House price prediction Predicting house prices is a common task in data science and machine learning. Here's a high-level overview of how you might approach it:

Data Collection: Gather a dataset containing features of houses (e.g., size, number of bedrooms, location, amenities) and their corresponding prices. Websites like Zillow, Kaggle, or government housing datasets are good sources.

Data Preprocessing: Clean the data by handling missing values, encoding categorical variables, and scaling numerical features if necessary. This step ensures that the data is in a suitable format for training a model. Feature Selection/Engineering: Choose relevant features that are likely to influence house prices. You may also create new features based on domain knowledge or data analysis.

Model Selection: Select a regression model suitable for predicting continuous target variables like house prices. Common choices include Linear Regression, Decision Trees, Random Forests, Gradient Boosting, and Neural Networks.

Model Training: Split your dataset into training and testing sets to train and evaluate the performance of your model. You can further split the training set for validation purposes or use cross-validation techniques.

Model Evaluation: Assess the performance of your model using appropriate evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), or Root Mean Squared Error (RMSE).

Hyperparameter Tuning: Fine-tune your model's hyperparameters to improve its performance. Techniques like grid search or random search can be employed for this purpose.

Deployment: Once satisfied with your model's performance, deploy it to make predictions on new data. This could be as simple as saving the trained model and creating an interface for users to input house features.

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.

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.

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.

House Price Index YoY in the United States decreased to 2.60 percent in June from 2.90 percent in May of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

The house price to income ratio in the United States has reached concerning levels, with the index hitting ***** in the fourth quarter of 2024. This indicates that house prices have outpaced income growth by over ** percent since 2015, highlighting a growing affordability crisis in the housing market. The widening gap between home prices and wages is putting homeownership out of reach for many Americans, particularly as real wages have remained stagnant. Rising home prices and stagnant wages While average annual real wages in the United States have increased slightly since 2014, home prices have soared. The median sales price of existing single-family homes reached a record-high in 2024, representing a substantial increase over the past five years. This disparity between wage growth and home price appreciation has led to a significant decrease in housing affordability across the country. Affordability challenges in the U.S. housing market The U.S. Housing Affordability Index, which measures whether a family earning the median income can afford a median-priced home, plummeted in 2024, marking the second-worst year for homebuyers since records began. This decline in affordability is reflected in homebuyer sentiment, with homebuyer sentiment plummeting.

The average house price in England started to increase in August 2024, after falling by over three percent year-on-year in December 2023. In May 2025, the house price index amounted to 101.7 index points, suggesting an increase in house prices of 3.4 percent since the same month in 2024 and roughly 2 percent rise since January 2023 - the baseline year for the index. Among the different regions in the UK, West and East Midlands experienced the strongest growth.

House prices in the second most populous state in the United States, Texas, have increased more than two-fold since 2011. In 2023, the median house price reached ******* U.S. dollars, a decrease of *** percent from the previous year. Texas is one of the more affordable states for buying a home with house prices below the national average.

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.

This dataset contains 500 entries of housing price data from various countries, regions, and cities worldwide, making it ideal for machine learning models and real estate market analysis. The dataset covers diverse geographic locations, including:

North America: USA, Canada, Mexico
Europe: Germany, France, UK, Italy, Spain
Asia: Japan, China, India, South Korea
Other Regions: Australia, Brazil, South Africa

Columns Included:

Country: The country where the house is located (e.g., USA, Japan, India).
State/Region: The state or region within the country (e.g., California, Bavaria).
City: The city where the property is located (e.g., Los Angeles, Tokyo).
Square Footage (SqFt): The size of the house in square feet (ranging from 500 to 5000 sq ft).
Bedrooms: The number of bedrooms in the house (ranging from 1 to 6).
Population Density: The population density of the area (people per sq km).
Price of House: The price of the house (in local currency, converted to USD where applicable).

This dataset can be used for:

Machine Learning Models: Training and evaluating models for house price prediction.
Market Analysis: Analyzing housing trends across different regions and countries.
Visualization: Creating insightful visualizations to understand price distributions and regional variations.

This dataset provides a balanced mix of geographic diversity and housing features for robust predictive modeling and analysis.

