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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Same dataset as "House Sales in King County, USA", but with treated content and with a split version (train-test) allowing direct use in machine learning models.

We have 14 columns in the dataset, as it follows:

• date: Date of the home sale
• price: Price of each home sold
• bedrooms: Number of bedrooms
• bathrooms: Number of bathrooms
• living_in_m2: Square meters of the apartments interior living space
• nice_view: A flag that indicates the view's quality of a property
• perfect_condition: A flag that indicates the maximum index of the apartment condition
• grade: An index from 1 to 5, where 1 falls short of quality level and 5 have a high quality level of construction and design
• has_basement: A flag indicating whether or not a property has a basement
• renovated: A flag if the property was renovated
• has_lavatory: Check for the presence of these incomplete/secondary bathrooms (bathtub, sink, toilet)
• single_floor: A flag indicating whether the property had only one floor
• month: The month of the home sale
• quartile_zone: A quartile distribution index of the most expensive zip codes, where 1 means less expansive and 4 most expansive.

This dataset contains data on all Real Property parcels that have sold since 2013 in Allegheny County, PA.

Before doing any market analysis on property sales, check the sales validation codes. Many property "sales" are not considered a valid representation of the true market value of the property. For example, when multiple lots are together on one deed with one price they are generally coded as invalid ("H") because the sale price for each parcel ID number indicates the total price paid for a group of parcels, not just for one parcel. See the Sales Validation Codes Dictionary for a complete explanation of valid and invalid sale codes.

Sales Transactions Disclaimer: Sales information is provided from the Allegheny County Department of Administrative Services, Real Estate Division. Content and validation codes are subject to change. Please review the Data Dictionary for details on included fields before each use. Property owners are not required by law to record a deed at the time of sale. Consequently the assessment system may not contain a complete sales history for every property and every sale. You may do a deed search at http://www.alleghenycounty.us/re/index.aspx directly for the most updated information. Note: Ordinance 3478-07 prohibits public access to search assessment records by owner name. It was signed by the Chief Executive in 2007.

This dataset provides values for AVERAGE HOUSE PRICES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

India's residential house prices - quarterly and annual changes in house prices across cities, expert analysis and comparison with global peers.

Explore how inflation affects UK house prices. Compare nominal and real (CPI-adjusted) property values using official UK HPI and CPI data to see the true long-term housing market trends.

This dataset contains property sales data, including information such as PropertyID, property type (e.g., Commercial or Residential), tax keys, property addresses, architectural styles, exterior wall materials, number of stories, year built, room counts, finished square footage, units (e.g., apartments), bedroom and bathroom counts, lot sizes, sale dates, and sale prices. Explore this dataset to gain insights into real estate trends and property characteristics.

Data.milwaukee.gov, (2023). Property Sales Data. [online] Available at: https://data.milwaukee.gov [Accessed 9th October 2023].

Open Definition. (n.d.). Creative Commons Attribution 4.0 International Public License (CC BY 4.0). [online] Available at: http://www.opendefinition.org/licenses/cc-by [Accessed 9th October 2023].

This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: - Price in dollars - Address of the property - Latitude and Longitude of the address obtained by using Google Geocoding service - Area Name of the property obtained by using Google Geocoding service

This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas)

However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes).

This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/

I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

Quarterly median house prices for metropolitan Adelaide by suburb

Code and data for "House Prices and Rents" House prices are from Case-Shiller and are not uploaded.

Abstract (en): How have house prices evolved over the long run? This paper presents annual house prices for 14 advanced economies since 1870. We show that real house prices stayed constant from the nineteenth to the mid-twentieth century, but rose strongly and with substantial cross-country variation in the second half of the twentieth century. Land prices, not replacement costs, are the key to understanding the trajectory of house prices. Rising land prices explain about 80 percent of the global house price boom that has taken place since World War II. Our findings have implications for the evolution of wealth-to-income ratios, the growth effects of agglomeration, and the price elasticity of housing supply.

This dataset provides values for RESIDENTIAL PROPERTY PRICES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The data was extracted from Zillow.. Zillow is a prominent online real estate marketplace and has data on around 100 million homes The goal is to create a rich and diverse dataset that encompasses a wide range of housing characteristics across different states, cities, and neighborhoods in the United States.This dataset provides valuable insights into real estate trends and property features. Each record represents a unique house listing and includes details such as location, property specifications, market estimates, and more. A total of 3 files are included, more about them in the file description.

Feature Description:

1. State: The state in which the property is located (AL:Alabama) . Includes all US states except Hawaii.
2. City: The city where the property is situated.
3. Street: The street address of the property.
4. Zipcode: The postal code associated with the property.
5. Bedroom: The number of bedrooms in the house.
6. Bathroom: The number of bathrooms in the house.
7. Area(sqft): The total area of the house.
8. PPSq(Price Per Square Foot): The cost per square foot of the property.
9. LotArea(acres): The total land area associated with the property.
10. MarketEstimate(Dollars $): Estimated market value of the property. This value is estimated using Zillow's own algorithm.
11. RentEstimate:(Dollars $) Estimated rental value of the property. This value is estimated using Zillow's own algorithm.
12. Latitude: The latitude coordinates of the property.
13. Longitude: The longitude coordinates of the property.
14. ListedPrice:(Dollars $) The listed price of the property.

