
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Disability status.

Figure II-13.

Share of Population by Disability Status, 2019
Source: ABAG Housing Needs Data Workbook
217
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Figure II-14.
% of Population with a Disability by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer 218
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 19
Familial status.

Figure II-15.

Age Distribution, Belmont, 2000-2019
Source: ABAG Housing Needs Data Workbook
Figure II-16.

Share of Households by Size, 2019
Source: ABAG Housing Needs Data Workbook
219
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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
220
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Figure II-19.

Housing Type by Tenure, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
Figure II-20.

Housing Units by Number of Bedrooms and Tenure, Belmont, 2019 Source: ABAG Housing Needs Data Workbook
221
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Figure II-21.
% of Children in Married Couple Households by Census Tract, 2019 222
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Source: California Department of Housing and Community Development AFFH Data Viewer Figure II-22. [legend missing in HCD provided map]
% Households with Single Female with Children by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer 223
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Figure II-23. [legend missing in HCD provided map]
% of Married Couple Households by Census Tract, 2019
Source: California Department of Housing and Community Development AFFH Data Viewer 224
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Figure II-24. [legend missing in HCD provided map]
% of Adults Living Alone by Census Tract, 2019
Source: California Department of Housing and Community Development AFFH Data Viewer 225
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
226
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 227
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 228
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 229
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 30
Figure II-29.

R/ECAPs and Edge 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). Edge R/ECAPs are census tracts that have a non230 ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 31
white population of 50 percent or more (majority-minority) AND the poverty rate is two times the average tract poverty rate for the County (13% in 2010).

Source: California Department of Housing and Community Development AFFH Data Viewer Figure II-30.

R/ECAPs and Edge R/ECAPs, 2019
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). Edge R/ECAPs are census tracts that have a non231 ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 32
white population of 50 percent or more (majority-minority) AND the poverty rate is two times the average tract poverty rate for the County (12.8% in 2019).

Source: California Department of Housing and Community Development AFFH Data Viewer SECTION III. Access to Opportunity
Education
[Available December 2021] Appendix item: Access to education supplement—findings from a countywide analysis of access to education and educational outcomes by protected class. 232
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 33
Figure III-1.

TCAC Opportunity Areas Education Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer 233
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 34
Employment
Figure III-2.

Jobs by Industry, Belmont, 2002-2018
Source: ABAG Housing Needs Data Workbook
234
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 35
Figure III-3.

Job Holders by Industry, Belmont, 2002-2018
Source: ABAG Housing Needs Data Workbook
Figure III-4.

Jobs to Household Ratio, Belmont, 2002-2018
Source: ABAG Housing Needs Data Workbook
235
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 36
Figure III-5.

Jobs to Worker Ratio by Wage, Belmont, 2002-2018
Source: ABAG Housing Needs Data Workbook
Figure III-6.

Unemployment Rate, 2010-2021
Source: ABAG Housing Needs Data Workbook
236
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 37
Figure III-7.

TCAC Opportunity Areas Economic Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer 237
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 38
Figure III-8.

Jobs Proximity Index by Block Group, 2017
Source: California Department of Housing and Community Development AFFH Data Viewer 238
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 39
Transportation
[TCAC’s transportation opportunity score and maps were not available at the time of this report]
Environment
239
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Figure III-9.

TCAC Opportunity Areas Environmental Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer 240
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 41
Figure III-10.

CalEnviroScreen by Census Tract, 2021
Source: California Department of Housing and Community Development AFFH Data Viewer 241
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 42
Figure III-11.

Healthy Places Index by Census Tract, 2021
Source: California Department of Housing and Community Development AFFH Data Viewer 242
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 43
Patterns in disparities in access to opportunity.

Figure III-12.

Population Living in Moderate and High Resource Ares by Race and Ethnicity, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
Figure III-13.

Population with Limited English Proficiency, Belmont, 2019 Source: ABAG Housing Needs Data Workbook
243
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 44
Figure III-14.

TCAC Opportunity Areas Composite Score by Census Tract, 2021 Source: California Department of Housing and Community Development AFFH Data Viewer 244
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 45
Figure III-15.

Social Vulnerability Index by Census Tract, 2018
Source: California Department of Housing and Community Development AFFH Data Viewer 245
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 46
Figure III-16.

