Graph and download economic data for New Privately-Owned Housing Units Completed: Units in Buildings with 5 Units or More (COMPU5MUSA) from Jan 1968 to May 2025 about 5-unit structures +, new, private, housing, and USA.

The Survey of Market Absorption of New Multifamily Units (SOMA) is sponsored by the Department of Housing and Urban Development (HUD) and administered by the U.S. Census Bureau. SOMA collects data for new residential construction of multifamily units, including apartment buildings and condominium buildings. SOMA provide information on amenities, rent/sales price levels, number of units, type of building, and the number of units taken off the market (absorbed).SOMA uses the Census Bureau's Survey of Construction (SOC) as its sampling base. Each month, a sample of residential buildings containing five or more units is selected for SOMA. The initial 3-month interview collects information on amenities, rent or sales price levels, number of units, type of building, and the number of units taken off the market (absorbed). Field representatives conduct subsequent interviews, if necessary, at 6, 9, and 12 months after completion.

Graph and download economic data for Rental Vacancy Rate in the United States (RRVRUSQ156N) from Q1 1956 to Q1 2025 about vacancy, rent, rate, and USA.

Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions:The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

Monthly Multifamily Insured Loan Data 2020

  Multifamily insured loan data that has had the PII removed, as of August 31, 2020 (updated monthly) About HUD Housing and Funding Allocation Data: Links to several different HUD datasets including CARES Act and Indian Housing Block Grant FY2020 allocations, and monthly single- and multi-family 2020 loan data with the PII removed. Other datasets contain sheltered/unsheltered/total homeless data by demographic, HUD Continuum of Care area, and State, shelter capacity by state yearly from 2007 to 2019, and American Community Survey 2014-2018 5-year county level estimates for median rent value.
  Geography Level: State, City, ZipItem Vintage: 2020
  Update Frequency: N/AAgency: HUD (Multiple)Available File Type: Excel with PDF Supplement (All links go to same FHA dataset) 

  Return to Other Federal Agency Datasets Page

Graph and download economic data for New Privately-Owned Housing Units Started: Units in Buildings with 5 Units or More (HOUST5F) from Jan 1959 to May 2025 about 5-unit structures +, housing starts, privately owned, housing, and USA.

Monthly Multifamily Terminated Loan Data 2020

  Multifamily terminated loan data that has had the PII removed, as of August 31, 2020 (updated monthly) About HUD Housing and Funding Allocation Data: Links to several different HUD datasets including CARES Act and Indian Housing Block Grant FY2020 allocations, and monthly single- and multi-family 2020 loan data with the PII removed. Other datasets contain sheltered/unsheltered/total homeless data by demographic, HUD Continuum of Care area, and State, shelter capacity by state yearly from 2007 to 2019, and American Community Survey 2014-2018 5-year county level estimates for median rent value.
  Geography Level: State, City, ZipItem Vintage: 2020
  Update Frequency: N/AAgency: HUD (Multiple)Available File Type: Excel with PDF Supplement (All links go to same FHA dataset) 

  Return to Other Federal Agency Datasets Page

Housing Starts Multi Family in the United States decreased to 316 Thousand units in May from 454 Thousand units in April of 2025. This dataset includes a chart with historical data for the United States Housing Starts Multi Family.

PI-provided abstract: The Census Bureau took the Residential Finance Survey (RFS) as part of the decennial census from 1950-2000. The RFS is the only survey designed to collect and produce data about the financing of nonfarm, privately-owned residential properties. The RFS is a unique survey for several reasons: It collects, tabulates, and presents data for properties, the standard unit of reference for financial transactions related to housing. In most other demographic surveys, the unit of reference is the person, household, or housing unit. It is the only source of information on property, mortgage, and financial characteristics for multi-unit rental properties. Information on multi-family loans and properties is particularly difficult to obtain, but is important to understand if progress is to be made in the development of standards for underwriting multi-family mortgages. It conducts interviews of property owners and mortgage lenders, resulting in more accurate information on property and mortgage characteristics. The RFS is the only survey which is able to provide a comprehensive view of mortgage finance in the USA, by providing information not only about the loan itself from the lender, but also information about the property owner's demographic characteristics. As part of the decennial census, it is mandatory. This is important in collecting information from mortgage lenders. The RFS is exempt from statutes prohibiting release of financial records by financial institutions. It is able to subdivide the industry into relevant components. Different parts of the industry have excellent information on their own loans and clients, but not that of the industry as a whole. Information on lending by individual investors or small groups of investors such as pension funds is collected only by the RFS.