This dataset is a snapshot from October 2022 of all 48 homes in a section of a neighborhood nearby a large university in Central Florida. All of the homes are single family homes featuring a garage, a driveway, and a fenced-in backyard. Data was gathered by hand (keyboard) via a collection of sites, including Zillow, Realtor, Redfin, Trulia, and Orange County Property Appraiser. All homes were built in the same year in the early 2000's and feature central air and all other utilities typical of contemporary suburban homes in the United States. The area is close to a university and a large portion of renters are college students and young professionals, as well as families and older adults.

There are 30 columns:

• HID: House ID, a unique identifier for each house (int from 1 to 48, not the actual address number) -Sqft: The Square Footage of the Interior of the house (int) -LandSqft: The Total Square Footage of the land (int) -Neighbors: The number of homes directly adjacent to each house (int) -Stories: The number of stories in each house (int) -Pool: Does the house have a pool (int, 0 for 'No', 1 for 'Yes') -Bedrooms: The number of bedrooms in each house (int) -Bathrooms: The number of bathrooms (full or half) in each house (int) -DateLastSold: The date on which the house was last sold (datetime) -PropertyTaxes2022: The annual property taxes for 2022 (float) -OwnedByBank: Is the house owned by a bank (int, 0 for 'No', 1 for 'Yes') -OuterPortion: Is the house on the Outer Portion of the Neighborhood (int, 0 for 'No', 1 for 'Yes') -NextToLoudRoad: Is the house directly adjacent to a loud road (int, 0 for 'No', 1 for 'Yes') -PriceLastSold: Price that the house was last sold for (float) -Zestimate: Zillow's Price Estimate for the house (float) -RentZestimate: Zillow's Estimate for the Monthly Price of rent for the house (float) -RealtorcomEstimate: Realtor dot com's Estimate for the house (float) -RedfinEstimate: Redfin's Estimate for the house (float) -TruliaEstimate: Trulia's Estimate for the house (float) -OCPALandValue2022: The Land Value on the county's 2022 records (float) -OCPABuildingValue2022: The Building Value on the county's 2022 records (float) -OCPAFeaturesValue2022: The Features Value on the county's 2022 records (float) -OCPAMarketValue2022: The Market Value on the county's 2022 records (float) -OCPAAssessedValue2022: The Assessed Value on the county's 2022 records (float), AKA what homeowners are taxed on -OCPALandValue2021: The Land Value on the county's 2021 records (float) -OCPABuildingValue2021: The Building Value on the county's 2021 records (float) -OCPAFeaturesValue2021: The Features Value on the county's 2021 records (float) -OCPAMarketValue2021: The Market Value on the county's 2021 records (float) -OCPAAssessedValue2021: The Assessed Value on the county's 2021 records (float), AKA what homeowners are taxed on -Notes: any notes on any of the homes (str)

Note that while the dataset is exhaustive in that it has all of the houses, some homes are missing some columns, typically because a home did not feature a estimate on a site or the one home not found on the property appraiser's site. This also is therefore not a randomized dataset, so the only population of homes that it can be used to infer on are those within this specific portion of the neighborhood. Personally, I am going to use the dataset to practice a couple of aspects of real-world data: Cleaning, Imputing, and Exploratory Data Analysis. Mainly, I want to compare different approaches to filling in the missing values of the dataset, then do some Model Building with some additional Dimensionality Reduction.

This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'.

Data available from: 1995

Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above.

Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024.

When will new figures be published? New figures are published approximately one to three months after the period under review.

