Draft Housing Needs Data Report — East Palo Alto City Council, July 19, 2022

1 HOUSING NEEDS DATA REPORT: EAST

PALO ALTO
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............................................................................................36

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: Substandard Housing Issues.....................................................................................36 Figure 23: Home Values of Owner-Occupied Units.......................................................................37 Figure 24: Zillow Home Value Index (ZHVI)...............................................................................37 Figure 25: Contract Rents for Renter-Occupied Units...................................................................38 Figure 26: Median Contract Rent ...........................................................................................39 Figure 27: Cost Burden by Tenure ..........................................................................................40 Figure 28: Cost Burden by Income Level ..................................................................................41 Figure 29: Cost Burden by Race.............................................................................................42 Figure 30: Cost Burden by Household Size ................................................................................43 Figure 31: Cost-Burdened Senior Households by Income Level ........................................................44 Figure 32: Overcrowding by Tenure and Severity ........................................................................45 Figure 33: Overcrowding by Income Level and Severity ................................................................46 Figure 34: Overcrowding by Race...........................................................................................47 Figure 35: Household Size by Tenure ......................................................................................48 Figure 36: Housing Units by Number of Bedrooms .......................................................................49 Figure 37: Household Type ..................................................................................................50 Figure 38: Female-Headed Households by Poverty Status ..............................................................51 Figure 39: Senior Households by Income and Tenure....................................................................52 Figure 40: Disability by Type ................................................................................................53 Figure 41: Homelessness by Household Type and Shelter Status, San Mateo County ..............................55 Figure 42: Racial Group Share of General and Homeless Populations, San Mateo County.........................56 Figure 43: Latinx Share of General and Homeless Populations, San Mateo County.................................57 Figure 44: Characteristics for the Population Experiencing Homelessness, San Mateo County...................58 Figure 45: Farm Operations and Farm Labor by County, San Mateo County .........................................60 Figure 46: Population with Limited English Proficiency.................................................................61

0.3 List of tables
4 Table 1: Illustrative Regional Housing Needs Allocation from Draft Methodology ..................................10 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 East Palo Alto. 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 East Palo Alto increased by 4.4% from 2000 to 2020, which is below the growth rate of the Bay Area.
• Age – In 2019, East Palo Alto’s youth population under the age of 18 was 8,381 and senior population 65 and older was 2,033. These age groups represent 28.3% and 6.9%, respectively, of East Palo Alto’s population.
• Race/Ethnicity – In 2020, 10.1% of East Palo Alto’s population was White while 10.9% was African American, 9.6% was Asian, and 66.1% was Latinx. People of color in East Palo Alto comprise a proportion above the overall proportion in the Bay Area as a whole.1 • Employment – East Palo Alto residents most commonly work in the Health & Educational Services industry. From January 2010 to January 2021, the unemployment rate in East Palo Alto decreased by 5.4 percentage points. Since 2010, the number of jobs located in the jurisdiction increased by 470 (13.2%). Additionally, the jobs-household ratio in East Palo Alto has increased from 0.37 in 2002 to 0.57 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 East Palo Alto increased, 1.3% 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 East Palo Alto residents to live and thrive in the community.
– Ownership The largest proportion of homes had a value in the range of $750k-$1M in 2019. Home prices increased by 160.0% from 2010 to 2020. – Rental Prices – The typical contract rent for an apartment in East Palo Alto was $1,630 in 2019. Rental prices increased by 65.7% from 2009 to 2019. To rent a typical apartment without cost burden, a household would need to make $65,560 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, 54.1% of homes in East Palo Alto were single family detached, 4.1% were single family attached, 3.4% were small multifamily (2-4 units), and 36.6% were medium or large multifamily (5+ units). Between 2010 and 2020, the number of

