Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Explore the Redfin USA Properties Dataset, available in CSV format. This extensive dataset provides valuable insights into the U.S. real estate market, including detailed property listings, prices, property types, and more across various states and cities. Perfect for those looking to conduct in-depth market analysis, real estate investment research, or financial forecasting.

Key Features:

• Comprehensive Property Data: Includes essential details such as listing prices, property types, square footage, and the number of bedrooms and bathrooms.
• Geographic Coverage: Encompasses a wide range of U.S. states and cities, providing a broad view of the national real estate market.
• Historical Trends: Analyze past market data to understand price movements, regional differences, and market trends over time.
• Geo-Location Details: Enables spatial analysis and mapping by including precise geographical coordinates of properties.

Who Can Benefit From This Dataset:

• Real Estate Investors: Identify lucrative opportunities by analyzing property values, market trends, and regional price variations.
• Market Analysts: Gain a deeper understanding of the U.S. housing market dynamics to inform research and reporting.
• Data Scientists and Researchers: Leverage detailed real estate data for modeling, urban studies, or economic analysis.
• Financial Analysts: Utilize the dataset for financial modeling, helping to predict market behavior and assess investment risks.

Download the Redfin USA Properties Dataset to access essential information on the U.S. housing market, ideal for professionals in real estate, finance, and data analytics. Unlock key insights to make informed decisions in a dynamic market environment.

Looking for deeper insights or a custom data pull from Redfin?
Send a request with just one click and explore detailed property listings, price trends, and housing data.
🔗 Request Redfin Real Estate Data

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.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q1 2025 about sales, median, housing, and USA.

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.

Nahb Housing Market Index in the United States increased to 33 points in July from 32 points in June 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.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Our Realtor.com (Multiple Listing Service) dataset represents one of the most exhaustive collections of real estate data available to the industry. It consolidates data from over 500 MLS aggregators across various regions, providing an unparalleled view of the property market.

Features:

Property Listings: Each listing provides comprehensive details about a property. This includes its physical address, number of bedrooms and bathrooms, square footage, lot size, type of property (e.g., single-family home, condo, townhome), and more.

Photographs and Virtual Tours: Visuals are crucial in the property market. Most listings are accompanied by high-quality photographs and, in many cases, virtual or 3D tours that allow potential buyers to explore properties remotely.

Pricing Information: Listings provide asking prices, and the dataset frequently updates to reflect price changes. Historical price data, which includes initial listing prices and any subsequent reductions or increases, is also available.

Transaction Histories: For sold properties, the dataset provides information about the date of sale, the sale price, and any discrepancies between the listing and sale prices.

Agent and Broker Information: Each listing typically has associated details about the property's real estate professional. This might include their name, contact details, and affiliated brokerage.

Open House Schedules: Open house dates and times are listed for properties that are actively being shown to potential buyers.

1. Analytical Insights:

Market Trends: By analyzing the dataset over time, one can glean insights into market dynamics, such as the rate of price appreciation or depreciation in certain areas, the average time properties stay on the market, and seasonality effects.

Neighborhood Data: With comprehensive geographical data, it becomes possible to understand neighborhood-specific trends. This is invaluable for potential buyers or real estate investors looking to identify burgeoning markets.

Price Comparisons: Realtors and potential buyers can benchmark properties against similar listings in the same area to determine if a property is priced appropriately.

1. Utility:

For Industry Professionals and Analysts: Beyond buyers and sellers, the dataset is a trove of information for real estate agents, brokers, analysts, and investors. They can harness this data to craft strategies, predict market movements, and serve their clients better.

This dataset uses data provided from Washington State’s Housing Market, a publication of the Washington Center for Real Estate Research (WCRER) at the University of Washington.

Median sales prices represent that price at which half the sales in a county (or the state) took place at higher prices, and half at lower prices. Since WCRER does not receive sales data on individual transactions (only aggregated statistics), the median is determined by the proportion of sales in a given range of prices required to reach the midway point in the distribution. While average prices are not reported, they tend to be 15-20 percent above the median.

Movements in sales prices should not be interpreted as appreciation rates. Prices are influenced by changes in cost and changes in the characteristics of homes actually sold. The table on prices by number of bedrooms provides a better measure of appreciation of types of homes than the overall median, but it is still subject to composition issues (such as square footage of home, quality of finishes and size of lot, among others).

There is a degree of seasonal variation in reported selling prices. Prices tend to hit a seasonal peak in summer, then decline through the winter before turning upward again, but home sales prices are not seasonally adjusted. Users are encouraged to limit price comparisons to the same time period in previous years.

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. Measured in €

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.

New Home Sales in the United States decreased to 623 Thousand units in May from 722 Thousand units in April of 2025. This dataset provides the latest reported value for - United States New Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Starts in the United States increased to 1321 Thousand units in June from 1263 Thousand units in May 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.

