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.

Title: Boston Housing Price Prediction Dataset

Description:

This dataset contains information about housing prices in Boston and is often used for regression analysis and predictive modeling. The dataset is based on the classic Boston Housing dataset, which is frequently used as a benchmark in machine learning.

Attributes:

1. CRIM (Per Capita Crime Rate): The per capita crime rate in the neighborhood.
2. ZN (Proportion of Residential Land Zoned for Large Lots): The proportion of residential land zoned for lots over 25,000 sq. ft.
3. INDUS (Proportion of Non-Retail Business Acres): The proportion of non-retail business acres per town.
4. CHAS (Charles River Dummy Variable): A binary variable indicating whether the Charles River bounds the tract (1 if bounded, 0 otherwise).
5. NOX (Nitric Oxides Concentration): Nitric oxides concentration (parts per 10 million).
6. RM (Average Number of Rooms per Dwelling): The average number of rooms per dwelling.
7. AGE (Proportion of Owner-Occupied Units Built Prior to 1940): The proportion of owner-occupied units built prior to 1940.
8. DIS (Weighted Distances to Employment Centers): Weighted distances to five Boston employment centers.
9. RAD (Index of Accessibility to Radial Highways): An index representing accessibility to radial highways.
10. TAX (Full-Value Property Tax Rate per $10,000): The full-value property tax rate per $10,000.
11. PTRATIO (Pupil-Teacher Ratio): The pupil-teacher ratio by town.
12. B (1000(Bk - 0.63)^2 where Bk is the Proportion of Black Residents): A measure of the proportion of Black residents adjusted for an offset.
13. LSTAT (Percentage of Lower Status of the Population): The percentage of lower-status residents in the population.
14. MEDV (Median Value of Owner-Occupied Homes): The median value of owner-occupied homes in $1000s (Target Variable).

Objective:

Predict the median value of owner-occupied homes (MEDV) based on various features to gain insights into factors influencing housing prices.

Usage:

This dataset is suitable for regression tasks, machine learning practice, and understanding the dynamics of housing markets.

Citation:

The dataset is derived from the UCI Machine Learning Repository and can be cited as follows:

Harrison Jr., D., & Rubinfeld, D. L. (1978). Hedonic prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81-102.

This data sets out the percentage of residents of the Cambridge housing sub-region who are unable to afford housing, based on contemporary income data and housing costs, broken down into percentage for 1, 2 and 3 bedroom homes. The data comes from the housing sub-region's Strategic Housing Market Assessment, or SHMA, which is updated regularly. The data provided in this open data set comes from: SHMA 2013, based on 2011/12 data SHMA 2012, based on 2009/10 data SHMA 2010, based on 2008/9 data SHMA 2009, based on mostly 2007/8 data The data is all published in chapters of our strategic housing market assessment which are used as part of our calculations around the need for affordable housing, particularly where we need to work out the proportion of people unlikely to be able to afford housing via the private market (owned or rented) and thus potentially in need of "sub market" or affordable housing.

Data on resident owners who are persons occupying one of their residential properties: sex, age, total income, the type and the assessment value of the owner-occupied property, as well as the number and the total assessment value of residential properties owned.

