17 datasets found
  1. o

    Rent increase dwellings; income class

    • data.overheid.nl
    • cbs.nl
    • +1more
    atom, json
    Updated May 20, 2025
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    Centraal Bureau voor de Statistiek (Rijk) (2025). Rent increase dwellings; income class [Dataset]. https://data.overheid.nl/dataset/14819-rent-increase-dwellings--income-class
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    json(KB), atom(KB)Available download formats
    Dataset updated
    May 20, 2025
    Dataset provided by
    Centraal Bureau voor de Statistiek (Rijk)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This table includes figures on the average increase of rent broken down by income class. A distinction is made here between rental of regulated dwellings by social and other landlords and liberalised rental.

    Data available from: 2015.

    Status of the figures: The figures in this table are definitive.

    Changes as of 20 May 2025: The figures broken down by income class have been removed from this table for the categories of liberalised rents and total. These figures are not applicable and were previously published in error. Landlords can only request income data for regulated rents, which form the basis for this table.

    Changes as of 4 September 2024: The figures of 2024 have been published.

    Changes as of 8 September 2023: The category 'middle income' has been added to the table.

    When will new figures be published? New figures of 2025 will become available in September 2025.

  2. 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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    csvAvailable download formats
    Dataset updated
    Oct 17, 2024
    Dataset authored and provided by
    Champaign County Regional Planning Commission
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    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).

  3. Median Rent as a Percentage of Income Map

    • data.wu.ac.at
    csv, json, xml
    Updated Dec 15, 2015
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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
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    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.

  4. T

    United States Price to Rent Ratio

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated May 27, 2025
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    TRADING ECONOMICS (2025). United States Price to Rent Ratio [Dataset]. https://tradingeconomics.com/united-states/price-to-rent-ratio
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    xml, json, excel, csvAvailable download formats
    Dataset updated
    May 27, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 31, 1970 - Dec 31, 2024
    Area covered
    United States
    Description

    Price to Rent Ratio in the United States increased to 134.20 in the fourth quarter of 2024 from 133.60 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

  5. Public Housing

    • data.bayareametro.gov
    Updated Dec 10, 2021
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    California Department of Housing and Community Development (2021). Public Housing [Dataset]. https://data.bayareametro.gov/Structures/Public-Housing/3bj7-zyaq
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    application/rdfxml, csv, application/rssxml, xml, tsv, application/geo+json, kml, kmzAvailable download formats
    Dataset updated
    Dec 10, 2021
    Dataset provided by
    California Department of Housing & Community Developmenthttps://hcd.ca.gov/
    Authors
    California Department of Housing and Community Development
    Description

    The feature set indicates the locations, and tenant characteristics of public housing development buildings for the San Francisco Bay Region. This feature set, extracted by the Metropolitan Transportation Commission, is from the statewide public housing buildings feature layer provided by the California Department of Housing and Community Development (HCD). HCD itself extracted the California data from the United States Department of Housing and Urban Development (HUD) feature service depicting the location of individual buildings within public housing units throughout the United States.

    According to HUD's Public Housing Program, "Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by some 3,300 housing agencies. HUD administers federal aid to local housing agencies that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments.

    HUD administers Federal aid to local Housing Agencies (HAs) that manage housing for low-income residents at rents they can afford. Likewise, HUD furnishes technical and professional assistance in planning, developing, and managing the buildings that comprise low-income housing developments. This feature set provides the location, and resident characteristics of public housing development buildings.

    Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:

    ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) 
    ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) 
    ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) 
    ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) 
    ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) 
     ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) 
    ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) 
    Null - Could not be geocoded (does not appear on the map) 
    

    For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. In an effort to protect Personally Identifiable Information, the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10.

    HCD downloaded the HUD data in April 2021. They sourced the data from https://hub.arcgis.com/datasets/fedmaps::public-housing-buildings.

    To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/.

