Housing Index in the United States decreased to 434.40 points in May from 435.10 points in April 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.

Dataset Overview

This dataset provides historical housing price indices for the United States, covering a span of 20 years from January 2000 onwards. The data includes housing price trends at the national level, as well as for major metropolitan areas such as San Francisco, Los Angeles, New York, and more. It is ideal for understanding how housing prices have evolved over time and exploring regional differences in the housing market.

Why This Dataset?

The U.S. housing market has experienced significant shifts over the last two decades, influenced by economic booms, recessions, and post-pandemic recovery. This dataset allows data enthusiasts, economists, and real estate professionals to analyze long-term trends, make forecasts, and derive insights into regional housing markets.

What’s Included?

Time Period: January 2000 to the latest available data (specific end date depends on the dataset). Frequency: Monthly data. Regions Covered: 20+ U.S. cities, states, and aggregates.

Columns Description

Each column represents the housing price index for a specific region or aggregate, starting with a date column:

Date: Represents the date of the housing price index measurement, recorded with a monthly frequency. U.S. National: The national-level housing price index for the United States. 20-City Composite: The aggregate housing price index for the top 20 metropolitan areas in the U.S. CA-San Francisco: The housing price index for San Francisco, California. CA-Los Angeles: The housing price index for Los Angeles, California. WA-Seattle: The housing price index for Seattle, Washington. NY-New York: The housing price index for New York City, New York. Additional Columns: The dataset includes more columns with housing price indices for various U.S. cities, which can be viewed in the full dataset preview.

Potential Use Cases

Time-Series Analysis: Investigate long-term trends and patterns in housing prices. Forecasting: Build predictive models to forecast future housing prices using historical data. Regional Comparisons: Analyze how housing prices have grown in different cities over time. Economic Insights: Correlate housing prices with economic factors like interest rates, GDP, and inflation.

Who Can Use This Dataset?

This dataset is perfect for:

Data scientists and machine learning practitioners looking to build forecasting models. Economists and policymakers analyzing housing market dynamics. Real estate investors and analysts studying regional trends in housing prices.

Example Questions to Explore

Which cities have experienced the highest housing price growth over the last 20 years? How do housing price trends in coastal cities (e.g., Los Angeles, Miami) compare to midwestern cities (e.g., Chicago, Detroit)? Can we predict future housing prices using time-series models like ARIMA or Prophet?

House Price Index YoY in the United States decreased to 2.80 percent in May from 3.20 percent in April of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

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.

New housing price index (NHPI). Monthly data are available from January 1981. The table presents data for the most recent reference period and the last four periods. The base period for the index is (201612=100).

This dataset provides a comprehensive overview of new housing price indexes in Canada. The data is sourced from a reliable statistical survey, offering a detailed breakdown of housing prices across different components such as total house and land, house only, and land only. The dataset is structured to include key metrics such as geographical location, price index classification, and specific price values, providing a robust foundation for analyzing housing price dynamics within the country.

Key Features

• Price Index Metrics: The dataset includes price indexes for total house and land, house only, and land only, providing a complete picture of housing price dynamics across different components.
• Geographical Focus: Data is specific to Canada, providing insights into national housing price trends and patterns.
• Unit of Measurement: Information is presented in index units (201612=100), allowing for straightforward analysis and comparison.
• Temporal Precision: The data is time-stamped for January 1981, ensuring relevance and accuracy for temporal analysis.

Potential Uses

• Real Estate Market Analysis: Assist in understanding the housing price dynamics in Canada, which is crucial for real estate market forecasting and planning.
• Investment Decisions: Provide insights into optimal investment strategies for real estate in various regions.
• Economic Policy: Support policymakers in monitoring and ensuring compliance with housing market trends and economic standards.
• Market-Specific Insights: Evaluate the impact of housing price trends on specific regions and potential growth or decline areas.
• Strategic Planning: Inform strategic planning for real estate developers and policymakers by providing a clear snapshot of current housing price levels and trends.

Housing Index in Hong Kong increased to 138.63 points in August 10 from 137.19 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Views: 1327 Openness rating: Dataset Actions View showcases with this dataset Contact dataset owner Residential Property Price Index

The FHFA House Price Index (FHFA HPI®) is a comprehensive 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.

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

The dataset contains year-, quarter- and city-wise data on the Housing Price Index in Indian and among its various cities such as Ahmedabad, Bangalore, Chennai, Delhi, Jaipur, Kanpur, Kochi, Kolkata, Lucknow, Mumbai, 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.

Housing Index in the United Kingdom increased to 514.30 points in July from 512.40 points in June of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

Analysis of ‘Home Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/PythonforSASUsers/hpindex on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Federal Housing Finance Agency House Price Index (HPI) is a broad measure of the movement of single-family house prices. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. The technical methodology for devising the index, collection, and publishing the data is at: http://www.fhfa.gov/PolicyProgramsResearch/Research/PaperDocuments/1996-03_HPI_TechDescription_N508.pdf

Content

Contains monthly and quarterly time series from January 1991 to August 2016 for the U.S., state, and MSA categories. Analysis variables are the aggregate non-seasonally adjusted value and seasonally adjusted index values. The index value is 100 beginning January 1991.

Acknowledgements

This data is found on Data.gov

Inspiration

Can this data be combined with the corresponding census growth projections either at the state or MSA level to forecast 24 months out the highest and lowest home index values?

--- Original source retains full ownership of the source dataset ---

Residential Property Price Indices (RPPIs) – also named House price indices (HPIs), are index numbers measuring the evolution of residential property prices over time. RPPIs are key statistics not only for citizens and households across the world, but also for economic and monetary policy makers. Among their professional uses, they serve, for example, to monitor macroeconomic imbalances and risk exposure of the financial sector.

“National and Regional House Price Indices” datasets include RPPI compiled by official statistical agencies following international statistical guidelines. It covers all OECD member countries and some non-member countries. Whenever possible, these RPPIs are broken down by region, dwelling type (single- and multi-family dwellings) and vintage (new and existing dwellings).

The dataset called “National and Regional House Price Indices” contains the full list of available RPPIs.

House Price Index MoM in the United States decreased by 0.20 percent in May from -0.30 percent in April of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index MoM.

HPM09 - Residential Property Price Index - Dataset - data.gov.ie

Outline of measurement concepts for the publication of Housing Price Index (HPI) data.