About Dataset

Source

The source of this dataset is REDFIN Data Center. To download the latest dataset available, please go to: https://www.redfin.com/news/data-center/

They also provide a page with the definitions for each metric used here: https://www.redfin.com/news/data-center-metrics-definitions/

For more informaton on Data and Data Quality, please visit: https://www.redfin.com/about/data-quality-on-redfin Reading the Data

The data is a .tsv format and can be imported using pandas as follows:

df = pd.read_csv("weekly_housing_market_data_most_recent.tsv000", sep='\t')

MOST RECENT DATAPOINT: 2022-07-11

Nahb Housing Market Index in the United States decreased to 32 points in August from 33 points in July of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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:

• Residential Real Estate Sales
• Mortgage Foreclosures
• Residential Vacancy
• Parcel Year Built
• Parcel Condition
• Building Violations
• Owner Occupancy
• Subsidized Housing Units

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.

About this Dataset

Real estate properties dataset from Homes.com. Crawl Feeds extracted more than 300K+ records. Last extracted on 2 july 2022.

Category

Real Estate

Keywords

Real Estate,real estate lists,housing,US Real Estate

Row Count

303036

Price

$400.00

Enrich your real estate strategies and market insights with our comprehensive New York Housing dataset. Analyzing this dataset can aid in understanding housing market dynamics and trends, empowering organizations to refine their investment strategies and business decisions. Access the entire dataset or tailor a subset to fit your requirements.

Popular use cases include optimizing investment strategies based on neighborhood engagement and property popularity, performing detailed user behavior analysis and segmentation by housing type, price range, and location to tailor marketing and engagement efforts, and identifying and forecasting emerging trends in the New York housing market to stay ahead in the competitive real estate industry.

Total Housing Inventory in the United States increased to 1550 Thousands in July from 1540 Thousands in June of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

The dataset "aus_real_estate.csv" encapsulates comprehensive real estate information pertaining to Australia, showcasing diverse attributes essential for property assessment and market analysis. This dataset, comprising 5000 entries across 10 distinct columns, offers a detailed portrayal of various residential properties in cities across Australia.

The dataset encompasses crucial factors influencing property valuation and purchase decisions. The 'Price' column represents the property's cost, spanning a range between $100,000 and $2,000,000. Attributes such as 'Bedrooms' and 'Bathrooms' highlight the accommodation specifics, varying from one to five bedrooms and one to three bathrooms, respectively. 'SqFt' denotes the square footage of the properties, varying between 800 and 4000 square feet, elucidating their size and spatial dimensions.

The 'City' column encompasses major Australian urban centers, including Sydney, Melbourne, Brisbane, Perth, and Adelaide, delineating the geographical distribution of the properties. 'State' further categorizes the locations into New South Wales (NSW), Victoria (VIC), Queensland (QLD), Western Australia (WA), and South Australia (SA).

The dataset encapsulates temporal information through the 'Year_Built' attribute, spanning from 1950 to 2023, providing insights into the age and vintage of the properties. Moreover, property types are delineated within the 'Type' column, encompassing variations such as 'Apartment,' 'House,' and 'Townhouse.' The binary 'Garage' column signifies the presence (1) or absence (0) of a garage, while 'Lot_Area' provides an understanding of the land area, ranging from 1000 to 10,000 square feet.

This dataset offers a comprehensive outlook into the Australian real estate landscape, facilitating multifaceted analyses encompassing property valuation, market trends, and regional preferences. Its diverse attributes make it a valuable resource for researchers, analysts, and stakeholders within the real estate domain, enabling robust investigations and informed decision-making processes regarding property investments and market dynamics in Australia.

This dataset was generated for analyzing the economic impacts of subway networks on housing prices in metropolitan areas. The provision of transit networks and accompanying improvement in accessibility induce various impacts and we focused on the economic impacts realized through housing prices. As a proxy of housing price, we consider the price of condominiums, the dominant housing type in South Korea. Although our focus is transit accessibility and housing prices, the presented dataset is applicable to other studies. In particular, it provides a wide range of variables closely related to housing price, including housing properties, local amenities, local demographic characteristics, and control variables for the seasonality. Many of these variables were scientifically generated by our research team. Various distance variables were constructed in a geographic information system environment based on public data and they are useful not only for exploring environmental impacts on housing prices, but also for other statistical analyses in regard to real estate and social science research. The four metropolitan areas covered by the data—Busan, Daegu, Daejeon, and Gwangju—are independent of the transit systems of Greater Seoul, providing accurate information on the metropolitan structure separate from the capital city.

