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.

New analysis of mortgage data in seven large urban counties in Ohio, Pennsylvania, and Kentucky finds that growth in home purchase originations was much stronger for Black borrowers than non-Black borrowers between 2018 and 2021. However, the Black homeownership rate remained far below the non-Black rate.

Data are aggregated from census tract to Countywide Statistical Area (CSA).Link to full report, State of Black LA.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. Field Descriptions:

Field

Description

Source

Source Year

csa

Countywide Statistical Area

eGIS

2022

sd

Supervisorial District

eGIS

2021

med_income_total

Average median household income for all residents

US Census ACS 5-year table S1903

2020

med_income_black

Average median household income for Black residents

US Census ACS 5-year table S1903

2020

homeownership_total

Homeownership rate for all residents

US Census ACS 5-year table B25003

2020

homeownership_black

Homeownership rate for Black residents

US Census ACS 5-year table B25003B

2020

eviction_filings_per100_renters

Eviction filings per 100 renter households

The Eviction Lab

2002-2018 (yearly average of available years)

life_expectancy

Average life expectancy

CDC

2015

black_pop

Black population (alone or in combination)

US Census ACS 5-year table DP05

2020

black_pct

% Black population (alone or in combination)

US Census ACS 5-year table DP05

2020

nh_black_pop

Non-Hispanic Black alone population

US Census ACS 5-year table DP05

2020

nh_black_pct

% Non-Hispanic Black alone population

US Census ACS 5-year table DP05

2020

college_grad

Population of residents age 25+ with bachelor degree or higher

US Census ACS 5-year table DP02

2020

college_grad_pct

% of all residents age 25+ with bachelor degree or higher

US Census ACS 5-year table DP02

2020

college_grad_black

Population of Black residents age 25+ with bachelor degree or higher

US Census ACS 5-year table S1501

2020

college_grad_black_pct

% of Black residents age 25+ with bachelor degree or higher

US Census ACS 5-year table S1501

2020

unemployment

Unemployment Rate

US Census ACS 5-year table S2301

2020

unemployment_black

Black (Alone) Unemployment Rate

US Census ACS 5-year table S2301

2020

total_pop

Total population

US Census ACS 5-year table DP05

2020

Shape

CSA Geometry

eGIS

2022

Housing Inventory: New Listing Count Year-Over-Year in Black Hawk County, IA was 20.83% in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: New Listing Count Year-Over-Year in Black Hawk County, IA reached a record high of 68.63 in February of 2020 and a record low of -43.02 in February of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: New Listing Count Year-Over-Year in Black Hawk County, IA - last updated from the United States Federal Reserve on December of 2025.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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, 2021 American Community Survey 1-Year Estimates.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..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 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.

The IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Race and Ethnicity measure in this release is an indicator of homeownership inequity, which includes the ratio between the proportion of householders identifying as White alone, not Hispanic or Latino, who own (as opposed to renting) their home and the proportion of householders identifying as a different race/ethnic group who own their home. Three ratios are provided for Black, Asian, and Hispanic groups. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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, 2017-2021 American Community Survey 5-Year Estimates.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..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 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.

Some Black households live in neighborhoods with lower incomes, as well as higher unemployment rates and lower educational attainment, than their own incomes might suggest, and this may impede their economic mobility. We investigate reasons for the neighborhood sorting patterns we observe and find that differences in financial factors such as income, wealth, or housing costs between Black and white households do not explain racial distributions across neighborhoods. Our findings suggest other factors are at work, including discrimination in the housing market, ongoing racial hostility, or preferences by Black households for the strength of social networks or other neighborhood amenities that some lower-socioeconomic locations provide.

This dataset represents ethnic group (19 tick-box level) by dwelling tenure and by occupancy rating, for England and Wales combined. The data are also broken down by age and by sex.

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity, or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options.

Total counts for some population groups may not match between published tables. This is to protect the confidentiality of individuals' data. Population counts have been rounded to the nearest 5 and any counts below 10 are suppressed, this is signified by a 'c' in the data tables.

"Asian Welsh" and "Black Welsh" ethnic groups were included on the census questionnaire in Wales only, these categories were new for 2021.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.

All housing data in these tables do not include commual establishments.

For quality information in general, please read more from here.

For specific quality information about housing, please read more from here

Ethnic Group (19 tick-box level)

These are the 19 ethnic group used in this dataset:

• Asian, Asian British or Asian Welsh
  • Bangladeshi
  • Chinese
  • Indian
  • Pakistani
  • Other Asian
• Black, Black British, Black Welsh, Caribbean or African
  • African
  • Caribbean
  • Other Black
• Mixed or Multiple ethnic groups
  • White and Asian
  • White and Black African
  • White and Black Caribbean
  • Other Mixed or Multiple ethnic groups
• White
  • English, Welsh, Scottish, Northern Irish or British
  • Gypsy or Irish Traveller
  • Irish
  • Roma
  • Other White
• Other ethnic group
  • Arab
  • Any other ethnic group

Occupancy rating of bedrooms: 0 or more

A household’s accommodation has an ideal number of bedrooms or more bedrooms than required (under-occupied)

Occupancy rating of bedrooms: -1 or less

A household’s accommodation has fewer bedrooms than required (overcrowded)

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 Housing is a basic necessity, and affordable housing is essential for individuals and families to live and thrive in DC.The rising cost of housing threatens residents’ access to safe and stable housing as well as their ability to cover other essential expenses like food, transportation, and childcare.Racial segregation, housing discrimination, and racist urban renewal programs, among other policies and practices, have meant that Black residents and residents of color in the District disproportionately experience the effects of rapidly rising housing costs. The District's Response Leading the nation in policies and investments for low-income rental households. Target of 12,000 new affordable housing units by 2025. Steps taken to preserve and expand affordable housing include the Housing Production Trust Fund, the Affordable Housing Preservation Fund, and the Home Purchasing Assistance Program, among others.

