100+ datasets found
  1. Share of income spent on mortgage or rent in England 2011-2024, by tenure

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Share of income spent on mortgage or rent in England 2011-2024, by tenure [Dataset]. https://www.statista.com/statistics/755883/income-spent-on-mortgage-or-rent-england-by-tenure/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 2023 - Mar 2024
    Area covered
    United Kingdom (England)
    Description

    When comparing the mortgage or rental costs incurred by owners with mortgage, private renters and social renters in England, private renters pay a considerably larger share of their income than the other two groups. While owner occupiers with mortgages paid approximately **** percent of their income on mortgage in 2024, private renters paid ** percent, or more than *********. In terms of average monthly costs, renting a three-bedroom house is more expensive than buying.

  2. s

    Spending on renting or buying a house

    • ethnicity-facts-figures.service.gov.uk
    csv
    Updated Apr 8, 2025
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    Race Disparity Unit (2025). Spending on renting or buying a house [Dataset]. https://www.ethnicity-facts-figures.service.gov.uk/housing/owning-and-renting/spending-on-renting-or-buying-a-house/latest
    Explore at:
    csv(16 KB), csv(25 KB), csv(23 KB), csv(27 KB)Available download formats
    Dataset updated
    Apr 8, 2025
    Dataset authored and provided by
    Race Disparity Unit
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    England
    Description

    In the 2 years to March 2023, White British households spent 28% of their weekly income on rent payments on average – the lowest percentage out of all ethnic groups.

  3. c

    Housing Affordability

    • data.ccrpc.org
    csv
    Updated Oct 17, 2024
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    Champaign County Regional Planning Commission (2024). Housing Affordability [Dataset]. https://data.ccrpc.org/dataset/housing-affordability
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    csv(2343)Available download formats
    Dataset updated
    Oct 17, 2024
    Dataset provided by
    Champaign County Regional Planning Commission
    Description

    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).

  4. Share of households under housing stress in the U.S. 2019, by income level

    • statista.com
    Updated Nov 6, 2020
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    Statista (2020). Share of households under housing stress in the U.S. 2019, by income level [Dataset]. https://www.statista.com/statistics/1011935/households-housing-stress-by-income-level-usa/
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    Dataset updated
    Nov 6, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    United States
    Description

    This statistic shows the share of households who spent half or more of their income on housing in the United States in 2019, by income level. In 2019, 52.8 percent of households who earned between 10,000 and 20,000 U.S. dollars annually spent fifty percent or more of their income on housing costs, whereas only 1.5 percent of households with annual incomes between 75,000 and 100,000 U.S. dollars did the same.

  5. Housing Cost as a Percentage of Income Map

    • data.wu.ac.at
    csv, json, xml
    Updated Aug 27, 2016
    + more versions
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    United States Census Bureau American Community Survey (2016). Housing Cost as a Percentage of Income Map [Dataset]. https://data.wu.ac.at/schema/performance_smcgov_org/aGY4bS03emFu
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    csv, xml, jsonAvailable download formats
    Dataset updated
    Aug 27, 2016
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Description

    This dataset contains information about the percent of income households spend on housingin cities in San Mateo County. This data is for owner occupied housing with or without a mortgage. This data was extracted from the United States Census Bureau's American Community Survey 2014 5 year estimates.

  6. Gross rent as a share of household income in the U.S. 2023

    • statista.com
    Updated May 7, 2025
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    Statista (2025). Gross rent as a share of household income in the U.S. 2023 [Dataset]. https://www.statista.com/statistics/186732/gross-rent-as-a-percent-of-household-income-in-the-us/
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    Dataset updated
    May 7, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    Approximately 42.5 percent of residents in renter-occupied housing units in the United States paid gross rent which exceeded 35 percent of their income in 2023. In comparison, about 12.3 percent paid less than 15 percent of their gross household income.