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 single-family units increased more than multi-family units. Generally, in East Palo Alto, the share of the 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 East Palo Alto, 28.8% of households spend 30%-50% of their income on housing, while 25.2% 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, 64.7% of households in East Palo Alto live in neighborhoods that are susceptible to or experiencing displacement, and 0.0% live in areas at risk of or undergoing gentrification. 0.0% of households in East Palo Alto 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 – 0.0% of residents in East Palo Alto live in neighborhoods identified as “Highest Resource” or “High Resource” areas by State-commissioned research, while 100.0% 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 East Palo Alto, 6.4% of residents have a disability of any kind and may require accessible housing. Additionally, 27.5% of East Palo Alto households are larger households with five or more people, who likely need larger housing units with three bedrooms or more. 20.6% 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

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 respondents had been reached. We use the five-year release to get a 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 East Palo Alto.
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 East Palo Alto, the proposed RHNA to be planned for this cycle is 829 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

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 their Housing Elements will be released at the end of 2021. The potential allocation that East Palo Alto 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 East Palo
Alto Units
San Mateo
County
Units
Bay Area
Units
East Palo
Alto Percent
San Mateo
County
Percent
Bay Area
Percent
Very Low Income
(<50% of AMI) 165 12196 114442 19.9% 25.6% 25.9%
Low Income (50%-
80% of AMI) 95 7023 65892 11.5% 14.7% 14.9%
Moderate Income
(80%-120% of AMI) 159 7937 72712 19.2% 16.6% 16.5%
Above Moderate
Income (>120% of
AMI)
410 20531 188130 49.5% 43.1% 42.6%
Total 829 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, East Palo Alto’s population has increased by

4.4%; this rate is below that of the region as a whole, at 14.8%. In East Palo Alto, roughly 10.6% of its population moved during the past year, a number 2.8 percentage points smaller than the regional rate of 13.4%.

Table 2: Population Growth Trends
Geography 1990 1995 2000 2005 2010 2015 2020
East Palo Alto 23451 24710 29506 32080 28155 30236 30794 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 East Palo Alto was estimated to be 30,794 (see Table 2). From 1990 to 2000, the population increased by 25.8%, while it decreased by 4.6% during the first decade of the 2000s. In the most recent decade, the population increased by 9.4%. The population of East Palo Alto makes up

4.0% 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 East Palo Alto, the median age in 2000 was 25.8; by 2019, this figure had increased, landing at around 30 years. More specifically, the population of those under 14 has decreased since 2010, while the 65-and-over 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 68.2% of seniors and 65.9% 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 today9. Since 2000, the percentage of residents in East Palo Alto identifying as White has increased – and by the same token the percentage of residents of all other races and ethnicities has decreased – by 3.4 percentage points, with the 2019 population standing at 2,991 (see Figure 4). In absolute terms, the Hispanic or Latinx population increased the most while the Black or African American, 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 East Palo Alto increased by 46.9% (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 15,101 employed residents, and 5,225 jobs10 in East Palo Alto - the ratio of jobs to resident workers is 0.35; East Palo Alto 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. East Palo Alto has more low-wage residents than low-wage jobs (where low-wage refers to jobs paying less than $25,000). At the other end of the

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 wage 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 East Palo Alto has increased from 0.37 in 2002, to 0.57 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 East Palo Alto residents work is Health & Educational 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 East Palo Alto, there was a 5.4 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 East Palo Alto, 17.4% of households make more than 100% of the Area Median Income (AMI)13, compared to 25.5% 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 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 East Palo Alto 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 East Palo Alto’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 50.0% of East Palo Alto’s very low-income RHNA is for extremely low-income households.

According to the data shown below (Figure 11), 3,837 of East Palo Alto’s households are 0-50% AMI while 1,918 are extremely low-income. Therefore, extremely low-income households represent 50.0% of households who are 0-50% AMI, as 1,918 divided by 3,837 is 50.0%. 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 East Palo Alto’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 of the AMI are moderate-income, those making 50 to 80 percent are low-income, those making 30 to 50 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 East Palo Alto, the largest proportion of renters falls in the 0%-30% 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.

