The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
 ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
Null - Could not be geocoded (does not appear on the map) 

For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

California State Income Limits reflect updated median income and household income levels for acutely low-, extremely low-, very low-, low- and moderate-income households for California’s 58 counties (required by Health and Safety Code Section 50093). These income limits apply to State and local affordable housing programs statutorily linked to HUD income limits and differ from income limits applicable to other specific federal, State, or local programs.

These maps display wildfire risk exposure of households in the state of California. Estimates of the number of households that fall within areas with the highest wildfire risk are aggregated within small area hexagons and then displayed on the map using a color-coding scheme described in the legend. Lighter yellow hues signify the highest concentration of individual households in highest risk areas. A hexagon that is orange or yellow does not necessarily indicate that wildfire risk is highest, overall, but rather that the hexagon contains the most households in high risk areas, relative to other hexagons. These orange and yellow areas, then, are the most likely to involve conflagrations that impact large populations. Therefore, estimates of concentrations can serve as a guide to conduct further data analysis and vulnerability assessments, emergency planning, and resource allocation.MethodologyTo produce these maps, researchers used 2017 United States Postal Service (USPS) address vacancy data1. The USPS vacancy data is a quarterly updated dataset of the universe of all addresses in the United States and are, thus, the most granular geographic housing unit data available to HUD. The data of occupied residential units can be geographically plotted at the Zip+4 centroid, which typically encompasses 10-30 actual housing units. To assess wildfire risk, researchers matched Zip+4 centroids to the 2018 Wildfire Hazard Potential (WHP) Map produced by the US Forest Service2. This national wildfire risk data is an index generated from multiple data sources that measure attributes such as wildfire likelihood and intensity, fuel and vegetation, and past fire occurrences. The result is a national map of wildfire potential at 270- m.² resolution. For this analysis, Zip+4 centroids that matched with WHP risk of High or Very High are used to convey high-risk households. The maps only display counts of households that are at high or very high risk. The household counts are then aggregated within small area hexagons measuring approximately four-mi.², which are assigned a color based on the number of at-risk households.A note of caution regarding USPS vacancy data. Though this data is more granular than Census data with regard to geographic location, the Zip+4 centroids associate with capture areas of varying geographic size, such that rural Zip+4 centroids tend to include addresses that are more geographically dispersed. As a result, and because of how we used the Zip+4 centroid data to match to granular wildfire risk data, the rural data may be less accurate in flagging wildfire risk, either over-counting or under-counting households within high-risk areas.

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, and gender. This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives. The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity are separate files but are now combined. Information updated as of 7/15/2024.

This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

This service denotes the locations of colonias communities as defined in Section 916 of the Cranston-Gonzalez National Affordable Housing Act of 1990.In order to better serve colonia residents, the National Affordable Housing Act of 1990 (as amended) included Section 916 which called for the border states of Arizona, California, New Mexico and Texas to set aside a percentage of their annual State CDBG allocations for use in the colonias. The use of these set aside funds is to help meet the needs of the colonias residents in relationship to the need for potable water, adequate sewer systems, or decent, safe and sanitary housing. Therefore, the set-aside funds may be utilized for any CDBG eligible activity that is, or is in conjunction with, a potable water, sewer or housing activity.Per Section 916 of the Cranston-Gonzalez National Affordable Housing Act of 1990, a "colonia" refers to any community that meets the following criteria:(A) is in the State of Arizona, California, New Mexico, or Texas;(B) is in the area of the United States within 150 miles of the border between the United States and Mexico, except that the term does not include any standard metropolitan statistical area that has a population exceeding 1,000,000;(C) is designated by the State or county in which it is located as a colonia;(D) is determined to be a colonia on the basis of objective criteria, including lack of potable water supply, lack of adequate sewage systems, and lack of decent, safe, and sanitary housing, and;(E) was in existence and generally recognized as a colonia before the date of the enactment of the Cranston-Gonzalez National Affordable Housing Act.To learn more about the State Community Development Block Grant Colonias Set-Aside visit: https://www.hudexchange.info/programs/cdbg-colonias/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 2019

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

Housing-Burdened Low-Income Households. Percent of households in a census tract that are both low income (making less than 80% of the HUD Area Median Family Income) and severely burdened by housing costs (paying greater than 50% of their income to housing costs). (5-year estimates, 2013-2017).