  7. Expenditure on mortgage and rent as a proportion of total expenditure and...

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Jul 14, 2023
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    Office for National Statistics (2023). Expenditure on mortgage and rent as a proportion of total expenditure and disposable income, UK [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/expenditure/datasets/expenditureonmortgageandrentasaproportionoftotalexpenditureanddisposableincomeuk
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    xlsxAvailable download formats
    Dataset updated
    Jul 14, 2023
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    Expenditure on rent by renters and mortgages by mortgage holders, by region and age from the Living Costs and Food Survey for the financial year ending 2022. Data is presented as a proportion of total expenditure and a proportion of disposable income.

  8. C

    Percent of Income Spent on Housing - 2017

    • chattadata.org
    • internal.chattadata.org
    Updated Oct 4, 2019
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    (2019). Percent of Income Spent on Housing - 2017 [Dataset]. https://www.chattadata.org/Economy/Percent-of-Income-Spent-on-Housing-2017/htwq-mawx
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    kml, csv, tsv, application/rssxml, application/geo+json, kmz, application/rdfxml, xmlAvailable download formats
    Dataset updated
    Oct 4, 2019
    Description

    Data form 2017 ACS

  9. Households who spend more than 30 percent of income on housing

    • data.amerigeoss.org
    esri rest, html
    Updated Jan 7, 2020
    + more versions
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    ESRI (2020). Households who spend more than 30 percent of income on housing [Dataset]. https://data.amerigeoss.org/id/dataset/households-who-spend-more-than-30-percent-of-income-on-housing
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    esri rest, htmlAvailable download formats
    Dataset updated
    Jan 7, 2020
    Dataset provided by
    Esrihttp://esri.com/
    Description

    This map shows households that spend more than 30 percent of their income on housing, a threshold widely used by many affordable housing advocates and official government sources including Housing and Urban Development. Census asks about income and housing costs to understand whether housing is affordable in local communities. When housing is not sufficient or not affordable, income data helps communities:

    • Enroll eligible households in programs designed to assist them.
    • Qualify for grants from the Community Development Block Grant (CDBG), HOME Investment Partnership Program, Emergency Solutions Grants (ESG), Housing Opportunities for Persons with AIDS (HOPWA), and other programs.
    When rental housing is not affordable, the Department of Housing and Urban Development (HUD) uses rent data to determine the amount of tenant subsidies in housing assistance programs.

    Map opens in Atlanta. Use the bookmarks or search bar to view other cities. Data is symbolized to show the relationship between burdensome housing costs for owner households with a mortgage and renter households:

    legned

    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.

  10. House-price-to-income ratio in selected countries worldwide 2024

    • statista.com
    • ai-chatbox.pro
    Updated May 6, 2025
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    Statista (2025). House-price-to-income ratio in selected countries worldwide 2024 [Dataset]. https://www.statista.com/statistics/237529/price-to-income-ratio-of-housing-worldwide/
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    Dataset updated
    May 6, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Worldwide
    Description

    Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

  11. F

    Expenditures: Housing by Quintiles of Income Before Taxes: Second 20 Percent...

    • fred.stlouisfed.org
    json
    Updated Sep 25, 2024
    + more versions
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    (2024). Expenditures: Housing by Quintiles of Income Before Taxes: Second 20 Percent (21st to 40th Percentile) [Dataset]. https://fred.stlouisfed.org/series/CXUHOUSINGLB0103M
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    jsonAvailable download formats
    Dataset updated
    Sep 25, 2024
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Expenditures: Housing by Quintiles of Income Before Taxes: Second 20 Percent (21st to 40th Percentile) (CXUHOUSINGLB0103M) from 1984 to 2023 about percentile, tax, expenditures, income, housing, and USA.

  12. c

    Where are people affected by high rent costs?

    • hub.scag.ca.gov
    • hub.arcgis.com
    Updated Feb 1, 2022
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    rdpgisadmin (2022). Where are people affected by high rent costs? [Dataset]. https://hub.scag.ca.gov/maps/3a3207d9b7f0438e96270ffdef07a51d
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    Dataset updated
    Feb 1, 2022
    Dataset authored and provided by
    rdpgisadmin
    Area covered
    Description

    This map shows housing costs as a percentage of household income. Severe housing cost burden is described as when over 50% of income in a household is spent on housing costs. For renters it is over 50% of household income going towards gross rent (contract rent plus tenant-paid utilities). Miami, Florida accounts for the having the highest population of renters with severe housing burden costs.The map's topic is shown by tract and county centroids. 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. Income is based on earnings in past 12 months of survey. Current Vintage: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map 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. 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

