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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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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 Name | Description |
|---|---|
Country | The country where the housing market data is recorded 🌍 |
Year | The year of observation 📅 |
Average House Price ($) | The average price of houses in USD 💰 |
Median Rental Price ($) | The median monthly rent for properties in USD 🏠 |
Mortgage Interest Rate (%) | The average mortgage interest rate percentage 📉 |
Household Income ($) | The average annual household income in USD 🏡 |
Population Growth (%) | The percentage increase in population over the year 👥 |
Urbanization Rate (%) | Percentage of the population living in urban areas 🏙️ |
Homeownership Rate (%) | The percentage of people who own their homes 🔑 |
GDP Growth Rate (%) | The annual GDP growth percentage 📈 |
Unemployment Rate (%) | The percentage of unemployed individuals in the labor force 💼 |
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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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Housing Index in the United States decreased to 435.40 points in September from 435.60 points in August of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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TwitterGlobal house prices experienced a significant shift in 2022, with advanced economies seeing a notable decline after a prolonged period of growth. The real house price index (adjusted for inflation) for advanced economies peaked at nearly *** index points in early 2022 before falling to around ***** points by the second quarter of 2023. In the second quarter of 2025, the index reached ***** points. This represents a reversal of the upward trend that had characterized the housing market for roughly a decade. Likewise, real house prices in emerging economies declined after reaching a high of ***** points in the third quarter of 2021. What is behind the slowdown? Inflation and slow economic growth have been the primary drivers for the cooling of the housing market. Secondly, the growing gap between incomes and house prices since 2012 has decreased the affordability of homeownership. Last but not least, homebuyers in 2024 faced dramatically higher mortgage interest rates, further contributing to worsening sentiment and declining transactions. Some markets continue to grow While many countries witnessed a deceleration in house price growth in 2022, some markets continued to see substantial increases. Turkey, in particular, stood out with a nominal increase in house prices of over ** percent in the first quarter of 2025. Other countries that recorded a two-digit growth include North Macedonia and Russia. When accounting for inflation, the three countries with the fastest growing residential prices in early 2025 were North Macedonia, Portugal, and Bulgaria.
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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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This dataset provides a comprehensive snapshot of the Texas real estate market as of 2024, featuring a curated selection of 500 property listings. It encompasses a wide array of properties, reflecting the diverse real estate landscape across Texas. This dataset serves as a foundational tool for understanding market dynamics, property valuations, and regional housing trends within the state.
Given its breadth and depth, this dataset is poised to facilitate a multitude of data science applications. Researchers and analysts can leverage this dataset for exploratory data analysis (EDA) to identify patterns, trends, and anomalies within the Texas real estate market. It is particularly suited for regression analyses to predict property prices based on various features, classification tasks to categorize properties into different market segments, and geographical data analysis to understand regional market dynamics. Despite the dataset's modest size, it offers a rich source for machine learning models aimed at providing insights into price determinants and market trends, ensuring practical applications remain within realistic and achievable bounds.
url: Web address for the property listing on Realtor.com.status: Current status of the listing, indicating availability.id: Unique identifier for each property listing.listPrice: The asking price for the property.baths: Total number of bathrooms, including partials.baths_full: Number of full bathrooms.baths_full_calc: Calculated number of full bathrooms, for consistency.beds: Number of bedrooms in the property.sqft: Total square footage of the property.stories: Number of levels or floors in the property.sub_type: Specific sub-category of the property, if applicable.text: Descriptive narrative provided for the property listing.type: General category of the property (e.g., single-family, condo).year_built: Year the property was constructed.This dataset has been meticulously compiled, adhering to ethical standards and ensuring all data is sourced from publicly available information. It respects privacy and copyright considerations, utilizing data that is openly accessible and intended for public consumption.
Gratitude is extended to Realtor.com for serving as an invaluable resource in the compilation of this dataset. The platform's commitment to providing comprehensive and accessible real estate data has significantly contributed to the depth and quality of this dataset.