Home Ownership Rate in Poland decreased to 87.10 percent in 2024 from 87.30 percent in 2023. This dataset provides the latest reported value for - Poland Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Home Ownership Rate in Canada decreased to 66.70 percent in 2023 from 69.30 percent in 2021. This dataset provides the latest reported value for - Canada Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This data collection includes data collected as part of the project Selfbuilding: the production and consumption of new homes from the perspective of households. It includes (1) survey data with self builders and potential self builders, alongside (2) transcripts for interviews conducted with key informants (KI) and (3) transcripts for interviews conducted with self builders. (1) The survey data submitted here was collected in 2012-13 via two online surveys, generated and hosted by Bristol Online Surveys. PDF copies of these surveys are included as part of this submission. (a) The first of these targeted contemporary self builders--people who were building or who had built their own homes in the last 20 years. It consisted for 54 questions, covering the desire to selfbuild; past and present residential choice; experiences of construction and self-build; planning your selfbuild; financing your self-build; details of individual self-build projects; and experience of self-building; while also collecting basic information about individual households. (b) The second of these targeted would-be self builders. A much shorter survey, this was designed to merely capture a sense of what people wanted to achieve through self-build and their motivations alongside basic information. (2) The submission to the archive include 30 transcripts of semi-structured interviews conducted with key informants--industry professionals, housing practitioners, local authority representatives and planners. These were conducted between 2013 and 2015. (3) The submission also includes transcripts from in-depth interviews conducted with 22 research participants. Where follow-up visits including recorded interviews took place, there are additional transcripts. The self-building of homes is being promoted as one solution to the shortage of affordable houses in Britain. Currently, self-build projects account for 14% of the new homes built, a percentage equivalent to or greater than that of any single house building firm. This form of housing provision is, however, understudied, with the result that there is very little understanding of who the people are who choose to build their homes, the motivations for this residential practice and experiences of self-building. This project aims to correct this lacuna of knowledge, conducting a systematic enquiry into self-building in Britain, taking seriously the need to know who is using self-build, what their characteristics are and to what extent they succeed in their aims, as well as exploring how the self-build market is structured. The project will be conducted over three years, and is comprised of several different modes of data collection and analysis, engaging self-build households, potential self-builders, industry professionals and experts, national and local planning authorities. This will be supplemented with a review of planning and housing policies, and analysis of popular representations of self-build in Britain. The research took place in three stages: (1) Semi-structured interviews with key stakeholders including local authority representatives, representatives from DCLG, national self-build association (NaSBA), selfbuild developers and supplies, housing practitioners. These were recruited through targeted enquiries via email or phone in the first instance; further parties were recruited via snowball sampling. (2) Internet survey of self-building households The target population for the survey was self-building households at all stages in the process, including those who are just thinking about self-build, and those who have completed their projects. Statistics on the numbers of households who self-build are disputed, and not disaggregated in terms of class background, household composition or the level of engagement. It is therefore impossible to obtain a representative sample in terms of these variables. The survey was promoted through a range of specialist networks and internet fora, with the aim of getting a wide coverage of Britain's self builders. (3) Ethnographic case studies with 20 self-building households A purposive sample of self-building households was recruited through various methods including survey responses, networks and forums. Self-builders at all stages in the self-build process will be recruited. The sample aimed to capture the diversity of the self-build population in terms of household composition, individual and collective self-build, the roles that households take within the self- build process, socio-economic background and the overall financial investment that they make in their projects. For each case study, the researcher spent up to three days with the household, conducting research using a variety of methods including: (1) in-depth interviews with households and individual household members, asking people to think back over their experiences of self-building; (2) the collection of housing and residential histories and trajectories; (3) innovative participatory research methods (a) visual ethnography on-site and (b) analysis of personal archives and architectural plans; (4) participant observation in the home and around the neighbourhood; (5) and the collection of basic socio-economic data.

Update 29-04-2020: The data is now split into two files based on the variable collection frequency (monthly and yearly). Additional variables added: area size in hectares, number of jobs in the area, number of people living in the area.

Context

I have been inspired by Xavier and his work on Barcelona to explore the city of London! 🇬🇧 💂

Content

The datasets is primarily centered around the housing market of London. However, it contains a lot of additional relevant data: - Monthly average house prices - Yearly number of houses - Yearly number of houses sold - Yearly percentage of households that recycle - Yearly life satisfaction - Yearly median salary of the residents of the area - Yearly mean salary of the residents of the area - Monthly number of crimes committed - Yearly number of jobs - Yearly number of people living in the area - Area size in hectares

The data is split by areas of London called boroughs (a flag exists to identify these), but some of the variables have other geographical UK regions for reference (like England, North East, etc.). There have been no changes made to the data except for melting it into a long format from the original tables.

Acknowledgements

The data has been extracted from London Datastore. It is released under UK Open Government License v2 and v3. The underlining datasets can be found here: https://data.london.gov.uk/dataset/uk-house-price-index https://data.london.gov.uk/dataset/number-and-density-of-dwellings-by-borough https://data.london.gov.uk/dataset/subjective-personal-well-being-borough https://data.london.gov.uk/dataset/household-waste-recycling-rates-borough https://data.london.gov.uk/dataset/earnings-place-residence-borough https://data.london.gov.uk/dataset/recorded_crime_summary https://data.london.gov.uk/dataset/jobs-and-job-density-borough https://data.london.gov.uk/dataset/ons-mid-year-population-estimates-custom-age-tables

Cover photo by Frans Ruiter from Unsplash

Inspiration

The dataset lends itself for extensive exploratory data analysis. It could also be a great supervised learning regression problem to predict house price changes of different boroughs over time.