As the renewable energy transition accelerates, housing, due to its high energy demand, can play a critical role in the clean energy shift. Specifically, multifamily housing provides a unique opportunity for solar photovoltaic (PV) system adoption, given the existing competing interests between landlords and tenants which has historically slowed this transition. To address this transition gap, this project identified and ranked Metropolitan Statistical Areas (MSAs) in the United States for ZNE Capital (the client) to acquire multifamily housing to install solar PV systems. The group identified seven criteria to determine favorable markets for rooftop solar PV on multifamily housing: landlord policy favorability, real estate market potential, CO2 abatement potential, electricity generation potential, solar installation internal rate of return, climate risk avoidance, and health costs associated with primary air pollutants. A total investment favorability score is calculated based on criteria importance assigned by the user. Investment favorability scores were investigated for different preferences to demonstrate the robustness and generalizability of the framework. The data analysis and criteria calculations were conducted using RStudio, ultimately to provide reproducible code to be used for future projects. The results are presented in a ranked list from best to worst metro areas to invest in. Future studies can utilize the reproducible code to inform decisions on where to invest in solar PV on multifamily housing anywhere in the United States by changing weights within the model depending on preferences. Methods

Collecting real estate and landlord data for metropolitan statistical areas (MSAs) from federal agency databases.

Real estate metrics: Six indicator metrics were selected to represent areas with growing housing demands. The metrics included were population growth, employment growth, average annual occupancy, annual rent change, the ratios of median annual rent to median income, and median income to median home price. The population estimates and median income data was downloaded from the Census Bureau. Median rent data was downloaded from HUDuser. Median home price data was downloaded from National Association of REALTORS®. Students were provided temporary memberships to Yardi Systems Matrix to obtain multifamily occupancy rates, and this data will not be redistributed. All the real estate metrics were combined into a single dataset using CBSA codes, which each MSA has a unique 5-digit identifier. Income-to-home price and rent-to-income ratios were calculated in R Studio.

Landlord data: the minimum security deposit and eviction notice data was collected for each state and manually compiled into an Excel. Security deposit information was provided as the number of months of rent. States with no maximum deposit limit received a score of 1.0, meaning it was the most favorable. Two month's rent was scored as 0.5, and one month's rent was given a score of 0.

Using NREL's REopt web tool to 1) model solar PV system on multifamily buildings in various cities and 2) obtain data to represent energy generation, CO2 abatement potential, avoided health costs from emissions, and solar project financial criteria.

An anchor city was identified within each MSA as the city with the highest population to input into the REopt tool. Default inputs were changed based on information provided by industry experts and changes in federal funding programs. Detailed instructions of inputs were created to ensure consistency when running the model for each city. The four outputs collected from the tool include: annual energy generation from renewables (%), lifecycle total CO2 emissions, health costs associated with primary air pollutants, and internal rate of return(%). The group divided up a list of cities, input the respective data for each one, obtained the outputs, then compiled it into a Google sheet. Outputs were checked by other members to ensure accuracy.

Collecting climate risk data from FEMA's National Risk Index Map.

Climate risk data was downloaded as a CSV file. The risk score was used to represent impacts of climate variability on long-term real estate investments. Risk scores were provided at the county level. The group identified the county each city resided in, to associate the correct score to each city in R Studio

Normalizing the data

Metrics were normalized by subtracting the minimum value for the metric from each value and dividing by the difference between the maximum and minimum values. This resulted in scores between 0 and 1 that were relative to the MSAs included in the analysis.

Weighing the data

Real Estate and Landlord Criteria metrics: these two criteria contained more than one metric, so the metrics within these criteria were weighted to produce real estate and landlord scores. Weights for each criterion sum to 1, in which higher weights indicate greater importance for multifamily real estate investments. Each weight was multiplied by the respective metric, then all weighted metrics within each criterion were summed to produce the criteria score. Investment Favorability Score: seven criteria were multiplied by respective weights based on the stakeholder's preferences. Weights sum to 1 to ensure consistency throughout the project. The sum of the seven weighted criteria is the investment favorability score.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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--- Original source retains full ownership of the source dataset ---

HUD’s Multifamily Housing property portfolio consist primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also include nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. HUD provides subsidies and grants to property owners and developers in an effort to promote the development and preservation of affordable rental units for low-income populations, and those with special needs such as the elderly, and disabled. The portfolio can be broken down into two basic categories: insured, and assisted. The three largest assistance programs for Multifamily Housing are Section 8 Project Based Assistance, Section 202 Supportive Housing for the Elderly, and Section 811 Supportive Housing for Persons with Disabilities. The Multifamily property locations represent the approximate location of the property. The locations of individual buildings associated with each property are not depicted here. 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 (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Multifamily Housing visit: https://www.hud.gov/program_offices/housing/mfh, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.Data Dictionary: DD_HUD Assisted Multifamily Properties Date of Coverage: 12/2023

The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.

