Atherton City Council
January 18, 2023

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.

1 HOUSING NEEDS DATA REPORT:

ATHERTON
ABAG/MTC Staff and Baird + Driskell Community Planning
2021-04-02

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 .............................................................................................................. 5 2 Summary of Key Facts .................................................................................................. 6 3 Looking to the Future: Regional Housing Needs .................................................................. 9

3.1 Regional Housing Needs Determination......................................................................... 9

3.2 Regional Housing Needs Allocation .............................................................................. 9 4 Population, Employment and Household Characteristics ..................................................... 11

4.1 Population ..........................................................................................................11

4.2 Age...................................................................................................................12

4.3 Race and Ethnicity ................................................................................................14

4.4 Employment Trends ...............................................................................................15

4.4.1 Balance of Jobs and Workers...............................................................................15

4.4.2 Sector Composition ..........................................................................................19

4.4.3 Unemployment ...............................................................................................20

4.5 Extremely Low-Income Households.............................................................................21

4.6 Tenure...............................................................................................................25

4.7 Displacement .......................................................................................................29 5 Housing Stock Characteristics ...................................................................................... 31

5.1 Housing Types, Year Built, Vacancy, and Permits............................................................31

5.2 Assisted Housing Developments At-Risk of Conversion......................................................34

5.3 Substandard Housing..............................................................................................35

5.4 Home and Rent Values ............................................................................................35

5.5 Overpayment and Overcrowding ................................................................................39 6 Special Housing Needs................................................................................................ 48

6.1 Large Households ..................................................................................................48

6.2 Female-Headed Households......................................................................................49

6.3 Seniors...............................................................................................................51

6.4 People with Disabilities ..........................................................................................52

6.5 Homelessness.......................................................................................................54

6.6 Farmworkers .......................................................................................................59

6.7 Non-English Speakers .............................................................................................60

3 0.2 List of figures
Figure 1: Population Growth Trends .......................................................................................12 Figure 2: Population by Age, 2000-2019...................................................................................13 Figure 3: Senior and Youth Population by Race ..........................................................................14 Figure 4: Population by Race, 2000-2019 .................................................................................15 Figure 5: Jobs in a Jurisdiction .............................................................................................16 Figure 6: Workers by Earnings, by Jurisdiction as Place of Work and Place of Residence.........................17 Figure 7: Jobs-Worker Ratios, By Wage Group ...........................................................................18 Figure 8: Jobs-Household Ratio .............................................................................................19 Figure 9: Resident Employment by Industry ..............................................................................20 Figure 10: Unemployment Rate.............................................................................................21 Figure 11: Households by Household Income Level......................................................................23 Figure 12: Household Income Level by Tenure ...........................................................................24 Figure 13: Poverty Status by Race..........................................................................................25 Figure 14: Housing Tenure...................................................................................................26 Figure 15: Housing Tenure by Race of Householder .....................................................................27 Figure 16: Housing Tenure by Age..........................................................................................28 Figure 17: Housing Tenure by Housing Type ..............................................................................29 Figure 18: Households by Displacement Risk and Tenure...............................................................30 Figure 19: Housing Type Trends ............................................................................................31 Figure 20: Housing Units by Year Structure Built ........................................................................32 Figure 21: Vacant Units by Type............................................................................................33 Figure 22: Home Values of Owner-Occupied Units ......................................................................36 Figure 23: Zillow Home Value Index (ZHVI)...............................................................................37 Figure 24: Contract Rents for Renter-Occupied Units...................................................................38 Figure 25: Median Contract Rent ...........................................................................................39 Figure 26: Cost Burden by Tenure ..........................................................................................40 Figure 27: Cost Burden by Income Level ..................................................................................41 Figure 28: Cost Burden by Race.............................................................................................42 Figure 29: Cost Burden by Household Size ................................................................................43 Figure 30: Cost-Burdened Senior Households by Income Level ........................................................44 Figure 31: Overcrowding by Tenure and Severity........................................................................45 Figure 32: Overcrowding by Income Level and Severity ................................................................46 Figure 33: Overcrowding by Race...........................................................................................47 Figure 34: Household Size by Tenure ......................................................................................48 Figure 35: Housing Units by Number of Bedrooms .......................................................................49 Figure 36: Household Type ..................................................................................................50 Figure 37: Female-Headed Households by Poverty Status ..............................................................51 Figure 38: Senior Households by Income and Tenure....................................................................52 Figure 39: Disability by Type ................................................................................................53 Figure 40: Homelessness by Household Type and Shelter Status, San Mateo County ..............................55 Figure 41: Racial Group Share of General and Homeless Populations, San Mateo County.........................56 Figure 42: Latinx Share of General and Homeless Populations, San Mateo County.................................57 Figure 43: Characteristics for the Population Experiencing Homelessness, San Mateo County ...................58 Figure 44: Farm Operations and Farm Labor by County, San Mateo County .........................................60 Figure 45: Population with Limited English Proficiency ................................................................61

0.3 List of tables
Table 1: Illustrative Regional Housing Needs Allocation from Draft Methodology ..................................10

4 Table 2: Population Growth Trends ........................................................................................11 Table 3: Housing Permitting.................................................................................................33 Table 4: Assisted Units at Risk of Conversion.............................................................................34 Table 5: Population with Developmental Disabilities by Age ..........................................................54 Table 6: Population with Developmental Disabilities by Residence ..................................................54 Table 7: Students in Local Public Schools Experiencing Homelessness ...............................................59 Table 8: Migrant Worker Student Population .............................................................................59

5 1 INTRODUCTION
The Bay Area continues to see growth in both population and jobs, which means more housing of various types and sizes is needed to ensure that residents across all income levels, ages, and abilities have a place to call home. While the number of people drawn to the region over the past 30 years has steadily increased, housing production has stalled, contributing to the housing shortage that communities are experiencing today. In many cities, this has resulted in residents being priced out, increased traffic congestion caused by longer commutes, and fewer people across incomes being able to purchase homes or meet surging rents.

The 2023-2031 Housing Element Update provides a roadmap for how to meet our growth and housing challenges. Required by the state, the Housing Element identifies what the existing housing conditions and community needs are, reiterates goals, and creates a plan for more housing. The Housing Element is an integral part of the General Plan, which guides the policies of Atherton.

6 2 SUMMARY OF KEY FACTS
• Population – Generally, the population of the Bay Area continues to grow because of natural growth and because the strong economy draws new residents to the region. The population of Atherton decreased by 2.3% from 2000 to 2020, which is below the growth rate of the Bay Area. • Age – In 2019, Atherton’s youth population under the age of 18 was 1,596 and senior population 65 and older was 1,604. These age groups represent 22.3% and 22.4%, respectively, of Atherton’s population.
• Race/Ethnicity – In 2020, 69.3% of Atherton’s population was White while 0.8% was African American, 21.0% was Asian, and 4.4% was Latinx. People of color in Atherton comprise a proportion below the overall proportion in the Bay Area as a whole.1 • Employment – Atherton residents most commonly work in the Financial & Professional Services industry. From January 2010 to January 2021, the unemployment rate in Atherton decreased by

3.1 percentage points. Since 2010, the number of jobs located in the jurisdiction increased by 560 (28.2%). Additionally, the jobs-household ratio in Atherton has increased from 0.82 in 2002 to 1.14 jobs per household in 2018.
• Number of Homes – The number of new homes built in the Bay Area has not kept pace with the demand, resulting in longer commutes, increasing prices, and exacerbating issues of displacement and homelessness. The number of homes in Atherton decreased, 1.0% from 2010 to 2020, which is below the growth rate for San Mateo County and below the growth rate of the region’s housing stock during this time period.
• Home Prices – A diversity of homes at all income levels creates opportunities for all Atherton residents to live and thrive in the community.
– Ownership The largest proportion of homes had a value in the range of $2M+ in 2019.

Home prices increased by 195.7% from 2010 to 2020.
– Rental Prices – The typical contract rent for an apartment in Atherton was $3,200 in 2019. Rental prices increased by 60.3% from 2009 to 2019. To rent a typical apartment without cost burden, a household would need to make $128,320 per year.2 • Housing Type – It is important to have a variety of housing types to meet the needs of a community today and in the future. In 2020, 97.9% of homes in Atherton were single family detached, 1.8% were single family attached, 0.2% were small multifamily (2-4 units), and 0.0% were medium or large multifamily (5+ units). Between 2010 and 2020, the number of multifamily units increased more than single-family units. Generally, in Atherton, the share of the 1 The Census Bureau’s American Community Survey accounts for ethnic origin separate from racial identity. The numbers reported here use an accounting of both such that the racial categories are shown exclusive of Latinx status, to allow for an accounting of the Latinx population regardless of racial identity. 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, but occasionally when discussing US Census data, we use Hispanic or Non-Hispanic, to clearly link to the data source. 2 Note that contract rents may differ significantly from, and often being lower than, current listing prices.

7 housing stock that is detached single family homes is above that of other jurisdictions in the region.
• Cost Burden – The U.S. Department of Housing and Urban Development considers housing to be affordable for a household if the household spends less than 30% of its income on housing costs.

A household is considered “cost-burdened” if it spends more than 30% of its monthly income on housing costs, while those who spend more than 50% of their income on housing costs are considered “severely cost-burdened.” In Atherton, 13.5% of households spend 30%-50% of their income on housing, while 17.6% of households are severely cost burden and use the majority of their income for housing.
• Displacement/Gentrification – According to research from The University of California, Berkeley, 0.0% of households in Atherton live in neighborhoods that are susceptible to or experiencing displacement, and 0.0% live in areas at risk of or undergoing gentrification. 100.0% of households in Atherton live in neighborhoods where low-income households are likely excluded due to prohibitive housing costs. There are various ways to address displacement including ensuring new housing at all income levels is built. • Neighborhood – 39.6% of residents in Atherton live in neighborhoods identified as “Highest Resource” or “High Resource” areas by State-commissioned research, while 0.0% of residents live in areas identified by this research as “Low Resource” or “High Segregation and Poverty” areas. These neighborhood designations are based on a range of indicators covering areas such as education, poverty, proximity to jobs and economic opportunities, low pollution levels, and other factors.3
• Special Housing Needs – Some population groups may have special housing needs that require specific program responses, and these groups may experience barriers to accessing stable housing due to their specific housing circumstances. In Atherton, 7.8% of residents have a disability of any kind and may require accessible housing. Additionally, 12.5% of Atherton households are larger households with five or more people, who likely need larger housing units with three bedrooms or more. 5.4% of households are female-headed families, which are often at greater risk of housing insecurity.

Note on Data
Many of the tables in this report are sourced from data from the Census Bureau’s American Community Survey or U.S. Department of Housing and Urban Development’s Comprehensive Housing
Affordability Strategy (CHAS) data, both of which are samples and as such, are subject to sampling variability. This means that data is an estimate, and that other estimates could be possible if another set of respondents had been reached. We use the five-year release to get a 3 For more information on the “opportunity area” categories developed by HCD and the California Tax Credit Allocation Committee, see this website: https://www.treasurer.ca.gov/ctcac/opportunity.asp. The degree to which different jurisdictions and neighborhoods have access to opportunity will likely need to be analyzed as part of new Housing Element requirements related to affirmatively furthering fair housing. ABAG/MTC will be providing jurisdictions with technical assistance on this topic this summer, following the release of additional guidance from HCD.

8 larger data pool to minimize this “margin of error” but particularly for the smaller cities, the data will be based on fewer responses, and the information should be interpreted accordingly.

Additionally, there may be instances where there is no data available for a jurisdiction for particular data point, or where a value is 0 and the automatically generated text cannot perform a calculation. In these cases, the automatically generated text is “NODATA.” Staff should reword these sentences before using them in the context of the Housing Element or other documents.

Note on Figures
Any figure that does not specify geography in the figure name represents data for Atherton.

9 3 LOOKING TO THE FUTURE: REGIONAL HOUSING NEEDS

3.1 Regional Housing Needs Determination
The Plan Bay Area 20504 Final Blueprint forecasts that the nine-county Bay Area will add 1.4 million new households between 2015 and 2050. For the eight-year time frame covered by this Housing Element Update, the Department of Housing and Community Development (HCD) has identified the region’s housing need as 441,176 units. The total number of housing units assigned by HCD is separated into four income categories that cover housing types for all income levels, from very low-income households to market rate housing.5 This calculation, known as the Regional Housing Needs Determination (RHND), is based on population projections produced by the California Department of Finance as well as adjustments that incorporate the region’s existing housing need. The adjustments result from recent legislation requiring HCD to apply additional adjustment factors to the baseline growth projection from California Department of Finance, in order for the regions to get closer to healthy housing markets. To this end, adjustments focus on the region’s vacancy rate, level of overcrowding and the share of cost burdened households, and seek to bring the region more in line with comparable ones.6 These new laws governing the methodology for how HCD calculates the RHND resulted in a significantly higher number of housing units for which the Bay Area must plan compared to previous RHNA cycles.

3.2 Regional Housing Needs Allocation
A starting point for the Housing Element Update process for every California jurisdiction is the Regional Housing Needs Allocation or RHNA – the share of the RHND assigned to each jurisdiction by the Association of Bay Area Governments (ABAG). State Housing Element Law requires ABAG to develop a methodology that calculates the number of housing units assigned to each city and county and distributes each jurisdiction’s housing unit allocation among four affordability levels. For this RHNA cycle, the RHND increased by 135%, from 187,990 to 441,776. For more information on the RHNA process this cycle, see ABAG’s website: https://abag.ca.gov/our-work/housing/rhna-regional-housing-needsallocation Almost all jurisdictions in the Bay Area are likely to receive a larger RHNA this cycle compared to the last cycle, primarily due to changes in state law that led to a considerably higher RHND compared to previous cycles.

In January 2021, ABAG adopted a Draft RHNA Methodology, which is currently being reviewed by HCD.

