The 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.

A housing market prediction that many experts agree on is that it will be a seller’s market. Home prices are expected to rise for some time due to increased demand and limited supply. Millennials are at the age to start investing in the real estate market for the first time. Hence, the demand for residential and commercial projects is rising with every passing day. The future of real estate will witness a rise in demand and limited supply, resulting in it being a seller’s market.

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House Price Prediction Dataset.

The dataset contains 2000 rows of house-related data, representing various features that could influence house prices. Below, we discuss key aspects of the dataset, which include its structure, the choice of features, and potential use cases for analysis.

1. Dataset Features

The dataset is designed to capture essential attributes for predicting house prices, including:

Area: Square footage of the house, which is generally one of the most important predictors of price. Bedrooms & Bathrooms: The number of rooms in a house significantly affects its value. Homes with more rooms tend to be priced higher. Floors: The number of floors in a house could indicate a larger, more luxurious home, potentially raising its price. Year Built: The age of the house can affect its condition and value. Newly built houses are generally more expensive than older ones. Location: Houses in desirable locations such as downtown or urban areas tend to be priced higher than those in suburban or rural areas. Condition: The current condition of the house is critical, as well-maintained houses (in 'Excellent' or 'Good' condition) will attract higher prices compared to houses in 'Fair' or 'Poor' condition. Garage: Availability of a garage can increase the price due to added convenience and space. Price: The target variable, representing the sale price of the house, used to train machine learning models to predict house prices based on the other features.

2. Feature Distributions

Area Distribution: The area of the houses in the dataset ranges from 500 to 5000 square feet, which allows analysis across different types of homes, from smaller apartments to larger luxury houses. Bedrooms and Bathrooms: The number of bedrooms varies from 1 to 5, and bathrooms from 1 to 4. This variance enables analysis of homes with different sizes and layouts. Floors: Houses in the dataset have between 1 and 3 floors. This feature could be useful for identifying the influence of multi-level homes on house prices. Year Built: The dataset contains houses built from 1900 to 2023, giving a wide range of house ages to analyze the effects of new vs. older construction. Location: There is a mix of urban, suburban, downtown, and rural locations. Urban and downtown homes may command higher prices due to proximity to amenities. Condition: Houses are labeled as 'Excellent', 'Good', 'Fair', or 'Poor'. This feature helps model the price differences based on the current state of the house. Price Distribution: Prices range between $50,000 and $1,000,000, offering a broad spectrum of property values. This range makes the dataset appropriate for predicting a wide variety of housing prices, from affordable homes to luxury properties.

3. Correlation Between Features

A key area of interest is the relationship between various features and house price: Area and Price: Typically, a strong positive correlation is expected between the size of the house (Area) and its price. Larger homes are likely to be more expensive. Location and Price: Location is another major factor. Houses in urban or downtown areas may show a higher price on average compared to suburban and rural locations. Condition and Price: The condition of the house should show a positive correlation with price. Houses in better condition should be priced higher, as they require less maintenance and repair. Year Built and Price: Newer houses might command a higher price due to better construction standards, modern amenities, and less wear-and-tear, but some older homes in good condition may retain historical value. Garage and Price: A house with a garage may be more expensive than one without, as it provides extra storage or parking space.

4. Potential Use Cases

The dataset is well-suited for various machine learning and data analysis applications, including:

House Price Prediction: Using regression techniques, this dataset can be used to build a model to predict house prices based on the available features. Feature Importance Analysis: By using techniques such as feature importance ranking, data scientists can determine which features (e.g., location, area, or condition) have the greatest impact on house prices. Clustering: Clustering techniques like k-means could help identify patterns in the data, such as grouping houses into segments based on their characteristics (e.g., luxury homes, affordable homes). Market Segmentation: The dataset can be used to perform segmentation by location, price range, or house type to analyze trends in specific sub-markets, like luxury vs. affordable housing. Time-Based Analysis: By studying how house prices vary with the year built or the age of the house, analysts can derive insights into the trends of older vs. newer homes.

