Graph and download economic data for Homeownership Rates by Race and Ethnicity: Black Alone in the United States (BOAAAHORUSQ156N) from Q1 1994 to Q2 2025 about African-American, homeownership, rate, and USA.

In 2023, the rate of homeownership among White people living in the United States was 74.3 percent. Comparatively, 45.7 percent of Black people owned a home in the same year.

In 2018, ** percent of African Americans living in Mississippi owned their home, which was the state with the highest Black homeownership rate. Mississippi also had the highest White homeownership rate, but it was considerably higher at ** percent. The homeownership rate among African Americans in Montana and North Dakota was only ***** percent.

Graph and download economic data for Homeownership Rates by Race and Ethnicity: Non-Hispanic White Alone in the United States (NHWAHORUSQ156N) from Q1 1994 to Q2 2025 about white, homeownership, non-hispanic, rate, and USA.

Home ownership persists as the primary way that families build wealth. Housing researchers and advocates often discuss the racial home ownership gap, particularly for Black and Hispanic households (Urban Institute, Pew Hispanic Center). Historical policies such as redlining, steering, and municipal underbounding have effects that stay with us today.This map shows the overall home ownership rate and the home ownership rate by race/ethnicity of householder in a chart in the pop-up. Map is multi-scale showing data for state, county, and tract.This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

2020 data points are the average of 2019 and 2021 data points and are included solely to maintain chart continuity. The U.S. Census Bureau did not release 2020 ACS 1-year estimates due to COVID-19. These figures should not be interpreted as an actual estimate for 2020. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1-Year Estimates

Why This Matters

Homeownership has historically been an important source of intergenerational wealth. For many, homeownership can provide financial and housing security.Rising home prices over the past two decades have outpaced wage growth, perpetuating significant racial disparities in homeownership rates and contributing to the displacement of Black residents and other people of color from the District.

A history of redlining and racist real estate practices, like racial covenants, barred Black and other people of color from homeownership.

The District's Response

Convening of the Black Homeownership Strikeforce to address past harms and increase equitable homeownership rates through targeted, evidence-based recommendations, and setting the goal of creating 20,000 new Black homeowners by 2030.

Programs to enable homeowning families and individuals to remain in their homes, including the Homestead Deduction and Senior Citizen or Disabled Property Owner Tax Relief and the Heir Property Assistance Program.

Inclusionary Zoning (IZ) Affordable Housing Program and financial assistance programs like the Home Purchase Assistance Program (HPAP), Employer Assisted Housing Program (EAHP), and Negotiated Employee Assistance Home Purchase Program (NEAHP) to support homeownership among District residents.

ACS 1-year estimates are based on data collected over one calendar year, offering more current information but with a higher margin of error. ACS 5-year estimates combine five years of data, providing more reliable information but less current. Both are based on probability samples. Some racial and ethnic categories are suppressed to avoid misleading estimates when the relative standard error exceeds 30%.

Data Source: American Community Survey (ACS) 1- & 5-Year Estimates

Why This Matters

Homeownership has historically been an important source of intergenerational wealth. For many, homeownership can provide financial and housing security.Rising home prices over the past two decades have outpaced wage growth, perpetuating significant racial disparities in homeownership rates and contributing to the displacement of Black residents and other people of color from the District.

A history of redlining and racist real estate practices, like racial covenants, barred Black and other people of color from homeownership.

The District's Response

Convening of the Black Homeownership Strikeforce to address past harms and increase equitable homeownership rates through targeted, evidence-based recommendations, and setting the goal of creating 20,000 new Black homeowners by 2030.

Programs to enable homeowning families and individuals to remain in their homes, including the Homestead Deduction and Senior Citizen or Disabled Property Owner Tax Relief and the Heir Property Assistance Program.

Inclusionary Zoning (IZ) Affordable Housing Program and financial assistance programs like the Home Purchase Assistance Program (HPAP), Employer Assisted Housing Program (EAHP), and Negotiated Employee Assistance Home Purchase Program (NEAHP) to support homeownership among District residents.

Graph and download economic data for Homeownership Rate (5-year estimate) for Black Hawk County, IA (HOWNRATEACS019013) from 2009 to 2023 about Black Hawk County, IA; Waterloo; IA; homeownership; 5-year; housing; rate; and USA.