For Atherton, the proposed RHNA to be planned for this cycle is 348 units, a slated increase from the last cycle. Please note that the previously stated figures are merely illustrative, as ABAG has yet to issue Final RHNA allocations. The Final RHNA allocations that local jurisdictions will use for their 4 Plan Bay Area 2050 is a long-range plan charting the course for the future of the nine-county San Francisco Bay Area. It covers four key issues: the economy, the environment, housing and transportation 5 HCD divides the RHND into the following four income categories:

Very Low-income: 0-50% of Area Median Income
Low-income: 50-80% of Area Median Income
Moderate-income: 80-120% of Area Median Income
Above Moderate-income: 120% or more of Area Median Income 6 For more information on HCD’s RHND calculation for the Bay Area, see this letter sent to ABAG from HCD on June 9, 2020: https://www.hcd.ca.gov/community-development/housing-element/docs/abagrhna-final060920(r).pdf

10 Housing Elements will be released at the end of 2021. The potential allocation that Atherton would receive from the Draft RHNA Methodology is broken down by income category as follows:

Table 1: Illustrative Regional Housing Needs Allocation from Draft Methodology Income Group Atherton
Units
San Mateo
County
Units
Bay Area
Units
Atherton
Percent
San Mateo
County
Percent
Bay Area
Percent
Very Low Income
(<50% of AMI) 94 12196 114442 27.0% 25.6% 25.9%
Low Income (50%-
80% of AMI) 54 7023 65892 15.5% 14.7% 14.9%
Moderate Income
(80%-120% of AMI) 56 7937 72712 16.1% 16.6% 16.5%
Above Moderate
Income (>120% of
AMI)
144 20531 188130 41.4% 43.1% 42.6%
Total 348 47687 441176 100.0% 100.0% 100.0%
Source: Association of Bay Area Governments Methodology and tentative numbers were approved by ABAG’s Executive board on January 21, 2021 (Resolution No. 02-2021). The numbers were submitted for review to California Housing and Community Development in February 2021, after which an appeals process will take place during the Summer and Fall of 2021.

THESE NUMBERS SHOULD BE CONSIDERED PRELIMINARY AND SUBJECT TO CHANGE PER HCD REVIEW

11 4 POPULATION, EMPLOYMENT AND HOUSEHOLD

CHARACTERISTICS

4.1 Population
The Bay Area is the fifth-largest metropolitan area in the nation and has seen a steady increase in population since 1990, except for a dip during the Great Recession. Many cities in the region have experienced significant growth in jobs and population. While these trends have led to a corresponding increase in demand for housing across the region, the regional production of housing has largely not kept pace with job and population growth. Since 2000, Atherton’s population has decreased by 2.3%; this rate is below that of the region as a whole, at 14.8%. In Atherton, roughly 13.8% of its population moved during the past year, a number 0.4 percentage points greater than the regional rate of 13.4%.

Table 2: Population Growth Trends
Geography 1990 1995 2000 2005 2010 2015 2020
Atherton 7163 7249 7194 7230 6914 6976 7031
San Mateo County 649623 685354 707163 719844 718451 761748 773244 Bay Area 6020147 6381961 6784348 7073912 7150739 7595694 7790537 Universe: Total population
Source: California Department of Finance, E-5 series
For more years of data, please refer to the Data Packet Workbook, Table POPEMP-01.

In 2020, the population of Atherton was estimated to be 7,031 (see Table 2). From 1990 to 2000, the population increased by 0.4%, while it decreased by 3.9% during the first decade of the 2000s. In the most recent decade, the population increased by 1.7%. The population of Atherton makes up 0.9% of San Mateo County.7
7 To compare the rate of growth across various geographic scales, Figure 1 shows population for the jurisdiction, county, and region indexed to the population in the year 1990. This means that the data points represent the population growth (i.e. percent change) in each of these geographies relative to their populations in 1990.

12 Figure 1: Population Growth Trends
Source: California Department of Finance, E-5 series Note: The data shown on the graph represents population for the jurisdiction, county, and region indexed to the population in the first year shown. The data points represent the relative population growth in each of these geographies relative to their populations in that year.

For some jurisdictions, a break may appear at the end of each decade (1999, 2009) as estimates are compared to census counts.

DOF uses the decennial census to benchmark subsequent population estimates.

For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-01.

4.2 Age
The distribution of age groups in a city shapes what types of housing the community may need in the near future. An increase in the older population may mean there is a developing need for more senior housing options, while higher numbers of children and young families can point to the need for more family housing options and related services. There has also been a move by many to age-in-place or downsize to stay within their communities, which can mean more multifamily and accessible units are also needed.

In Atherton, the median age in 2000 was 45.2; by 2019, this figure had increased, landing at around 47 years. More specifically, the population of those under 14 has increased since 2010, while the 65-andover population has increased (see Figure 2).

13 Figure 2: Population by Age, 2000-2019
Universe: Total population
Source: U.S. Census Bureau, Census 2000 SF1, Table P12; U.S. Census Bureau, Census 2010 SF1, Table P12; U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B01001 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-04.

Looking at the senior and youth population by race can add an additional layer of understanding, as families and seniors of color are even more likely to experience challenges finding affordable housing.

People of color8 make up 11.2% of seniors and 38.7% of youth under 18 (see Figure 3). 8 Here, we count all non-white racial groups

14 Figure 3: Senior and Youth Population by Race
Universe: Total population
Notes: In the sources for this table, the Census Bureau does not disaggregate racial groups by Hispanic/Latinx ethnicity, and an overlapping category of Hispanic / non-Hispanic groups has not been shown to avoid double counting in the stacked bar chart.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B01001(A-G) For the data table behind this figure, please refer to the Data Packet Workbook, Table SEN-02.

4.3 Race and Ethnicity
Understanding the racial makeup of a city and region is important for designing and implementing effective housing policies and programs. These patterns are shaped by both market factors and government actions, such as exclusionary zoning, discriminatory lending practices and displacement that has occurred over time and continues to impact communities of color today 9 . Since 2000, the
percentage of residents in Atherton identifying as White has decreased – and by the same token the percentage of residents of all other races and ethnicities has increased – by 16.3 percentage points, with the 2019 population standing at 4,966 (see Figure 4). In absolute terms, the Asian / API, NonHispanic population increased the most while the White, Non-Hispanic population decreased the most. 9 See, for example, Rothstein, R. (2017). The color of law : a forgotten history of how our government segregated America. New York, NY & London, UK: Liveright Publishing.

15 Figure 4: Population by Race, 2000-2019
Universe: Total population
Notes: Data for 2019 represents 2015-2019 ACS estimates. The Census Bureau defines Hispanic/Latinx ethnicity separate from racial categories. For the purposes of this graph, the “Hispanic or Latinx” racial/ethnic group represents those who identify as having Hispanic/Latinx ethnicity and may also be members of any racial group. All other racial categories on this graph represent those who identify with that racial category and do not identify with Hispanic/Latinx ethnicity.

Source: U.S. Census Bureau, Census 2000, Table P004; U.S. Census Bureau, American Community Survey 5-Year Data (2015- 2019), Table B03002
For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-02.

4.4 Employment Trends

4.4.1 Balance of Jobs and Workers
A city houses employed residents who either work in the community where they live or work elsewhere in the region. Conversely, a city may have job sites that employ residents from the same city, but more often employ workers commuting from outside of it. Smaller cities typically will have more employed residents than jobs there and export workers, while larger cities tend to have a surplus of jobs and import workers. To some extent the regional transportation system is set up for this flow of workers to the region’s core job centers. At the same time, as the housing affordability crisis has illustrated, local imbalances may be severe, where local jobs and worker populations are out of sync at a sub-regional scale.

One measure of this is the relationship between workers and jobs. A city with a surplus of workers “exports” workers to other parts of the region, while a city with a surplus of jobs must conversely “import” them. Between 2002 and 2018, the number of jobs in Atherton increased by 29.3% (see Figure 5).

16 Figure 5: Jobs in a Jurisdiction
Universe: Jobs from unemployment insurance-covered employment (private, state and local government) plus United States Office of Personnel Management-sourced Federal employment Notes: The data is tabulated by place of work, regardless of where a worker lives. The source data is provided at the census block level. These are crosswalked to jurisdictions and summarized.

Source: U.S. Census Bureau, Longitudinal Employer-Household Dynamics, Workplace Area Characteristics (WAC) files, 2002-2018 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-11.

There are 2,870 employed residents, and 2,726 jobs10 in Atherton - the ratio of jobs to resident workers is 0.95; Atherton is a net exporter of workers.

Figure 6 shows the balance when comparing jobs to workers, broken down by different wage groups, offering additional insight into local dynamics. A community may offer employment for relatively lowincome workers but have relatively few housing options for those workers - or conversely, it may house residents who are low wage workers but offer few employment opportunities for them. Such relationships may cast extra light on potentially pent-up demand for housing in particular price categories. A relative surplus of jobs relative to residents in a given wage category suggests the need to import those workers, while conversely, surpluses of workers in a wage group relative to jobs means the community will export those workers to other jurisdictions. Such flows are not inherently bad, though over time, sub-regional imbalances may appear. Atherton has more low-wage jobs than lowwage residents (where low-wage refers to jobs paying less than $25,000). At the other end of the wage 10 Employed residents in a jurisdiction is counted by place of residence (they may work elsewhere) while jobs in a jurisdiction are counted by place of work (they may live elsewhere). The jobs may differ from those reported in Figure 5 as the source for the time series is from administrative data, while the cross-sectional data is from a survey.

17 spectrum, the city has more high-wage residents than high-wage jobs (where high-wage refers to jobs paying more than $75,000) (see Figure 6).11
Figure 6: Workers by Earnings, by Jurisdiction as Place of Work and Place of Residence
Universe: Workers 16 years and over with earnings
Source: U.S. Census Bureau, American Community Survey 5-Year Data 2015-2019, B08119, B08519 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-10.

Figure 7 shows the balance of a jurisdiction’s resident workers to the jobs located there for different wage groups as a ratio instead - a value of 1 means that a city has the same number of jobs in a wage group as it has resident workers - in principle, a balance. Values above 1 indicate a jurisdiction will need to import workers for jobs in a given wage group. At the regional scale, this ratio is 1.04 jobs for each worker, implying a modest import of workers from outside the region (see Figure 7). 11 The source table is top-coded at $75,000, precluding more fine grained analysis at the higher end of the wage spectrum.

18 Figure 7: Jobs-Worker Ratios, By Wage Group
Universe: Jobs in a jurisdiction from unemployment insurance-covered employment (private, state and local government) plus United States Office of Personnel Management-sourced Federal employment Notes: The ratio compares job counts by wage group from two tabulations of LEHD data: Counts by place of work relative to counts by place of residence. See text for details.

Source: U.S. Census Bureau, Longitudinal Employer-Household Dynamics, Workplace Area Characteristics (WAC) files (Jobs); Residence Area Characteristics (RAC) files (Employed Residents), 2010-2018 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-14.

Such balances between jobs and workers may directly influence the housing demand in a community.

New jobs may draw new residents, and when there is high demand for housing relative to supply, many workers may be unable to afford to live where they work, particularly where job growth has been in relatively lower wage jobs. This dynamic not only means many workers will need to prepare for long commutes and time spent on the road, but in the aggregate it contributes to traffic congestion and time lost for all road users.

If there are more jobs than employed residents, it means a city is relatively jobs-rich, typically also with a high jobs to household ratio. Thus bringing housing into the measure, the jobs-household ratio in Atherton has increased from 0.82 in 2002, to 1.14 jobs per household in 2018 (see Figure 8).

19 Figure 8: Jobs-Household Ratio
Universe: Jobs in a jurisdiction from unemployment insurance-covered employment (private, state and local government) plus United States Office of Personnel Management-sourced Federal employment; households in a jurisdiction Notes: The data is tabulated by place of work, regardless of where a worker lives. The source data is provided at the census block level. These are crosswalked to jurisdictions and summarized. The ratio compares place of work wage and salary jobs with households, or occupied housing units. A similar measure is the ratio of jobs to housing units. However, this jobs-household ratio serves to compare the number of jobs in a jurisdiction to the number of housing units that are actually occupied. The difference between a jurisdiction’s jobs-housing ratio and jobs-household ratio will be most pronounced in jurisdictions with high vacancy rates, a high rate of units used for seasonal use, or a high rate of units used as short-term rentals.

Source: U.S. Census Bureau, Longitudinal Employer-Household Dynamics, Workplace Area Characteristics (WAC) files (Jobs), 2002-2018; California Department of Finance, E-5 (Households) For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-13.

4.4.2 Sector Composition
In terms of sectoral composition, the largest industry in which Atherton residents work is Financial & Professional Services, and the largest sector in which San Mateo residents work is Health & Educational Services (see Figure 9). For the Bay Area as a whole, the Health & Educational Services industry employs the most workers.

20 Figure 9: Resident Employment by Industry
Universe: Civilian employed population age 16 years and over Notes: The data displayed shows the industries in which jurisdiction residents work, regardless of the location where those residents are employed (whether within the jurisdiction or not). Categories are derived from the following source tables:

Agriculture & Natural Resources: C24030_003E, C24030_030E; Construction: C24030_006E, C24030_033E; Manufacturing, Wholesale & Transportation: C24030_007E, C24030_034E, C24030_008E, C24030_035E, C24030_010E, C24030_037E; Retail:

C24030_009E, C24030_036E; Information: C24030_013E, C24030_040E; Financial & Professional Services: C24030_014E, C24030_041E, C24030_017E, C24030_044E; Health & Educational Services: C24030_021E, C24030_024E, C24030_048E, C24030_051E; Other: C24030_027E, C24030_054E, C24030_028E, C24030_055E Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table C24030 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-06.

4.4.3 Unemployment
In Atherton, there was a 3.1 percentage point decrease in the unemployment rate between January 2010 and January 2021. Jurisdictions through the region experienced a sharp rise in unemployment in 2020 due to impacts related to the COVID-19 pandemic, though with a general improvement and recovery in the later months of 2020.

21 Figure 10: Unemployment Rate
Universe: Civilian noninstitutional population ages 16 and older Notes: Unemployment rates for the jurisdiction level is derived from larger-geography estimates. This method assumes that the rates of change in employment and unemployment are exactly the same in each sub-county area as at the county level. If this assumption is not true for a specific sub-county area, then the estimates for that area may not be representative of the current economic conditions. Since this assumption is untested, caution should be employed when using these data. Only not seasonallyadjusted labor force (unemployment rates) data are developed for cities and CDPs.

Source: California Employment Development Department, Local Area Unemployment Statistics (LAUS), Sub-county areas monthly updates, 2010-2021.

For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-15.

4.5 Extremely Low-Income Households
Despite the economic and job growth experienced throughout the region since 1990, the income gap has continued to widen. California is one of the most economically unequal states in the nation, and the Bay Area has the highest income inequality between high- and low-income households in the state12.

