The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.
Statistics on the availability and affordability of housing, homelessness, and homebuilding in rural and urban areas.
Indicators:
Data source: Department for Levelling up, Housing and Communities & Ministry for Housing, Communities and Local Government
Coverage: England
Rural classification used: Local Authority Rural-Urban Classification
Next release date: tbc
Defra statistics: rural
Email mailto:rural.statistics@defra.gov.uk">rural.statistics@defra.gov.uk
<p class="govuk-body">You can also contact us via Twitter: <a href="https://twitter.com/DefraStats" class="govuk-link">https://twitter.com/DefraStats</a></p>
The Housing Affordability Data System (HADS) is a set of files derived from the 1985 and later national American Housing Survey (AHS) and the 2002 and later Metro AHS. This system categorizes housing units by affordability and households by income, with respect to the Adjusted Median Income, Fair Market Rent (FMR), and poverty income. It also includes housing cost burden for owner and renter households. These files have been the basis for the worst case needs tables since 2001. The data files are available for public use, since they were derived from AHS public use files and the published income limits and FMRs. These dataset give the community of housing analysts the opportunity to use a consistent set of affordability measures. The most recent year HADS is available as a Public Use File (PUF) is 2013. For 2015 and beyond, HADS is only available as an IUF and can no longer be released on a PUF. Those seeking access to more recent data should reach to the listed point of contact.
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Housing Starts in the United States decreased to 1256 Thousand units in May from 1392 Thousand units in April of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Jun 2025 about median and USA.
These files are no longer being updated to include any late revisions local authorities may have reported to the department. Please use instead the Local authority housing statistics open data file for the latest data.
<p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute">MS Excel Spreadsheet</span>, <span class="gem-c-attachment_attribute">728 KB</span></p>
<p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute"><abbr title="Comma-separated Values" class="gem-c-attachment_abbr">CSV</abbr></span>, <span class="gem-c-attachment_attribute">286 KB</span></p>
<p class="gem-c-attachment_metadata"><a class="govuk-link" aria-label="View Local authority housing statistics - full data 2019 to 2020 online" href="/media/62b319028fa8f5356d206d53/LAHS_all_data_2019_2020_-_06_2022.csv/preview">View online</a></p>
Following a period of stagnation over most of the 2010s, the number of owner occupied housing units in the United States started to grow in 2017. In 2023, there were over 86 million owner-occupied homes. Owner-occupied housing is where the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing. Homeownership sentiment in the U.S. Though homeownership is still a cornerstone of the American dream, an increasing share of people see themselves as lifelong renters. Millennials have been notoriously late to enter the housing market, with one in four reporting that they would probably continue to always rent in the future, a 2022 survey found. In 2017, just five years before that, this share stood at about 13 percent. How many renter households are there? Renter households are roughly half as few as owner-occupied households in the U.S. In 2023, the number of renter occupied housing units amounted to almost 45 million. Climbing on the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home.
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New housing price index (NHPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (201612=100).
The dataset contains current data on low rent and Section 8 units in PHA's administered by HUD. The Section 8 Rental Voucher Program increases affordable housing choices for very low-income households by allowing families to choose privately owned rental housing. Through the Section 8 Rental Voucher Program, the administering housing authority issues a voucher to an income-qualified household, which then finds a unit to rent. If the unit meets the Section 8 quality standards, the PHA then pays the landlord the amount equal to the difference between 30 percent of the tenant's adjusted income (or 10 percent of the gross income or the portion of welfare assistance designated for housing) and the PHA-determined payment standard for the area. The rent must be reasonable compared with similar unassisted units.
Housing stock in units is an economic estimate of the number of housing units in Canada, the provinces and territories by institutional sector, dwelling occupation, dwelling type, and tenure type. These data are used to estimate gross domestic product by income and expenditure. The units are benchmarked to dwelling data from the census at the national, provincial and territorial levels. Dwelling type and tenure type are also aligned with census data.
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Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q1 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.
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Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q1 2025 about homeownership, housing, rate, and USA.
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In 2021, Allegheny County Economic Development (ACED), in partnership with Urban Redevelopment Authority of Pittsburgh(URA), completed the a Market Value Analysis (MVA) for Allegheny County. This analysis services as both an update to previous MVA’s commissioned separately by ACED and the URA and combines the MVA for the whole of Allegheny County (inclusive of the City of Pittsburgh). The MVA is a unique tool for characterizing markets because it creates an internally referenced index of a municipality’s residential real estate market. It identifies areas that are the highest demand markets as well as areas of greatest distress, and the various markets types between. The MVA offers insight into the variation in market strength and weakness within and between traditional community boundaries because it uses Census block groups as the unit of analysis. Where market types abut each other on the map becomes instructive about the potential direction of market change, and ultimately, the appropriateness of types of investment or intervention strategies.