70% of White British households owned their own homes – the highest percentage out of all ethnic groups.

This statistic presents the homeownership rate in the United States among African Americans as of May 2016. The results of the the survey revealed that ** percent of the respondents owned their home, while ** percent of the respondents rented their primary place of residence.

The homeownership among White people in the United States was **** percent, the highest out of all ethnicities, in 2023. American Dream Part of the “American Dream” is the idea of owning a home. It is seen as a status symbol and an indicator of wealth. People take a lot of pride in owning a home, and hope to do so at the earliest age possible. It is the idea of having a white picket fence with a nuclear family, a dog, and a car or two which is seen as the stereotypical “end goal”. However, in the aftermath of the 2008 recession, the rate of homeownership in the United States fell steadily until 2016. The recession hindered people’s chances of owning a home, due to less credit being available and their own fears about being stuck with a home in negative equity if another recession were to occur. As a result, the homeownership rate in the United States has barely increased in the past few years. Factors affecting homeownership Homeownership varies based on different factors. Married-couple families have the highest homeownership rates among different family statuses. Unsurprisingly, households with high incomes have the highest homeownership rates.

Graph and download economic data for Consumer Unit Characteristics: Percent Black or African American by Housing Tenure: Homeowner with Mortgage (CXU980270LB1703M) from 2003 to 2023 about consumer unit, homeownership, mortgage, African-American, percent, housing, and USA.

Financial overview and grant giving statistics of African American Alliance for Home Ownership Inc.

In a September 2020 survey among adults in the United States, over half of respondents said that their interest in buying a home had not changed due to the COVID-19 pandemic (** percent). However, Hispanic respondents were more likely to have changed their plans (** percent) compared to white respondents (** percent). In the United States, the 2020 homeownership rate reached **** percent.

In a September 2020 survey among adults in the United States, around ** percent of Hispanic respondents said that they were currently saving up to buy a house, while just ** percent of white respondents said that they were doing so. Similarly, just ** percent of Hispanics said that they never plan or expect to own a home, while ** percent of White respondents said so.In the United States, the 2020 homeownership rate reached **** percent.

While experimental studies of local election officials have found evidence of racial discrimination, we know little about whether these biases manifest in bureaucracies that provide access to valuable government programs and are less tied to politics. We address these issues in the context of affordable housing programs using a randomized field experiment. We explore responsiveness to putative white, black, and Hispanic requests for aid in the housing application process. In contrast to prior findings, public housing officials respond at equal rates to black and white email requests. We do, however, find limited evidence of responsiveness discrimination towards Hispanics. Moreover, we observe substantial differences in email tone. Hispanic housing applicants were twenty percentage points less likely to be greeted by name than were their black and white counterparts. This disparity in tone is somewhat more muted in more diverse locations, but it does not depend on whether a housing official is Hispanic.

Graph and download economic data for Consumer Unit Characteristics: Percent White, Asian, and All Other Races, Not Including African American by Housing Tenure: Homeowner without Mortgage (CXUWHTNDOTHLB1704M) from 2003 to 2023 about consumer unit, homeownership, asian, mortgage, white, percent, housing, and USA.

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

This paper examines the association between the Great Recession and real assets among families with young children. Real assets such as homes and cars are key indicators of economic well-being that may be especially valuable to low-income families. Using longitudinal data from the Fragile Families and Child Wellbeing Study (N = 4,898), we investigate the association between the city unemployment rate and home and car ownership and how the relationship varies by family structure (married, cohabiting, and single parents) and by race/ethnicity (White, Black, and Hispanic mothers). Using mother fixed-effects models, we find that a one percentage point increase in the unemployment rate is associated with a -0.5 percentage point decline in the probability of home ownership and a -0.7 percentage point decline in the probability of car ownership. We also find that the recession was associated with lower levels of home ownership for cohabiting families and for Hispanic families, as well as lower car ownership among single mothers and among Black mothers, whereas no change was observed among married families or White households. Considering that homes and cars are the most important assets among middle and low-income households in the U.S., these results suggest that the rise in the unemployment rate during the Great Recession may have increased household asset inequality across family structures and race/ethnicities, limiting economic mobility, and exacerbating the cycle of poverty.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.