In Atherton, 78.9% of households make more than 100% of the Area Median Income (AMI)13, compared to

6.5% making less than 30% of AMI, which is considered extremely low-income (see Figure 11). 12 Bohn, S.et al. 2020. Income Inequality and Economic Opportunity in California. Public Policy Institute of California.
13 Income groups are based on HUD calculations for Area Median Income (AMI). HUD calculates the AMI for different metropolitan areas, and the nine county Bay Area includes the following metropolitan areas: Napa Metro Area (Napa County), Oakland-Fremont Metro Area (Alameda and Contra Costa Counties), San Francisco Metro Area (Marin, San Francisco, and San Mateo Counties), San Jose-Sunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo-Fairfield Metro Area (Solano County). The AMI levels in this chart are based on the HUD metro area where this jurisdiction is located. Households making between 80 and 120 percent of the AMI are moderate-income, those making 50 to 80 percent are low-income, those making 30 to 50

22 Regionally, more than half of all households make more than 100% AMI, while 15% make less than 30% AMI. In San Mateo County, 30% AMI is the equivalent to the annual income of $44,000 for a family of four. Many households with multiple wage earners – including food service workers, full-time students, teachers, farmworkers and healthcare professionals – can fall into lower AMI categories due to relatively stagnant wages in many industries.

Note on Estimating the Projected Number of Extremely Low-Income Households Local jurisdictions are required to provide an estimate for their projected extremely low-income households in their Housing Elements. HCD’s official Housing Element guidance notes that jurisdictions can use their RHNA for very low-income households (those making 0-50% AMI) to calculate their projected extremely low-income households. For more information, visit HCD’s Building Blocks page on Extremely Low-Income Housing Needs.

This document does not contain the required data point of projected extremely low-income households, as Bay Area jurisdictions have not yet received their final RHNA numbers. Once Atherton receives its 6th Cycle RHNA, staff can estimate the projected extremely low-income households using one of the following three methodologies:

Option A: Assume that 59.8% of Atherton’s very low-income RHNA is for extremely low-income households.

According to HCD’s Regional Housing Need Determination for the Bay Area, 15.5% of the region’s housing need is for 0-30% AMI households while 25.9% is for 0-50% AMI households. Therefore, extremely low-income housing need represents 59.8% of the region’s very low-income housing need, as 15.5 divided by 25.9 is 59.8%. This option aligns with HCD’s guidance to use U.S. Census data to calculate the percentage of very low-income RHNA that qualifies for extremely low-income households, as HCD uses U.S. Census data to calculate the Regional Housing Need Determination.

Option B: Assume that 50.7% of Atherton’s very low-income RHNA is for extremely low-income households.

According to the data shown below (Figure 11), 294 of Atherton’s households are 0-50% AMI while 149 are extremely low-income. Therefore, extremely low-income households represent 50.7% of households who are 0-50% AMI, as 149 divided by 294 is 50.7%. This option aligns with HCD’s guidance to use U.S. Census data to calculate the percentage of very low-income RHNA that qualifies for extremely low-income households, as the information in Figure 11 represents a tabulation of Census Bureau Data.

Option C: Assume that 50% of Atherton’s very low-income RHNA is for extremely low-income households.

HCD’s guidance notes that instead of using use U.S. Census data to calculate the percentage of very low-income RHNA that qualifies for extremely low-income households, local jurisdictions can presume that 50% of their RHNA for very low-income households qualifies for extremely low-income households. percent are very low-income, and those making less than 30 percent are extremely low-income. This is then adjusted for household size.

23 Figure 11: Households by Household Income Level
Universe: Occupied housing units
Notes: Income groups are based on HUD calculations for Area Median Income (AMI). HUD calculates the AMI for different metropolitan areas, and the nine county Bay Area includes the following metropolitan areas: Napa Metro Area (Napa County), Oakland-Fremont Metro Area (Alameda and Contra Costa Counties), San Francisco Metro Area (Marin, San Francisco, and San Mateo Counties), San Jose-Sunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo-Fairfield Metro Area (Solano County). The AMI levels in this chart are based on the HUD metro area where this jurisdiction is located. The data that is reported for the Bay Area is not based on a regional AMI but instead refers to the regional total of households in an income group relative to the AMI for the county where that household is located. Local jurisdictions are required to provide an estimate for their projected extremely low-income households (0-30% AMI) in their Housing Elements. HCD’s official Housing Element guidance notes that jurisdictions can use their RHNA for very low-income households (those making 0-50% AMI) to calculate their projected extremely low-income households. As Bay Area jurisdictions have not yet received their final RHNA numbers, this document does not contain the required data point of projected extremely low-income households. The report portion of the housing data needs packet contains more specific guidance for how local staff can calculate an estimate for projected extremely low-income households once jurisdictions receive their 6th cycle RHNA numbers.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table ELI-01.

Throughout the region, there are disparities between the incomes of homeowners and renters.

Typically, the number of low-income renters greatly outpaces the amount of housing available that is affordable for these households.

In Atherton, the largest proportion of renters falls in the Greater than 100% of AMI income group, while the largest proportion of homeowners are found in the Greater than 100% of AMI group (see Figure 12).

24 Figure 12: Household Income Level by Tenure
Universe: Occupied housing units
Notes: Income groups are based on HUD calculations for Area Median Income (AMI). HUD calculates the AMI for different metropolitan areas, and the nine county Bay Area includes the following metropolitan areas: Napa Metro Area (Napa County), Oakland-Fremont Metro Area (Alameda and Contra Costa Counties), San Francisco Metro Area (Marin, San Francisco, and San Mateo Counties), San Jose-Sunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo-Fairfield Metro Area (Solano County). The AMI levels in this chart are based on the HUD metro area where this jurisdiction is located.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-21.

Currently, people of color are more likely to experience poverty and financial instability as a result of federal and local housing policies that have historically excluded them from the same opportunities extended to white residents.14 These economic disparities also leave communities of color at higher risk for housing insecurity, displacement or homelessness. In Atherton, Asian / API (Hispanic and NonHispanic) residents experience the highest rates of poverty, followed by White (Hispanic and NonHispanic) residents (see Figure 13). 14 Moore, E., Montojo, N. and Mauri, N., 2019. Roots, Race & Place: A History of Racially Exclusionary Housing the San Francisco Bay Area. Hass Institute.

25 Figure 13: Poverty Status by Race
Universe: Population for whom poverty status is determined Notes: The Census Bureau uses a federally defined poverty threshold that remains constant throughout the country and does not correspond to Area Median Income. For this table, the Census Bureau does not disaggregate racial groups by Hispanic/Latinx ethnicity. However, data for the white racial group is also reported for white householders who are not Hispanic/Latinx. Since residents who identify as white and Hispanic/Latinx may have very different experiences within the housing market and the economy from those who identify as white and non-Hispanic/Latinx, data for multiple white sub-groups are reported here. The racial/ethnic groups reported in this table are not all mutually exclusive. Therefore, the data should not be summed as the sum exceeds the population for whom poverty status is determined for this jurisdiction. However, all groups labelled “Hispanic and Non-Hispanic” are mutually exclusive, and the sum of the data for these groups is equivalent to the population for whom poverty status is determined.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B17001(A-I) For the data table behind this figure, please refer to the Data Packet Workbook, Table ELI-03.

4.6 Tenure
The number of residents who own their homes compared to those who rent their homes can help identify the level of housing insecurity – ability for individuals to stay in their homes – in a city and region. Generally, renters may be displaced more quickly if prices increase. In Atherton there are a total of 2,215 housing units, and fewer residents rent than own their homes: 6.8% versus 93.2% (see Figure 14). By comparison, 39.8% of households in San Mateo County are renters, while 44% of Bay Area households rent their homes.

26 Figure 14: Housing Tenure
Universe: Occupied housing units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25003 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-16.

Homeownership rates often vary considerably across race/ethnicity in the Bay Area and throughout the country. These disparities not only reflect differences in income and wealth but also stem from federal, state, and local policies that limited access to homeownership for communities of color while facilitating homebuying for white residents. While many of these policies, such as redlining, have been formally disbanded, the impacts of race-based policy are still evident across Bay Area communities.15 In Atherton, NODATA of Black households owned their homes, while homeownership rates were 93.5% for Asian households, 62.5% for Latinx households, and 93.3% for White households. Notably, recent changes to state law require local jurisdictions to examine these dynamics and other fair housing issues when updating their Housing Elements.
15 See, for example, Rothstein, R. (2017). The color of law : a forgotten history of how our government segregated America. New York, NY & London, UK: Liveright Publishing.

27 Figure 15: Housing Tenure by Race of Householder
Universe: Occupied housing units
Notes: For this table, the Census Bureau does not disaggregate racial groups by Hispanic/Latinx ethnicity. However, data for the white racial group is also reported for white householders who are not Hispanic/Latinx. Since residents who identify as white and Hispanic/Latinx may have very different experiences within the housing market and the economy from those who identify as white and non-Hispanic/Latinx, data for multiple white sub-groups are reported here. The racial/ethnic groups reported in this table are not all mutually exclusive. Therefore, the data should not be summed as the sum exceeds the total number of occupied housing units for this jurisdiction. However, all groups labelled “Hispanic and Non-Hispanic” are mutually exclusive, and the sum of the data for these groups is equivalent to the total number of occupied housing units.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25003(A-I) For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-20.

The age of residents who rent or own their home can also signal the housing challenges a community is experiencing. Younger households tend to rent and may struggle to buy a first home in the Bay Area due to high housing costs. At the same time, senior homeowners seeking to downsize may have limited options in an expensive housing market.

In Atherton, 20.2% of householders between the ages of 25 and 44 are renters, while 2.3% of householders over 65 are (see Figure 16).

28 Figure 16: Housing Tenure by Age
Universe: Occupied housing units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25007 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-18.

In many cities, homeownership rates for households in single-family homes are substantially higher than the rates for households in multi-family housing. In Atherton, 93.1% of households in detached single-family homes are homeowners, while NODATA of households in multi-family housing are homeowners (see Figure 17).

29 Figure 17: Housing Tenure by Housing Type
Universe: Occupied housing units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25032 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-22.

4.7 Displacement
Because of increasing housing prices, displacement is a major concern in the Bay Area. Displacement has the most severe impacts on low- and moderate-income residents. When individuals or families are forced to leave their homes and communities, they also lose their support network.

The University of California, Berkeley has mapped all neighborhoods in the Bay area, identifying their risk for gentrification. They find that in Atherton, 0.0% of households live in neighborhoods that are susceptible to or experiencing displacement and 0.0% live in neighborhoods at risk of or undergoing gentrification.

Equally important, some neighborhoods in the Bay Area do not have housing appropriate for a broad section of the workforce. UC Berkeley estimates that 100.0% of households in Atherton live in neighborhoods where low-income households are likely to be excluded due to prohibitive housing costs.16
16 More information about this gentrification and displacement data is available at the Urban Displacement Project’s webpage: https://www.urbandisplacement.org/. Specifically, one can learn more about the different gentrification/displacement typologies shown in Figure 18 at this link:
https://www.urbandisplacement.org/sites/default/files/typology_sheet_2018_0.png. Additionally, one can view maps that show which typologies correspond to which parts of a jurisdiction here:
https://www.urbandisplacement.org/san-francisco/sf-bay-area-gentrification-and-displacement

30 Figure 18: Households by Displacement Risk and Tenure Universe: Households
Notes: Displacement data is available at the census tract level. Staff aggregated tracts up to jurisdiction level using census 2010 population weights, assigning a tract to jurisdiction in proportion to block level population weights. Total household count may differ slightly from counts in other tables sourced from jurisdiction level sources. Categories are combined as follows for simplicity: At risk of or Experiencing Exclusion: At Risk of Becoming Exclusive; Becoming Exclusive; Stable/Advanced Exclusive At risk of or Experiencing Gentrification: At Risk of Gentrification; Early/Ongoing Gentrification; Advanced Gentrification Stable Moderate/Mixed Income: Stable Moderate/Mixed Income Susceptible to or Experiencing Displacement: LowIncome/Susceptible to Displacement; Ongoing Displacement Other: High Student Population; Unavailable or Unreliable Data Source: Urban Displacement Project for classification, American Community Survey 5-Year Data (2015-2019), Table B25003 for tenure.

For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-25.

31 5 HOUSING STOCK CHARACTERISTICS

5.1 Housing Types, Year Built, Vacancy, and Permits
In recent years, most housing produced in the region and across the state consisted of single-family homes and larger multi-unit buildings. However, some households are increasingly interested in “missing middle housing” – including duplexes, triplexes, townhomes, cottage clusters and accessory dwelling units (ADUs). These housing types may open up more options across incomes and tenure, from young households seeking homeownership options to seniors looking to downsize and age-in-place.

The housing stock of Atherton in 2020 was made up of 97.9% single family detached homes, 1.8% single family attached homes, 0.2% multifamily homes with 2 to 4 units, 0.0% multifamily homes with 5 or more units, and 0.0% mobile homes (see Figure 19). In Atherton, the housing type that experienced the most growth between 2010 and 2020 was Single-Family Home: Attached.

Figure 19: Housing Type Trends
Universe: Housing units
Source: California Department of Finance, E-5 series
For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-01.

Production has not kept up with housing demand for several decades in the Bay Area, as the total number of units built and available has not yet come close to meeting the population and job growth experienced throughout the region. In Atherton, the largest proportion of the housing stock was built 1940 to 1959, with 1,087 units constructed during this period (see Figure 20). Since 2010, 7.3% of the current housing stock was built, which is 180 units.

32 Figure 20: Housing Units by Year Structure Built
Universe: Housing units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25034 For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-04.