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 East Palo Alto, American Indian or Alaska Native (Hispanic and Non-Hispanic) residents experience the highest rates of poverty, followed by Other Race or Multiple Races (Hispanic and Non-Hispanic) 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 East Palo Alto there are a total of 7,724 housing units, and more residents rent than own their homes: 60.2% versus 39.8% (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 East Palo Alto, 60.2% of Black households owned their homes, while homeownership rates were 60.0% for Asian households, 27.7% for Latinx households, and 31.9% 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 East Palo Alto, 74.3% of householders between the ages of 25 and 44 are renters, while 26.4% 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 East Palo Alto, 63.6% of households in detached single-family homes are homeowners, while 6.0% 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 East Palo Alto, 64.7% 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 0.0% of households in East Palo Alto 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 East Palo Alto in 2020 was made up of 54.1% single family detached homes, 4.1% single family attached homes, 3.4% multifamily homes with 2 to 4 units, 36.6% multifamily homes with 5 or more units, and 1.9% mobile homes (see Figure 19). In East Palo Alto, the housing type that experienced the most growth between 2010 and 2020 was Single-Family Home: Detached.

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 East Palo Alto, the largest proportion of the housing stock was built 1940 to 1959, with 3,262 units constructed during this period (see Figure 20). Since 2010, 1.5% of the current housing stock was built, which is 128 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 7.4% of the overall housing stock in East Palo Alto. The rental vacancy stands at

8.5%, while the ownership vacancy rate is 2.4%. Of the vacant units, the most common type of vacancy is For Rent (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 (7.4%). 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, 95 housing units were issued permits in East Palo Alto. 6.3% of permits issued in East Palo Alto were for above moderate-income housing, 36.8% were for moderate-income housing, and 56.8% were for low- or very low-income housing (see Table 3).

Table 3: Housing Permitting
Income Group value
Moderate Income Permits 35
Low Income Permits 34
Very Low Income Permits 20
Above Moderate Income Permits 6
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 466 assisted units in East Palo Alto 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 East Palo Alto San Mateo County Bay Area
Low 466 4656 110177
Moderate 0 191 3375
High 0 359 1854
Very High 0 58 1053
Total Assisted Units in Database 466 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 East Palo Alto. For example, 2.0% of renters in East Palo Alto reported lacking a kitchen and 1.6% of renters lack plumbing, compared to 0.0% of owners who lack a kitchen and 1.5% 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. 36 Figure 22: Substandard Housing Issues
Universe: Occupied housing units
Notes: Per HCD guidance, this data should be supplemented by local estimates of units needing to be rehabilitated or replaced based on recent windshield surveys, local building department data, knowledgeable builders/developers in the community, or nonprofit housing developers or organizations.

Source: U.S. Census Bureau, American Community Survey 5-Year Data (2015-2019), Table B25053, Table B25043, Table B25049 For the data table behind this figure, please refer to the Data Packet Workbook, Table HSG-06.

5.4 Home and Rent Values
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 East Palo Alto was estimated at $936,680 by December of 2020, per data from Zillow. The largest proportion of homes were valued between $750k-$1M (see Figure 23). 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 151.0% in East Palo Alto from $373,130 to $936,680. This change is above the change in San Mateo County, and above the change for the region (see Figure 24). 37 Figure 23: 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.

Figure 24: 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 38 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 East Palo Alto, the largest proportion of rental units rented in the Rent $1500-$2000 category, totaling 30.7%, followed by 25.6% of units renting in the Rent $1000-$1500 category (see Figure 25).

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.

Figure 25: 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 65.7% in East Palo Alto, from $1,210 to $1,630 per month (see Figure 26). 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 24 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 39 Figure 26: 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.

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. 40 Figure 27: 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 East Palo Alto, 26.2% of renters spend 30% to 50% of their income on housing compared to 19.8% of those that own (see Figure 27). Additionally, 29.4% of renters spend 50% or more of their income on housing, while 21.9% of owners are severely cost-burdened.

In East Palo Alto, 25.2% of households spend 50% or more of their income on housing, while 28.8% spend 30% to 50%. However, these rates vary greatly across income categories (see Figure 28). For example,

67.5% of East Palo Alto households making less than 30% of AMI spend the majority of their income on housing. For East Palo Alto residents making more than 100% of AMI, just 0.0% are severely costburdened, and 93.2% of those making more than 100% of AMI spend less than 30% of their income on housing.
41 Figure 28: 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.

