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?

Taiwan Housing Future Price Confidence Score data was reported at 89.900 Point in Dec 2014. This records a decrease from the previous number of 98.900 Point for Sep 2014. Taiwan Housing Future Price Confidence Score data is updated quarterly, averaging 106.350 Point from Mar 2003 (Median) to Dec 2014, with 45 observations. The data reached an all-time high of 138.300 Point in Mar 2013 and a record low of 52.740 Point in Dec 2008. Taiwan Housing Future Price Confidence Score data remains active status in CEIC and is reported by Construction and Planning Agency, Ministry of the Interior. The data is categorized under Global Database’s Taiwan – Table TW.EB016: Real Estate Confidence Score.

Gisborne Futures Housing Change Areas

The US residential real estate market, a significant component of the global market, is characterized by a moderate but steady growth trajectory. With a projected Compound Annual Growth Rate (CAGR) of 2.04% from 2025 to 2033, the market demonstrates resilience despite fluctuating economic conditions. The 2025 market size, while not explicitly provided, can be reasonably estimated based on available data and considering recent market trends. Assuming a continuation of the observed growth pattern in preceding years, a substantial market value in the trillions is plausible. Key drivers include sustained population growth, particularly in urban areas, increasing household formations among millennials and Gen Z, and ongoing demand for both rental properties (apartments and condominiums) and owner-occupied homes (landed houses and villas). However, challenges persist, including rising interest rates which impact affordability, supply chain constraints affecting new construction, and the potential for macroeconomic shifts to influence buyer confidence. Segmentation analysis highlights the varying performance across property types, with apartments and condominiums potentially experiencing higher demand in urban centers while landed houses and villas appeal to a different demographic profile and geographic distribution. The competitive landscape includes a mix of large publicly traded real estate investment trusts (REITs) like AvalonBay Communities and Equity Residential, regional developers like Mill Creek Residential, and established brokerage firms such as RE/MAX and Keller Williams Realty Inc., all vying for market share within distinct segments. The geographical distribution of the market shows significant concentration within North America, particularly in the US, reflecting established infrastructure, economic stability, and favorable regulatory environments. While other regions like Europe and Asia-Pacific contribute to the global market, the US continues to be a dominant force. The forecast period (2025-2033) suggests continued expansion, albeit at a moderate pace, indicating a relatively stable and mature market that remains attractive for investment and development. Future growth hinges upon addressing affordability concerns, navigating fluctuating interest rates, and managing supply-demand dynamics to ensure sustainable market expansion. Government policies influencing housing affordability and construction regulations will play a crucial role in shaping the future trajectory of the US residential real estate sector. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Notable trends are: Existing Home Sales Witnessing Strong Growth.

Number of units from projected future housing growth in the City as used in enrollment projection.

In April 2019, ** percent of Gen X renters said they planned to continue renting in the United States and the only ** percent said they planned to purchase a home. In February 2018, ** percent planned to buy a home in the near future, so the trend towards homeownership has fallen slightly in this age group.

Taiwan Housing Future Price Confidence Score: Home Seeker (HS) data was reported at 75.100 Point in Jun 2017. This records an increase from the previous number of 67.200 Point for Dec 2016. Taiwan Housing Future Price Confidence Score: Home Seeker (HS) data is updated quarterly, averaging 98.370 Point from Jun 2002 (Median) to Jun 2017, with 53 observations. The data reached an all-time high of 138.900 Point in Mar 2013 and a record low of 50.200 Point in Dec 2008. Taiwan Housing Future Price Confidence Score: Home Seeker (HS) data remains active status in CEIC and is reported by Construction and Planning Agency, Ministry of the Interior. The data is categorized under Global Database’s Taiwan – Table TW.EB016: Real Estate Confidence Score.

🇦🇺 호주

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.

Lumber futures have seen a rise in open interest, suggesting renewed investor interest and potential shifts in market dynamics despite recent volatility.

This statistic illustrates the level of optimism among French real estate professionals regarding the future of the housing market in their region between 2015 and 2019, depending on their profession. According to the survey, ** percent of builders of individual houses were optimistic. They appeared to be the most confident about the housing market situation as of January 2019.

Financial overview and grant giving statistics of Housing Hawaiis Future

This dataset is about book series. It has 1 row and is filtered where the books is The future of housing markets : a new appraisal. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

In April 2019, ** percent of Millennials said that losing their job would have the greatest negative impact on their future housing plans in the United States, whereas only ***** percent of said that an impending economic recession would have a large impact on their future housing plans.

We conduct a field experiment with US households to study how expectations about long-run home price growth shape spending decisions. We exogenously vary survey respondents’ expectations by providing different expert forecasts. Homeowners’ spending, measured using rich home-scanner data, is inelastic to home price expectations. By contrast, renters reduce their spending when expecting higher home price growth. These patterns reflect positive endowment effects for owners from higher future wealth and negative income effects for both groups due to higher future housing costs. Our study highlights consequences of asset price growth and long-term expectations about the economy for household behavior.

Data tables from the Jersey’s Future Housing Needs report. The latest Future Housing Needs report is available here on gov.je.

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.

Lennar Housing Market: (LEN) Building a Brighter Future for Shareholders?

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

The global student housing software market is projected to reach a valuation of USD 2.5 billion by 2033, growing at a compound annual growth rate (CAGR) of 12.3% from 2025 to 2033.

This dataset is about book subjects. It has 3 rows and is filtered where the books is Homes for the future : a new analysis of housing need and demand in England. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

ITW17 - Future expectation of intergenerational wealth transfers. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Future expectation of intergenerational wealth transfers...