This MVA utilized data that helps to define the local real estate market. The data used covers the 2017-2019 period, and data used in the analysis includes:
The MVA uses a statistical technique known as cluster analysis, forming groups of areas (i.e., block groups) that are similar along the MVA descriptors, noted above. The goal is to form groups within which there is a similarity of characteristics within each group, but each group itself different from the others. Using this technique, the MVA condenses vast amounts of data for the universe of all properties to a manageable, meaningful typology of market types that can inform area-appropriate programs and decisions regarding the allocation of resources.
Please refer to the presentation and executive summary for more information about the data, methodology, and findings.
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This tool is a searchable data catalogue containing links to a range of official statistics on housing. It forms a part of the ONS Housing Statistics Portal.
The primary reasons for purchasing a home in the United States in 2024 varied among home buyers. Approximately one in four homebuyers bought a home because they desired to have their own home. Having one's own home was mainly considered by millennial buyers during their home buying process.
Most of the German population rented their housing. In 2023, around ** million people did so, compared to roughly **** million who had their own house. The German real estate market does offer different housing options, but it is also an increasingly tough one for tenants and future homeowners to navigate amid the ongoing recession. Competitive and expensive Becoming a homeowner is getting more and more difficult in Germany. After almost a decade of uninterrupted growth, the market has entered a period of downturn. For years, homebuyers could access cheap credit, with mortgage rates as low as *** percent. However, in 2022 and 2023, mortgage rates have increased strongly to over **** percent, making it much more expensive to invest in residential property. In addition to that, prices for owner occupied houses have increased by over ** percent since 2015, house price growth had also overtaken that of rentals the same year, making renting the cheaper living option, especially for younger people. The summary of the housing situation sounds familiar worldwide: fierce competition in urban areas when searching for rentals, with demand far outstripping supply, as well as rising property prices for those considering a house purchase. Somewhere to live The decision to rent rather than buy may occur for various reasons. Tenants may simply not be ready financially to buy a home, be that a house or apartment, or they would not be considered by a bank for a loan based on their current earnings. They may be pressed for time and hope to find a place to rent quicker, while buying a home is a long-term commitment, leading to different types of costs and legalities. A ***************** of people lived in shared apartments in recent years, but figures had not changed so much as to rule this type of housing out as a popular option. Shared or not, the average rent prices of residential property in Germany have been going up year after year, both for new buildings and older ones.
Dataset of all the data supplied by each local authority and imputed figures used for national estimates.
This file is no longer being updated to include any late revisions local authorities may have reported to the department. Please use instead the Local authority housing statistics open data file for the latest data.
MS Excel Spreadsheet, 1.26 MB
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Request an accessible format.In 2024, there were approximately **** million housing units occupied by renters in the United States. This number has been gradually increasing since 2010 as part of a long-term upward swing since 1975. Meanwhile, the number of unoccupied rental housing units has followed a downward trend, suggesting a growing demand and supply failing to catch up. Why are rental homes in such high demand? This high demand for rental homes is related to the shortage of affordable housing. Climbing the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home. How many owner occupied homes are there in the U.S.? In 2023, there were over ** million owner occupied homes. Owner occupied housing is when the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing.
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House Price Index YoY in the United States decreased to 3 percent in April from 3.90 percent in March of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.
A full time series of the data since 1978-79 is available alongside these tables. The next update is scheduled for January/February 2025.
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Notes on Local Authority Housing Statistics (LAHS) 2023-24
Some figures in the main LAHS tables are calculated as weighted averages which cannot be
The NYC Department of City Planning’s (DCP) Housing Database contains all NYC Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. It includes the three primary construction job types that add or remove residential units: new buildings, major alterations, and demolitions, and can be used to determine the change in legal housing units across time and space. Records in the Housing Database Project-Level Files are geocoded to the greatest level of precision possible, subject to numerous quality assurance and control checks, recoded for usability, and joined to other housing data sources relevant to city planners and analysts. Data are updated semiannually, at the end of the second and fourth quarters of each year. Please see DCP’s annual Housing Production Snapshot summarizing findings from the 21Q4 data release here. Additional Housing and Economic analyses are also available. The NYC Department of City Planning’s (DCP) Housing Database Unit Change Summary Files provide the net change in Class A housing units since 2010, and the count of units pending completion for commonly used political and statistical boundaries (Census Block, Census Tract, City Council district, Community District, Community District Tabulation Area (CDTA), Neighborhood Tabulation Area (NTA). These tables are aggregated from the DCP Housing Database Project-Level Files, which is derived from Department of Buildings (DOB) approved housing construction and demolition jobs filed or completed in NYC since January 1, 2010. Net housing unit change is calculated as the sum of all three construction job types that add or remove residential units: new buildings, major alterations, and demolitions. These files can be used to determine the change in legal housing units across time and space.