Vacant units make up 10.4% of the overall housing stock in Atherton. The rental vacancy stands at

9.6%, while the ownership vacancy rate is 3.2%. Of the vacant units, the most common type of vacancy is For Seasonal, Recreational, Or Occasional Use (see Figure 21).17 Throughout the Bay Area, vacancies make up 2.6% of the total housing units, with homes listed for rent; units used for recreational or occasional use, and units not otherwise classified (other vacant) making up the majority of vacancies. The Census Bureau classifies a unit as vacant if no one is occupying it when census interviewers are conducting the American Community Survey or Decennial Census. Vacant units classified as “for recreational or occasional use” are those that are held for shortterm periods of use throughout the year. Accordingly, vacation rentals and short-term rentals like AirBnB are likely to fall in this category. The Census Bureau classifies units as “other vacant” if they are vacant due to foreclosure, personal/family reasons, legal proceedings, repairs/renovations, abandonment, preparation for being rented or sold, or vacant for an extended absence for reasons such as a work assignment, military duty, or incarceration.18 In a region with a thriving economy and housing market like the Bay Area, units being renovated/repaired and prepared for rental or sale are likely to represent a large portion of the “other vacant” category. Additionally, the need for seismic retrofitting 17 The vacancy rates by tenure is for a smaller universe than the total vacancy rate first reported, which in principle includes the full stock (10.4%). The vacancy by tenure counts are rates relative to the rental stock (occupied and vacant) and ownership stock (occupied and vacant) - but exclude a a significant number of vacancy categories, including the numerically significant other vacant. 18 For more information, see pages 3 through 6 of this list of definitions prepared by the Census Bureau:
https://www.census.gov/housing/hvs/definitions.pdf.

33 in older housing stock could also influence the proportion of “other vacant” units in some jurisdictions.19
Figure 21: Vacant Units by Type
Universe: Vacant housing units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25004 For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-03.

Between 2015 and 2019, 131 housing units were issued permits in Atherton. 59.5% of permits issued in Atherton were for above moderate-income housing, 2.3% were for moderate-income housing, and 38.2% were for low- or very low-income housing (see Table 3).

Table 3: Housing Permitting
Income Group value
Above Moderate Income Permits 78
Very Low Income Permits 35
Low Income Permits 15
Moderate Income Permits 3
Universe: Housing permits issued between 2015 and 2019
Notes: HCD uses the following definitions for the four income categories: Very Low Income: units affordable to households making less than 50% of the Area Median Income for the county in which the jurisdiction is located. Low Income: units affordable to households making between 50% and 80% of the Area Median Income for the county in which the jurisdiction is located. Moderate Income: units affordable to households making between 80% and 120% of the Area Median Income for the 19 See Dow, P. (2018). Unpacking the Growth in San Francisco’s Vacant Housing Stock: Client Report for the San Francisco Planning Department. University of California, Berkeley.

34 county in which the jurisdiction is located. Above Moderate Income: units affordable to households making above 120% of the Area Median Income for the county in which the jurisdiction is located.

Source: California Department of Housing and Community Development (HCD), 5th Cycle Annual Progress Report Permit Summary (2020)
This table is included in the Data Packet Workbook as Table HSG-11.

5.2 Assisted Housing Developments At-Risk of Conversion While there is an immense need to produce new affordable housing units, ensuring that the existing affordable housing stock remains affordable is equally important. Additionally, it is typically faster and less expensive to preserve currently affordable units that are at risk of converting to market-rate than it is to build new affordable housing.

The data in the table below comes from the California Housing Partnership’s Preservation Database, the state’s most comprehensive source of information on subsidized affordable housing at risk of losing its affordable status and converting to market-rate housing. However, this database does not include all deed-restricted affordable units in the state, so there may be at-risk assisted units in a jurisdiction that are not captured in this data table. There are 0 assisted units in Atherton in the Preservation Database. Of these units, 0.0% are at High Risk or Very High Risk of conversion.20 Note on At-Risk Assisted Housing Developments
HCD requires that Housing Elements list the assisted housing developments at risk of converting to market-rate uses. For more information on the specific properties that are at Moderate Risk, High Risk, or Very High Risk of conversion, local jurisdiction staff should contact Danielle Mazzella, Preservation & Data Manager at the California Housing Partnership, at [email protected].

Table 4: Assisted Units at Risk of Conversion
Income Atherton San Mateo County Bay Area
Low 0 4656 110177
Moderate 0 191 3375
High 0 359 1854
Very High 0 58 1053
Total Assisted Units in Database 0 5264 116459
Universe: HUD, Low-Income Housing Tax Credit (LIHTC), USDA, and CalHFA projects. Subsidized or assisted developments that do not have one of the aforementioned financing sources may not be included. 20 California Housing Partnership uses the following categories for assisted housing developments in its database:

Very-High Risk: affordable homes that are at-risk of converting to market rate within the next year that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer.

High Risk: affordable homes that are at-risk of converting to market rate in the next 1-5 years that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer.

Moderate Risk: affordable homes that are at-risk of converting to market rate in the next 5-10 years that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer.

Low Risk: affordable homes that are at-risk of converting to market rate in 10+ years and/or are owned by a large/stable non-profit, mission-driven developer.

35 Notes: While California Housing Partnership’s Preservation Database is the state’s most comprehensive source of information on subsidized affordable housing at risk of losing its affordable status and converting to market-rate housing, this database does not include all deed-restricted affordable units in the state. Consequently, there may be at-risk assisted units in a jurisdiction that are not captured in this data table. Per HCD guidance, local jurisdictions must also list the specific affordable housing developments at-risk of converting to market rate uses. This document provides aggregate numbers of at-risk units for each jurisdiction, but local planning staff should contact Danielle Mazzella with the California Housing Partnership at [email protected] to obtain a list of affordable properties that fall under this designation. California Housing Partnership uses the following categories for assisted housing developments in its database: Very-High Risk: affordable homes that are atrisk of converting to market rate within the next year that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer. High Risk: affordable homes that are at-risk of converting to market rate in the next 1-5 years that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer. Moderate Risk: affordable homes that are at-risk of converting to market rate in the next 5-10 years that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer. Low Risk: affordable homes that are atrisk of converting to market rate in 10+ years and/or are owned by a large/stable non-profit, mission-driven developer.

Source: California Housing Partnership, Preservation Database (2020) This table is included in the Data Packet Workbook as Table RISK-01.

5.3 Substandard Housing
Housing costs in the region are among the highest in the country, which could result in households, particularly renters, needing to live in substandard conditions in order to afford housing. Generally, there is limited data on the extent of substandard housing issues in a community. However, the Census Bureau data included in the graph below gives a sense of some of the substandard conditions that may be present in Atherton. For example, 0.0% of renters in Atherton reported lacking a kitchen and 0.0% of renters lack plumbing, compared to 0.0% of owners who lack a kitchen and 0.0% of owners who lack plumbing.

Note on Substandard Housing
HCD requires Housing Elements to estimate the number of units in need of rehabilitation and replacement. As a data source for housing units in need of rehabilitation and replacement is not available for all jurisdictions in the region, ABAG was not able to provide this required data point in this document. To produce an estimate of housing needs in need of rehabilitation and replacement, staff can supplement the data below on substandard housing issues with additional local information from code enforcement, recent windshield surveys of properties, building department data, knowledgeable builders/developers in the community, or nonprofit housing developers or organizations. For more information, visit HCD’s Building Blocks page on Housing Stock Characteristics.

Universe: HUD, Low-Income Housing Tax Credit (LIHTC), USDA, and CalHFA projects. Subsidized or assisted developments that do not have one of the aforementioned financing sources may not be included.

Notes: While California Housing Partnership’s Preservation Database is the state’s most comprehensive source of information on subsidized affordable housing at risk of losing its affordable status and converting to market-rate housing, this database does not include all deed-restricted affordable units in the state. Consequently, there may be at-risk assisted units in a jurisdiction that are not captured in this data table. Per HCD guidance, local jurisdictions must also list the specific affordable housing developments at-risk of converting to market rate uses. This document provides aggregate numbers of at-risk units for each jurisdiction, but local planning staff should contact Danielle Mazzella with the California Housing Partnership at [email protected] to obtain a list of affordable properties that fall under this designation. California Housing Partnership uses the following categories for assisted housing developments in its database: Very-High Risk: affordable homes that are atrisk of converting to market rate within the next year that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer. High Risk: affordable homes that are at-risk of converting to market rate in the next 1-5 years that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer. Moderate Risk: affordable homes that are at-risk of converting to market rate in the next 5-10 years that do not have a known overlapping subsidy that would extend affordability and are not owned by a large/stable non-profit, mission-driven developer. Low Risk: affordable homes that are atrisk of converting to market rate in 10+ years and/or are owned by a large/stable non-profit, mission-driven developer.

Source: California Housing Partnership, Preservation Database (2020) For the data table behind this figure, please refer to the Data Packet Workbook, Table RISK-01.

5.4 Home and Rent Values

36 Home prices reflect a complex mix of supply and demand factors, including an area’s demographic profile, labor market, prevailing wages and job outlook, coupled with land and construction costs. In the Bay Area, the costs of housing have long been among the highest in the nation. The typical home value in Atherton was estimated at $6,593,960 by December of 2020, per data from Zillow. The largest proportion of homes were valued between $2M+ (see Figure 22). By comparison, the typical home value is $1,418,330 in San Mateo County and $1,077,230 the Bay Area, with the largest share of units valued $1m-$1.5m (county) and $500k-$750k (region).

The region’s home values have increased steadily since 2000, besides a decrease during the Great Recession. The rise in home prices has been especially steep since 2012, with the median home value in the Bay Area nearly doubling during this time. Since 2001, the typical home value has increased 248.2% in Atherton from $1,893,730 to $6,593,960. This change is above the change in San Mateo County, and above the change for the region (see Figure 23).

Figure 22: Home Values of Owner-Occupied Units
Universe: Owner-occupied units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25075 For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-07.

37 Figure 23: Zillow Home Value Index (ZHVI)
Universe: Owner-occupied housing units
Notes: Zillow describes the ZHVI as a smoothed, seasonally adjusted measure of the typical home value and market changes across a given region and housing type. The ZHVI reflects the typical value for homes in the 35th to 65th percentile range. The ZHVI includes all owner-occupied housing units, including both single-family homes and condominiums. More information on the ZHVI is available from Zillow. The regional estimate is a household-weighted average of county-level ZHVI files, where household counts are yearly estimates from DOF’s E-5 series For unincorporated areas, the value is a population weighted average of unincorporated communities in the county matched to census-designated population counts.

Source: Zillow, Zillow Home Value Index (ZHVI)
For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-08.

Similar to home values, rents have also increased dramatically across the Bay Area in recent years.

Many renters have been priced out, evicted or displaced, particularly communities of color. Residents finding themselves in one of these situations may have had to choose between commuting long distances to their jobs and schools or moving out of the region, and sometimes, out of the state.

In Atherton, the largest proportion of rental units rented in the Rent $3000 or more category, totaling

67.4%, followed by 18.6% of units renting in the Rent $2500-$3000 category (see Figure 24). Looking beyond the city, the largest share of units is in the $3000 or more category (county) compared to the $1500-$2000 category for the region as a whole.

38 Figure 24: Contract Rents for Renter-Occupied Units
Universe: Renter-occupied housing units paying cash rent Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25056 For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-09.

Since 2009, the median rent has increased by 60.3% in Atherton, from $2,000 to $3,200 per month (see Figure 25). In San Mateo County, the median rent has increased 41.1%, from $1,560 to $2,200. The median rent in the region has increased significantly during this time from $1,200 to $1,850, a 54% increase.21
21 While the data on home values shown in Figure 23 comes from Zillow, Zillow does not have data on rent prices available for most Bay Area jurisdictions. To have a more comprehensive dataset on rental data for the region, the rent data in this document comes from the U.S. Census Bureau’s American Community Survey, which may not fully reflect current rents. Local jurisdiction staff may want to supplement the data on rents with local realtor data or other sources for rent data that are more current than Census Bureau data.

39 Figure 25: Median Contract Rent
Universe: Renter-occupied housing units paying cash rent Notes: For unincorporated areas, median is calculated using distribution in B25056.

Source: U.S. Census Bureau, American Community Survey 5-Year Data releases, starting with 2005-2009 through 2015-2019, B25058, B25056 (for unincorporated areas). County and regional counts are weighted averages of jurisdiction median using B25003 rental unit counts from the relevant year.

For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-10.

5.5 Overpayment and Overcrowding
A household is considered “cost-burdened” if it spends more than 30% of its monthly income on housing costs, while those who spend more than 50% of their income on housing costs are considered “severely cost-burdened.” Low-income residents are the most impacted by high housing costs and experience the highest rates of cost burden. Spending such large portions of their income on housing puts low-income households at higher risk of displacement, eviction, or homelessness.

40 Figure 26: Cost Burden by Tenure
Universe: Occupied housing units
Notes: Cost burden is the ratio of housing costs to household income. For renters, housing cost is gross rent (contract rent plus utilities). For owners, housing cost is “select monthly owner costs”, which includes mortgage payment, utilities, association fees, insurance, and real estate taxes. HUD defines cost-burdened households as those whose monthly housing costs exceed 30% of monthly income, while severely cost-burdened households are those whose monthly housing costs exceed 50% of monthly income.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25070, B25091 For the data table behind this figure, please refer to the Data Packet Workbook, Table OVER-06.

Renters are often more cost-burdened than owners. While the housing market has resulted in home prices increasing dramatically, homeowners often have mortgages with fixed rates, whereas renters are more likely to be impacted by market increases. When looking at the cost burden across tenure in Atherton, 11.3% of renters spend 30% to 50% of their income on housing compared to 12.1% of those that own (see Figure 26). Additionally, 2.7% of renters spend 50% or more of their income on housing, while 16.2% of owners are severely cost-burdened.

In Atherton, 17.6% of households spend 50% or more of their income on housing, while 13.5% spend 30% to 50%. However, these rates vary greatly across income categories (see Figure 27). For example, 87.7% of Atherton households making less than 30% of AMI spend the majority of their income on housing. For Atherton residents making more than 100% of AMI, just 6.9% are severely cost-burdened, and 80.0% of those making more than 100% of AMI spend less than 30% of their income on housing.

41 Figure 27: Cost Burden by Income Level
Universe: Occupied housing units
Notes: Cost burden is the ratio of housing costs to household income. For renters, housing cost is gross rent (contract rent plus utilities). For owners, housing cost is “select monthly owner costs”, which includes mortgage payment, utilities, association fees, insurance, and real estate taxes. HUD defines cost-burdened households as those whose monthly housing costs exceed 30% of monthly income, while severely cost-burdened households are those whose monthly housing costs exceed 50% of monthly income. Income groups are based on HUD calculations for Area Median Income (AMI). HUD calculates the AMI for different metropolitan areas, and the nine county Bay Area includes the following metropolitan areas: Napa Metro Area (Napa County), Oakland-Fremont Metro Area (Alameda and Contra Costa Counties), San Francisco Metro Area (Marin, San Francisco, and San Mateo Counties), San Jose-Sunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo-Fairfield Metro Area (Solano County). The AMI levels in this chart are based on the HUD metro area where this jurisdiction is located.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table OVER-05.