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

Technical information The house price index measures the price evolution of real estate prices on the market of private property. The index follows price changes of new or existing residential real estate purchased by households, irrespective of their purpose (letting or owner-occupying). Only market prices are taken into account. Houses built by their owners are therefore not included. The price of the building plot is included in the house price. The house price index is based on real estate transaction data from the General Administration of the Patrimonial Documentation of the FPS Finances. The prices used are those included in the deeds of sale. Given the time between the date on which the preliminary sales agreement is signed and the date on which the deed is executed (between three and four months), this index measures the price evolution with a delay compared to the actual date on which the sales price is set. This delay is inherent to the data source. The house price index is calculated by the European Union Member States, Norway and Iceland. Eurostat calculates the index for the Euro area (as well as for the European Union as a whole) using the harmonised indices of the Member States. Given the role of the housing market in the economic and financial crisis of 2008, the house price index was included in the indicators used in the procedure for macroeconomic imbalances procedure of the European Union. The house price index is calculated under the European Regulation 2016/792 on harmonised indices of consumer prices and the house price index and 2023/1470 laying down the methodological and technical specifications as regards the house price index and the owner-occupied housing price index. Data are available from 2005 onward for Belgium as well as for the European Union and the majority of European countries. The house price index can be broken down by new houses and existing houses. The weights of these two items in the overall index are determined by the gross fixed capital formation in houses (for the new houses) and the total value of transactions of the previous year (for the existing houses). Until 2013, the house price index of new houses was roughly estimated based on the output price index in the construction sector. Since 2014, it is also based on real estate transaction data. House price index for existing houses is available per region since 2010. Therefore, data were completely reviewed for the publication of results in the 4th quarter of 2023 in 2024. Since the houses that are put up for sale differ from one quarter to another, the changes in characteristics are processed with hedonic regression models to eliminate price fluctuations due to changes in characteristics of the properties sold. These models aim to estimate the theoretical price based on the characteristics and location of the houses sold. This theoretical price is then compared to the actual price. Two indices are calculated, one with the actually observed transaction prices and the other with the prices estimated by the regression models. The final index is obtained by calculating the ratio of the index obtained with the actual transaction prices compared to the index obtained with the estimated prices. Therefore, the house price index may be evolving differently from the observed average prices.

Housing Index in China decreased by 2.80 percent in July from -3.20 percent in June of 2025. This dataset provides the latest reported value for - China Newly Built House Prices YoY Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The house price data are collected from the official website of China's National Bureau of Statistics . We acquired the month-on-month growth data of house prices since January 2006, then compiled the house price index based on January 2006 as 100. The Shanghai Stock Exchange Index (SSEI) data which are treated as stock market prices are derived from the CSMAR database. After that, we calculate the monthly house price and stock price return as , where are proxied by the monthly house price index and SSEI, and represent the returns series. 157 observations from January 2006 to March 2019 are obtained.

After a period of rapid increase, house price growth in the UK has moderated. In 2025, house prices are forecast to increase by ****percent. Between 2025 and 2029, the average house price growth is projected at *** percent. According to the source, home building is expected to increase slightly in this period, fueling home buying. On the other hand, higher borrowing costs despite recent easing of mortgage rates and affordability challenges may continue to suppress transaction activity. Historical house price growth in the UK House prices rose steadily between 2015 and 2020, despite minor fluctuations. In the following two years, prices soared, leading to the house price index jumping by about 20 percent. As the market stood in April 2025, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next five years. Growth is forecast to be stronger in 2025 and slow slightly until 2029. The rental market in London is expected to follow a similar trend, with Outer London slightly outperforming Central London.

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.

This is the unadjusted lower quartile house priced for residential property sales (transactions) in the area for a 12 month period with April in the middle (year-ending September). These figures have been produced by the ONS (Office for National Statistics) using the Land Registry (LR) Price Paid data on residential dwelling transactions.

The LR Price Paid data are comprehensive in that they capture changes of ownership for individual residential properties which have sold for full market value and covers both cash sales and those involving a mortgage.

The lower quartile is the value determined by putting all the house sales for a given year, area and type in order of price and then selecting the price of the house sale which falls three quarters of the way down the list, such that 75Percentage of transactions lie above and 25Percentage lie below that value. These are particularly useful for assessing housing affordability when viewed alongside average and lower quartile income for given areas.

Note that a transaction occurs when a change of freeholder or leaseholder takes place regardless of the amount of money involved and a property can transact more than once in the time period.

The LR records the actual price for which the property changed hands. This will usually be an accurate reflection of the market value for the individual property, but it is not always the case. In order to generate statistics that more accurately reflect market values, the LR has excluded records of houses that were not sold at market value from the dataset. The remaining data are considered a good reflection of market values at the time of the transaction. For full details of exclusions and more information on the methodology used to produce these statistics please see http://www.ons.gov.uk/peoplepopulationandcommunity/housing/qmis/housepricestatisticsforsmallareasqmi

The LR Price Paid data are not adjusted to reflect the mix of houses in a given area. Fluctuations in the types of house that are sold in that area can cause differences between the lower quartile transactional value of houses and the overall market value of houses.

If, for a given year, for house type and area there were fewer than 5 sales records in the LR Price Paid data, the house price statistics are not reported." Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Housing Index in Germany increased to 219.06 points in July from 218.19 points in June 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.