Redfin is a real estate brokerage and publishes the US housing market data on a regular basis. Using this dataset, you can analyze and visualize housing market data for US cities. Timeline: Starting from February 2012 until the present time (Data is refreshed and updated on a monthly basis)

The dataset has the following columns: - period_begin - period_end - period_duration
- region_type
- region_type_id - table_id - is_seasonally_adjusted. (indicates if prices are seasonally adjusted; f represents False) - region - city - state - state_code - property_type - property_type_id - median_sale_price
- median_sale_price_mom (median sale price changes month over month) - median_sale_price_yoy (median sale price changes year over year) - median_list_price
- median_list_price_mom (median list price changes month over month) - median_list_price_yoy (median list price changes year over year) - median_ppsf (median sale price per square foot) - median_ppsf_mom (median sale price per square foot changes month over month) - median_ppsf_yoy (median sale price per square foot changes year over year) - median_list_ppsf (median list price per square foot) - median_list_ppsf_mom (median list price per square foot changes month over month) - median_list_ppsf_yoy. (median list price per square foot changes year over year) - homes_sold (number of homes sold) - homes_sold_mom (number of homes sold month over month) - homes_sold_yoy (number of homes sold year over year) - pending_sales
- pending_sales_mom
- pending_sales_yoy
- new_listings - new_listings_mom
- new_listings_yoy
- inventory - inventory_mom
- inventory_yoy
- months_of_supply
- months_of_supply_mom - months_of_supply_yoy
- median_dom (median days on market until property is sold) - median_dom_mom (median days on market changes month over month) - median_dom_yoy (median days on market changes year over year) - avg_sale_to_list (average sale price to list price ratio) - avg_sale_to_list_mom (average sale price to list price ratio changes month over month) - avg_sale_to_list_yoy (average sale price to list price ratio changes year over year) - sold_above_list
- sold_above_list_mom - sold_above_list_yoy - price_drops - price_drops_mom - price_drops_yoy - off_market_in_two_weeks (number of properties that will be taken off the market within 2 weeks) - off_market_in_two_weeks_mom (changes in number of properties that will be taken off the market within 2 weeks, month over month) - off_market_in_two_weeks_yoy (changes in number of properties that will be taken off the market within 2 weeks, year over year) - parent_metro_region - parent_metro_region_metro_code - last_updated

Filetype: gzip (gz) Support for gzip files in Python: https://docs.python.org/3/library/gzip.html

Data Source & Credit: Redfin.com

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

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.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Description:

This dataset contains prices of New York houses, providing valuable insights into the real estate market in the region. It includes information such as broker titles, house types, prices, number of bedrooms and bathrooms, property square footage, addresses, state, administrative and local areas, street names, and geographical coordinates.

Key Features:

• BROKERTITLE: Title of the broker
• TYPE: Type of the house
• PRICE: Price of the house
• BEDS: Number of bedrooms
• BATH: Number of bathrooms
• PROPERTYSQFT: Square footage of the property
• ADDRESS: Full address of the house
• STATE: State of the house
• MAIN_ADDRESS: Main address information
• ADMINISTRATIVE_AREA_LEVEL_2: Administrative area level 2 information
• LOCALITY: Locality information
• SUBLOCALITY: Sublocality information
• STREET_NAME: Street name
• LONG_NAME: Long name
• FORMATTED_ADDRESS: Formatted address
• LATITUDE: Latitude coordinate of the house
• LONGITUDE: Longitude coordinate of the house

Potential Use Cases:

• Price analysis: Analyze the distribution of house prices to understand market trends and identify potential investment opportunities.
• Property size analysis: Explore the relationship between property square footage and prices to assess the value of different-sized houses.
• Location-based analysis: Investigate geographical patterns to identify areas with higher or lower property prices.
• Bedroom and bathroom trends: Analyze the impact of the number of bedrooms and bathrooms on house prices.
• Broker performance analysis: Evaluate the influence of different brokers on the pricing of houses.

If you find this dataset useful, your support through an upvote would be greatly appreciated ❤️🙂 Thank you

Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

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

Snapshot img

Residential Real Estate Market Size 2025-2029

The residential real estate market size is valued to increase USD 485.2 billion, at a CAGR of 4.5% from 2024 to 2029. Growing residential sector globally will drive the residential real estate market.

Major Market Trends & Insights

APAC dominated the market and accounted for a 55% growth during the forecast period.
By Mode Of Booking - Sales segment was valued at USD 926.50 billion in 2023
By Type - Apartments and condominiums segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 41.01 billion
Market Future Opportunities: USD 485.20 billion
CAGR : 4.5%
APAC: Largest market in 2023

Market Summary

The market is a dynamic and ever-evolving sector that continues to shape the global economy. With increasing marketing initiatives and the growing residential sector globally, the market presents significant opportunities for growth. However, regulatory uncertainty looms large, posing challenges for stakeholders. According to recent reports, technology adoption in residential real estate has surged, with virtual tours and digital listings becoming increasingly popular. In fact, over 40% of homebuyers in the US prefer virtual property viewings. Core technologies such as artificial intelligence and blockchain are revolutionizing the industry, offering enhanced customer experiences and streamlined processes.
Despite these advancements, regulatory compliance remains a major concern, with varying regulations across regions adding complexity to market operations. The market is a complex and intriguing space, with ongoing activities and evolving patterns shaping its future trajectory.

What will be the Size of the Residential Real Estate Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

How is the Residential Real Estate Market Segmented and what are the key trends of market segmentation?