Currently, people of color are more likely to experience poverty and financial instability as a result of federal and local housing policies that have historically excluded them from the same opportunities extended to white residents. As a result, they often pay a greater percentage of their income on housing, and in turn, are at a greater risk of housing insecurity.

Asian / API, Non-Hispanic residents are the most cost burdened with 14.5% spending 30% to 50% of their income on housing, and Other Race or Multiple Races, Non-Hispanic residents are the most severely cost burdened with 55.6% spending more than 50% of their income on housing (see Figure 28).

42 Figure 28: Cost Burden by Race
Universe: Occupied housing units
Notes: Cost burden is the ratio of housing costs to household income. For renters, housing cost is gross rent (contract rent plus utilities). For owners, housing cost is “select monthly owner costs”, which includes mortgage payment, utilities, association fees, insurance, and real estate taxes. HUD defines cost-burdened households as those whose monthly housing costs exceed 30% of monthly income, while severely cost-burdened households are those whose monthly housing costs exceed 50% of monthly income. For the purposes of this graph, the “Hispanic or Latinx” racial/ethnic group represents those who identify as having Hispanic/Latinx ethnicity and may also be members of any racial group. All other racial categories on this graph represent those who identify with that racial category and do not identify with Hispanic/Latinx ethnicity.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table OVER-08.

Large family households often have special housing needs due to a lack of adequately sized affordable housing available. The higher costs required for homes with multiple bedrooms can result in larger families experiencing a disproportionate cost burden than the rest of the population and can increase the risk of housing insecurity.

In Atherton, 15.4% of large family households experience a cost burden of 30%-50%, while 11.2% of households spend more than half of their income on housing. Some 13.2% of all other households have a cost burden of 30%-50%, with 18.4% of households spending more than 50% of their income on housing (see Figure 29).

43 Figure 29: Cost Burden by Household Size
Universe: Occupied housing units
Notes: Cost burden is the ratio of housing costs to household income. For renters, housing cost is gross rent (contract rent plus utilities). For owners, housing cost is “select monthly owner costs”, which includes mortgage payment, utilities, association fees, insurance, and real estate taxes. HUD defines cost-burdened households as those whose monthly housing costs exceed 30% of monthly income, while severely cost-burdened households are those whose monthly housing costs exceed 50% of monthly income.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table OVER-09.

When cost-burdened seniors are no longer able to make house payments or pay rents, displacement from their homes can occur, putting further stress on the local rental market or forcing residents out of the community they call home. Understanding how seniors might be cost-burdened is of particular importance due to their special housing needs, particularly for low-income seniors. 100.0% of seniors making less than 30% of AMI are spending the majority of their income on housing. For seniors making more than 100% of AMI, 78.5% are not cost-burdened and spend less than 30% of their income on housing (see Figure 30).

44 Figure 30: Cost-Burdened Senior Households by Income Level Universe: Senior households
Notes: For the purposes of this graph, senior households are those with a householder who is aged 62 or older. Cost burden is the ratio of housing costs to household income. For renters, housing cost is gross rent (contract rent plus utilities). For owners, housing cost is “select monthly owner costs”, which includes mortgage payment, utilities, association fees, insurance, and real estate taxes. HUD defines cost-burdened households as those whose monthly housing costs exceed 30% of monthly income, while severely cost-burdened households are those whose monthly housing costs exceed 50% of monthly income. Income groups are based on HUD calculations for Area Median Income (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 JoseSunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo-Fairfield Metro Area (Solano County). The AMI levels in this chart are based on the HUD metro area where this jurisdiction is located.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table SEN-03.

Overcrowding occurs when the number of people living in a household is greater than the home was designed to hold. There are several different standards for defining overcrowding, but this report uses the Census Bureau definition, which is more than one occupant per room (not including bathrooms or kitchens). Additionally, the Census Bureau considers units with more than 1.5 occupants per room to be severely overcrowded.

Overcrowding is often related to the cost of housing and can occur when demand in a city or region is high. In many cities, overcrowding is seen more amongst those that are renting, with multiple households sharing a unit to make it possible to stay in their communities. In Atherton, 0.0% of households that rent are severely overcrowded (more than 1.5 occupants per room), compared to 0.8% of households that own (see Figure 31). In Atherton, 0.0% of renters experience moderate overcrowding (1 to 1.5 occupants per room), compared to 0.2% for those own.

45 Figure 31: Overcrowding by Tenure and Severity
Universe: Occupied housing units
Notes: The Census Bureau defines an overcrowded unit as one occupied by 1.01 persons or more per room (excluding bathrooms and kitchens), and units with more than 1.5 persons per room are considered severely overcrowded.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table OVER-01.

Overcrowding often disproportionately impacts low-income households. 0.0% of very low-income households (below 50% AMI) experience severe overcrowding, while 0.2% of households above 100% experience this level of overcrowding (see Figure 32).

46 Figure 32: Overcrowding by Income Level and Severity Universe: Occupied housing units
Notes: The Census Bureau defines an overcrowded unit as one occupied by 1.01 persons or more per room (excluding bathrooms and kitchens), and units with more than 1.5 persons per room are considered severely overcrowded. Income groups are based on HUD calculations for Area Median Income (AMI). HUD calculates the AMI for different metropolitan areas, and the nine county Bay Area includes the following metropolitan areas: Napa Metro Area (Napa County), Oakland-Fremont Metro Area (Alameda and Contra Costa Counties), San Francisco Metro Area (Marin, San Francisco, and San Mateo Counties), San Jose-Sunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo-Fairfield Metro Area (Solano County). The AMI levels in this chart are based on the HUD metro area where this jurisdiction is located.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table OVER-04.

Communities of color are more likely to experience overcrowding similar to how they are more likely to experience poverty, financial instability, and housing insecurity. People of color tend to experience overcrowding at higher rates than White residents. In Atherton, the racial group with the largest overcrowding rate is Other Race or Multiple Races (Hispanic and Non-Hispanic) (see Figure 33)

47 Figure 33: Overcrowding by Race
Universe: Occupied housing units
Notes: The Census Bureau defines an overcrowded unit as one occupied by 1.01 persons or more per room (excluding bathrooms and kitchens), and units with more than 1.5 persons per room are considered severely overcrowded. For this table, the Census Bureau does not disaggregate racial groups by Hispanic/Latinx ethnicity. However, data for the white racial group is also reported for white householders who are not Hispanic/Latinx. Since residents who identify as white and Hispanic/Latinx may have very different experiences within the housing market and the economy from those who identify as white and nonHispanic/Latinx, data for multiple white sub-groups are reported here. The racial/ethnic groups reported in this table are not all mutually exclusive. Therefore, the data should not be summed as the sum exceeds the total number of occupied housing units for this jurisdiction. However, all groups labelled “Hispanic and Non-Hispanic” are mutually exclusive, and the sum of the data for these groups is equivalent to the total number of occupied housing units.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25014 For the data table behind this figure, please refer to the Data Packet Workbook, Table OVER-03.

48 6 SPECIAL HOUSING NEEDS

6.1 Large Households
Large households often have different housing needs than smaller households. If a city’s rental housing stock does not include larger apartments, large households who rent could end up living in overcrowded conditions. In Atherton, for large households with 5 or more persons, most units (91.0%) are owner occupied (see Figure 34). In 2017, 0.0% of large households were very low-income, earning less than 50% of the area median income (AMI).

Figure 34: Household Size by Tenure
Universe: Occupied housing units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25009 For the data table behind this figure, please refer to the Data Packet Workbook, Table LGFEM-01.

The unit sizes available in a community affect the household sizes that can access that community.

Large families are generally served by housing units with 3 or more bedrooms, of which there are 2,093 units in Atherton. Among these large units with 3 or more bedrooms, 5.8% are owner-occupied and

94.2% are renter occupied (see Figure 35).

49 Figure 35: Housing Units by Number of Bedrooms
Universe: Housing units
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25042 For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-05.

6.2 Female-Headed Households
Households headed by one person are often at greater risk of housing insecurity, particularly femaleheaded households, who may be supporting children or a family with only one income. In Atherton, the largest proportion of households is Married-couple Family Households at 74.4% of total, while FemaleHeaded Households make up 5.4% of all households.

50 Figure 36: Household Type
Universe: Households
Notes: For data from the Census Bureau, a “family household” is a household where two or more people are related by birth, marriage, or adoption. “Non-family households” are households of one person living alone, as well as households where none of the people are related to each other.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B11001 For the data table behind this figure, please refer to the Data Packet Workbook, Table POPEMP-23.

Female-headed households with children may face particular housing challenges, with pervasive gender inequality resulting in lower wages for women. Moreover, the added need for childcare can make finding a home that is affordable more challenging.

In Atherton, 7.8% of female-headed households with children fall below the Federal Poverty Line, while

0.0% of female-headed households without children live in poverty (see Figure 37).

51 Figure 37: Female-Headed Households by Poverty Status Universe: Female Households
Notes: The Census Bureau uses a federally defined poverty threshold that remains constant throughout the country and does not correspond to Area Median Income.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B17012 For the data table behind this figure, please refer to the Data Packet Workbook, Table LGFEM-05.

6.3 Seniors
Senior households often experience a combination of factors that can make accessing or keeping affordable housing a challenge. They often live on fixed incomes and are more likely to have disabilities, chronic health conditions and/or reduced mobility.

Seniors who rent may be at even greater risk for housing challenges than those who own, due to income differences between these groups. The largest proportion of senior households who rent make Greater than 100% of AMI, while the largest proportion of senior households who are homeowners falls in the income group Greater than 100% of AMI (see Figure 38).

52 Figure 38: Senior Households by Income and Tenure
Universe: Senior households
Notes: For the purposes of this graph, senior households are those with a householder who is aged 62 or older. Income groups are based on HUD calculations for Area Median Income (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 JoseSunnyvale-Santa Clara Metro Area (Santa Clara County), Santa Rosa Metro Area (Sonoma County), and Vallejo-Fairfield Metro Area (Solano County). The AMI levels in this chart are based on the HUD metro area where this jurisdiction is located.

Source: U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) ACS tabulation, 2013-2017 release
For the data table behind this figure, please refer to the Data Packet Workbook, Table SEN-01.

6.4 People with Disabilities
People with disabilities face additional housing challenges. Encompassing a broad group of individuals living with a variety of physical, cognitive and sensory impairments, many people with disabilities live on fixed incomes and are in need of specialized care, yet often rely on family members for assistance due to the high cost of care.

When it comes to housing, people with disabilities are not only in need of affordable housing but accessibly designed housing, which offers greater mobility and opportunity for independence.

Unfortunately, the need typically outweighs what is available, particularly in a housing market with such high demand. People with disabilities are at a high risk for housing insecurity, homelessness and institutionalization, particularly when they lose aging caregivers. Figure 39 shows the rates at which different disabilities are present among residents of Atherton. Overall, 7.8% of people in Atherton have a disability of any kind.22
22 These disabilities are counted separately and are not mutually exclusive, as an individual may report more than one disability. These counts should not be summed.

53 Figure 39: Disability by Type
Universe: Civilian noninstitutionalized population 18 years and over Notes: These disabilities are counted separately and are not mutually exclusive, as an individual may report more than one disability. These counts should not be summed. The Census Bureau provides the following definitions for these disability types:

Hearing difficulty: deaf or has serious difficulty hearing. Vision difficulty: blind or has serious difficulty seeing even with glasses. Cognitive difficulty: has serious difficulty concentrating, remembering, or making decisions. Ambulatory difficulty: has serious difficulty walking or climbing stairs. Self-care difficulty: has difficulty dressing or bathing. Independent living difficulty:
has difficulty doing errands alone such as visiting a doctor’s office or shopping.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B18102, Table B18103, Table B18104, Table B18105, Table B18106, Table B18107.

For the data table behind this figure, please refer to the Data Packet Workbook, Table DISAB-01.

State law also requires Housing Elements to examine the housing needs of people with developmental disabilities. Developmental disabilities are defined as severe, chronic, and attributed to a mental or physical impairment that begins before a person turns 18 years old. This can include Down’s Syndrome, autism, epilepsy, cerebral palsy, and mild to severe mental retardation. Some people with developmental disabilities are unable to work, rely on Supplemental Security Income, and live with family members. In addition to their specific housing needs, they are at increased risk of housing insecurity after an aging parent or family member is no longer able to care for them.23 In Atherton, of the population with a developmental disability, children under the age of 18 make up

27.8%, while adults account for 72.2%.
23 For more information or data on developmental disabilities in your jurisdiction, contact the Golden Gate Regional Center for Marin, San Francisco and San Mateo Counties; the North Bay Regional Center for Napa, Solano and Sonoma Counties; the Regional Center for the East Bay for Alameda and Contra Costa Counties; or the San Andreas Regional Center for Santa Clara County.

54 Table 5: Population with Developmental Disabilities by Age Age Group value
Age 18+ 13
Age Under 18 5
Universe: Population with developmental disabilities
Notes: The California Department of Developmental Services is responsible for overseeing the coordination and delivery of services to more than 330,000 Californians with developmental disabilities including cerebral palsy, intellectual disability, Down syndrome, autism, epilepsy, and related conditions. The California Department of Developmental Services provides ZIP code level counts. To get jurisdiction-level estimates, ZIP code counts were crosswalked to jurisdictions using census block population counts from Census 2010 SF1 to determine the share of a ZIP code to assign to a given jurisdiction.

Source: California Department of Developmental Services, Consumer Count by California ZIP Code and Age Group (2020) This table is included in the Data Packet Workbook as Table DISAB-04.

The most common living arrangement for individuals with disabilities in Atherton is the home of parent /family /guardian.

Table 6: Population with Developmental Disabilities by Residence Residence Type value
Home of Parent /Family /Guardian 20
Independent /Supported Living 5
Other 0
Foster /Family Home 0
Intermediate Care Facility 0
Community Care Facility 0
Universe: Population with developmental disabilities
Notes: The California Department of Developmental Services is responsible for overseeing the coordination and delivery of services to more than 330,000 Californians with developmental disabilities including cerebral palsy, intellectual disability, Down syndrome, autism, epilepsy, and related conditions. The California Department of Developmental Services provides ZIP code level counts. To get jurisdiction-level estimates, ZIP code counts were crosswalked to jurisdictions using census block population counts from Census 2010 SF1 to determine the share of a ZIP code to assign to a given jurisdiction.

