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

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Description:

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?

Acknowledgement:

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.

Objective:

• Understand the Dataset & cleanup (if required).
• Build Regression models to predict the sales w.r.t a single & multiple feature.
• Also evaluate the models & compare thier respective scores like R2, RMSE, etc.

Summary of UK House Price Index (HPI) price statistics covering England, Scotland, Wales and Northern Ireland. Full UK HPI data are available on GOV.UK.

Affordability ratios calculated by dividing house prices by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

Description:

This dataset contains various features of residential properties along with their corresponding prices. It is suitable for exploring and analyzing factors influencing housing prices and for building predictive models to estimate the price of a property based on its attributes.

Usage:

• This dataset can be used for exploratory data analysis to understand the relationships between different housing features and prices.
• It can also be used to build machine learning models for predicting housing prices based on the given features.

License: This dataset is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

House prices in the UK rose dramatically during the coronavirus pandemic, with growth slowing down in 2022 and turning negative in 2023. The year-on-year annual house price change peaked at 14 percent in July 2022. In April 2025, house prices increased by 3.5 percent. As of late 2024, the average house price was close to 290,000 British pounds. Correction in housing prices: a European phenomenon The trend of a growing residential real estate market was not exclusive to the UK during the pandemic. Likewise, many European countries experienced falling prices in 2023. When comparing residential property RHPI (price index in real terms, e.g. corrected for inflation), countries such as Germany, France, Italy, and Spain also saw prices decline. Sweden, one of the countries with the fastest growing residential markets, saw one of the largest declines in prices. How has demand for UK housing changed since the outbreak of the coronavirus? The easing of the lockdown was followed by a dramatic increase in home sales. In November 2020, the number of mortgage approvals reached an all-time high of over 107,000. One of the reasons for the housing boom were the low mortgage rates, allowing home buyers to take out a loan with an interest rate as low as 2.5 percent. That changed as the Bank of England started to raise the base lending rate, resulting in higher borrowing costs and a decline in homebuyer sentiment.

Median price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

Global 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.

Key information about House Prices Growth

• US house prices grew 3.3% YoY in Sep 2025, following an increase of 4.1% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Sep 2025, with an average growth rate of -12.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

Task Description: Real Estate Price Prediction

This task involves predicting the price of real estate properties based on various features that influence the value of a property. The dataset contains several attributes of real estate properties such as square footage, the number of bedrooms, bathrooms, floors, the year the property was built, whether the property has a garden or pool, the size of the garage, the location score, and the distance from the city center.

The goal is to build a regression model that can predict the Price of a property based on the provided features.

Dataset Columns:

ID: A unique identifier for each property.

Square_Feet: The area of the property in square meters.

Num_Bedrooms: The number of bedrooms in the property.

Num_Bathrooms: The number of bathrooms in the property.

Num_Floors: The number of floors in the property.

Year_Built: The year the property was built.

Has_Garden: Indicates whether the property has a garden (1 for yes, 0 for no).

Has_Pool: Indicates whether the property has a pool (1 for yes, 0 for no).

Garage_Size: The size of the garage in square meters.

Location_Score: A score from 0 to 10 indicating the quality of the neighborhood (higher scores indicate better neighborhoods).

Distance_to_Center: The distance from the property to the city center in kilometers.

Price: The target variable that represents the price of the property. This is the value we aim to predict.

Objective: The goal of this task is to develop a regression model that predicts the Price of a real estate property using the other features as inputs. The model should be able to learn the relationship between these features and the price, providing an accurate prediction for unseen data.

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.

House Prices Dataset

Subtitle:

Detailed Real Estate Data for Predicting House Prices and Analyzing Market Trends

Description:

This dataset contains information on 21,613 properties, making it a comprehensive resource for exploring real estate market trends and building predictive models for house prices. The data includes various features capturing property details, location, and market conditions, providing ample opportunities for data exploration, visualization, and machine learning applications.

Key Features:

• General Information:

  • id: Unique identifier for each property.
  • date: Date of sale.

• Price Details:

  • price: Sale price of the house.

• Property Features:

  • bedrooms: Number of bedrooms.
  • bathrooms: Number of bathrooms (including partials as fractions).
  • sqft_living: Living space area in square feet.
  • sqft_lot: Lot size in square feet.
  • floors: Number of floors.
  • waterfront: Whether the property has a waterfront view.
  • view: Quality of the view rating.
  • condition: Overall condition of the house.
  • grade: Grade of construction and design (scale of 1–13).

• Additional Metrics:

  • sqft_above: Square footage of the property above ground.
  • sqft_basement: Basement area in square feet.
  • yr_built: Year the property was built.
  • yr_renovated: Year of last renovation.

• Location Coordinates:

  • zipcode: ZIP code of the property.
  • lat and long: Latitude and longitude coordinates.

• Neighbor Comparisons:

  • sqft_living15: Average living space of 15 nearest properties.
  • sqft_lot15: Average lot size of 15 nearest properties.

Use Cases:

• Predicting house prices using regression models.
• Identifying the impact of various features (e.g., number of bedrooms, location) on property prices.
• Analyzing market trends and spatial distribution of real estate prices.

This dataset is a valuable resource for anyone interested in real estate analytics, machine learning, or geographic data visualization.

The 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.

Summary statistics for housing transactions by local authority in England and Wales, on an annual basis, updated quarterly using HM Land Registry Price Paid Data. Select values from the Year and Month dimensions for data for a 12-month period ending that month and year (e.g. selecting June and 2018 will return the twelve months to June 2018).

According to a survey conducted in South Korea in January 2025, around ** percent of respondents stated that housing prices would fall over the next year. According to the source, most respondents in the previous year's survey had an overall negative opinion on the government's policies on real estate.

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.

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.

View monthly updates and historical trends for Windsor, ON New Housing Price Index. Source: Statistics Canada. Track economic data with YCharts analytics.

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data for 1969/1970 is not available for Cork, Limerick, Galway, Waterford and Other areas The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. National and Other Areas figure changed for 2015 on 27/6/15 as revised data received from Local Authorities Prices includes houses and apartments measured in € .hidden { display: none }