  13. Detailed household expenditure as a percentage of total expenditure by...

    • ons.gov.uk
    • cy.ons.gov.uk
    xls
    Updated Jan 24, 2019
    + more versions
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    Office for National Statistics (2019). Detailed household expenditure as a percentage of total expenditure by disposable income decile group: Table 3.2 [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/expenditure/datasets/detailedhouseholdexpenditureasapercentageoftotalexpenditurebydisposableincomedecilegroupuktable32e
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jan 24, 2019
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Description

    Average weekly household expenditure on goods and services in the UK. Data are shown by region, age, income (including equivalised) group (deciles and quintiles), economic status, socio-economic class, housing tenure, output area classification, urban and rural areas (Great Britain only), place of purchase and household composition.

  14. Percentage of total monthly household income spent on private rent, England...

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Mar 19, 2020
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    Office for National Statistics (2020). Percentage of total monthly household income spent on private rent, England 2012 to 2018 [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/housing/datasets/percentageoftotalmonthlyhouseholdincomespentonprivaterentengland2012to2018
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Mar 19, 2020
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    England
    Description

    Percentage of total monthly household income that is spent on monthly private rent, by region and country, England, 2012 to 2018.

  15. c

    ACS Housing Costs Variables - Tract

    • hub.scag.ca.gov
    Updated Feb 3, 2022
    + more versions
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    rdpgisadmin (2022). ACS Housing Costs Variables - Tract [Dataset]. https://hub.scag.ca.gov/items/ee9ceca4fa554392a47e3a4d06d63a06
    Explore at:
    Dataset updated
    Feb 3, 2022
    Dataset authored and provided by
    rdpgisadmin
    Area covered
    Description

    This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state centroids. 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. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the count and percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). 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: 2015-2019ACS Table(s): B25070, B25091Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

  16. Least affordable metro areas U.S. 2018, by income spent on mortgage

    • statista.com
    Updated Jul 10, 2025
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    Statista (2025). Least affordable metro areas U.S. 2018, by income spent on mortgage [Dataset]. https://www.statista.com/statistics/980747/least-affordable-metro-areas-usa-by-income-spent-on-mortgage/
    Explore at:
    Dataset updated
    Jul 10, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2018
    Area covered
    United States
    Description

    This statistic shows the share of income spent on mortgage payments in selected metro areas in the Unites States in 2018. In 2018, Los Angeles, California was the third least affordable metro area because **** percent of the median household income was spent on median mortgage payments. For comparison, this is higher than the **** percent homeowners spent, on average, on mortgage payments in the United States in 2018.

  17. T

    Vital Signs: Housing Affordability - County by Income (2022)

    • data.bayareametro.gov
    application/rdfxml +5
    Updated Jan 3, 2023
    + more versions
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    (2023). Vital Signs: Housing Affordability - County by Income (2022) [Dataset]. https://data.bayareametro.gov/dataset/Vital-Signs-Housing-Affordability-County-by-Income/va7e-4jn9
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    csv, application/rdfxml, tsv, xml, json, application/rssxmlAvailable download formats
    Dataset updated
    Jan 3, 2023
    Description

    VITAL SIGNS INDICATOR
    Housing Affordability (EQ2)

    FULL MEASURE NAME
    Housing Affordability

    LAST UPDATED
    December 2022

    DATA SOURCE
    U.S. Census Bureau: Decennial Census - https://nhgis.org
    Form STF3 – https://nhgis.org (1980-1990)
    Form SF3a – https://nhgis.org (2000)

    U.S. Census Bureau: American Community Survey - https://data.census.gov/
    Form B25074 (2009-2021)
    Form B25095 (2009-2021)

    CONTACT INFORMATION
    vitalsigns.info@bayareametro.gov

    METHODOLOGY NOTES (across all datasets for this indicator)
    The share of income brackets used for different Census and American Community Survey (ACS) forms vary over time. To allow for historical comparisons, the Census Bureau merges housing expenditure brackets into three consistent bins (less than 20 percent, 20 percent to 34 percent, and more than 35 percent) that work for all years. The highest income bracket for renters in the ACS data was $100,000 or more, while the homeowner dataset included brackets for $100,000 to $149,999 and $150,000 and above. These brackets were merged together to allow for uniform comparison across tenure. While some studies use 30 percent as the affordability threshold, Vital Signs uses 35 percent as this is the closest break point using the standardized affordability brackets above.

