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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 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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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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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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License information was derived automatically
Single Family Home Prices in the United States increased to 415200 USD in October from 412300 USD in September of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.
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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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Key information about House Prices Growth
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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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House Price Index YoY in the United States decreased to 1.70 percent in September from 2.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.
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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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Twitterttd22/house-price dataset hosted on Hugging Face and contributed by the HF Datasets community
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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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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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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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View monthly updates and historical trends for US Existing Home Median Sales Price. from United States. Source: National Association of Realtors. Track ec…
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Average House Prices in the United States increased to 534100 USD in August from 478200 USD in July of 2025. This dataset includes a chart with historical data for the United States New Home Average Sales Price.
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Case-Shiller Index of US residential house prices. Data comes from S&P Case-Shiller data and includes both the national index and the indices for 20 metropolitan regions. The indices are created us...
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TwitterIn the United States, Hawaii was the state with the most expensive housing, with the typical value of single-family homes in the 35th to 65th percentile range exceeding ******* U.S. dollars. Unsurprisingly, Hawaii also ranked top as the state with the highest cost of living. Meanwhile, a property was the least expensive in West Virginia, where it cost under ******* U.S. dollars to buy the typical single-family home. Single-family home prices increased across most states in the United States between December 2023 and December 2024, except in Louisiana, Florida, and the District of Colombia. According to the Federal Housing Association, house appreciation in 13 states exceeded **** percent in 2023.
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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:
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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:
Who Can Benefit From This Dataset:
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
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https://raw.githubusercontent.com/Masterx-AI/Project_Housing_Price_Prediction_/main/hs.jpg" alt="">
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.