The residential real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Mode Of Booking

  Sales
  Rental or lease


Type

  Apartments and condominiums
  Landed houses and villas


Location

  Urban
  Suburban
  Rural


End-user

  Mid-range housing
  Affordable housing
  Luxury housing


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    Australia
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Mode Of Booking Insights

The sales segment is estimated to witness significant growth during the forecast period.

Request Free Sample

The Sales segment was valued at USD 926.50 billion in 2019 and showed a gradual increase during the forecast period.

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Regional Analysis

APAC is estimated to contribute 55% to the growth of the global market during the forecast period.Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

See How Residential Real Estate Market Demand is Rising in APAC Request Free Sample

The market in the Asia Pacific (APAC) region holds a significant share and is projected to lead the global market growth. Factors fueling this expansion include the region's rapid urbanization and increasing consumer spending power. Notably, residential and commercial projects in countries like India and China are experiencing robust development. The residential real estate sector in China plays a pivotal role in the economy and serves as a major growth driver for the market.

With these trends continuing, the APAC the market is poised for continued expansion during the forecast period.

Market Dynamics

Our researchers analyzed the data with 2024 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

In the Residential Real Estate Market, understanding the impact property tax rates home values and effect interest rates mortgage affordability is essential for buyers and investors. Key factors affecting home price appreciation and factors influencing housing affordability shape market trends, while the importance property due diligence process and requirements environmental site assessment ensure informed decisions. Investors benefit from methods calculating rental property roi, process home equity loan application, and benefits real estate portfolio diversification. Tools like property management software efficiency and techniques effective property marketing help tackle challenges managing rental properties. Additionally, strategies successf

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.

In 2025, India was the country with the highest increase in house prices since 2010 among the Asia-Pacific (APAC) countries under observation. In the second quarter of the year, the nominal house price index in India reached over 359 index points. This suggests an increase of 259 percent since 2010, the baseline year when the index value was set to 100. It is important to note that the nominal index does not account for the effects of inflation, meaning when adjusted for inflation, price growth in real terms was slower.

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 Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Oct 2025 about median and USA.

Dataset

This dataset was created by Wen Li

Released under Apache 2.0

Contents

Nahb Housing Market Index in the United States increased to 38 points in November from 37 points in October 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.

Innsbruck was the most expensive Austrian city to buy an apartment in, with average values of 7,700 euros per square meter in the first quarter of 2025. The price of an apartment in Graz was significantly lower at 4,590 euros per square meter.

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Description:

A simple yet challenging project, to predict the housing price based on certain factors like house area, bedrooms, furnished, nearness to mainroad, etc. The dataset is small yet, it's complexity arises due to the fact that it has strong multicollinearity. Can you overcome these obstacles & build a decent predictive model?

Acknowledgement:

Harrison, D. and Rubinfeld, D.L. (1978) Hedonic prices and the demand for clean air. J. Environ. Economics and Management 5, 81–102. Belsley D.A., Kuh, E. and Welsch, R.E. (1980) Regression Diagnostics. Identifying Influential Data and Sources of Collinearity. New York: Wiley.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

The Housing Data Extracted from Homes.com (USA) dataset is a comprehensive collection of 2 million real estate listings sourced from Homes.com, one of the leading real estate platforms in the United States. This dataset offers detailed insights into the U.S. housing market, making it an invaluable resource for real estate professionals, investors, researchers, and analysts.

The dataset contains extensive property details, including location, price, property type (single-family homes, condos, apartments), number of bedrooms and bathrooms, square footage, lot size, year built, and availability status. Organized in CSV format, it provides users with easy access to structured data for analyzing trends, developing investment strategies, or building real estate applications.

Key Features:

• Record Count: 2 million housing listings from across the USA.
• Data Fields: Property address, price, property type, bedrooms, bathrooms, square footage, lot size, year built, and availability.
• Format: CSV format for easy integration with data analysis platforms, machine learning models, and real estate tools.
• Source: Directly sourced from Homes.com’s USA real estate listings.
• Geographical Focus: Comprehensive coverage of properties across all regions of the United States.

Use Cases:

• Real Estate Market Research: Analyze property prices, market trends, and housing demand in various U.S. regions.
• Investment Analysis: Use data to identify high-potential properties and regions for real estate investments.
• Property Comparison: Compare listings by price, location, and features to evaluate market conditions across different cities and states.
• Machine Learning Models: Build predictive models for price forecasting, property valuation, and real estate recommendation systems.
• Content Creation: Create real estate-related content, reports, and insights for the U.S. housing market using up-to-date data.

The United States real estate market was valued at USD 3.43 Trillion in 2024. The industry is expected to grow at a CAGR of 2.80% during the forecast period of 2025-2034 to reach a value of USD 4.52 Trillion by 2034. The market growth is mainly driven by the rising corporate investment, particularly in addressing the nation’s affordable housing shortage.

Major corporations are actively investing to integrate housing stability with social responsibility, supporting both new construction and the preservation of existing homes. In September 2024, UnitedHealth Group surpassed USD 1 billion in investments for affordable and mixed-income housing through direct capital and tax credits. These projects span 31 states and have delivered over 25,000 homes, simultaneously improved community health and providing secure housing for low- and moderate-income households.

Such corporate involvements are reshaping trends in United States real estate market by expanding the supply of affordable housing, reducing barriers for renters and homeowners, and stimulating development in high-demand urban and suburban areas. By aligning financial resources with strategic planning, corporations are enabling scalable solutions that meet social and economic objectives while enhancing overall market efficiency.

Graph and download economic data for Average Sales Price of Houses Sold for the United States (ASPUS) from Q1 1963 to Q2 2025 about sales, housing, and USA.

Sea level rise will pose increased risks to U.S. coastal real estate markets in the coming decades, though the direct economic costs depend on the severity and uncertainty within climate-change scenarios.