Source: California Department of Developmental Services, Consumer Count by California ZIP Code and Residence Type (2020) This table is included in the Data Packet Workbook as Table DISAB-05.

6.5 Homelessness
Homelessness remains an urgent challenge in many communities across the state, reflecting a range of social, economic, and psychological factors. Rising housing costs result in increased risks of community members experiencing homelessness. Far too many residents who have found themselves housing insecure have ended up unhoused or homeless in recent years, either temporarily or longer term.

Addressing the specific housing needs for the unhoused population remains a priority throughout the region, particularly since homelessness is disproportionately experienced by people of color, people with disabilities, those struggling with addiction and those dealing with traumatic life circumstances. In San Mateo County, the most common type of household experiencing homelessness is those without children in their care. Among households experiencing homelessness that do not have children, 75.5% are unsheltered. Of homeless households with children, most are sheltered in transitional housing (see Figure 40).

55 Figure 40: Homelessness by Household Type and Shelter Status, San Mateo County Universe: Population experiencing homelessness
Notes: This data is based on Point-in-Time (PIT) information provided to HUD by CoCs in the application for CoC Homeless Assistance Programs. The PIT Count provides a count of sheltered and unsheltered homeless persons on a single night during the last ten days in January. Each Bay Area county is its own CoC, and so the data for this table is provided at the county-level. Per HCD’s requirements, jurisdictions will need to supplement this county-level data with local estimates of people experiencing homelessness.

Source: U.S. Department of Housing and Urban Development (HUD), Continuum of Care (CoC) Homeless Populations and Subpopulations Reports (2019)
For the data table behind this figure, please refer to the Data Packet Workbook, Table HOMELS-01.

People of color are more likely to experience poverty and financial instability as a result of federal and local housing policies that have historically excluded them from the same opportunities extended to white residents. Consequently, people of color are often disproportionately impacted by homelessness, particularly Black residents of the Bay Area. In San Mateo County, White (Hispanic and Non-Hispanic) residents represent the largest proportion of residents experiencing homelessness and account for

66.6% of the homeless population, while making up 50.6% of the overall population (see Figure 41).

56 Figure 41: Racial Group Share of General and Homeless Populations, San Mateo County
Universe: Population experiencing homelessness
Notes: This data is based on Point-in-Time (PIT) information provided to HUD by CoCs in the application for CoC Homeless Assistance Programs. The PIT Count provides a count of sheltered and unsheltered homeless persons on a single night during the last ten days in January. Each Bay Area county is its own CoC, and so the data for this table is provided at the county-level. Per HCD’s requirements, jurisdictions will need to supplement this county-level data with local estimates of people experiencing homelessness. HUD does not disaggregate racial demographic data by Hispanic/Latinx ethnicity for people experiencing homelessness. Instead, HUD reports data on Hispanic/Latinx ethnicity for people experiencing homelessness in a separate table.

Accordingly, the racial group data listed here includes both Hispanic/Latinx and non-Hispanic/Latinx individuals.

Source: U.S. Department of Housing and Urban Development (HUD), Continuum of Care (CoC) Homeless Populations and Subpopulations Reports (2019); U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B01001(A-I) For the data table behind this figure, please refer to the Data Packet Workbook, Table HOMELS-02.

In San Mateo, Latinx residents represent 38.1% of the population experiencing homelessness, while Latinx residents comprise 24.7% of the general population (see Figure 42).

57 Figure 42: Latinx Share of General and Homeless Populations, San Mateo County Universe: Population experiencing homelessness
Notes: This data is based on Point-in-Time (PIT) information provided to HUD by CoCs in the application for CoC Homeless Assistance Programs. The PIT Count provides a count of sheltered and unsheltered homeless persons on a single night during the last ten days in January. Each Bay Area county is its own CoC, and so the data for this table is provided at the county-level. Per HCD’s requirements, jurisdictions will need to supplement this county-level data with local estimates of people experiencing homelessness. The data from HUD on Hispanic/Latinx ethnicity for individuals experiencing homelessness does not specify racial group identity. Accordingly, individuals in either ethnic group identity category (Hispanic/Latinx or non-Hispanic/Latinx) could be of any racial background.

Source: U.S. Department of Housing and Urban Development (HUD), Continuum of Care (CoC) Homeless Populations and Subpopulations Reports (2019); U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B01001(A-I) For the data table behind this figure, please refer to the Data Packet Workbook, Table HOMELS-03.

Many of those experiencing homelessness are dealing with severe issues – including mental illness, substance abuse and domestic violence – that are potentially life threatening and require additional assistance. In San Mateo County, homeless individuals are commonly challenged by severe mental illness, with 305 reporting this condition (see Figure 12). Of those, some 62.0% are unsheltered, further adding to the challenge of handling the issue.

Note on Homelessness Data
Notably all the data on homelessness provided above is for the entire county. This data comes from the Department of Housing and Urban Development’s (HUD) Point in Time count, which is the most comprehensive publicly available data source on people experiencing homelessness. HUD only provides this data at the countylevel and not for specific jurisdictions. However, Housing Element law requires local jurisdictions to estimate or count of the daily average number of people lacking shelter. Therefore, staff will need to supplement the data in this document with additional local data on the number of people experiencing homelessness. If staff do not have estimates of people experiencing homelessness in their jurisdiction readily available, HCD recommends contacting local service providers such as continuum-of-care providers, local homeless shelter and service providers, food

58 programs, operators of transitional housing programs, local drug and alcohol program service providers, and county mental health and social service departments.24
Figure 43: Characteristics for the Population Experiencing Homelessness, San Mateo County
Universe: Population experiencing homelessness
Notes: This data is based on Point-in-Time (PIT) information provided to HUD by CoCs in the application for CoC Homeless Assistance Programs. The PIT Count provides a count of sheltered and unsheltered homeless persons on a single night during the last ten days in January. Each Bay Area county is its own CoC, and so the data for this table is provided at the county-level. Per HCD’s requirements, jurisdictions will need to supplement this county-level data with local estimates of people experiencing homelessness. These challenges/characteristics are counted separately and are not mutually exclusive, as an individual may report more than one challenge/characteristic. These counts should not be summed.

Source: U.S. Department of Housing and Urban Development (HUD), Continuum of Care (CoC) Homeless Populations and Subpopulations Reports (2019)
For the data table behind this figure, please refer to the Data Packet Workbook, Table HOMELS-04.

In Atherton, there were no reported students experiencing homeless in the 2019-20 school year. By comparison, San Mateo County has seen a 37.5% decrease in the population of students experiencing homelessness since the 2016-17 school year, and the Bay Area population of students experiencing homelessness decreased by 8.5%. During the 2019-2020 school year, there were still some 13,718 students experiencing homelessness throughout the region, adding undue burdens on learning and thriving, with the potential for longer term negative effects. 24 For more information, see HCD’s Building Blocks webpage for People Experiencing Homelessness:
https://www.hcd.ca.gov/community-development/building-blocks/housing-needs/people-experiencinghomelessness.shtml

59 Table 7: Students in Local Public Schools Experiencing Homelessness AcademicYear Atherton San Mateo County Bay Area
2016-17 0 1910 14990
2017-18 34 1337 15142
2018-19 0 1934 15427
2019-20 0 1194 13718
Universe: Total number of unduplicated primary and short-term enrollments within the academic year (July 1 to June 30), public schools
Notes: The California Department of Education considers students to be homeless if they are unsheltered, living in temporary shelters for people experiencing homelessness, living in hotels/motels, or temporarily doubled up and sharing the housing of other persons due to the loss of housing or economic hardship. The data used for this table was obtained at the school site level, matched to a file containing school locations, geocoded and assigned to jurisdiction, and finally summarized by geography.

Source: California Department of Education, California Longitudinal Pupil Achievement Data System (CALPADS), Cumulative Enrollment Data (Academic Years 2016-2017, 2017-2018, 2018-2019, 2019-2020) This table is included in the Data Packet Workbook as Table HOMELS-05.

6.6 Farmworkers
Across the state, housing for farmworkers has been recognized as an important and unique concern.

Farmworkers generally receive wages that are considerably lower than other jobs and may have temporary housing needs. Finding decent and affordable housing can be challenging, particularly in the current housing market.

In Atherton, the migrant worker student population totaled 16 during the 2019-20 school year and has decreased by 56.2% since the 2016-17 school year. The trend for the region for the past few years has been a decline of 2.4% in the number of migrant worker students since the 2016-17 school year. The change at the county level is a 57.1% decrease in the number of migrant worker students since the 2016-17 school year.

Table 8: Migrant Worker Student Population
AcademicYear Atherton San Mateo County Bay Area
2016-17 32 657 4630
2017-18 11 418 4607
2018-19 16 307 4075
2019-20 14 282 3976
Universe: Total number of unduplicated primary and short-term enrollments within the academic year (July 1 to June 30), public schools
Notes: The data used for this table was obtained at the school site level, matched to a file containing school locations, geocoded and assigned to jurisdiction, and finally summarized by geography.

Source: California Department of Education, California Longitudinal Pupil Achievement Data System (CALPADS), Cumulative Enrollment Data (Academic Years 2016-2017, 2017-2018, 2018-2019, 2019-2020) This table is included in the Data Packet Workbook as Table FARM-01.

60 According to the U.S. Department of Agriculture Census of Farmworkers, the number of permanent farm workers in San Mateo County has decreased since 2002, totaling 978 in 2017, while the number of seasonal farm workers has decreased, totaling 343 in 2017 (see Figure 44).

Figure 44: Farm Operations and Farm Labor by County, San Mateo County Universe: Hired farm workers (including direct hires and agricultural service workers who are often hired through labor contractors)
Notes: Farm workers are considered seasonal if they work on a farm less than 150 days in a year, while farm workers who work on a farm more than 150 days are considered to be permanent workers for that farm.

Source: U.S. Department of Agriculture, Census of Farmworkers (2002, 2007, 2012, 2017), Table 7: Hired Farm Labor For the data table behind this figure, please refer to the Data Packet Workbook, Table FARM-02.

6.7 Non-English Speakers
California has long been an immigration gateway to the United States, which means that many languages are spoken throughout the Bay Area. Since learning a new language is universally challenging, it is not uncommon for residents who have immigrated to the United States to have limited English proficiency. This limit can lead to additional disparities if there is a disruption in housing, such as an eviction, because residents might not be aware of their rights or they might be wary to engage due to immigration status concerns. In Atherton, 1.0% of residents 5 years and older identify as speaking English not well or not at all, which is below the proportion for San Mateo County.

Throughout the region the proportion of residents 5 years and older with limited English proficiency is 8%.

61 Figure 45: Population with Limited English Proficiency Universe: Population 5 years and over
Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B16005 For the data table behind this figure, please refer to the Data Packet Workbook, Table AFFH-03.

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 1
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
Racial bias 3 11% 1 20% -2
Disability bias 13 48% 5 100% -8
Familial status bias 11 41% 1 20% -10
Total cases 27 100% 5 100% -22
Per 1,000 population 0.04 0.01
2010
Cases % of Total Cases % of Total
2020 Change
(2010-2020) Page 364 of 443
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 2
Figure I-3.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 3
Figure I-4.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 4
Figure I-5.

HCD Fair Housing Inquiries by Bias, January 2013-March 2021 Source: California Department of Housing and Community Development AFFH Data Viewer Jurisdiction
Atherton 0 0 0 0 0 0 0 0
Belmont 2 0 1 0 0 0 0 9
Brisbane 0 0 0 0 0 0 0 0
Burlingame 3 0 2 0 0 0 0 6
Colma 0 0 0 0 0 0 0 0
Daly City 1 2 1 3 0 0 0 17
East Palo Alto 1 1 0 0 0 0 0 7
Foster City 4 0 0 0 0 0 0 7
Half Moon Bay 0 0 0 0 0 0 0 1
Hillsborough 0 0 0 0 0 0 0 0
Menlo Park 3 0 0 0 0 1 0 11
Millbrae 0 0 0 0 0 0 0 0
Pacifica 3 0 0 1 0 1 0 9
Portola Valley 0 0 0 0 0 0 0 0
Redwood City 5 1 1 1 0 1 0 24
San Bruno 0 0 0 0 0 0 0 5
San Carlos 1 0 1 0 0 0 0 4
San Mateo 4 2 2 2 0 0 0 27
South San Francisco 0 0 0 1 0 0 0 6
Woodside 0 0 0 0 0 0 0 2
Disability Race Total
Familial
Status
National
Origin Religion Sex Color

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 5
Figure I-6.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 6
Figure I-7.

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

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

Figure II-1.

Population by Race and Ethnicity, 2019
Source: ABAG Housing Needs Data Workbook
Figure II-2.

Population by Race and Ethnicity, Atherton, 2000-2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 8
Figure II-3.

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

Area Median Income by Race and Ethnicity, Atherton, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 9
Figure II-5.

Poverty Rate by Race and Ethnicity, Atherton, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 10
Figure II-6.
% Non-White Population by Census Block Groups, 2018
Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 11
Figure II-7.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 12
Figure II-8.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 13
Figure II-9.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 14
Figure II-10.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 15
Figure II-11.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 16
Figure II-12.

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

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

Figure II-13.

Share of Population by Disability Status, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 18
Figure II-14.
% of Population with a Disability by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

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

Figure II-15.

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

Share of Households by Size, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 20
Figure II-17.

Share of Households by Type, 2019
Source: ABAG Housing Needs Data Workbook
Figure II-18.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 21
Figure II-19.

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

Housing Units by Number of Bedrooms and Tenure, Atherton, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 22
Figure II-21.
% of Children in Married Couple Households by Census Tract, 2019 Source: California Department of Housing and Community Development AFFH Data Viewer

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 23
Figure II-22. [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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 24
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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 25
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

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

Figure II-25.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 27
Figure II-26.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 28
Figure II-27.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 29
Figure II-28.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 30
Figure II-29.

R/ECAPs 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 nonwhite 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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 31
Figure II-30.

Areas of High Segregation and Poverty, 2021
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 nonwhite 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

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

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 34
Employment
Figure III-2.