This contains details about home ownership in King County. It has been developed for the Determinant of Equity - Community Economic Development presentation, Home Ownership Rates equity indicator. Fields describe the total number of people (Denominator), number of people that own a home (Numerator), the type of equity indicator being measured (Indicator), and the value that describes this measurement (Indicator Value).

The data for this dataset was compiled from the American Community Survey (ACS) 1-year and 5-year estimates. Vintages

1-year estimates: 2013-2017 5-year estimates: 2013-2017, 2014-2018, 2015-2019, 2016-2020, 2017-2021, 2018 - 2022

Variables

1-year estimates: B25003 - TENURE 5-year estimates: B25003B - TENURE (BLACK OR AFRICAN AMERICAN ALONE HOUSEHOLDER) - B25003I - TENURE (HISPANIC OR LATINO HOUSEHOLDER), B25093 - AGE OF HOUSEHOLDER BY SELECTED MONTHLY OWNER COSTS AS A PERCENTAGE OF HOUSEHOLD INCOME IN THE PAST 12 MONTHS

For more information about King County's equity efforts, please see:

Equity, Racial & Social Justice Vision Ordinance 16948 describing the determinates of equity Determinants of Equity and Data Tool

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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, 2017-2021 American Community Survey 5-Year Estimates.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..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 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.

Current-Ratio Time Series for Mitsubishi Estate Co Ltd. Mitsubishi Estate Co., Ltd. engages in the real estate activities in Japan and internationally. The company develops, leases, manages, and sells office buildings and commercial facilities; operates rental offices, coworking space, virtual offices, hourly meeting rooms, home delivery storage service, commercial nursing homes, and building garages; offers real estate management, as well as building management services, such as security, facility management, cleaning, and planting services; and operates hotels and airports. It also engages in the construction, sales, management, and leasing of developed condominiums and residential houses; design and contract construction of custom-built houses; renovation and sales of condominiums; real estate brokerage; dark fiber leasing and data center housing business; provision of real estate investment, such as asset management services to investment corporations and real estate funds; architectural design and engineering business; cooling and heating supply business; delivery and takeout; and parking management business. In addition, the company leases, operates, and manages logistics facilities; sells gasoline products; purchases, manufactures, processes, and sells construction materials; constructs prefabricated housing using cross-laminated timber and laminated wood; constructs, manufactures, and sells furniture and household items; offers financial consulting and investment advisory services; and develops and manages information systems and software. Further, it plans, develops, and operates GYYM, a platform service for fitness facilities; Ele-Cinema, an elevator projection type media solution; and Machi Pass FACE, a collaboration platform that enables facial recognition services. Additionally, the company offers human resources, land management, and landscaping services. Mitsubishi Estate Co., Ltd. was founded in 1890 and is headquartered in Tokyo, Japan.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.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, 2017-2021 American Community Survey 5-Year Estimates.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..Between 2018 and 2019 the American Community Survey retirement income question changed. These changes resulted in an increase in both the number of households reporting retirement income and higher aggregate retirement income at the national level. For more information see Changes to the Retirement Income Question ..The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2017-2021 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 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.

Housing Inventory: Median Home Size in Square Feet in Black Hawk County, IA was 1620.00000 Level in October of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Home Size in Square Feet in Black Hawk County, IA reached a record high of 1763.00000 in October of 2022 and a record low of 1285.00000 in October of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Home Size in Square Feet in Black Hawk County, IA - last updated from the United States Federal Reserve on November of 2025.

Number-of-Days-of-Payables Time Series for Mitsubishi Estate Co Ltd. Mitsubishi Estate Co., Ltd. engages in the real estate activities in Japan and internationally. The company develops, leases, manages, and sells office buildings and commercial facilities; operates rental offices, coworking space, virtual offices, hourly meeting rooms, home delivery storage service, commercial nursing homes, and building garages; offers real estate management, as well as building management services, such as security, facility management, cleaning, and planting services; and operates hotels and airports. It also engages in the construction, sales, management, and leasing of developed condominiums and residential houses; design and contract construction of custom-built houses; renovation and sales of condominiums; real estate brokerage; dark fiber leasing and data center housing business; provision of real estate investment, such as asset management services to investment corporations and real estate funds; architectural design and engineering business; cooling and heating supply business; delivery and takeout; and parking management business. In addition, the company leases, operates, and manages logistics facilities; sells gasoline products; purchases, manufactures, processes, and sells construction materials; constructs prefabricated housing using cross-laminated timber and laminated wood; constructs, manufactures, and sells furniture and household items; offers financial consulting and investment advisory services; and develops and manages information systems and software. Further, it plans, develops, and operates GYYM, a platform service for fitness facilities; Ele-Cinema, an elevator projection type media solution; and Machi Pass FACE, a collaboration platform that enables facial recognition services. Additionally, the company offers human resources, land management, and landscaping services. Mitsubishi Estate Co., Ltd. was founded in 1890 and is headquartered in Tokyo, Japan.

Housing Inventory: Median Days on Market Year-Over-Year in Black Hawk County, IA was -21.86% in September of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Days on Market Year-Over-Year in Black Hawk County, IA reached a record high of 134.00 in May of 2024 and a record low of -49.21 in June of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Days on Market Year-Over-Year in Black Hawk County, IA - last updated from the United States Federal Reserve on November of 2025.

14% of White British households rented their home privately in the 2 years from April 2021 to May 2023 – the lowest percentage out of all ethnic groups.