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TwitterRedfin 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
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License information was derived automatically
A dataset comprising various variables around housing and demographics for the top 50 American cities by population.
Variables:
Zip Code: Zip code within which the listing is present.
Price: Listed price for the property.
Beds: Number of beds mentioned in the listing.
Baths: Number of baths mentioned in the listing.
Living Space: The total size of the living space, in square feet, mentioned in the listing.
Address: Street address of the listing.
City: City name where the listing is located.
State: State name where the listing is located.
Zip Code Population: The estimated number of individuals within the zip code. Data from Simplemaps.com.
Zip Code Density: The estimated number of individuals per square mile within the zip code. Data from Simplemaps.com.
County: County where the listing is located.
Median Household income: Estimated median household income. Data from the U.S. Census Bureau.
Latitude: Latitude of the zip code. ** Data from Simplemaps.com.**
Longitude: Longitude of the zip code. Data from Simplemaps.com.
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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.
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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.
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TwitterThe average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.
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TwitterThe year-end value of the S&P Case Shiller National Home Price Index amounted to 321.45 in 2024. The index value was equal to 100 as of January 2000, so if the index value is equal to 130 in a given year, for example, it means that the house prices increased by 30 percent since 2000. S&P/Case Shiller U.S. home indices – additional informationThe S&P Case Shiller National Home Price Index is calculated on a monthly basis and is based on the prices of single-family homes in nine U.S. Census divisions: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain and Pacific. The index is the leading indicator of the American housing market and one of the indicators of the state of the broader economy. The index illustrates the trend of home prices and can be helpful during house purchase decisions. When house prices are rising, a house buyer might want to speed up the house purchase decision as the transaction costs can be much higher in the future. The S&P Case Shiller National Home Price Index has been on the rise since 2011.The S&P Case Shiller National Home Price Index is one of the indices included in the S&P/Case-Shiller Home Price Index Series. Other indices are the S&P/Case Shiller 20-City Composite Home Price Index, the S&P/Case Shiller 10-City Composite Home Price Index and twenty city composite indices.
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TwitterThe primary reasons for purchasing a home in the United States in 2024 varied among home buyers. Approximately one in four homebuyers bought a home because they desired to have their own home. Having one's own home was mainly considered by millennial buyers during their home buying process.
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License information was derived automatically
Key information about House Prices Growth
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TwitterThis dataset comes from Zillow and provides a comprehensive look at U.S. housing market trends from 2018 to May 2024. It includes detailed data on median home values, average days outstanding for property sales, and their impact on reducing prices in several cities. This dataset is ideal for analyzing the correlation between home values, time to market, and price adjustments, offering valuable insights for real estate professionals, economists, and data analysts interested in the dynamics of the U.S. housing market.
About the license, taken from the Zillow website:
“For research and academic projects, we provide the following metrics that have more flexible Terms of Use regarding data storage and manipulation – https://www.zillow.com/research/data/”
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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?
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.
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Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q3 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.
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Graph and download economic data for Real Residential Property Prices for United States (QUSR628BIS) from Q1 1970 to Q2 2025 about residential, HPI, housing, real, price index, indexes, price, and USA.
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TwitterThe number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.
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This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.
Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.
Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith
For more datasets, click here.
- 🚨 Your notebook can be here! 🚨!
- Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
- Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
- Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc
If you use this dataset in your research, please credit the original authors. Data Source
See the dataset description for more information.
File: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...
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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.
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The Sales segment was valued at USD 926.50 billion in 2019 and showed a gradual increase during the forecast period.
Request Free Sample
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
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The North America Prefabricated Housing Market Report is Segmented by Material Type (Concrete, Glass, Metal, Timber, Other Materials), by Type (Single Family, Multi Family), by Product Type (Modular Homes, Panelized & Componentized Systems, Manufactured Homes, Other Prefab Types), and by Country (United States, Canada, Mexico). The Market Forecasts are Provided in Terms of Value (USD).
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View monthly updates and historical trends for US House Price Index. from United States. Source: Federal Housing Finance Agency. Track economic data with …
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TwitterThe U.S. housing market continues to evolve, with the median price for existing homes forecast to fall to ******* U.S. dollars by 2027. This projection comes after a period of significant growth and recent fluctuations, reflecting the complex interplay of economic factors affecting the real estate sector. The rising costs have not only impacted home prices but also down payments, with the median down payment more than doubling since 2012. Regional variations in housing costs Home prices and down payments vary dramatically across the United States. While the national median down payment stood at approximately ****** U.S. dollars in early 2024, homebuyers in states like California, Massachusetts, and Hawaii faced down payments exceeding ****** U.S. dollars. This disparity highlights the challenges of homeownership in high-cost markets and underscores the importance of location in determining housing affordability. Market dynamics and future outlook The housing market has shown signs of cooling after years of rapid growth, with a modest price increase of *** percent in 2024. This slowdown can be attributed in part to rising mortgage rates, which have tempered demand. Despite these challenges, most states continued to see year-over-year price growth in 2025, with Rhode Island and West Virginia leading the packby home appreciation. As the market adjusts to new economic realities, potential homebuyers and investors alike will be watching closely for signs of stabilization or renewed growth in the coming years.
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U.S. Housing Market size is estimated to be valued at USD 52.14 Tn in 2025 and is expected to expand at a CAGR of 4.9%, reaching USD 72.88 Tn by 2032.
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Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Oct 2025 about median and USA.
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TwitterRedfin 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