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.

A dataset of indicators of the state of the UK housing market This is a collection of indicators from diverse sources on different aspects of the state of the UK housing market. Some indicators are updated monthly, others quarterly. Publication of this dataset began in August 2012. The choice of which indicators are included in this dataset may be subject to revision, but the intention is to update the dataset regularly as new data become available. Historical time series have been added for some (but not yet all) of the indicators.

In 2017, the County Department of Economic Development, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for Allegheny County. A similar MVA was completed with the Pittsburgh Urban Redevelopment Authority in 2016. The Market Value Analysis (MVA) offers an approach for community revitalization; it recommends applying interventions not only to where there is a need for development but also in places where public investment can stimulate private market activity and capitalize on larger public investment activities. 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. The 2016 Allegheny County MVA does not include the City of Pittsburgh, which was characterized at the same time in the fourth update of the City of Pittsburgh’s MVA. All calculations herein therefore do not include the City of Pittsburgh. While the methodology between the City and County MVA's are very similar, the classification of communities will differ, and so the data between the two should not be used interchangeably. Allegheny County's MVA utilized data that helps to define the local real estate market. Most data used covers the 2013-2016 period, and data used in the analysis includes: •Residential Real Estate Sales; • Mortgage Foreclosures; • Residential Vacancy; • Parcel Year Built; • Parcel Condition; • Owner Occupancy; and • Subsidized Housing Units. 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. During the research process, staff from the County and Reinvestment Fund spent an extensive amount of effort ensuring the data and analysis was accurate. In addition to testing the data, staff physically examined different areas to verify the data sets being used were appropriate indicators and the resulting MVA categories accurately reflect the market. Please refer to the report (included here as a pdf) for more information about the data, methodology, and findings.

ttd22/house-price dataset hosted on Hugging Face and contributed by the HF Datasets community

Annual house price data based on a sub-sample of the Regulated Mortgage Survey.

Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. Excluding apartments, measured in EUR Figure changed on the 27/6/16 as revised data received from the Local authority

House Price Index YoY in the United States decreased to 2.60 percent in June from 2.90 percent in May of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

This dataset contains 500 entries of housing price data from various countries, regions, and cities worldwide, making it ideal for machine learning models and real estate market analysis. The dataset covers diverse geographic locations, including:

North America: USA, Canada, Mexico
Europe: Germany, France, UK, Italy, Spain
Asia: Japan, China, India, South Korea
Other Regions: Australia, Brazil, South Africa

Columns Included:

Country: The country where the house is located (e.g., USA, Japan, India).
State/Region: The state or region within the country (e.g., California, Bavaria).
City: The city where the property is located (e.g., Los Angeles, Tokyo).
Square Footage (SqFt): The size of the house in square feet (ranging from 500 to 5000 sq ft).
Bedrooms: The number of bedrooms in the house (ranging from 1 to 6).
Population Density: The population density of the area (people per sq km).
Price of House: The price of the house (in local currency, converted to USD where applicable).

This dataset can be used for:

Machine Learning Models: Training and evaluating models for house price prediction.
Market Analysis: Analyzing housing trends across different regions and countries.
Visualization: Creating insightful visualizations to understand price distributions and regional variations.

This dataset provides a balanced mix of geographic diversity and housing features for robust predictive modeling and analysis.

This dataset is about books. It has 1 row and is filtered where the book is An econometric analysis of the urban housing market. It features 7 columns including author, publication date, language, and book publisher.

Median price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

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.

Treasury and the U.S. Department of Housing and Urban Development (HUD) jointly produce a Monthly Housing Scorecard on the health of the nation’s housing market. The Scorecard is generally released during the first week of each month.