The dataset thumbnail image is credited to Realtor.com, as featured on their official Facebook page. The image serves as a visual representation of the diverse and dynamic nature of the Texas real estate market, captured in this comprehensive dataset. View Image
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Key information about House Prices Growth
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TwitterThe U.S. housing market has slowed, after ** consecutive years of rising home prices. In 2021, house prices surged by an unprecedented ** percent, marking the highest increase on record. However, the market has since cooled, with the Freddie Mac House Price Index showing more modest growth between 2022 and 2024. In 2024, home prices increased by *** percent. That was lower than the long-term average of *** percent since 1990. Impact of mortgage rates on homebuying The recent cooling in the housing market can be partly attributed to rising mortgage rates. After reaching a record low of **** percent in 2021, the average annual rate on a 30-year fixed-rate mortgage more than doubled in 2023. This significant increase has made homeownership less affordable for many potential buyers, contributing to a substantial decline in home sales. Despite these challenges, forecasts suggest a potential recovery in the coming years. How much does it cost to buy a house in the U.S.? In 2023, the median sales price of an existing single-family home reached a record high of over ******* U.S. dollars. Newly built homes were even pricier, despite a slight decline in the median sales price in 2023. Naturally, home prices continue to vary significantly across the country, with West Virginia being the most affordable state for homebuyers.
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TwitterAbout the dataset (cleaned data)
The dataset (parquet file) contains approximately 1,5 million residential household sales from Denmark during the periode from 1992 to 2024. All cleaned data is merged into one parquet file here on Kaggle. Note some cleaning might still be nessesary, see notebook under code.
Also, added a random sample (100k) of the dataset as a csv file.
Done in Python version: 2.6.3.
Raw data
Raw data and more info is avaible on Github repositary: https://github.com/MartinSamFred/Danish-residential-housingPrices-1992-2024.git
The dataset has been scraped and cleaned (to some extent). Cleaned files are located in: \Housing_data_cleaned \ named DKHousingprices_1 and 2. Saved in parquet format (and saved as two files due to size).
Cleaning from raw files to above cleaned files is outlined in BoligsalgConcatCleanigGit.ipynb. (done in Python version: 2.6.3)
Webscraping script: Webscrape_script.ipynb (done in Python version: 2.6.3)
Provided you want to clean raw files from scratch yourself:
Uncleaned scraped files (81 in total) are located in \Housing_data_raw \ Housing_data_batch1 and 2. Saved in .csv format and compressed as 7-zip files.
Additional files added/appended to the Cleaned files are located in \Addtional_data and named DK_inflation_rates, DK_interest_rates, DK_morgage_rates and DK_regions_zip_codes. Saved in .xlsx format.
Content
Each row in the dataset contains a residential household sale during the period 1992 - 2024.
“Cleaned files” columns:
0 'date': is the transaction date
1 'quarter': is the quarter based on a standard calendar year
2 'house_id': unique house id (could be dropped)
3 'house_type': can be 'Villa', 'Farm', 'Summerhouse', 'Apartment', 'Townhouse'
4 'sales_type': can be 'regular_sale', 'family_sale', 'other_sale', 'auction', '-' (“-“ could be dropped)
5 'year_build': range 1000 to 2024 (could be narrowed more)
6 'purchase_price': is purchase price in DKK
7 '%_change_between_offer_and_purchase': could differ negatively, be zero or positive
8 'no_rooms': number of rooms
9 'sqm': number of square meters
10 'sqm_price': 'purchase_price' divided by 'sqm_price'
11 'address': is the address
12 'zip_code': is the zip code
13 'city': is the city
14 'area': 'East & mid jutland', 'North jutland', 'Other islands', 'Capital, Copenhagen', 'South jutland', 'North Zealand', 'Fyn & islands', 'Bornholm'
15 'region': 'Jutland', 'Zealand', 'Fyn & islands', 'Bornholm'
16 'nom_interest_rate%': Danish nominal interest rate show pr. quarter however actual rate is not converted from annualized to quarterly
17 'dk_ann_infl_rate%': Danish annual inflation rate show pr. quarter however actual rate is not converted from annualized to quarterly
18 'yield_on_mortgage_credit_bonds%': 30 year mortgage bond rate (without spread)
Uses
Various (statistical) analysis, visualisation and I assume machine learning as well.