The cost and availability of housing is an important determinant of well- being. Households with lower incomes may spend a larger proportion of their income on housing. The inability of households to afford necessary non-housing goods after paying for shelter is known as housing-induced poverty. California has very high housing costs relative to much of the country, making it difficult for many to afford adequate housing. Within California, the cost of living varies significantly and is largely dependent on housing cost, availability, and demand.

Areas where low-income households may be stressed by high housing costs can be identified through the Housing and Urban Development (HUD) Comprehensive Housing Affordability Strategy (CHAS) data. We measure households earning less than 80% of HUD Area Median Family Income by county and paying greater than 50% of their income to housing costs. The indicator takes into account the regional cost of living for both homeowners and renters, and factors in the cost of utilities. CHAS data are calculated from US Census Bureau's American Community Survey (ACS).

The United States Department of Housing and Urban Development’s (HUD) Housing Choice Voucher (HCV) Program assists very low-income families, the elderly, and the disabled in obtaining decent, safe, and sanitary housing in the private market. Public Housing Authorities (PHAs) receive federal funds from HUD to administer the voucher program, and housing subsidies are paid to the landlord directly by the PHA on behalf of the participating family. The voucher recipient remains responsible for paying any difference that exists between the actual rent charged by the landlord and the amount subsidized by the program. Voucher recipients are responsible for finding a suitable housing unit where the owner agrees to rent under the program. Because housing assistance is provided on behalf of the family or individual, participants are free to choose their own housing, including single-family homes, townhouses, and apartments provided that the chosen housing meets the requirements of the program, and is not limited to units located in subsidized housing projects. Qualified housing may also include the family's present residence. Furthermore, under certain circumstances, and if authorized by the PHA, a family may use its voucher to purchase a modest home.

Public data pertaining to the locations of HCV program participants are only available as United States Census Tract aggregations. Moreover, to protect the confidentiality of those receiving Housing Choice Voucher Program assistance, tracts containing 10 or fewer voucher holders have been omitted from this service.

This feature set includes both tenant-based vouchers and project-based vouchers.

The California Department of Housing and Community Development (HCD) sourced the original data from: https://hudgis-hud.opendata.arcgis.com/datasets/housing-choice-vouchers-by-tract.

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, gender, veteran status, and disability status.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity are separate files but are now combined.

Information updated as of 2/06/2025.

This conference aims to introduce and explain the fair housing laws under the HUD federal provisions and state proposed measures. Housing policies mandated by HUD funds aim to reduce housing segregation where it exists (e.g. building public housing in places of opportunity). Community participation is an integral part of assessing any fair housing process. The conference will also provide an opportunity to find existing data narratives that support the stories of residents in the community by providing evidence in the form of data. Aside from the local and regional data HUD provides, other data sources (e.g. CalEnviro Screen, Regional Opportunity Index, EnviroAtlas) can be used to show racially and/or ethnically concentrated areas of poverty, disparities in access to opportunities, and the overall outcomes of segregated neighborhoods. These data are described in Appendices A-D. In order to understand the legal requirements of fair housing, the local legal services organization Central California Legal Services, and a researcher roundtable, informed by community narratives, will collectively identify further research needed in the areas of affordable housing, education, active transportation, and economic development. Additionally, the goal is that participants locate themselves within the fair housing conversation and are able to collaborate across sectors to provide a full picture of housing needs, and potential for the rural and urban communities in Fresno County.

The Point In Time Unsheltered Homeless Census by Gender data. The Housing Inventory Count Submitted to the U.S. Department of Housing and Urban Development (HUD) Additional information available at: https://www.hudexchange.info/resources/documents/Notice-CPD-17-08-2018-HIC-PIT-Data-Collection-Notice.pdf 2017 data collected January 26, 2017, 2018 data collected February 22, 2018.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

Data is from the Continuum of Care’s annual housing inventory count required by HUD and reported annually.

Housing Inventory Count Submitted to the U.S. Department of Housing and Urban Development (HUD) Additional information available at: https://www.hudexchange.info/resources/documents/Notice-CPD-16-060-2017-HIC-PIT-Data-Collection-Notice.pdf