The dataset is a catalog of major residential development projects in Somerset County, NJ. This includes Affordable Housing, Senior housing options, and Market-rate rentalsAffordable Housing Options: With New Jersey having some of the highest housing costs in the county, the state government has implemented several initiatives and programs to provide housing options for low- and moderate-income eligible households. In addition, several municipalities have implemented inclusionary zoning laws, that require property developers to allocate a certain percentage of the units for affordable housing. Somerset county has several affordable housing programs to help low-and moderate-income eligible households and first-time homebuyers, including the Mt. Laurel Doctrine, New Jersey Balanced Housing Program, HUD Public Housing Program, HUD Housing Choice Voucher Program (Section 8). This dataset provides a comprehensive list of all affordable housing projects in the county. The dataset includes ‘inclusionary’ developments that are comprised of both market-rate units and affordable units. It also includes municipality-sponsored and other 100% affordable housing projects, as well as affordable housing created through the redevelopment process. The total number of market rate and affordable housing units in each project is provided. Some projects include a blend of both rental and for-purchase units. Senior Housing Options: There are several housing options in Somerset County for older adults seeking assistance with daily living or those who want to maintain their independence or those who seek to live in communities designed for older adults. These options include – Active Adult Communities: These are communities designed for older adults who can live independently but want to live in a community specifically for older adults. They typically offer amenities such as fitness centers, swimming pools, and social activities. Many independent living communities also offer additional services such as transportation, housekeeping, and meals. Assisted Living Communities: These communities aid with daily living activities such as bathing, dressing, and medication management. They offer a range of services, depending on the level of care needed. Some assisted living communities also offer memory care services for individuals with dementia or Alzheimer's disease. Continuing Care Retirement Communities: These communities offer a continuum of care that includes independent living, assisted living, and skilled nursing care. This allows residents to "age in place" and receive additional care as needed without having to move to a different community. Senior Residence: These communities are restricted to residents who are 55 years of age or older. They typically offer amenities like active adult communities and may have additional features such as golf courses, community centers, and events. Market Rate Rentals: These properties are typically owned/operated by private landlords and are not considered affordable housing and are not subject to government subsidies. These include apartments, condominiums, town homes, single-family homes. The information included in this dataset represents a point-in-time (November 2023) and is subject to change. Furthermore, new, or alternative housing projects may be proposed in future years, which will be incorporated into subsequent dataset updates. Updates to this dataset will take place on an as-needed basis.

Fuel poverty is the requirement to spend 10% or more of household income to maintain an adequate level or warmth. The energy efficiency of a house can be measured using the Standard Assessment Procedure (SAP). The procedure calculates a number between 1 and 100, low numbers generally indicates a house that has low levels of insulation and an inefficient heating system where as numbers closer to 100 indicate a very energy efficient house. SAP is the Government's recommended system for energy rating of dwellings. SAP is being used as a proxy for fuel poverty in households of people claiming income based benefits, given the link between income poverty and fuel poverty.

Home Ownership Rate in Germany decreased to 47.20 percent in 2024 from 47.60 percent in 2023. This dataset provides the latest reported value for - Germany Home Ownership Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Key Table Information.Table Title.Selected Housing Characteristics.Table ID.ACSDP1Y2024.DP04.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Data Profiles.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, and towns and estimates of ...

This dataset and map service provides information on the U.S. Housing and Urban Development's (HUD) low to moderate income areas. The term Low to Moderate Income, often referred to as low-mod, has a specific programmatic context within the Community Development Block Grant (CDBG) program. Over a 1, 2, or 3-year period, as selected by the grantee, not less than 70 percent of CDBG funds must be used for activities that benefit low- and moderate-income persons. HUD uses special tabulations of Census data to determine areas where at least 51% of households have incomes at or below 80% of the area median income (AMI). This dataset and map service contains the following layer.

This dataset displays data from the 2005 Census of Japan. It displays data on Private Households throughout prefectures in Japan. This dataset specifically deals with number of Rented Households Issued Housing, Number of Rented Households Issued Housing Members, Average number of Members per Rented Households Issued Housing, Area of Floor Space per Household of Rented Households Issued Housing, and Area of Floor Space per Person of Rented Households Issued Housing. This data comes from Japan's Ministry of Internal Affairs and Communication's Statistics Bureau.

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.

This dataset is a summary of the OpenFEMA Individuals and Households Program - Valid Registrations (NEMIS) dataset and contains aggregated, non-PII data from Housing Assistance Program reporting authority within FEMA's Recovery Directorate to share data on registrations and Individuals and Households Program (IHP). The data contains counts of program eligibility, referrals and registration methods as well as program award amounts segmented by city where registration is valid. Additionally disaster number, county and zip code are provided.rnrnPlease Note: IHP is intended to help with critical expenses that cannot be covered in other ways. The IHP is not intended to return all homes or belongings to their pre†disaster condition. In some cases, IHP may only provide enough money, up to the program limits, for you to return an item to service. Secondary or vacation residencies do not qualify. Visit for more information about the program: https://www.fema.gov/assistance/public . rnrnThis is raw, unedited data from FEMA's National Emergency Management Information System (NEMIS) and as such is subject to a small percentage of human error. rnrnThe financial information is derived from NEMIS and not FEMA's official financial systems. Due to differences in reporting periods, status of obligations and how business rules are applied, this financial information may differ slightly from official publication on public websites such as usaspending.gov; this dataset is not intended to be used for any official federal financial reporting.rnrnCitation: The Agency's preferred citation for datasets (API usage or file downloads) can be found on the OpenFEMA Terms and Conditions page, Citing Data section: https://www.fema.gov/about/openfema/terms-conditions .rnrnThis dataset is not intended to be an official federal report, and should not be considered an official federal report.rnrnIf you have media inquiries about this dataset, please email the FEMA News Desk FEMA-News-Desk@dhs.gov or call (202) 646-3272. For inquiries about FEMA's data and Open government program please contact the OpenFEMA team via email OpenFEMA@fema.dhs.gov.