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.

Other Race or Multiple Races, Non-Hispanic residents are the most cost burdened with 42.2% spending 30% to 50% of their income on housing, and Hispanic or Latinx residents are the most severely cost burdened with 29.6% spending more than 50% of their income on housing (see Figure 29). 42 Figure 29: 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.

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 East Palo Alto, 29.0% of large family households experience a cost burden of 30%-50%, while 24.8% of households spend more than half of their income on housing. Some 28.7% of all other households have a cost burden of 30%-50%, with 25.3% of households spending more than 50% of their income on housing (see Figure 30).
43 Figure 30: 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.

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. 55.8% 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, 84.7% are not cost-burdened and spend less than 30% of their income on housing (see Figure 31).
44 Figure 31: 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.

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 East Palo Alto, 17.5% of households that rent are severely overcrowded (more than 1.5 occupants per room), compared to 3.2% of households that own (see Figure 32). In East Palo Alto, 18.3% of renters experience moderate overcrowding (1 to 1.5 occupants per room), compared to 7.9% for those own. 45 Figure 32: 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.

Overcrowding often disproportionately impacts low-income households. 8.6% of very low-income households (below 50% AMI) experience severe overcrowding, while 7.9% of households above 100% experience this level of overcrowding (see Figure 33).
46 Figure 33: 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.

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 East Palo Alto, the racial group with the largest overcrowding rate is Hispanic or Latinx (see Figure 34) 47 Figure 34: 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 East Palo Alto, for large households with 5 or more persons, most units (63.3%) are renter occupied (see Figure 35). In 2017, 48.3% of large households were very low-income, earning less than 50% of the area median income (AMI).

Figure 35: 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 3,678 units in East Palo Alto. Among these large units with 3 or more bedrooms, 32.3% are owner-occupied and 67.7% are renter occupied (see Figure 36).
49 Figure 36: 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 East Palo Alto, the largest proportion of households is Married-couple Family Households at 48.1% of total, while Female-Headed Households make up 20.6% of all households. 50 Figure 37: 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 East Palo Alto, 30.9% of female-headed households with children fall below the Federal Poverty Line, while 1.0% of female-headed households without children live in poverty (see Figure 38). 51 Figure 38: 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 0%-30% of AMI, while the largest proportion of senior households who are homeowners falls in the income group 0%-30% of AMI (see Figure 39).
52 Figure 39: 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.

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 40 shows the rates at which different disabilities are present among residents of East Palo Alto. Overall, 6.4% of people in East Palo Alto 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 40: 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 East Palo Alto, of the population with a developmental disability, children under the age of 18 make up 50.0%, while adults account for 50.0%.

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+ 93
Age Under 18 93
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 East Palo Alto is the home of parent /family /guardian.

Table 6: Population with Developmental Disabilities by Residence Residence Type value
Home of Parent /Family /Guardian 173
Independent /Supported Living 13
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 41).
55 Figure 41: 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 42). 56 Figure 42: 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 43). 57 Figure 43: 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.

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 44: 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.

In East Palo Alto, the student population experiencing homelessness totaled 524 during the 2019-20 school year and decreased by 31.7% since the 2016-17 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.

The number of students in East Palo Alto experiencing homelessness in 2019 represents 43.9% of the San Mateo County total and 3.8% of the Bay Area total.

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 East Palo Alto San Mateo County Bay Area
2016-17 767 1910 14990
2017-18 441 1337 15142
2018-19 946 1934 15427
2019-20 524 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 East Palo Alto, there were no reported students of migrant workers in the 2019-20 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 East Palo Alto San Mateo County Bay Area
2016-17 58 657 4630
2017-18 0 418 4607
2018-19 0 307 4075
2019-20 0 282 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.

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 45). 60 Figure 45: 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 East Palo Alto, 13.8% of residents 5 years and older identify as speaking English not well or not at all, which is above 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 46: 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.

East Palo Alto City CouncilJuly 19, 2022

East Palo Alto City Council
July 19, 2022