Potential Use Cases:

• Real Estate Market Analysis: Explore trends in housing prices, market estimates, and rental values across different states and cities.
• Predictive Modeling: Build predictive models to estimate property prices or rental values based on various features.
• Feature Engineering: Create new features or derive insights to enhance machine learning models.

Dataset

This dataset was created by K.ABHIRAM

Contents

The house price index in London reached 99.1 index points in May 2025, which was an increase of 2.2 percent year on year. The house price index (HPI) is an easy way of illustrating trends in the house sales market and help simplify house purchase decisions. By using hedonic regression, the index models property price data for all dwellings and shows how much the price has changed since January 2023. Average house prices in Londnon boroughs Location plays a huge role in the price of a home. Kensington and Chelsea and City of Westminster are undoubtedly the most expensive boroughs in London, with an average house price that can exceed one million British pounds. In comparison, a house in Barking and Dagenham cost approximately one third. Nevertheless, the housing market is the busiest in the boroughs with average house prices. How have regional house prices in the UK developed? House prices in other UK regions have risen even more than in London. In Northern Ireland, the house price index reached nearly 120 index points in May 2025, ranking it among the regions with the highest property appreciation. The UK house price index stood at 103 index points, suggesting an increase of 51 percent since 2015.

The UK housing market continued to show significant regional variations in 2025, with London maintaining its position as the most expensive city for homebuyers. The average house price in the capital stood at ******* British pounds in February, nearly double the national average. However, the market dynamics are shifting, with London experiencing only a modest *** percent annual increase, while other cities like Belfast and Liverpool saw more substantial growth of over **** percent respectively. Affordability challenges and market slowdown Despite the continued price growth in many cities, the UK housing market is facing headwinds. The affordability of mortgage repayments has become the biggest barrier to property purchases, with the majority of the respondents in a recent survey citing it as their main challenge. Moreover, a rising share of Brits have reported affordability as a challenge since 2021, reflecting the impact of rising house prices and higher mortgage rates. The market slowdown is evident in the declining housing transaction volumes, which have plummeted since 2021. European context The stark price differences are mirrored in the broader European context. While London boasts some of the highest property prices among European cities, a comparison of the average transaction price for new homes in different European countries shows a different picture. In 2023, the highest prices were found in Austria, Germany, and France.

The average bid price of new housing in Europe was the highest in Luxembourg, at 8,760 euros per square meter. Since there is no central body that collects and tracks transaction activity or house prices across the whole continent or the European Union, only bid prices were considered. House prices have been soaring, with Sweden topping the ranking Considering the RHPI of houses in Europe (the price index in real terms, which measures price changes of single-family properties adjusted for the impact of inflation), however, the picture changes. Sweden, Luxembourg and Norway top this ranking, meaning residential property prices have surged the most in these countries. Real values were calculated using the so-called Personal Consumption Expenditure Deflator (PCE), This PCE uses both consumer prices as well as consumer expenditures, like medical and health care expenses paid by employers. It is meant to show how expensive housing is compared to the way of living in a country. Home ownership highest in Eastern Europe The home ownership rate in Europe varied from country to country. In 2020, roughly half of all homes in Germany were owner-occupied whereas home ownership was at nearly ** percent in Romania or around ** percent in Slovakia and Lithuania. These numbers were considerably higher than in France or Italy, where homeowners made up ** percent and ** percent of their respective populations.For more information on the topic of property in Europe, visit the following pages as a starting point for your research: real estate investments in Europe and residential real estate in Europe.

This dataset contains detailed information on current real estate listings in Houston, Texas, sourced from Zillow, and provides a comprehensive snapshot of the Houston housing market as of 5th June 2024.

The data was extracted from Zillow using a combination of two scraping tools from Apify: Zillow ZIP Code Scraper 🔗 https://apify.com/maxcopell/zillow-zip-search and Zillow Details Scraper 🔗 https://apify.com/maxcopell/zillow-detail-scraper.

The data includes key details for each listing for sale, such as:

• 📍 Complete address, city, state, zip code, latitude/longitude coordinates
• 🏡 Property type (single family, condo, apartment, etc.)
• 💵 Listing price
• 🛏️ Number of bedrooms and bathrooms
• 📐 Square footage
• 🌳 Lot size in acres (if applicable)
• 🏗️ Year of construction
• 🏘️ HOA fees (if applicable)
• 💸 Property tax history
• ✨ Amenities such as rooftop terraces, concierge services, etc.
• 🏫 Nearby schools and their GreatSchools ratings
• 🧑‍💼 Property and listing agents, brokers, and their contact information
• 🕒 Availability for tours and open houses
• 🖼️ Links to listing photos

With 25,900 current listings, this dataset is ideal for in-depth analysis of the Houston housing market and the Houston real estate market. Potential use cases include:

• Comparing listing prices, price per square foot across different neighborhoods, property types
• Mapping listings to visualize the spatial distribution of for-sale inventory
• Analyzing the age of for-sale housing stock from year-built data
• Evaluating typical HOA fees, and property taxes for listings
• Identifying listings with sought-after amenities
• Assessing school quality near listings from GreatSchools ratings
• Contacting listing agents programmatically using the included agent info

Whether you're a real estate professional, market researcher, data scientist, or just curious about the Houston housing market, this dataset provides a wealth of information to explore. You can start investigating Houston real estate today.

Of all the capital cities in Europe, prices in Paris had the highest disproportion to the national average in 2024. A new house in the French capital cost more than ***** times the price of a house outside the city. This was followed by Athens, Munich, and Barcelona.