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 2.60 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.

Table B.3.1 presents quarterly mortgage rate data specific to the Irish market. These data include all euro and non-euro denominated mortgage lending in the Republic of Ireland only. New business refers to new mortgage lending drawdowns during the quarter, broken down by type of interest rate product (i.e. fixed, tracker and SVR). The data also provide further breakdown of mortgages for principal dwelling house (PDH) and buy-to-let (BTL) properties. Renegotiations of existing loans are not included.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Exploring the Positive Correlation Between Interest Rates and Housing Market Recessions

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.

In May 29, 2019, FHFA published its final Monthly Interest Rate Survey (MIRS), due to dwindling participation by financial institutions. MIRS had provided information on a monthly basis on interest rates, loan terms, and house prices by property type (all, new, previously occupied); by loan type (fixed- or adjustable-rate), and by lender type (savings associations, mortgage companies, commercial banks and savings banks); as well as information on 15-year and 30-year, fixed-rate loans. Additionally, MIRS provided quarterly information on conventional loans by major metropolitan area and by Federal Home Loan Bank district, and was used to compile FHFA’s monthly adjustable-rate mortgage index entitled the “National Average Contract Mortgage Rate for the Purchase of Previously Occupied Homes by Combined Lenders,” also known as the ARM Index.

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.

Annual house price inflation, simple and mix-adjusted average house prices, by dwelling, type of buyer, number of transactions, mortgage advances, distribution of borrowers' ages/incomes, interest rates, land prices, average valuations, Land Registry data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Interest Rates, High Prices, and Inventory Shortage to Slow Down Housing Market

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007. From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank. From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here: http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter. Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office. 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.

Through reading this publication you will: • gain an understanding of how house prices are set in economics terms, how they are measured, and why the cost of housing matters for London’s economy and its residents • see whether incomes and earnings in London have kept pace with the costs of home ownership in London, and see how affordability may be affected by future changes in interest rates • find out about the drivers of demand for residential property in London, and how the supply of homes has responded to changing conditions

Annual house price inflation, simple and mix-adjusted average house prices, by dwelling, type of buyer, number of transactions, mortgage advances, distribution of borrowers' ages/incomes, interest rates, land prices, average valuations, Land Registry data

An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Understanding the Dynamics and Implications of a Housing Market Recession

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Annual house price inflation, simple and mix-adjusted average house prices, by dwelling, type of buyer, number of transactions, mortgage advances, distribution of borrowers' ages/incomes, interest rates, land prices, average valuations, Land Registry data

This dataset provides a comprehensive view of the Portuguese housing market, integrating both listing and official transaction data. Initially compiled from historical reports by Idealista, it includes €/m² prices for sales and rentals across various Portuguese regions.

Now, this dataset has been significantly enhanced with official transaction data from the Instituto Nacional de Estatística (INE) of Portugal. This addition includes quarterly values and counts of housing transactions at a national level, providing a crucial perspective on actual market activity beyond listing prices.

This consolidated dataset is a core component of a broader case study exploring housing affordability, investment potential, and regional development across Portugal. It enables a more robust analysis by allowing comparison between asking prices and actual transaction values, as well as insights into market volume.

Additional socioeconomic data will be gradually integrated to further enrich the analysis, such as:

• Minimum and average wages (INE)
• New construction volume (INE)
• Migration and population trends
• Interest rates (Banco de Portugal)

🔗 Full pipeline and source files, including data cleaning scripts and analysis notebooks, are available on GitHub: https://github.com/igor-marques/portugal-housing-market-capstone

Data Sources Included: * Idealista: Historical listing prices (€/m²) for sales and rentals across Portuguese regions. * Instituto Nacional de Estatística (INE): Official quarterly data on housing transaction values and counts for Portugal (from Q1 2009 to Q1 2025).

🇮🇪 아일랜드 English House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007. From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank. From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here: http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter. Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office. 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.