Jobs by Industry, Atherton, 2002-2018
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 35
Figure III-3.

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

Jobs to Household Ratio, Atherton, 2002-2018
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 36
Figure III-5.

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

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 37
Figure III-7.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 38
Figure III-8.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 39
Transportation
[TCAC’s transportation opportunity score and maps were not available at the time of this report]
Environment

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 40
Figure III-9.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 41
Figure III-10.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 42
Figure III-11.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 43
Patterns in disparities in access to opportunity.

Figure III-12.

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

Population with Limited English Proficiency, Atherton, 2019 Source: ABAG Housing Needs Data Workbook

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

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

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

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, Atherton, 2019
Source: ABAG Housing Needs Data Workbook
Figure III-18.

Disability by Type for the Non-Institutionalized Population 18 Years and Over, Atherton, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 48
Figure III-19.

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

Employment by Disability Status, Atherton, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 49
Figure III-21.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 50
Figure III-22
[PLACEHOLDER] San Mateo County Housing Policies and Programs Analysis
Source: ABAG.

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 51
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, Atherton,
2015-2019
Source:

ABAG Housing Needs Data
Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 52
Figure IV-3.

Housing Units by Year
Built, Atherton
Source:

ABAG Housing Needs Data Workbook
Figure IV-4.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 53
Figure IV-5.

Zillow Home Value Index, 2001-2020
Source: ABAG Housing Needs Data Workbook
Figure IV-6.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 54
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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 55
Figure IV-9.

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

Overpayment (Cost Burden) by Area Median Income (AMI), Atherton, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 56
Figure IV-11.

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

Overpayment (Cost Burden) by Family Size, Atherton, 2019 Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 57
Figure IV-13.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 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

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, Atherton, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 60
Figure IV-17.

Overcrowding by Race and Ethnicity, Atherton, 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, Atherton, 2019
Source: ABAG Housing Needs Data Workbook

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 61
Figure IV-19.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 62
Substandard housing.

Please note Figure IV-20 is not applicable in Atherton because there are no units lacking complete kitchen and plumbing facilities.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 63
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

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, Atherton, 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 Page 427 of 443
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 65
Figure IV-26.

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

Assisted Units at Risk of Conversion, Atherton, 2019
Source: ABAG Housing Needs Data Workbook
Atherton 0 0 0 0 0
San Mateo County 4,656 191 359 58 5,264
Bay Area 110,177 3,375 1,854 1,053 116,459
Low Moderate High Very High
Total Assisted
Units in Database Page 428 of 443
ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 66
Figure IV-28.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 67
Figure IV-29.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 68
Figure IV-30.

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

ROOT POLICY RESEARCH MAP AND DATA PACKET, PAGE 69
Figure IV-31.

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

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

Figure IV-32.

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

Mortgage Application Denial Rate by Race and Ethnicity, Atherton, 2018- 2019
Source: ABAG Housing Needs Data Workbook

INTRODUCTION
San Mateo County is a great place to work, live and play. But like the rest of the region, we are experiencing housing challenges. While developing enough housing to meet the demands of our strong economy and growing workforce remains a key issue, our housing needs are also diverse and changing. Just as our individual housing needs change over the course of our lifetime, the housing needed by our communities change too. Understanding those changes is critical to shaping housing policies and programs that ensure our communities are places where all of us can thrive, regardless of our age, income, and specific circumstances.

Here are some trends related to the people, jobs, and households of San MateoCounty, and what they mean for our housing needs today and into the future.

KEYTAKEAWAYS:

People
Housing
Jobs
▪ By 2026, one out of five residents will be 65 or over ▪ San Mateo County’s population is becoming more diverse ▪ The number of households will continue to grow
▪ Housing prices and rents will continue to increase
▪ The number of jobs will continue to grow
▪ Although the median income is high, many jobs paylow
wages

PEOPLE
By 2026, one outof
five residents will
be 65 or over
Under 25 25-44 45-64 65+
San Mateo County makes up 10 percent of the total
Bay Area population, which is the fifth largest
metropolitan area in the country. The number of people
living here has steadily grown over the past few
decades. In 2020, our population was estimated to be
773,244, an increase of 19 percent since 1990.
1 That
trend is expected to continue–despite the impact of
the pandemic–because jobs continue to be added.

People are also living longer, with those 65 and over
expected to make up nearly 20 percent ofthe
population by 2026. Equally important is the fact that
Millennials recently surpassed the Baby Boomers as
our largest generation. As Millennials enter their 40s, they will continue to shape countywide housing needs.

By 2026, people 25-44 and 45-64 will make up more
than 50 percent of the population.
2 What does this mean for housing needs?
Both seniors and Millennials have shown a preference
for more walkable, mixed-use neighborhoods that are
close to work, schools, parks, and amenities. The majority of seniors prefer to stay in their homes and communities, known as aging-in-place. Yet many live on fixed incomes and may have mobility issues as they age, which require supportive services.

Simultaneously, Millennials are less likely to own homes and have less savings than previous generations; they
are more likely to live alone and delay marriage; and as they start families, may be in greater need of support
when purchasing their first home. Coupled with
increasing housing prices, it is more difficult for
younger generations to rent or purchase a home than it
was for current residents.

We must address how to support our seniors as they get
older so they can stay in their homes and communities,
and make sure young people, new families, and our
workers can find housing they can afford that meets
their needs.
1 U.S. Census, American Community Survey
2 Claritias Population Facts 2021
15%
28%
Age
2020
27% 29%
19%
28%
Age
2026
27% 26%

Our population is
becoming more
diverse
Asian Latinx Other *
.5%

21.8%

4.6%

29.6%
Black White
*
San Mateo County is a very diverse place to live, even
when compared to the State of California. Countywide,
more than one-third of the population is foreign-born
and almost half speaks a language other than English
at home. By contrast, a quarter of all Californians are foreign-born and less than a quarter speak a language
other than English at home. Over 120 identified
languages are spoken in San Mateo County, with top
languages including Spanish (17 percent), Chinese (8
percent) and Tagalog (6 percent).

Our population has become increasingly more diverse
over time. In 2000, more than half of people
identified as White, which fell to 39 percent in 2019,
and is expected to decrease further to 35 percent by
2026. However, while the Asian and Latinx populations
increased during that time, but the Black population
decreased by almost half, from 3.5 to 2.2 percent.
3 What does this mean for housing needs?
When planning for housing, we need to consider a
variety of housing needs—like larger homes for multigenerational families or those with more children—and how to create opportunities for everyone to access
quality, affordable housing near schools, transit, jobs, and services.

Past exclusionary practices have prevented people
of color from purchasing homes, living in certain
neighborhoods, and building wealth over time. As a
result, they are more likely to experience poverty,
housing insecurity, displacement, and homelessness.

And while many of our communities are very diverse,
we are still contending with segregation and a lack of
equitable opportunities. To help prevent
displacement due to gentrification and to create a
future where it is possible for everyone to find the
housing they need, it will be important to plan for a
variety of housing types and affordability options in
all neighborhoods.
3 U.S. Census, American Community Survey

39.2% 24.4%

2.2%
Race +
Ethnicity
2019

51.5% 22.6%
Race +
Ethnicity
2000

3.5%

DIVERSITY
Past and projected percentage of Black,
Indigenous and People of Color (BIPOC)
49%
2000 2019 2026
Since 2000:

BIPOC population growth
from 48.5% to 60.8%
Asian Population growth
from 21.8% to 29.6%
Black Population decline
from 3.5% to 2.2%

12.3%

7.8%

2.3%
35%
46%
120
of the population is foreign born
speak a language other thanEnglish
different languages are spoken
*Due to small percentage, Other is grouped as American Indian, Alaska Native, “Other” or Multiple Races

DIVERSITY
Past and projected percentage of Black,
Indigenous and People of Color (BIPOC)
49%
2000 2019 2026
2000 to 2019:

BIPOC population growth
from 48.5% to 60.8%
Asian Population growth
from 21.8% to 29.6%
Black Population decline
from 3.5% to 2.2%

12.3%

7.8%

2.2%
35%
46%
120
of the population is foreign born
speak a language other than
English
different languages are spoken
61%
65%

for
every
HOUSEHOLDS + HOUSING
The number
of households
will continue to
grow
That’s a
48%
increase
Over the past 30 years, new home construction has not
kept up with the number of jobs added to the economy.

This has led to a housing shortage.

In 2020, there were 265,000 households in San
Mateo County. By 2050 we expect that to increase
by almost 50 percent, to 394,000.
4 This growing
demand will continue to put pressure on home prices
and rents. Given that nearly 75 percent of our housing
was built before 1980, there will also be a need to
upgrade older homes. While upgrades will be essential
to make sure housing is of high quality and safe to
residents, redevelopment or repair can sometimes
result in a loss of affordable housing, especially in older multifamily or apartment buildings.

For every six low-wage jobs ($20/hour) there is one
home in the county that is affordable to such a
worker (monthly rent of $1,500).
5 What does this mean for housing needs?
Along with planning for more housing, we also need to
consider how to best support the development of low
and moderate income housing options while preserving
existing affordable homes. This includes transitional
and supportive housing options for the unhoused and
universal design to meet accessibility and mobility
needs.

Although the majority of housing produced in the past
few decades has been single-family homes or larger
multifamily buildings, some households have become
increasingly interested in “missing middle” housing—
smaller homes that include duplexes, triplexes,
townhomes, cottage clusters, garden apartments, and
accessory dwelling units (ADUs). These smaller homes
may provide more options to a diversity of community
members across income, age, and household size.
28%
5+ unit
multifamily
Housing
stock
55%
single family
homes
68%
of households
are families
76%
of existing housing
was built before 1980
2-4 unit multifamily
and mobile homes
6 1 17% 5,264
4 Plan Bay Area 2050 Projected Growth Pattern, U.S. Census, American Community Survey 5 Association of Bay Area Governments Jobs Housing Fit
61%
17%
Built 1975
$

265K
Households in 2020
394K
Households in 2050
low wage jobs
(153,000 in total)
affordable home
(25,000 in total)
2-4 unit multifamilyand
mobile homes
of households spend
half or more of their income on
housing
existing affordable units are at
risk of being converted to
market rate

Housing rent
and prices
continue to
increase
2009 to 2020
per m

The Bay Area is a great place to live, but throughout the region and county there just isn’t enough housing for
all income levels, which has caused costs to go up.

Home prices and rents have been steadily increasing
the past two decades, but in recent years the jump has
been dramatic. Since 2009, median rent increased 41
percent to $2,200, and median home values have
more than doubled to $1,445,000.
6 Overall, many residents are paying too much for
housing, while many others have been priced out
entirely. If a household spends more than 30 percent
of its monthly income on housing, it is considered
cost-burdened. If it spends more than 50 percent, it
is considered severely cost-burdened. Renters are
usually more cost-burdened than homeowners. While
home prices have increased dramatically, homeowners
often benefit from mortgages at fixed rates, whereas
renters are subject to ups and downs of the market.

In San Mateo County, 17 percent of households spend
half or more of their income on housing, while 19
percent spend between a one-third to half. However,
these rates vary greatly across income and race. Of
those who are extremely low income—making 30
percent or less of the area median income (AMI)—88
percent spend more than half of their income on
housing. Latino renters and Black homeowners are
disproportionately cost burdened and severely costburdened. Given that people in this situation have a small amount ofincome to start with, spending more
than half what they make on housing leaves them
with very little to meet other costs, such as food and
healthcare. Very low-income households paying more
than 50 percent of their income on rent are often at a
greater risk of homelessness.7
As a result, more people are living in overcrowded or
unsafe living conditions. They are also making the
6 San Mateo County Association of Realtors, Zillow
7 U.S. Census, American Community Survey
tough choice to move further away and commute long
distances to work or school, which has created more
traffic. Since low income residents and communities of
color are the most cost burdened, they are at the highest risk for eviction, displacement, and homelessness.

What does this mean for housing needs?
Although there are complex supply, demand, and
economic factors impacting costs, not having enough
housing across all incomes has meant rent and prices are just higher. Programs and policies that can support more homes across all income levels, particularly very low,
low, and moderate income, are essential, as
are more safe, affordable housing options to address
homelessness.
$2.2K
per month

RENTER SNAPSHOT
54% are under 44 years old
76% are people of color and ata
higher risk of being displaced
1 in 4 renters...
spend 50% of income on rent
live in overcrowded households –
89% of these renters are BIPOC
Latinx are the most cost burdened
31% spend more than half and
18% spend a third to half of their
income on rent
$1.4M
$1.56K
Median rent increased 41% Home values more than doubled $$675KK

JOBS
The number of jobs
will continueto grow
The Bay Area and San Mateo County have had very
strong economies for decades. While some
communities have more jobs and some have less, we
have all been impacted by the imbalance of job growth
and housing.

Since 2010, we have added over 100,000 jobs but
only 10,000 homes.
8 At the same time, our population
is growing naturally, meaning more people are living
longer while our children are growing up and moving
out into homes of their own. All of this impacts housing demand and contributes to the rising cost of homes.

We need more housing to create a better balance.

In 2020, there were 416,700 jobs, and by 2050 we
expect that to increase 22 percent to 507,000.
9 While some jobs pay very well, wages for many others
haven’t kept up with how costly it is to live here.

That’s a
22%
increase
What does this mean for housing needs?
As we plan for housing, we need to consider the needs
of our workforce—folks who are a part of our
communities but often end their day by commuting long
distances to a place they can afford. Many have been
displaced in recent decades or years, as housing rent
and prices soared along with a job-generating economy.

The lack of workforce housing affects us all, with
teachers, fire fighters, health care professionals, food service providers, and many essential workers being
excluded from the communities they contribute to every
day. The long-term sustainability of our communities
depends on our ability to create more affordable and
equitable housing options.

NEW JOBS TO NEW HOUSING
2010 - 2020
New jobs continue to
outpace new homes
added to the County
Jobs Homes
More people
living longer
8 U.S. Census American Community Survey, State of CA Employment Development Dept (EDD) 9 Plan Bay Area 2050 Projected Growth Pattern
children
growing up and
moving out
natural growth
and more
housing needed
100K
416.7K
Jobs in 2020
507K
Jobs in 2050
10K

Our median
income is high, but
the wage gap
continues to grow
2021 Household Income:

H
To be considered low or moderate income in the Bay
Area means a very different thing than in most parts of the country. The income or wage gap—the difference
between the highest and lowest wages—is large in our
region. Affordable housing here can mean that your
favorite hairstylist, your child’s principal, or the friendly medical assistant at your doctor’s office can qualify
for—and often needs—below marketrate or subsidized
affordable housing so they can live close to their work.