Practice exercises etc.
Uncleaned scraped files are great to practice cleaning, especially string cleaning. I’m not an expect as seen in the coding ;-).
Disclaimer
The data and information in the data set provided here are intended to be used primarily for educational purposes only. I do not own any data, and all rights are reserved to the respective owners as outlined in “Acknowledgements/sources”. The accuracy of the dataset is not guaranteed accordingly any analysis and/or conclusions is solely at the user's own responsibly and accountability.
Acknowledgements/sources
All data is publicly available on:
Boliga: https://www.boliga.dk/
Finans Danmark: https://finansdanmark.dk/
Danmarks Statistik: https://www.dst.dk/da
Statistikbanken: https://statistikbanken.dk/statbank5a/default.asp?w=2560
Macrotrends: https://www.macrotrends.net/
PostNord: https://www.postnord.dk/
World Data: https://www.worlddata.info/
Dataset picture / cover photo: Nick Karvounis (https://unsplash.com/)
Have fun… :-)
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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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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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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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Graph and download economic data for All-Transactions House Price Index for Los Angeles County, CA (ATNHPIUS06037A) from 1975 to 2024 about Los Angeles County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.
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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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Key information about House Prices Growth
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TwitterThe UK House Price Index is a National Statistic.
Download the full UK House Price Index data below, or use our tool to https://landregistry.data.gov.uk/app/ukhpi?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=tool&utm_term=9.30_20_03_24" class="govuk-link">create your own bespoke reports.
Datasets are available as CSV files. Find out about republishing and making use of the data.
This file includes a derived back series for the new UK HPI. Under the UK HPI, data is available from 1995 for England and Wales, 2004 for Scotland and 2005 for Northern Ireland. A longer back series has been derived by using the historic path of the Office for National Statistics HPI to construct a series back to 1968.
Download the full UK HPI background file:
If you are interested in a specific attribute, we have separated them into these CSV files:
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price&utm_term=9.30_20_03_24" class="govuk-link">Average price (CSV, 9.4MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-prices-Property-Type-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average_price_property_price&utm_term=9.30_20_03_24" class="govuk-link">Average price by property type (CSV, 28MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Sales-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=sales&utm_term=9.30_20_03_24" class="govuk-link">Sales (CSV, 5MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Cash-mortgage-sales-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=cash_mortgage-sales&utm_term=9.30_20_03_24" class="govuk-link">Cash mortgage sales (CSV, 7MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/First-Time-Buyer-Former-Owner-Occupied-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=FTNFOO&utm_term=9.30_20_03_24" class="govuk-link">First time buyer and former owner occupier (CSV, 6.3MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/New-and-Old-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=new_build&utm_term=9.30_20_03_24" class="govuk-link">New build and existing resold property (CSV, 17MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index&utm_term=9.30_20_03_24" class="govuk-link">Index (CSV, 6.1MB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Indices-seasonally-adjusted-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=index_season_adjusted&utm_term=9.30_20_03_24" class="govuk-link">Index seasonally adjusted (CSV, 209KB)
https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Average-price-seasonally-adjusted-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=average-price_season_adjusted&utm_term=9.30_20_03_24" class="govuk-link">Average price seasonally adjusted (CSV, 218KB)
<a rel="external" href="https://publicdata.landregistry.gov.uk/market-trend-data/house-price-index-data/Repossession-2024-01.csv?utm_medium=GOV.UK&utm_source=datadownload&utm_campaign=repossession&utm_term=9.30_20_03_24" class
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Graph and download economic data for All-Transactions House Price Index for Orange County, CA (ATNHPIUS06059A) from 1975 to 2024 about Orange County, CA; Los Angeles; CA; HPI; housing; price index; indexes; price; and USA.
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Housing Index in Hong Kong increased to 143.46 points in November 23 from 142.49 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Housing Index in Germany increased to 220.43 points in October from 219.91 points in September of 2025. This dataset provides the latest reported value for - Germany House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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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/”