This dataset contains an history of nearly all of the real estate transactions concerning a single house/appartment in France from 2014 to today. Some variables likely to have an impact on the price of real estate are also provided as time series: the households income levels per city, the average debt level of french peoples, the average amount of savings of french people, the interest rates of loans, the price of the rent per city, the number of houses and number of vacant houses per city.

This dataset is provided under a permissive licence, and is free to use for commercial uses. It has a vocation of helping research concerning the dynamics of real estate prices.

The dataset consist in extraction from several openly available datasets put together in a practical format: The DVF+ database of real estate transactions, the IRCOM dataset of household incomes and income taxes, average interest rates of real estate loans from the banque de france website, the LOVAC dataset of number of vacant and occupied housings per city, the OECD dataset of financial assets per capita, the "carte des loyers" dataset of 2018 and 2022 which list the average price of the rent per square meter, the Indice de Référence des Loyers (IRL) time series which is an index defining the maximum rent increase that can be applied to an already rented housing and is calculated every 3 months as the inflation adjusted buying power of 100€ in 1998, the TEC00104 eurostat dataset of debt levels.

Context

A local Vermont/New Hampshire real estate firm is looking into modeling closed prices for houses. This dataset contains features of houses in three towns in Vermont, which make up a sizable chunk of the real estate firm's business.

Content

MLS is the real estate information platform that is publicly available. Features were exported from an MLS web platform. Features include # of baths, # of bedrooms, and # of acres. There are also categorical features, such as town and address.

Hint: Natural language processing techniques that identify and leverage the road that a house is on may improve prediction accuracy.

Acknowledgements

Thank you to AH.

Goal

There is a Train, Validate, and, Test. Can you show a cross validated result that beats 10.0% error in closed price? You can use any measure to train your model - RMSE, RMSLE, etc.; however, the accuracy metric is simply mean percent error!

Please Note: These houses can be uniquely identified on the MLS website, which does also have photos of the houses. Computer Vision techniques that retrieve information from photos on the data are of interest to the company, but are not encouraged for this simple dataset, which serves as a jumping off point for future endeavors as it contains data that is already compiled and understood by the firm.

Extract detailed property data points — address, URL, prices, floor space, overview, parking, agents, and more — from any real estate listings. The Rankings data contains the ranking of properties as they come in the SERPs of different property listing sites. Furthermore, with our real estate agents' data, you can directly get in touch with the real estate agents/brokers via email or phone numbers.

A. Usecase/Applications possible with the data:

1. Property pricing - accurate property data for real estate valuation. Gather information about properties and their valuations from Federal, State, or County level websites. Monitor the real estate market across the country and decide the best time to buy or sell based on data

2. Secure your real estate investment - Monitor foreclosures and auctions to identify investment opportunities. Identify areas within special economic and opportunity zones such as QOZs - cross-map that with commercial or residential listings to identify leads. Ensure the safety of your investments, property, and personnel by analyzing crime data prior to investing.

3. Identify hot, emerging markets - Gather data about rent, demographic, and population data to expand retail and e-commerce businesses. Helps you drive better investment decisions.

4. Profile a building’s retrofit history - a building permit is required before the start of any construction activity of a building, such as changing the building structure, remodeling, or installing new equipment. Moreover, many large cities provide public datasets of building permits in history. Use building permits to profile a city’s building retrofit history.

5. Study market changes - New construction data helps measure and evaluate the size, composition, and changes occurring within the housing and construction sectors.

6. Finding leads - Property records can reveal a wealth of information, such as how long an owner has currently lived in a home. US Census Bureau data and City-Data.com provide profiles of towns and city neighborhoods as well as demographic statistics. This data is available for free and can help agents increase their expertise in their communities and get a feel for the local market.

7. Searching for Targeted Leads - Focusing on small, niche areas of the real estate market can sometimes be the most efficient method of finding leads. For example, targeting high-end home sellers may take longer to develop a lead, but the payoff could be greater. Or, you may have a special interest or background in a certain type of home that would improve your chances of connecting with potential sellers. In these cases, focused data searches may help you find the best leads and develop relationships with future sellers.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization