The US residential real estate market, a cornerstone of the American economy, is projected to experience steady growth over the next decade. While the provided CAGR of 2.04% is a modest figure, it reflects a market maturing after a period of significant expansion. This sustained growth is driven by several key factors. Firstly, population growth and urbanization continue to fuel demand for housing, particularly in densely populated areas and emerging suburban markets. Secondly, low interest rates (historically, though this can fluctuate) have made mortgages more accessible, stimulating buyer activity. Thirdly, a robust construction sector, though facing challenges in material costs and labor shortages, is gradually increasing the housing supply, mitigating some of the upward pressure on prices. However, challenges remain. Rising inflation and potential interest rate hikes pose a risk to affordability, potentially dampening demand. Furthermore, the ongoing evolution of remote work is reshaping residential preferences, with a shift toward larger homes in suburban or exurban locations. This trend impacts the relative demand for various property types, potentially increasing the appeal of landed houses and villas compared to apartments and condominiums in certain regions. The segmentation of the market into apartments/condominiums and landed houses/villas provides crucial insights into consumer preferences and investment strategies. High-density urban areas will continue to see strong demand for apartments and condos, while suburban and rural areas are likely to experience a greater increase in landed property sales. Major players like Simon Property Group, Mill Creek Residential, and others are strategically adapting to these trends, focusing on both development and management across various property types and geographic locations. Analyzing regional data within the US (e.g., comparing growth in the Northeast versus the Southwest) will highlight market nuances and potential investment opportunities. While the global data provided is valuable for understanding broader market forces, focusing the analysis on the US market allows for a more granular understanding of the specific drivers, trends, and challenges within this significant segment of the real estate sector. The forecast period (2025-2033) suggests continued, albeit measured, expansion. 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.. Key drivers for this market are: Investment Plan Towards Urban Rail Development. Potential restraints include: Italy’s Fragmented Approach to Tenders. Notable trends are: Existing Home Sales Witnessing Strong Growth.

Nahb Housing Market Index in the United States increased to 33 points in July from 32 points in June of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

From 2007 to 2016, the EU prefabricated buildings market showed a mixed trend pattern. A significant drop in 2008 (79% Y-o-Y) was followed by a gradual increase over the next three years until it plunged again in 2013 (91% Y-o-Y).

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Jul 2025 about median and USA.

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.

About Dataset

Source

The source of this dataset is REDFIN Data Center. To download the latest dataset available, please go to: https://www.redfin.com/news/data-center/

They also provide a page with the definitions for each metric used here: https://www.redfin.com/news/data-center-metrics-definitions/

For more informaton on Data and Data Quality, please visit: https://www.redfin.com/about/data-quality-on-redfin Reading the Data

The data is a .tsv format and can be imported using pandas as follows:

df = pd.read_csv("weekly_housing_market_data_most_recent.tsv000", sep='\t')

MOST RECENT DATAPOINT: 2022-07-11

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

Housing Index in China decreased by 3.20 percent in June from -3.50 percent in May 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.

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

Graph and download economic data for Equity Market Volatility Tracker: Macroeconomic News and Outlook: Real Estate Markets (EMVMACRORE) from Jan 1985 to Jun 2025 about volatility, uncertainty, equity, real estate, and USA.

Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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

Discover how proposed tariffs on Canadian lumber are creating a divide in the U.S. housing market, with stakeholders debating their impact on home building and affordability.

This poll, the first of two fielded April 2012, is a part of a continuing series of monthly surveys that solicits public opinion on a range of political and social issues. Respondents were asked how well Barack Obama was handling the presidency, terrorism, the economy, the war in Afghanistan, the housing market, and the issue of gasoline prices. Opinions were collected on whether respondents thought the country was headed in the right direction, the most important problem facing the nation, whether Congress was performing their job well, and the national economy. Respondents were also queried on their opinions of Barack Obama and Mitt Romney, as well as whether either of the two presidential candidates would be able to bring real change to Washington, whether they would be able to make the right decisions on various issues, and whether they would be an effective military leader. Additional topics included economic concerns, the suspension of Rick Santorum's presidential campaign, women's health issues, the future of the next generation of Americans, gasoline prices, the home mortgage crisis, federal income tax policies and the capital gains tax policy, the John Edwards trial, and the college education of the respondent's child. Finally, respondents were asked whether they voted in the 2008 presidential election and who they voted for, whether they supported the Tea Party movement, whether they usually vote Democratic or Republican, whether they planned to vote in a 2012 primary or caucus, how much attention they have paid to the 2012 presidential campaign, and whether they were registered to vote. Demographic information includes sex, age, race, social class, marital status, household makeup, education level, household income, employment status, religious preference, type of residential area (e.g., urban or rural), political party affiliation, political philosophy, and whether respondents thought of themselves as born-again Christians.

In 2016, the global prefab housing imports stood at 5.3M tons, coming up by 3% against the previous year figure. Overall, prefab housing imports continue to indicate a relatively flat trend pattern....

Housing Index in Hong Kong decreased to 137.19 points in August 3 from 138.84 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

After a long period of steady increase in real estate prices in Spain, the market was hit by the global financial crisis of 2007, resulting in the burst of the Spanish property bubble. House prices have since picked up and in 2023, the average square meter price reached 2,809 euros - just slightly below 2008 levels. Though prices have risen across the whole country, some regions, such as the Balearic Islands, Catalonia, Madrid, and Andalusia, experienced faster growth than others. Additionally, the gap between newly built and existing home prices has widened. Spain’s real estate market behind others The property market has made great progress, but it is still far off the rest of its European counterparts, and it is positioned, in fact, at the bottom of the European list of the EMF’s house price index, which is led by Czechia and Portugal. Supply is a major factor influencing the price development. Many European countries suffer housing shortages due to sluggish construction activity, and Spain is no exception. In 2022, ranked among the countries with the lowest number of residential construction starts per 1,000 citizens in Europe. Buying vs renting As happens with many other countries, the affordability of buying a home and renting will differ considerably dependent on the area. In 2022, the average Spanish citizen needed between five and 18 years to purchase an average priced property in their region with their full salary, with Murcia and La Rioja being the most affordable regions. The house price to rent index shows that house price growth has been much faster than rental growth. That is good news for homeowners whose homes appreciate over time, but an issue for renters who are yet to purchase a property.

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.

LRE: Will the Housing Market's Strength Continue to Lift This Stock?

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

Discover how the market for bearing housing with ball bearing in Japan is expected to grow over the next decade, with an anticipated increase in both volume and value terms. Explore the forecasted CAGR and projected market volume and value by the end of 2035.

The average Canadian house price declined slightly in 2023, after four years of consecutive growth. The average house price stood at ******* Canadian dollars in 2023 and was forecast to reach ******* Canadian dollars by 2026. Home sales on the rise The number of housing units sold is also set to increase over the two-year period. From ******* units sold, the annual number of home sales in the country is expected to rise to ******* in 2025. British Columbia and Ontario have traditionally been housing markets with prices above the Canadian average, and both are set to witness an increase in sales in 2025. How did Canadians feel about the future development of house prices? When it comes to consumer confidence in the performance of the real estate market in the next six months, Canadian consumers in 2024 mostly expected that the market would go up. A slightly lower share of the respondents believed real estate prices would remain the same.