  6. a

    2016 Counties Annual Income - Rent

    • data-hub-lacrossecounty.hub.arcgis.com
    Updated May 5, 2021
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    La Crosse County (2021). 2016 Counties Annual Income - Rent [Dataset]. https://data-hub-lacrossecounty.hub.arcgis.com/datasets/2016-counties-annual-income-rent
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    Dataset updated
    May 5, 2021
    Dataset authored and provided by
    La Crosse County
    Description

    Chart and data created/acquired for the La Crosse County Comprehensive Plan 2022 from the American Community Survey.

  7. F

    Consumer Price Index for All Urban Consumers: Rent of Primary Residence in...

    • fred.stlouisfed.org
    json
    Updated Jun 11, 2025
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    (2025). Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average [Dataset]. https://fred.stlouisfed.org/series/CUUR0000SEHA
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jun 11, 2025
    License

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

    Area covered
    United States
    Description

    Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in U.S. City Average (CUUR0000SEHA) from Dec 1914 to May 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

  8. Housing Cost Burden

    • data.ca.gov
    • data.chhs.ca.gov
    • +4more
    pdf, xlsx, zip
    Updated Aug 28, 2024
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    California Department of Public Health (2024). Housing Cost Burden [Dataset]. https://data.ca.gov/dataset/housing-cost-burden
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    xlsx, pdf, zipAvailable download formats
    Dataset updated
    Aug 28, 2024
    Dataset authored and provided by
    California Department of Public Healthhttps://www.cdph.ca.gov/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This table contains data on the percent of households paying more than 30% (or 50%) of monthly household income towards housing costs for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Department of Housing and Urban Development (HUD), Consolidated Planning Comprehensive Housing Affordability Strategy (CHAS) and the U.S. Census Bureau, American Community Survey (ACS). The table is part of a series of indicators in the [Healthy Communities Data and Indicators Project of the Office of Health Equity] Affordable, quality housing is central to health, conferring protection from the environment and supporting family life. Housing costs—typically the largest, single expense in a family's budget—also impact decisions that affect health. As housing consumes larger proportions of household income, families have less income for nutrition, health care, transportation, education, etc. Severe cost burdens may induce poverty—which is associated with developmental and behavioral problems in children and accelerated cognitive and physical decline in adults. Low-income families and minority communities are disproportionately affected by the lack of affordable, quality housing. More information about the data table and a data dictionary can be found in the Attachments.

  9. Households in 25% Housing Stress - Dataset - data.sa.gov.au

    • data.sa.gov.au
    Updated May 28, 2013
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    data.sa.gov.au (2013). Households in 25% Housing Stress - Dataset - data.sa.gov.au [Dataset]. https://data.sa.gov.au/data/dataset/households-in-25-housing-stress
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    Dataset updated
    May 28, 2013
    Dataset provided by
    Government of South Australiahttp://sa.gov.au/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    South Australia
    Description

    Housing Affordability Supply and Demand Data. Number of South Australian households paying more than 25% of their household income on housing (rent or mortgage) broken down by very low, low and moderate income brackets. This dataset relates to section 4, Housing Stress, of the Affordability master reports produced by the SA Housing Authority. Each master report covers one Local Government Area and is entitled ‘Housing Affordability – Demand and Supply by Local Government Area’. The Demand for Supply for LGA reports are available online at: https://data.sa.gov.au/data/dataset/housing-affordability-demand-and-supply-by-local-government-area Explanatory Notes: Data sourced from the Australian Bureau of Statistics (ABS), Census for Population and Housing and it is updated every 5 years in line with the ABS Census. The nature of the income imputation means that the reported proportion may significantly overstate the true proportion. Census housing stress data is best used in comparing results over Censuses (ie did it increase or decrease in an area) rather than using it to ascertain what proportion of households were in rental stress. Income bands are based on household income. High income households can also experience rental stress. These households are included in the total but not identified separately. Data is representative of households in very low, low and moderate income brackets. Please note that there are small random adjustments made to all cell values to protect the confidentiality of data. These adjustments may cause the sum of rows or columns to differ by small amounts from table totals.

