Home Ownership Rate in the United States decreased to 65.10 percent in the first quarter of 2025 from 65.70 percent in the fourth quarter of 2024. This dataset provides the latest reported value for - United States Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q1 2025 about homeownership, housing, rate, and USA.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Analysis of ‘Department of Housing & Community Development Performance Metrics FY 2011-2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4899bbff-6087-4694-9682-f48ec6a1dc81 on 26 January 2022.

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

The Maryland D​epartment of Housing and Community Development is proud to be at the forefront in implementing housing policy that promotes and preserves homeownership and creating innovative community development initiatives to meet the challenges of a growing Maryland.

Through the Maryland Mortgage Program, the department has empowered thousands of Maryland families to realize the American dream of homeownership and for existing homeowners.

The department’s rental housing programs increase and preserve the supply of affordable housing and provide good choices for working families, senior citizens, and individuals with special needs.

Community development and revitalization programs like Neighborhood BusinessWorks, Community Legacy, and Main Street Maryland help our cities and towns remain rich, vibrant communities.

The Maryland Department of Housing and Community Development remains committed to building on our past successes to maintain our reputation as an innovator in community revitalization and a national leader in housing finance.

DISCLAIMER: Some of the information may be tied to the Department’s bond funded loan programs and should not be relied upon in making an investment decision. The Department provides comprehensive quarterly and annual financial information and operating data regarding its bonds and bond funded loan programs, all of which is posted on the publicly-accessible Electronic Municipal Market Access system website (commonly known as EMMA) that is maintained by the Municipal Securities Rulemaking Board, and on the Department’s website under Investor Information.

More information accessible here: http://dhcd.maryland.gov/Investors/Pages/default.aspx

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

The Maryland D​epartment of Housing and Community Development is proud to be at the forefront in implementing housing policy that promotes and preserves homeownership and creating innovative community development initiatives to meet the challenges of a growing Maryland.

Through the Maryland Mortgage Program, the department has empowered thousands of Maryland families to realize the American dream of homeownership and for existing homeowners.

The department’s rental housing programs increase and preserve the supply of affordable housing and provide good choices for working families, senior citizens, and individuals with special needs.

Community development and revitalization programs like Neighborhood BusinessWorks, Community Legacy, and Main Street Maryland help our cities and towns remain rich, vibrant communities.

The Maryland Department of Housing and Community Development remains committed to building on our past successes to maintain our reputation as an innovator in community revitalization and a national leader in housing finance.

DISCLAIMER: Some of the information may be tied to the Department’s bond funded loan programs and should not be relied upon in making an investment decision. The Department provides comprehensive quarterly and annual financial information and operating data regarding its bonds and bond funded loan programs, all of which is posted on the publicly-accessible Electronic Municipal Market Access system website (commonly known as EMMA) that is maintained by the Municipal Securities Rulemaking Board, and on the Department’s website under Investor Information.

More information accessible here: http://dhcd.maryland.gov/Investors/Pages/default.aspx

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers,

"HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous."

Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. "

HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas."

These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality."

Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

description: The 2006-2010 ACS 5-Year Selected Population Tables (SPT) use ACS data aggregated over a 5-year period to provide more reliable estimates of detailed social, economic, and housing characteristics for many race, tribal, Hispanic, and ancestry population groups at multiple levels of geography. Detailed tables on topics such as educational attainment, fertility, nativity, citizenship, income, poverty, and homeownership are iterated for many racial and ethnic population groups. For the SPT, detailed tables are presented for up to 392 population groups in geographies down to the tract level where population thresholds were met.; abstract: The 2006-2010 ACS 5-Year Selected Population Tables (SPT) use ACS data aggregated over a 5-year period to provide more reliable estimates of detailed social, economic, and housing characteristics for many race, tribal, Hispanic, and ancestry population groups at multiple levels of geography. Detailed tables on topics such as educational attainment, fertility, nativity, citizenship, income, poverty, and homeownership are iterated for many racial and ethnic population groups. For the SPT, detailed tables are presented for up to 392 population groups in geographies down to the tract level where population thresholds were met.

description: The 2006-2010 ACS 5-Year American Indian and Alaska Native Tables (AIANT) use ACS data aggregated over a 5-year period to provide reliable estimates of detailed social, economic, and housing characteristics for many tribal population groups at multiple levels of geography. Detailed tables on topics such as educational attainment, fertility, nativity, citizenship, income, poverty, and homeownership are iterated for many tribal population groups. For the AIAN, detailed tables are presented for up to 950 population groups in selected geographies such as American Indian and Alaska Native areas where population thresholds were met.; abstract: The 2006-2010 ACS 5-Year American Indian and Alaska Native Tables (AIANT) use ACS data aggregated over a 5-year period to provide reliable estimates of detailed social, economic, and housing characteristics for many tribal population groups at multiple levels of geography. Detailed tables on topics such as educational attainment, fertility, nativity, citizenship, income, poverty, and homeownership are iterated for many tribal population groups. For the AIAN, detailed tables are presented for up to 950 population groups in selected geographies such as American Indian and Alaska Native areas where population thresholds were met.

Graph and download economic data for All-Transactions House Price Index for Connecticut (CTSTHPI) from Q1 1975 to Q1 2025 about CT, appraisers, HPI, housing, price index, indexes, price, and USA.