The starting point for this calculation is the Area
Median Income (AMI)—the middle spot between the
lowest and highest incomes earned in San Mateo
County. Simply put, half of households make more,
and half of households make less. Moderate income is
80 to 120 percent of the AMI, low income is 50 to 80
percent AMI, and very low income is 30 to 50 percent
AMI. Below 30 percent AMI is considered extremely
low income. The rule of thumb is households should
expect to pay about a third of their income on housing.

In San Mateo County, the AMI is $104,700 for a
single person, $119,700 for a household of two and
$149,600 for a family of four. When we talk about
affordable housing, we mean housing that is
moderately priced for low or moderate income
residents so that new families and the workforce can
live in our communities. Affordable housing programs
are generally for those who earn 80 percent or below
the AMI, which is $102,450 for a single person,
$117,100 for a household of two, and $146,350 a
yearfor a household of four.
10 What does this mean for housing needs?
Given the price of land in San Mateo County and what it costs to build new housing, creating affordable housing is extremely challenging—and often impossible without
some form of subsidy. Sometimes this is in the form
of donated land from a local government or school
district. Sometimes this is in the form ofincentives to 10 State of CA Dept of Housing and Community Development (HCD) developers or zoning rules requiring affordable units to be included. Most commonly, subsidies happen through
special financing, grants, and tax credits. Often all of these factors and more are needed to make affordable
housing work. The housing element update process is an
opportunity for each community to look at what is
possible and put in place policies and programs to help make affordability a reality.
$117K
$102K
$120K
$105K
80% AMI
100% AMI
INCOME LEVELS + WAGES Extremely Low 30% AMI
Grocery Store Clerkor
Barista
$29K/Yr or $15/Hr
83% of income spent on housing*
Very Low
50% AMI
Hair Stylist
or Administrative Assistant
$38K/Yr or $20/Hr
63% of income spent on housing*
Low
80% AMI
Medical Assistant
or Preschool Teacher
$52K/Yr or $27/Hr
46% of income spent on housing*
School Administrator or
Social Worker
$86K/Yr or $45/Hr
28% of income spent on housing*
*income spent on housing based on 2k per month/studio or 1 bedroom $146K
Household of 1 Household of 2 Household of 4
Income
Income
Income
$150K

Table A: Housing Element Sites Inventory, Table Starts in Cell A2 Jurisdiction Name Site Address/Intersection 5 Digit ZIP Code Assessor Parcel Number Consoli dated Sites General Plan Designation (Current) Zoning Designatio n (Current) Minim um Densi ty
Max
Density
Allowed
(units/ac
Parcel Size
(Acres)
Existing
Use/Vacancy Infrastructure Publicly-Owned Site Status Identified in Last/Last Two Planning Cycle(s) Lower
Income
Capacit
y
Modera
te
Income
Capacit
Above Moder
ate
Incom
Total
Capa
city
Optional
Information1
Optional
Information2
ATHERTON 1000 El Camino Real 94027 070 250 190 Public Facilities & Schools PFS 0 no max 22 private school YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 30 30 60 High Resource Opportunity Area ATHERTON 50 Valparaiso 94027 070 360 070 Public Facilities & Schools PFS 0 no max 14 private school YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 10 10 20 High Resource Opportunity AreaATHERTON 170 Atherton Ave. 94027 070 070 280 Low Density SF
Residential Base: R‐1A, Ov 0 4 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 4 4 High Resource Opportunity Area ATHERTON 97 Santiago 94027 070 343 100 Low Density SF Base: R‐1A, Ov 0 1.42 vacant YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 1 1 High Resource Opportunity Area ATHERTON 60 Parkwood 94027 061 260 140 Low Density SF R‐1A 0 1 2.4 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 260 Oakgrove 94027 061 232 140 Low Density SF R‐1A 0 1 2.48 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 175 Fair Oaks 94027 060 210 210 Low Density SF R‐1A 0 1 2.47 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 188 Fair Oaks 94027 060 220 060 Low Density SF R‐1A 0 1 2.35 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 2 Fair Oaks 94027 060 321 080 Low Density SF R‐1B 0 3 0.74 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 62 Fair Oaks 94027 060 321 110 Low Density SF R‐1B 0 3 0.8 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 77 Fair Oaks 94027 060 313 070 Low Density SF R‐1B 0 3 0.8 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 4 Barmetta 94027 060 312 060 Low Density SF R‐1B 0 3 1.03 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 1 Winchester 94027 070 222 150 Low Density SF R‐1B 0 3 0.75 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 3 Odell 94027 070 221 020 Low Density SF R‐1B 0 3 0.8 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 1 Odell 94027 070 221 010 Low Density SF R‐1B 0 3 0.8 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 101 Isabella 94027 070 250 100 Low Density SF R‐1A 0 1 2.29 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 101 Britton 94027 070 214 110 Low Density SF R‐1A 0 1 2.5 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 124 Isabella 94027 070 221 450 Low Density SF R‐1A 0 1 2.7 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 75‐85 Isabella 94027 070 250 130 Low Density SF R‐1A 0 1 5.09 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 4 4 High Resource Opportunity AreaATHERTON 82 Isabella 94027 070 240 050 Low Density SF R‐1A 0 1 4 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 3 3 High Resource Opportunity AreaATHERTON 137 Almendral 94027 070 060 030 Low Density SF R‐1A 0 1 2.43 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 163 Almendral 94027 070 050 080 Low Density SF R‐1A 0 1 2.56 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 75 Tuscaloosa 94027 070 110 210 Low Density SF R‐1A 0 1 2.32 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 29 Atherton 94027 070 230 270 Low Density SF R‐1A 0 1 2.39 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 38 Atherton 94027 070 120 140 Low Density SF R‐1A 0 1 2.72 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 30 Atherton 94027 070 120 130 Low Density SF R‐1A 0 1 3 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 3 3 High Resource Opportunity AreaATHERTON 52 Atherton 94027 070 110 090 Low Density SF R‐1A 0 1 2.88 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 170 Atherton 94027 070 070 270 Low Density SF R‐1A 0 1 4.81 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 4 4 High Resource Opportunity AreaATHERTON 396 Atherton 94027 070 032 310 Low Density SF R‐1A 0 1 2.6 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 150 Elena 94027 070 180 180 Low Density SF R‐1A 0 1 2 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 2 Prado Secoya 94027 070 343 170 Low Density SF R‐1A 0 1 2 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 43 Santiago 94027 070 343 310 Low Density SF R‐1A 0 1 2.51 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 207 Atherton 94027 070 191 120 Low Density SF R‐1A 0 1 2.43 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 98 Faxon 94027 070 380 030 Low Density SF R‐1A 0 1 2.63 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 95 Faxon 94027 070 180 400 Low Density SF R‐1A 0 1 1.13 vacant YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 1 1 High Resource Opportunity AreaATHERTON 266 Park 94027 070 341 030 Low Density SF R‐1A 0 1 3.3 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 3 3 High Resource Opportunity AreaATHERTON 25 Camino al Lago 94027 070 310 010 Low Density SF R‐1A 0 1 2.23 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 220 Camino al Lago 94027 070 320 160 Low Density SF R‐1A 0 1 5.03 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 4 4 High Resource Opportunity AreaATHERTON 236 Camino al Lago 94027 070 143 030 Low Density SF R‐1A 0 1 2 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 95 Monte Vista 94027 070 180 380 Low Density SF R‐1A 0 1 5.6 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 4 4 High Resource Opportunity AreaATHERTON 73 Monte Vista 94027 070 180 230 Low Density SF R‐1A 0 1 3.45 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 3 3 High Resource Opportunity AreaATHERTON 15 Monte Vista 94027 070 131 010 Low Density SF R‐1A 0 1 1.78 vacant YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 1 1 High Resource Opportunity AreaATHERTON 237 Atherton 94027 070 170 010 Low Density SF R‐1A 0 1 9.52 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 7 7 High Resource Opportunity AreaATHERTON 34 Linda Vista 94027 070 151 030 Low Density SF R‐1A 0 1 2.18 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 83 Fairvie w 94027 070 161 090 Low Density SF R‐1A 0 1 2 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity Area ATHERTON 260 Atherton 94027 070 032 440 Low Density SF R‐1A 0 1 5.75 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 4 4 High Resource Opportunity AreaATHERTON 270 Atherton 94027 070 032 190 Low Density SF R‐1A 0 1 2 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 268 Atherton 94027 070 032 200 Low Density SF R‐1A 0 1 2.35 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 282 Polhemus 94027 070 012 350 Low Density SF R‐1A 0 1 2.12 vacant YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 290 Polhemus 94027 070 012 180 Low Density SF R‐1A 0 1 5.3 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 4 4 High Resource Opportunity AreaATHERTON 346 Walsh 94027 073 220 020 Low Density SF R‐1A 0 1 2.9 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 354 Walsh 94027 073 220 010 Low Density SF R‐1A 0 1 2.5 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 86 Tallwood 94027 074 130 300 Low Density SF R‐1A 0 1 2.8 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 383 Walsh 94027 074 130 320 Low Density SF R‐1A 0 1 10.35 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 8 8 High Resource Opportunity AreaATHERTON 75 Reservoir 94027 073 263 010 Low Density SF R‐1A 0 1 3.27 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 200 Polhemus 94027 070 022 410 Low Density SF R‐1A 0 1 2.6 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 78 Winchester 94027 070 230 080 Low Density SF R‐1B 0 3 1.04 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 28 Isabella 94027 070 223 060 Low Density SF R‐1B 0 3 0.74 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 2 2 High Resource Opportunity AreaATHERTON 48 Lloyden 94027 060 304 020 Low Density SF R‐1B 0 3 1.2 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Used in Prior Housing Element ‐ Non‐Vacant 3 3 High Resource Opportunity AreaATHERTON 198 Fair Oaks 94027 060 220 270 Low Density SF R‐1A 0 1 2.35 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 2 2 High Resource Opportunity AreaATHERTON 18 Fair Oaks 94027 060 321 090 Low Density SF R‐1B 0 3 1.05 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 3 3 High Resource Opportunity AreaATHERTON 131 Britton 94027 072 214 160 Low Density SF R‐1A 0 1 2.61 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 2 2 High Resource Opportunity AreaATHERTON 191 Britton 94027 072 214 170 Low Density SF R‐1A 0 1 2.68 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 2 2 High Resource Opportunity AreaATHERTON 167 Isabella 94027 070 214 060 Low Density SF R‐1A 0 1 2.26 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 2 2 High Resource Opportunity AreaATHERTON 12 Faxon Forrest 94027 070 180 240 Low Density SF R‐1A 0 1 0.95 vacant YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 1 1 High Resource Opportunity AreaATHERTON 16 Faxon Forrest 94027 070 180 350 Low Density SF R‐1A 0 1 0.85 vacant YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 1 1 High Resource Opportunity AreaATHERTON 25 Monte Vista 94027 070 131 020 Low Density SF R‐1A 0 1 0.98 vacant YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 1 1 High Resource Opportunity AreaATHERTON 99 Broadacres 94027 073 220 070 Low Density SF R‐1A 0 1 3 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 2 2 High Resource Opportunity AreaATHERTON 172 Tuscaloosa 94027 070 050 220 Low Density SF R‐1A 0 1 2 residential 1 YES ‐ Current NO ‐ Privately‐Owned Available Not Used in Prior Housing Element 2 2 High Resource Opportunity Area ATTACHMENT 5

Table B: Candidate Sites Identified to be Rezoned to Accommodate Shortfall Housing Need, Table Starts in Cell A2 Jurisdiction Site 5 Digit ZIP Code Asse Very Low- Moder Abo v
Type of Parcel Size Current Current Zoning Proposed Propose Minim Maxim Total Vacant/ Description Optional Optional
ATHERTON 23 Oakwood 94070 4 0 12 Both 1.62 single family single family multi family RM 10 0 10 16 Non-Vacant single family hodeveloper interestATHERTON 97 Cebalo 94070 Both 0.37 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 98 Cebalo 94070 Both 0.34 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 94 Cebalo 94070 Both 0.41 single family single family multi family RM 20 0 20 4 Non-Vacant single family homeATHERTON 75 Cebalo 94070 Both 0.934 single family single family multi family RM 20 0 20 9 Non-Vacant single family homeATHERTON 8 Selby Lane 94070 Both 0.37 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 2 Selby Lane 94070 Both 0.33 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 1 Gresham Lane 94070 Both 0.32 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 25 Gresham Lane 94070 Both 0.32 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 37 Gresham Lane 94070 Both 0.32 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 49 Gresham Lane 94070 Both 0.32 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 61 Greham Lane 94070 Both 0.32 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 73 Greham Lane 94070 Both 0.32 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 85 Greham Lane 94070 Both 0.32 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 97 Gresham Lane 94070 Both 0.41 single family single family multi family RM 20 0 20 4 Non-Vacant single family homeATHERTON 182 el Camino 94070 Both 0.34 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 190 El Camino 94070 Both 0.34 single family single family multi family RM 20 0 20 3 Non-Vacant single family homeATHERTON 2 Stockbridge 94070 Both 0.24 single family single family multi family RM 20 0 20 2 Non-Vacant single family home 1 ATTACHMENT 5

Table C: Land Use, Table Starts in A2
Zoning Designation
(From Table A, Column G) General Land Uses Allowed
Base:

R‐1A Residential District, RM 20 Multifamily Resid
e Single family and multifamiy residential uses
Base:

R‐1A Residential District, RM 10 Multifamily Resid
e Single family and multifamiy residential uses
R‐1A Residential District, max. 1 du/ac Single family residential uses, https://atherton.municipal.codes/Code/17.32R‐1B Residential District, max. 3 du/ac Single family residential uses, https://atherton.municipal.codes/Code/17.33 PFS Public Facilities
& Schools District Public uses (Town Hall, public utilities, etc.) public & private schools, churches, country clubs, and other assembly and civic uses, convent housing, teacher housing, POS Parks
& Open Space District Parks, recreation, and open space uses, including caretaker housing, https://atherton.municipal.codes/Code/17.34 ATTACHMENT 5