  10. Private rental affordability, England and Wales

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Oct 28, 2024
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    Office for National Statistics (2024). Private rental affordability, England and Wales [Dataset]. https://www.ons.gov.uk/peoplepopulationandcommunity/housing/datasets/privaterentalaffordabilityengland
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    xlsxAvailable download formats
    Dataset updated
    Oct 28, 2024
    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, Wales
    Description

    Percentage of total monthly household income spent on private rent, by country and by regions of England, financial years ending 2013 to 2023.

  11. Philippines PTE: House Rent

    • ceicdata.com
    Updated Dec 15, 2022
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    CEICdata.com (2022). Philippines PTE: House Rent [Dataset]. https://www.ceicdata.com/en/philippines/family-income-and-expenditure-survey-percentage-distribution-of-family-expenditure-by-income-class/pte-house-rent
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    Dataset updated
    Dec 15, 2022
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2015
    Area covered
    Philippines
    Variables measured
    Household Income and Expenditure Survey
    Description

    Philippines PTE: House Rent data was reported at 12.200 % in 2015. Philippines PTE: House Rent data is updated yearly, averaging 12.200 % from Dec 2015 (Median) to 2015, with 1 observations. Philippines PTE: House Rent data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H026: Family Income and Expenditure Survey: Percentage Distribution of Family Expenditure: By Income Class.

  12. Real estate rental and leasing and property management, summary statistics

    • open.canada.ca
    • www150.statcan.gc.ca
    • +2more
    csv, html, xml
    Updated Mar 31, 2025
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    Statistics Canada (2025). Real estate rental and leasing and property management, summary statistics [Dataset]. https://open.canada.ca/data/en/dataset/13425ff1-aa23-495f-a80d-7178af53bc84
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    csv, html, xmlAvailable download formats
    Dataset updated
    Mar 31, 2025
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    License

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

    Description

    The summary statistics by North American Industry Classification System (NAICS) which include: operating revenue (dollars x 1,000,000), operating expenses (dollars x 1,000,000), salaries wages and benefits (dollars x 1,000,000), and operating profit margin (by percent), of lessors of residential buildings and dwellings (except social housing projects) (NAICS 531111), annual, for five years of data.

  13. a

    2010 Annual Local Income - Rent

    • data-hub-lacrossecounty.hub.arcgis.com
    Updated Apr 24, 2021
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    La Crosse County (2021). 2010 Annual Local Income - Rent [Dataset]. https://data-hub-lacrossecounty.hub.arcgis.com/datasets/2010-annual-local-income-rent
    Explore at:
    Dataset updated
    Apr 24, 2021
    Dataset authored and provided by
    La Crosse County
    Description

    Chart and data created/acquired for the La Crosse County Comprehensive Plan 2022 from the American Community Survey.

  14. Data from: Public Housing Authorities

    • data.lojic.org
    • hudgis-hud.opendata.arcgis.com
    • +1more
    Updated Nov 12, 2024
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    Department of Housing and Urban Development (2024). Public Housing Authorities [Dataset]. https://data.lojic.org/maps/HUD::public-housing-authorities-1
    Explore at:
    Dataset updated
    Nov 12, 2024
    Dataset provided by
    United States Department of Housing and Urban Developmenthttp://www.hud.gov/
    Authors
    Department of Housing and Urban Development
    Area covered
    Description

    Public Housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. Public housing comes in all sizes and types, from scattered single family houses to high-rise apartments for elderly families. There are approximately 1.2 million households living in public housing units, managed by over 3,300 housing agencies (HAs). HUD administers Federal aid to local housing agencies (HAs) that manage the housing for low-income residents at rents they can afford. HUD furnishes technical and professional assistance in planning, developing and managing these developments. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes: ‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green) ‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green) ‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow) ‘T’ - Census tract centroid (low degree of accuracy, symbolized as red) ‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red) ‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red) ‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red) Null - Could not be geocoded (does not appear on the map) For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block. The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about Public Housing visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Public Housing Authorities Date Updated: Q1 2025

  15. a

    2010 Peer Cities Annual Income - Rent

    • data-hub-lacrossecounty.hub.arcgis.com
    Updated May 5, 2021
    + more versions
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    La Crosse County (2021). 2010 Peer Cities Annual Income - Rent [Dataset]. https://data-hub-lacrossecounty.hub.arcgis.com/datasets/2010-peer-cities-annual-income-rent
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    Dataset updated
    May 5, 2021
    Dataset authored and provided by
    La Crosse County
    Description

    Chart and data created/acquired for the La Crosse County Comprehensive Plan 2022 from the American Community Survey.

