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

In 2024, Chile was the country with the highest inflation-adjusted increase in house prices since 2010 among the countries under observation. In the fourth quarter of the year, the real house price index in Chile hit 202.57 index points. This means that, adjusted for inflation, house prices grew by 102.57 percent since 2010, the baseline year when the index value was set to 100. According to the nominal house price index, which does not adjust for the effects of inflation, the price increase was higher.

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.

In 2024, Israel was the country with the highest inflation-adjusted increase in house prices since 2010 among the Middle Eastern and African countries under observation. In the fourth quarter of the year, the real house price index in Israel exceeded 185 index points, suggesting that, adjusted for inflation, house prices grew 85 percent since 2010, the baseline year when the index value was set to 100. According to the nominal house price index, which does not adjust for the effects of inflation, the price increase was higher.

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.

In 2024, Chile was the country with the highest increase in house prices since 2010 among the countries under observation. In the fourth quarter of the year, the nominal house price index in Chile exceeded 366 index points. That suggests an increase of 266 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 that adjusted for inflation, price growth in real terms was slower.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q2 2025 about appraisers, HPI, housing, price index, indexes, price, 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.

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

House Price Index YoY in the United States decreased to 2.30 percent in July from 2.70 percent in June of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

As a Data scientist, who yearns to experiment, learn and explore different techniques applied in this field, one cannot overlook the importance of application of Exploratory Data Analysis on various datasets out there.

This housing dataset provides a thorough analysis of the current state of the housing market. It includes information on housing prices, availability, and key trends, allowing you to gain a better understanding of the market and make informed decisions. Whether you're a homebuyer, investor, or simply interested in the state of the housing market, this dataset has valuable insights to offer.

Key information about House Prices Growth

• Mexico house prices grew 8.8% YoY in Dec 2024, following an increase of 9.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Dec 2024, with an average growth rate of 7.4%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

Annual house price data based on a sub-sample of the Regulated Mortgage Survey.

Single Family Home Prices in the United States decreased to 415200 USD in September from 422600 USD in August of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, Israel was the country with the highest increase in house prices since 2010 among the Middle Eastern and African countries under observation. In the fourth quarter of the year, the house price index in Israel exceeded 229 index points, suggesting an increase of 129 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 that, adjusted for inflation, price growth in real terms was slower.

Graph and download economic data for All-Transactions House Price Index for Michigan (MISTHPI) from Q1 1975 to Q2 2025 about MI, appraisers, HPI, housing, price index, indexes, price, and USA.

Danish residential house prices (1992-2024)

About 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… :-)

Inspired by the quintessential House Prices Starter Competition and the popular Melbourne Housing Dataset, this dataset captures 4K+ condominium unit listings on the Malaysian housing website mudah.my.

Like the above datasets, your job is to predict the house prices given certain parameters.

The data was scraped directly from the website using this data collection notebook. I might adapt the code to include houses as well in the future, but scraping the data takes a while due to having to wait for the website to load and having to timeout to account for CloudFlare's protections.

Note: This data is a lot less clean and organized than the data in the two datasets mentioned above. However, this is a good opportunity to practice data cleaning techniques, as this is something that is often overlooked on Kaggle. That being said, I made a starter notebook that goes through the data cleaning steps and outputs a fairly cleaned version of the dataset.

Data Description

• description: The full (unfiltered) description for the unit listing.
• Ad List: The ID of the listing on the website.
• Category: The category of the listing. It will most likely be Apartment / Condominium.
• Facilities: The facilities that the apartment has, in a comma-separated list.
• Building Name: The name of the building.
• Developer: The developer for the building.
• Tenure Type: The type of tenure for the building.
• Address: The address of the building. You can refer to this link for a description of what Malaysian addresses look like.
• Completion Year: The completion year of the building. If the building is still under construction, this is listed as -.
• # of Floors: The number of floors in the building.
• Total Units: The total number of units in the building.
• Property Type: The type of property.
• Bedroom: The number of bedrooms in the unit.
• Bathroom: The number of bathrooms in the unit.
• Parking Lot: The number of parking lots assigned to the unit, if any.
• Floor Range: The floor range for the building.
• Property Size: The size of the unit.
• Land Title: The title given to the land. This link explains what land titles are.
• Firm Type: The type of firm who posted the listing.
• Firm Number: The ID of the firm who posted the listing.
• REN Number: The REN number of the firm who posted the listing. Refer to this link for what REN numbers are.
• price: The price of the unit. This is what you are trying to predict.
• Nearby School/School: If there is a nearby school to the unit, which school it is.
• Park: If there is a nearby park to the unit, which park it is.
• Nearby Railway Station: If there is a nearby railway station to the unit, which railway station it is.
• Bus Stop: If there is a nearby bus stop to the unit, which station it is.
• Nearby Mall/Mall: If there is a nearby mall to the unit, which mall it is.
• Highway: If there is a nearby highway to the unit, which highway it is.

Data description

The House Price Index (HPI) measures inflation in the residential property market. The HPI captures price changes of all types of dwellings purchased by households (flats, detached houses, terraced houses, etc.). Only transacted dwellings are considered, self-build dwellings are excluded. The land component of the dwelling is included.

The HPI is available for all European Union Member States (except Greece), the United Kingdom (only until the third quarter of 2020), Iceland, Norway, Switzerland and Turkey. In addition to the individual country series, Eurostat produces indices for the euro area and for the European Union (EU). As from the first quarter of 2020 onwards, the EU HPI aggregate no longer includes the HPI from the United Kingdom.

The national HPIs are produced by National Statistical Offices (NSIs) and the European aggregates by Eurostat, by combining the national indices. The data released quarterly on Eurostat's website include the national and European price indices, weights and their rates of change.

In order to provide a more comprehensive picture of the housing market, house sales indicators are also provided. Available house sales indicators refer to the total number and value of dwellings transactions at national level where the purchaser is a household. Eurostat publishes in its database a quarterly and annual house sales index as well as quarterly and annual rates of change.

Statistical concepts and definitions

The HPI is based on market prices of dwellings. Non-marketed prices are ruled out from the scope of this indicator. Self-build dwellings, dwellings purchased by sitting tenants at discount prices or dwellings transacted between family members are out of the scope of the indicator. It covers all monetary dwelling transactions regardless of its type (e.g., carried out through a cash purchase or financed through a mortgage loan).

The HPI measures the price developments of all dwellings purchased by households, regardless of which institutional sector they were bought from and the purpose of the purchase. As such, a dwelling bought by a household for a purpose other than owner-occupancy (e.g., for being rented out) is within the scope of the indicator. The HPI includes all purchases of new and existing dwellings, including those of dwellings transacted between households.

The number and value of house sales cover the total annual value of dwellings transactions at national level where the purchaser is a household. Transactions between households are included. Transfers in dwellings due to donations and inheritances are excluded.

The house sales value reflect the prices paid by household buyers and include both the price of land and the price of the structure of the dwelling. The prices for new dwellings include VAT. Other costs related to the acquisition of the dwelling (e.g., notary fees, registration fees, real estate agency commission, bank fees) are excluded.

Statistical unit

Each published index or rate of change refers to transacted dwellings purchased at market prices by the household sector in the corresponding geographical entity. All transacted dwellings are covered, regardless of which institutional sector they were bought from and of the purchase purpose.

more: https://ec.europa.eu/eurostat/cache/metadata/en/prc_hpi_inx_esms.htm