Real estate datasets from various websites cover all major real estate data points including: property type, size, location, price, bedrooms, baths, address, history, images, and much more. Popular use cases include: forecast housing demand, analyze price fluctuations, improve customer satisfaction, see past prices to monitor market trends, and more.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q1 2025 about sales, housing, and USA.

In late 2016, the URA, in conjunction with Reinvestment Fund, completed the 2016 Market Value Analysis (MVA) for the City of Pittsburgh. 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 neighborhood 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.

Pittsburgh’s 2016 MVA utilized data that helps to define the local real estate market between July, 2013 and June, 2016:

• Median Sales Price

• Variance of Sales Price

• Percent Households Owner Occupied

• Density of Residential Housing Units

• Percent Rental with Subsidy

• Foreclosures as a Percent of Sales

• Permits as a Percent of Housing Units

• Percent of Housing Units Built Before 1940

• Percent of Properties with Assessed Condition “Poor” or worse

• Vacant Housing Units as a Percentage of Habitable 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 URA 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.

Displacement risk indicator classifying census tracts according to apartment rent prices in census tracts. We classify apartment rent along two dimensions:The median rents within the census tract for the specified year, balancing between nominal rental price and rental price per square foot.The change in median rent price (again balanced between nominal rent price and price per square foot) from the previous year.Note: Median rent calculations include market-rate and mixed-income multifamily apartment properties with 5 or more rental units in Seattle, excluding special types like student, senior, corporate or military housing.Source: Data from CoStar Group, www.costar.com, prepared by City of Seattle, Office of Planning and Community Development

EnhancedHousingMarketData.csv is an auxiliary dataset for the "Housing Prices" competition, containing key economic and demographic indicators vital for real estate market analysis. It includes data on non-farm employment, housing price index, per capita income, total quarterly wages, quantitative indexes of real GDP, total GDP, real GDP, stable population, employed individuals, and the average weekly wage in the private sector, along with the unemployment rate. This dataset aids in better understanding the factors influencing housing prices and allows for a more in-depth analysis of the real estate market.

"**TotalNonfarmEmployees**" - reflects the total number of employees working outside the agricultural sector. This figure includes workers in industries such as manufacturing, construction, trade, transportation, education, healthcare, and other non-agricultural sectors, making it a key indicator of economic activity and employment in the region.

"**HousingPriceIndex**" - represents a housing price index, reflecting changes in real estate prices in a specific region for a given month. This index can be used to analyze trends in the real estate market and assess the overall economic conditions.

"**AnnualPerCapitaIncome**" - represents the annual per capita income, measured yearly. This indicator reflects the average income per resident in a specific region over a year, serving as an important measure of the population's economic well-being.

"**QuarterlyTotalWages**" - represents the total quarterly wages, measured in dollars and adjusted for seasonal variations. This metric reflects the sum of wages paid by employers insured for unemployment insurance over a calendar quarter. It includes components such as vacation pay, bonuses, and tips.

"**TotalRealGDPChainIndex**" - represents the total annual quantitative index of real GDP, encompassing data from all private sectors and the government. It is based on the Fisher chain-weighted method, tracking changes in production volume or expenditures while eliminating the effects of price changes. This index is useful for comparing the volumes of production or expenditures across different time periods.

"**TotalGDP**" - describes the total Gross Domestic Product (GDP), measured in millions of dollars and calculated annually without seasonal adjustments. This metric encompasses all private sectors and the government, reflecting the market value of all final goods and services produced within an agglomeration. The agglomeration GDP represents the gross output minus intermediate costs, serving as a key indicator of economic activity and production volume.

"**TotalRealGDP**" - represents the total real Gross Domestic Product, measured in millions of chained 2012 dollars and calculated annually without seasonal adjustments. This metric includes data from all private sectors and the government. The real GDP for agglomerations is a measure of the gross product of each agglomeration, adjusted for inflation, and based on national prices for goods and services produced in the agglomeration.

"**StablePopulation**" - reflects the stable population, measured in thousands of people and calculated annually without seasonal adjustments. This metric represents population estimates as of July 1st each year, providing reliable data for analyzing demographic trends and planning purposes.

"**EmployedIndividuals**" - represents the number of employed individuals, measured in persons without seasonal adjustment and updated monthly. The data are derived from the Current Population Survey (CPS). Employed individuals include those who did any paid work, owned a business or farm, worked 15 hours or more as unpaid workers in a family business, or were temporarily absent from their job for various reasons. This metric is important for analyzing employment levels and the economic activity of the population.

"**AverageWeeklyWagePrivate**" - denotes the average weekly wage of private enterprise employees, measured in dollars per week and calculated quarterly without seasonal adjustment. It includes payments made by employers insured against unemployment over the quarter, encompassing vacation pay, bonuses, stock options, tips, and other components. This metric is important for assessing the level of wages in the private sector.

"**UnemploymentRate**" - represents the unemployment rate, measured in percentages and calculated monthly without seasonal adjustments. This metric indicates the proportion of the unemployed within the total labor force, providing key information about the labor market's condition and the population's economic activity.

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. Measured in €