SB 535 Disadvantaged Communities
Source: California Department of Housing and Community Development AFFH Data Viewer 246
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 47
Disparities in access to opportunity for persons with disabilities.

Figure III-17.

Population by Disability Status, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
Figure III-18.

Disability by Type for the Non-Institutionalized Population 18 Years and Over, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
247
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 48
Figure III-19.

Disability by Type for Seniors (65 years and over), Belmont, 2019 Source: ABAG Housing Needs Data Workbook
Figure III-20.

Employment by Disability Status, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
248
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Figure III-21.

Share of Population with a Disability by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer 249
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Figure III-22
[PLACEHOLDER] San Mateo County Housing Policies and Programs Analysis
[Updated Matrix Available December 2021]
Source: ABAG.
250
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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, Belmont,
2015-2019
Source:

ABAG Housing Needs Data
Workbook
251
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Figure IV-3.

Housing Units by Year
Built, Belmont
Source:

ABAG Housing Needs Data Workbook
Figure IV-4.

Distribution of Home Value for Owner Occupied Units, 2019 Source: ABAG Housing Needs Data Workbook
252
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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
253
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Figure IV-7.

Median Contract Rent, 2009-2019
Source: ABAG Housing Needs Data Workbook
Cost burden and severe cost burden.

Figure IV-8.

Overpayment (Cost Burden) by Jurisdiction, 2019
Source: ABAG Housing Needs Data Workbook
254
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 55
Figure IV-9.

Overpayment (Cost Burden) by Tenure, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
Figure IV-10.

Overpayment (Cost Burden) by Area Median Income (AMI), Belmont, 2019 Source: ABAG Housing Needs Data Workbook
255
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Figure IV-11.

Overpayment (Cost Burden) by Race and Ethnicity, Belmont, 2019 Source: ABAG Housing Needs Data Workbook
Figure IV-12.

Overpayment (Cost Burden) by Family Size, Belmont, 2019 Source: ABAG Housing Needs Data Workbook
256
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Figure IV-13.

Overpayment (Cost Burden) for Renter Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer 257
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 58
Figure IV-14.

Overpayment (Cost Burden) for Owner Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer 258
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 59
Overcrowding.

Figure IV-15.

Occupants per Room by Jurisdiction, 2019
Source: ABAG Housing Needs Data Workbook
Figure IV-16.

Occupants per Room by Tenure, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
259
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 60
Figure IV-17.

Overcrowding by Race and Ethnicity, Belmont, 2019
Note: Overcrowding is indicated by more than 1 person per room.

Source: ABAG Housing Needs Data Workbook
Figure IV-18.

Occupants per Room by AMI, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
260
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Figure IV-19.

Overcrowded Households by Census Tract, 2019
Source: California Department of Housing and Community Development AFFH Data Viewer 261
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 62
Substandard housing.

Figure IV-20.

Percent of Units Lacking Complete Kitchen and Plumbing Facilities, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
Homelessness.

Figure IV-21.

Homelessness by
Household Type
and Shelter Status,
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
262
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Figure IV-22.

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

Share of General and Homeless Populations by Ethnicity, San Mateo County, 2019
Source: ABAG Housing Needs Data Workbook
263
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 64
Figure IV-24.

Characteristics of the Population Experiencing Homelessness, San Mateo County, 2019
Source: ABAG Housing Needs Data Workbook
Displacement.

Figure IV-25.

Location of Population One Year Ago, Belmont, 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
Chronic
Substance Abuse HIV/AIDS
Severely
Mentally Ill Veterans
Victims of Domestic
Violence
264
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 65
Figure IV-26.

Tenure by Year Moved to Current Residence, Belmont, 2019 Source: ABAG Housing Needs Data Workbook
Figure IV-27.

Assisted Units at Risk of Conversion, Belmont, 2019
Source: ABAG Housing Needs Data Workbook
265
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 266
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 267
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 268
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 269
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 70
Other considerations.

Figure IV-32.

Mortgage Applications by Race and Ethnicity, Belmont, 2018-2019 Source: ABAG Housing Needs Data Workbook
Figure IV-33.

Mortgage Application Denial Rate by Race and Ethnicity, Belmont, 2018- 270
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 71
2019
Source: ABAG Housing Needs Data Workbook
271