5. Limitations and ...

The house prices of all house types in Norway increased steadily between 2009 and 2022, followed by a slight decline in 2023. Unlike houses, prices for multi-dwellings did not fall in 2023. Multi-dwelling were also the property type that experienced the strongest growth. At ***** index points, the index for multi-dwelling properties suggests an increase of ** percent since 2015 - the baseline year. How much did Norwegians pay for dwellings in 2021? Oslo appeared to be the most expensive city by dwelling prices that year, followed by Tromsø and Bergen.  Number of residential buildings The number of residential buildings in Norway constantly increased during the past decade, peaking in 2023. There were nearly *** million residences in the country. That was an increase of over 100 thousand units, compared to 2010. More than half of Norwegians lived in detached houses The share of residents by housing type was distributed unevenly in Norway in 2023. Approximately ** percent of Norwegian citizens lived in detached houses, whereas ** percent lived in multi-dwelling buildings. The least common housing type was houses with two dwellings that year.

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.

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 provides historical housing prices scraped from Centaline Property Hong Kong, one of the largest real estate agencies in Hong Kong. The dataset includes information on the date of the transaction, the property address, floor plan, saleable area, unit rate, source, and district. The dataset covers a period of time spanning several years, allowing for analysis of trends and changes in the Hong Kong housing market.

Columns: Date: the date of the property transaction Address: the address of the property Floor Plan: -- Price: the price of the property Changes: any changes made to the property since the last transaction Saleable Area: the area of the property that can be sold to a buyer Unit Rate: the price per square foot of saleable area Source: the source of the data (Centaline Property Hong Kong/ Land Registry) District: the district in which the property is located in Hong Kong

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.

Housing Index in Sweden increased to 959 points in the third quarter of 2025 from 945 points in the second quarter of 2025. This dataset provides - Sweden House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

Purpose and brief description The house price index measures the inflation in the residential property market. The house price index reflects price developments for all residential properties purchased by households (apartments, terraced houses, detached houses), regardless of whether they are new or existing. Only market prices are taken into account, so self-build homes are excluded. The price of the land is included in the price of the properties. Population Real estate transactions involving residential properties Periodicity Quarterly. Release calendar Results available 3 months after the reference period Definitions House price index: The house price index measures changes in the prices of new or existing dwellings, regardless of their use or previous owner. Inflation - house price index: Inflation is defined as the ratio between the value of a given quarter and that of the same quarter of the previous year. Weighting - house price index: Weighting based on the national accounts (gross fixed capital formation in housing) and the total number of real estate transactions involving residential properties. Type of dwelling according to the classification set out in Regulation (EU) No 93/2013 on housing price indices. 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 to prevent and correct macroeconomic imbalances in 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. The data have therefore been completely reviewed when the results for the fourth quarter of 2023 were published in March 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. The index is then calculated based on changes in the average prices observed and adjusted by a factor depending on the differences in quality observed between dwellings sold during the different periods.

Key information about House Prices Growth

• Mexico house prices grew 8.9% YoY in Sep 2025, following an increase of 8.7% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Sep 2025, with an average growth rate of 10.2%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

House Price Index MoM in the United States decreased to 0 percent in September from 0.40 percent in August of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index MoM.

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

The 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.

The FHFA House Price Index (FHFA HPI®) is the nation’s only collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data. ​ What does the FHFA HPI represent? The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas. U.S. Federal Housing Finance Agency, All-Transactions House Price Index for Connecticut [CTSTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTSTHPI, August 2, 2023.

The figures of existing own homes are related to the stock of existing own homes. Besides the price indices, figures are also published about the numbers sold, the average purchase price, and the total sum of the purchase prices of the sold dwellings. The House Price Index of existing own homes is based on a complete registration of sales of dwellings by the Dutch Land Registry Office (Kadaster) and the (WOZ) value of all dwellings in the Netherlands. Indices may fluctuate, for example if a small number of dwellings are sold in a certain region. In such cases we recommended using the long-term figures. The average purchase price of existing own homes may differ from the price index of existing own homes. The change in the average purchase price, however, is not an indicator for price developments of existing own homes.

Data available from: 1st quarter 1995 to 4th quarter 2023

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 numbers of existing owner-occupied sold homes can be recalculated 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 6 June 2024: This table has been discontinued. This table is followed by Existing own homes; purchase prices, price index 2020=100, region. See paragraph 3.

From reporting period 2024 quarter 1, the base year of the House Price Index for Existing Dwellings (PBK) will be adjusted from 2015 to 2020. In April 2024, the first figures of this new series will be released. These figures will be available in a new StatLine table. The old series (base year = 2015) can still be consulted via StatLine, but will no longer be updated.

House prices in the Houston-The Woodlands-Sugar Land metropolitan area have almost doubled since 2011. In 2023, the median house price reached ******* U.S. dollars, down by *** percent from 2022. This was close to the average house price in Texas.

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.