  16. G

    Net income of non-farm unincorporated business including rent, annual, 1926...

    • open.canada.ca
    • datasets.ai
    • +2more
    csv, html, xml
    Updated Jan 17, 2023
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    Statistics Canada (2023). Net income of non-farm unincorporated business including rent, annual, 1926 - 1990 [Dataset]. https://open.canada.ca/data/en/dataset/182177b4-79a6-4dc5-a62d-e1ae57c87bf2
    Explore at:
    csv, html, xmlAvailable download formats
    Dataset updated
    Jan 17, 2023
    Dataset provided by
    Statistics Canada
    License

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

    Description

    This table contains 14 series, with data for years 1926 - 1990 (not all combinations necessarily have data for all years), and was last released on 2000-02-19. This table contains data described by the following dimensions (Not all combinations are available): Geography (14 items: Canada; Newfoundland and Labrador; Nova Scotia; Prince Edward Island ...), Net income (1 items: Net income of non-farm unincorporated business including rent ...).

  17. d

    Housing Tenure and Costs - Seattle Neighborhoods

    • catalog.data.gov
    Updated Feb 14, 2025
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    City of Seattle ArcGIS Online (2025). Housing Tenure and Costs - Seattle Neighborhoods [Dataset]. https://catalog.data.gov/dataset/housing-tenure-and-costs-seattle-neighborhoods
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    Dataset updated
    Feb 14, 2025
    Dataset provided by
    City of Seattle ArcGIS Online
    Area covered
    Seattle
    Description

    Table from the American Community Survey (ACS) 5-year series on housing tenure and cost related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B25003 Tenure of Occupied Housing Units, B25070 Gross Rent as a Percentage of Household Income in the Past 12 Months, B25063 Gross Rent, B25091 Mortgage Status by Selected Monthly Owner Costs as a Percentage of Household Income in the Past 12 Months, B25087 Mortgage Stauts and Selected Monthly Owner Costs, B25064 Median Gross Rent, B25088 Median Selected Monthly Owner Costs by Mortgage Status. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.Table created for and used in the Neighborhood Profiles application.Vintages: 2023ACS Table(s): B25003, B25070, B25063, B25091, B25087, B25064, B25088Data downloaded from: Census Bureau's Explore Census Data The United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACS<a href='https://www.census.gov/programs-surveys/acs/technical-documentation.html' style='color:rgb(0, 121, 193); text-decoration-line:none; font-family:inherit;' target='

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Centraal Bureau voor de Statistiek (Rijk) (2025). Rent increase dwellings; income class [Dataset]. https://data.overheid.nl/dataset/14819-rent-increase-dwellings--income-class

Rent increase dwellings; income class

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json(KB), atom(KB)Available download formats
Dataset updated
May 20, 2025
Dataset provided by
Centraal Bureau voor de Statistiek (Rijk)
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

This table includes figures on the average increase of rent broken down by income class. A distinction is made here between rental of regulated dwellings by social and other landlords and liberalised rental.

Data available from: 2015.

Status of the figures: The figures in this table are definitive.

Changes as of 20 May 2025: The figures broken down by income class have been removed from this table for the categories of liberalised rents and total. These figures are not applicable and were previously published in error. Landlords can only request income data for regulated rents, which form the basis for this table.

Changes as of 4 September 2024: The figures of 2024 have been published.

Changes as of 8 September 2023: The category 'middle income' has been added to the table.

When will new figures be published? New figures of 2025 will become available in September 2025.

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