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 Q2 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).

This layer shows basic population and housing context. This is shown by county boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the homeownership rate, or the percent of occupied housing units that are owner-occupied.

Key Table Information.Table Title.Monthly Homeowners Association and/or Condominium Fee.Table ID.ACSDT1Y2024.B25142.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.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.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..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.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.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..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.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, an...

This layer shows tenure (owner or renter) by race of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25003, B25003B, B25003C, B25003D, B25003E, B25003F, B25003G, B25003H, B25003IData downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census: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 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 Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

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.

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

This dataset explores Homeowner expenditure on repairs and renovations by province in Canada for 2002. 1. Average Expenditure: Averages are based on all of the households in the sample including those that reported no expenditure for the category. Source: Income Statistics Division, Homeowner Repair and Renovation Expenditure 2002, Catalogue no. 62-201-XIB. Last modified: 2005-01-12.

Visit Mapping Inequality for full details.History of this spatial dataThe majority of this dataset is derived from maps made by the Home Owners’ Loan Corporation (HOLC), a New Deal agency. Using data and evaluations gathered from local real estate professionals, HOLC created color-coded maps for more than 200 American cities. The maps used four colors to represent the “security,” or the determined relative riskiness of mortgage lending, for residential areas of each city.Green areas on the maps were called "A," "First Grade," or "Best" and were considered to be safest for loans. These areas were typically populated with wealthy, white residents that were born in the United States.Blue areas were called "B," "Second Grade," or "Still Desirable". Yellow areas were called "C," "Third Grade," or "Definitely Declining".Red areas were called "D," "Fourth Grade," or "Hazardous". HOLC recommended lenders "refuse to make loans in these areas [or] only on a conservative basis." These areas typically overlapped with Black and immigrant communities, which usually had lower incomes.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.This dataset also includes spatial data for more than 100 municipalities from redlining maps that were not made as part of HOLC’s City Survey Program. These places were typically smaller in size, falling below the population threshold of 40,000 that HOLC used to determine which cities they would survey. As these maps were not made as part of HOLC’s City Survey Program, the vast majority use different categories and colors than those used by HOLC.The Residential Security Map created by the Home Owner's Loan Corporation for Decatur, IL.A new version of Mapping InequalityThe University of Richmond Digital Scholarship Lab began the Mapping Inequality project in 2016. Using scanned images of HOLC City Survey maps, a team of students and scholars georeferenced the images and digitally traced their color-coded residential areas, creating a spatial dataset that has since been used in numerous studies and research projects. Over the course of the project, more cities and their maps were added, including redlining maps of smaller cities that were not a part of HOLC’s City Survey. Non-residential areas shown on the maps, such as industrial and commercial areas, were also traced and added to the spatial database. The spatial dataset has grown, and now contains 10,000 polygons that were created from maps of 328 cities in 43 states.Previous versions of this feature layer, which are missing cities and non-residential areas, are available here and here.Images of the redlining maps maps, and their derived data, as well as more in-depth reading on the history are available on the the Mapping Inequality website. The site also includes a searchable archive of the detailed area descriptions that accompanied redlining maps. These texts provide important nuance in the grades and are invaluable for laying bare the racist, nativist, and often anti-semitic prejudices underlying real estate practice and federal housing policy during the Great Depression.What's in this feature layerEach feature in this dataset is a polygon that represents an area that was drawn on a 1930s redlining map. They include the following fields:area_id (integer) is a unique identifier for each area.city (string) is the name of the city, town, county, etc.state (string) is the 2-letter U.S. Postal Service abbreviation for the state.city_survey (boolean, true=1, false=0) denotes whether the map was created as part of the HOLC City Survey Program or not.category (string) is the assigned category from a redlining map. On standard HOLC City Survey Program maps, the category values are “Best”, “Still Desirable”, “Declining”, or “Hazardous.”grade (string) is the letter grade used to grade the area. For non-residential areas and most cities that were not part of the City Survey, the value is null.label (string) is the label from a redlining map. For most HOLC City Survey Program maps, this value is a letter and number, which often corresponds to an area description viewable on the Mapping Inequality website. commercial (boolean, true=1, false=0) denotes whether or not an area is labeled explicitly as commercial or inferred to be commercial from a redlining map.industrial (boolean, true=1, false=0) denotes whether or not an area is labeled explicitly as industrial, or inferred to be industrial.residential (boolean, true=1, false=0) denotes whether or not an area is labeled explicitly as residential or inferred to be residential.fill (string) is a hexadecimal color code for symbology. The value is typically an approximation of the color shown on a redlining map. You can use this attribute field as a feature symbology in ArcGIS Pro.This spatial dataset is available in geojson and geopackage format on the Mapping Inequality downloads page. Images of the scanned redlining maps, including in spatially referenced geotiff format are also available. UpdatesMarch 1st, 2024Fixed category errors on areas in Hudson County, NJAdded missing areas to Mt. Morris, MI (inset of Flint MI)

Pretax-Margin Time Series for eXp World Holdings Inc. eXp World Holdings, Inc., together with its subsidiaries, provides cloud-based real estate brokerage services for residential homeowners and homebuyers. It operates through three segments: North American Realty, International Realty, and Other Affiliated Services. The company acts as a licensed broker for processing residential and commercial real estate transactions; and provides other real estate support services. It also offers FrameVR.io, a web-accessible proprietary technology offering immersive 3D platforms; magazine and its related media properties which provide training, classes, resources, and tools under the SUCCESS brand; SUCCESS Space, a coworking solution offering rental workspaces for individual and group use, access to professional development coaching, media production services, virtual-world communications technology, and full-service cafes. It operates in North America, Canada, the United Kingdom, Australia, South Africa, India, Mexico, Portugal, France, Puerto Rico, Brazil, Italy, Hong Kong, Colombia, Spain, Israel, Panama, Germany, the Dominican Republic, Greece, New Zealand, Chile, Poland, and Dubai. The company was formerly known as eXp Realty International Corporation and changed its name to eXp World Holdings, Inc. in May 2016. eXp World Holdings, Inc. was incorporated in 2008 and is headquartered in Bellingham, Washington.

Change-To-Liabilities Time Series for eXp World Holdings Inc. eXp World Holdings, Inc., together with its subsidiaries, provides cloud-based real estate brokerage services for residential homeowners and homebuyers. It operates through three segments: North American Realty, International Realty, and Other Affiliated Services. The company acts as a licensed broker for processing residential and commercial real estate transactions; and provides other real estate support services. It also offers FrameVR.io, a web-accessible proprietary technology offering immersive 3D platforms; magazine and its related media properties which provide training, classes, resources, and tools under the SUCCESS brand; SUCCESS Space, a coworking solution offering rental workspaces for individual and group use, access to professional development coaching, media production services, virtual-world communications technology, and full-service cafes. It operates in North America, Canada, the United Kingdom, Australia, South Africa, India, Mexico, Portugal, France, Puerto Rico, Brazil, Italy, Hong Kong, Colombia, Spain, Israel, Panama, Germany, the Dominican Republic, Greece, New Zealand, Chile, Poland, and Dubai. The company was formerly known as eXp Realty International Corporation and changed its name to eXp World Holdings, Inc. in May 2016. eXp World Holdings, Inc. was incorporated in 2008 and is headquartered in Bellingham, Washington.

Graph and download economic data for All-Transactions House Price Index for Connecticut (CTSTHPI) from Q1 1975 to Q2 2025 about CT, appraisers, HPI, housing, price index, indexes, price, and USA.