Knowing who your consumers are is essential for businesses, marketers, and researchers. This detailed demographic file offers an in-depth look at American consumers, packed with insights about personal details, household information, financial status, and lifestyle choices. Let's take a closer look at the data:

Personal Identifiers and Basic Demographics At the heart of this dataset are the key details that make up a consumer profile:

Unique IDs (PID, HHID) for individuals and households Full names (First, Middle, Last) and suffixes Gender and age Date of birth Complete location details (address, city, state, ZIP) These identifiers are critical for accurate marketing and form the base for deeper analysis.

Geospatial Intelligence This file goes beyond just listing addresses by including rich geospatial data like:

Latitude and longitude Census tract and block details Codes for Metropolitan Statistical Areas (MSA) and Core-Based Statistical Areas (CBSA) County size codes Geocoding accuracy This allows for precise geographic segmentation and localized marketing.

Housing and Property Data The dataset covers a lot of ground when it comes to housing, providing valuable insights for real estate professionals, lenders, and home service providers:

Homeownership status Dwelling type (single-family, multi-family, etc.) Property values (market, assessed, and appraised) Year built and square footage Room count, amenities like fireplaces or pools, and building quality This data is crucial for targeting homeowners with products and services like refinancing or home improvement offers.

Wealth and Financial Data For a deeper dive into consumer wealth, the file includes:

Estimated household income Wealth scores Credit card usage Mortgage info (loan amounts, rates, terms) Home equity estimates and investment property ownership These indicators are invaluable for financial services, luxury brands, and fundraising organizations looking to reach affluent individuals.

Lifestyle and Interests One of the most useful features of the dataset is its extensive lifestyle segmentation:

Hobbies and interests (e.g., gardening, travel, sports) Book preferences, magazine subscriptions Outdoor activities (camping, fishing, hunting) Pet ownership, tech usage, political views, and religious affiliations This data is perfect for crafting personalized marketing campaigns and developing products that align with specific consumer preferences.

Consumer Behavior and Purchase Habits The file also sheds light on how consumers behave and shop:

Online and catalog shopping preferences Gift-giving tendencies, presence of children, vehicle ownership Media consumption (TV, radio, internet) Retailers and e-commerce businesses will find this behavioral data especially useful for tailoring their outreach.

Demographic Clusters and Segmentation Pre-built segments like:

Household, neighborhood, family, and digital clusters Generational and lifestage groups make it easier to quickly target specific demographics, streamlining the process for market analysis and campaign planning.

Ethnicity and Language Preferences In today's multicultural market, knowing your audience's cultural background is key. The file includes:

Ethnicity codes and language preferences Flags for Hispanic/Spanish-speaking households This helps ensure culturally relevant and sensitive communication.

Education and Occupation Data The dataset also tracks education and career info:

Education level and occupation codes Home-based business indicators This data is essential for B2B marketers, recruitment agencies, and education-focused campaigns.

Digital and Social Media Habits With everyone online, digital behavior insights are a must:

Internet, TV, radio, and magazine usage Social media platform engagement (Facebook, Instagram, LinkedIn) Streaming subscriptions (Netflix, Hulu) This data helps marketers, app developers, and social media managers connect with their audience in the digital space.

Political and Charitable Tendencies For political campaigns or non-profits, this dataset offers:

Political affiliations and outlook Charitable donation history Volunteer activities These insights are perfect for cause-related marketing and targeted political outreach.

Neighborhood Characteristics By incorporating census data, the file provides a bigger picture of the consumer's environment:

Population density, racial composition, and age distribution Housing occupancy and ownership rates This offers important context for understanding the demographic landscape.

Predictive Consumer Indexes The dataset includes forward-looking indicators in categories like:

Fashion, automotive, and beauty products Health, home decor, pet products, sports, and travel These predictive insights help businesses anticipate consumer trends and needs.

Contact Information Finally, the file includes ke...