    ACS 1-year data is used for larger geographies – Bay counties and most metropolitan area counties – while smaller geographies rely upon 5-year rolling average data due to their smaller sample sizes. Note that 2020 data uses the 5-year estimates because the ACS did not collect 1-year data for 2020.

    Income breakdown data is only provided for one year as it is not possible to compare consistent inflation-adjusted income brackets over time given Census data limitations. For the county breakdown, Napa was missing ACS 1-Year renter data for all years except 2012 and 2013, and Marin was missing ACS 1-Year renter data for 2019 — these counties used 5-Year data for those years.

  18. Median Rent as a Percentage of Income Map

    • data.wu.ac.at
    csv, json, xml
    Updated Dec 15, 2015
    + more versions
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    United States Census Bureau American Community Survey (2015). Median Rent as a Percentage of Income Map [Dataset]. https://data.wu.ac.at/schema/performance_smcgov_org/aWptcy15ZHln
    Explore at:
    xml, json, csvAvailable download formats
    Dataset updated
    Dec 15, 2015
    Dataset provided by
    United States Census Bureauhttp://census.gov/
    Description

    This dataset contains information about the percent of income households spend on rent in cities in San Mateo County. This data is for renters only, not those who live in owner-occupied homes with or without a mortgage. This data was extracted from the United States Census Bureau's American Community Survey 2014 5 year estimates.

  19. G

    Quality of Life - Housing

    • open.canada.ca
    • ouvert.canada.ca
    • +1more
    jp2, zip
    Updated Mar 14, 2022
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    Natural Resources Canada (2022). Quality of Life - Housing [Dataset]. https://open.canada.ca/data/en/dataset/edc64940-8893-11e0-9cfd-6cf049291510
    Explore at:
    zip, jp2Available download formats
    Dataset updated
    Mar 14, 2022
    Dataset provided by
    Natural Resources Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    Housing is one of the basic needs for well-being. Poor quality of housing may have adverse effects on health and well-being. Housing is one of the basic needs of all individuals, and the standard of housing impacts on physical health and well-being. Housing requiring major repairs inversely affects housing quality, while a high number of people per room inversely affects liveability. The third indicator measures affordability: a higher percentage of household income spent on rent or a mortgage impacts inversely on affordability.

  20. F

    Mortgage Debt Service Payments as a Percent of Disposable Personal Income

    • fred.stlouisfed.org
    json
    Updated Mar 21, 2025
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    (2025). Mortgage Debt Service Payments as a Percent of Disposable Personal Income [Dataset]. https://fred.stlouisfed.org/series/MDSP
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    jsonAvailable download formats
    Dataset updated
    Mar 21, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Description

    Graph and download economic data for Mortgage Debt Service Payments as a Percent of Disposable Personal Income (MDSP) from Q1 1980 to Q4 2024 about disposable, payments, mortgage, debt, personal income, percent, personal, services, income, and USA.

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Statista (2025). Share of income spent on mortgage or rent in England 2011-2024, by tenure [Dataset]. https://www.statista.com/statistics/755883/income-spent-on-mortgage-or-rent-england-by-tenure/
Organization logo

Share of income spent on mortgage or rent in England 2011-2024, by tenure

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Dataset updated
Jul 9, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Apr 2023 - Mar 2024
Area covered
United Kingdom (England)
Description

When comparing the mortgage or rental costs incurred by owners with mortgage, private renters and social renters in England, private renters pay a considerably larger share of their income than the other two groups. While owner occupiers with mortgages paid approximately **** percent of their income on mortgage in 2024, private renters paid ** percent, or more than *********. In terms of average monthly costs, renting a three-bedroom house is more expensive than buying.

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