The FHA insured Multifamily Housing portfolio consists primarily of rental housing properties with five or more dwelling units such as apartments or town houses, but can also be nursing homes, hospitals, elderly housing, mobile home parks, retirement service centers, and occasionally vacant land. Please note that this dataset overlaps the Multifamily Properties Assisted layer. The Multifamily property locations represent the approximate location of the property. 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 (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about HUD Insured Multifamily Properties visit: https://www.hud.gov/program_offices/housing/mfh Data Dictionary: DD_HUD Insured Multifamilly Properties Date of Coverage: 02/2025

Special Tabulations of Householdsby Income, Tenure, Age of Householder, and Housing ConditionsThe Economic and Market Analysis Division (EMAD) "Special Tabulations" data retrieval system produces tabular statistical summaries of counts of households by tenure, by income intervals, by age of householder, by size of household, by housing conditions based on the 1990 and 2000 Census, for select geographic areas in the United States. This system allows a user to extract data to conduct a longitudinal analysis of changes in a particular area.These special cross tabulations of decennial and ACS census data are the most detailed available for a qualitative analysis of housing demand based on incomes and age of householder. These data are a key element in the allocation formulae for the Section 8 and the Section 202 rental assistance programs, as well as a key element in EMAD qualitative demand market analysis activities for review of program applications and multifamily mortgage insurance applications submitted to FHA.For 1990 and 2000, the system contains decennial data for all counties and county equivalents in the United States, places with populations of 50,000 (subject to disclosure requirements), the nation, all states and the District of Columbia, and MSAs and PMSAs (except those in New England) based on the 1999 OMB definitions in effect at the time of the 2000 Census. Year 2000 data are also provided for selected areas in the Commonwealth of Puerto Rico. Beginning in 2010, the system uses data from the Census ACS 5-year survey, which is available at the CBSA, State, and County level. A detailed description of the exact content and format of the database is presented in the Help section of the system (Uploader's note: this help section was not available due to 404 error).

Graph and download economic data for New Privately-Owned Housing Units Started: Single-Family Units (HOUST1F) from Jan 1959 to May 2025 about housing starts, privately owned, 1-unit structures, family, housing, and USA.

Graph and download economic data for Housing Inventory Estimate: Total Housing Units in the United States (ETOTALUSQ176N) from Q2 2000 to Q1 2025 about inventories, housing, and USA.

The majority of the housing stock in the United States is single-family detached houses. Of the total ***** million housing units in 2023, about **** million were detached homes and *** million were attached single-family homes. In comparison, roughly **** million units were in multifamily buildings.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. Table Name Indicator Name Universe Timeframe Source Race Notes Source URL

homeownership_pct % Homeownership Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

renters_pct % Renters Occupied Housing Units 2016-2020 American Community Survey - Table B25003B-I Race alone; White is Non-Hispanic White https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSDT5Y2020.B25003

mean_home_value Mean Home Value Households 2021 Public Use Microdata Sample (PUMS) All races are Non-Hispanic LA County eGIS-Demography

accepted_mortgage_pct Accepted Mortgate Rate Mortgage Applications 2021 Home Mortgage Disclosure Act HMDA categories - https://files.consumerfinance.gov/f/documents/cfpb_reportable-hmda-data_regulatory-and-reporting-overview-reference-chart-2019.pdf https://ffiec.cfpb.gov/data-browser/data/2021

rent_burden_pct Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

rent_burden_severe_pct Severely Rent Burdened Renter Households 2019 California Housing Partnership All races are Non-Hispanic https://chpc.net/housingneeds/?view=37.405074,-119.26758,5&county=California,Los+Angeles&group=housingneed&chart=shortfall|current,cost-burden|current,cost-burden-re|current,homelessness,historical-rents,vacancy,asking-rents|2022,budgets|2021,funding|current,state-funding,lihtc|2010:2021:historical,rhna-progress,multifamily-production

eviction_per_100_hh Eviction Rate Renter Households 2014-2017 The Eviction Lab at Princeton University

https://data-downloads.evictionlab.org/#data-for-analysis/

homeless_count Homeless Count Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_homeless_pct % Homeless Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

homeless_county_pct % County Population Population excluding Long Beach, Glendale, and Pasadena 2022 LAHSA

https://www.lahsa.org/documents?id=6545-2022-greater-los-angeles-homeless-count-deck

unable_pay_mortgage_rent% Delayed or Were Unable to Pay Mortgage or Rent in the past 2 Years Households 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

homeless_ever% Who Reported Ever Being Homeless or Not Having Their Own Place to Live or Sleep in the past Five Years Adults 2018 LAC Health Survey https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm