Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

This study examines the dynamic short- and long-run causal relationship between South African real house prices and key macroeconomic fundamentals (gross domestic product(GDP), mortgage rate, exchange rate-USDZAR, affordability, household debt to disposable income, unemployment rate, share prices (JSE ALL share index), foreign direct investment, and producer price index) over the period 2000Q1-2019Q4. The study uses a vector error correction model (VECM) to estimate the relationships while accounting for endogeneity and reverse causality. Although, there seems to be a significant association(both short and long-run) between house prices and all macroeconomic fundamental variables, GDP and producer price index appear to have the greatest impact. Further, our results suggest that any short-term disequilibrium in house prices always self-corrects in the long-run.

Portugal, the Netherlands and Austria are among the countries where house prices grew the most in comparison to income since 2015. In the fourth quarter of 2024, the house price to income ratio in the Netherlands and Austria exceeded *** index points, indicating that since 2015, house price growth has outpaced income growth by ** percent. In Portugal, the index amounted to *** index points in the same period. This was not the case in all countries in the ranking: In Finland, Bulgaria, and Romania, the opposite trend was observed, showing that incomes grew faster than house prices. The house price to income ratio is calculated as the nominal house prices divided by nominal income per capita, with 2015 chosen as the base year of the index. The ratio signifies the development of housing affordability, with higher figures meaning housing is more unaffordable. There are other indices, such as RHPI (or house price indices corrected by inflation rates) which look at this as well.

This study examines the dynamic short- and long-run causal relationship between South African real house prices and key macroeconomic fundamentals (gross domestic product(GDP), mortgage rate, exchange rate-USDZAR, affordability, household debt to disposable income, unemployment rate, share prices (JSE ALL share index), foreign direct investment, and producer price index) over the period 2000Q1-2019Q4. The study uses a vector error correction model (VECM) to estimate the relationships while accounting for endogeneity and reverse causality. Although, there seems to be a significant association(both short and long-run) between house prices and all macroeconomic fundamental variables, GDP and producer price index appear to have the greatest impact. Further, our results suggest that any short-term disequilibrium in house prices always self-corrects in the long-run.

Egypt GDP: Real Estate data was reported at 302,141.920 EGP mn in Dec 2024. This records a decrease from the previous number of 312,317.353 EGP mn for Sep 2024. Egypt GDP: Real Estate data is updated quarterly, averaging 43,900.300 EGP mn from Sep 2001 (Median) to Dec 2024, with 94 observations. The data reached an all-time high of 312,317.353 EGP mn in Sep 2024 and a record low of 3,383.500 EGP mn in Sep 2001. Egypt GDP: Real Estate data remains active status in CEIC and is reported by Ministry of Planning, Economic Development and International Cooperation. The data is categorized under Global Database’s Egypt – Table EG.A014: GDP: by Industry: Current Price.

Germany GDP: GVA: Real Estate Activities data was reported at 95.631 EUR bn in Dec 2024. This records a decrease from the previous number of 96.523 EUR bn for Sep 2024. Germany GDP: GVA: Real Estate Activities data is updated quarterly, averaging 65.048 EUR bn from Mar 1991 (Median) to Dec 2024, with 136 observations. The data reached an all-time high of 96.759 EUR bn in Mar 2024 and a record low of 29.832 EUR bn in Mar 1991. Germany GDP: GVA: Real Estate Activities data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.A031: ESA 2010: GDP: by Industry: Current Price.

In 2023, the gross domestic product (GDP) from real estate in Singapore amounted to ***** billion Singapore dollars. 2020 saw the lowest GDP contribution from real estate in the past ten years, likely due to the COVID-19 pandemic.

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Residential Real Estate Market Size 2025-2029

The residential real estate market size is forecast to increase by USD 485.2 billion at a CAGR of 4.5% between 2024 and 2029.

The market is experiencing significant growth, fueled by increasing marketing initiatives that attract potential buyers and tenants. This trend is driven by the rising demand for housing solutions that cater to the evolving needs of consumers, particularly in urban areas. However, the market's growth trajectory is not without challenges. Regulatory uncertainty looms large, with changing policies and regulations posing a significant threat to market stability. Notably, innovative smart home technologies, such as voice-activated assistants and energy-efficient appliances, are gaining traction, offering enhanced convenience and sustainability for homeowners.
As such, companies seeking to capitalize on the opportunities presented by the growing the market must navigate these challenges with agility and foresight. The residential construction industry's expansion is driven by urbanization and the rising standard of living in emerging economies, including India, China, Thailand, Malaysia, and Indonesia. By staying abreast of regulatory changes and implementing innovative marketing strategies, they can effectively meet the evolving needs of consumers and maintain a competitive edge. These regulatory shifts can impact everything from property prices to financing options, making it crucial for market players to stay informed and adapt quickly.

What will be the Size of the Residential Real Estate Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic housing market analysis, small flats continue to be a popular choice for both investors and first-time homebuyers, driven by affordability and urban growth. International investment in housing projects, including apartments and condominiums, remains strong, offering attractive investment returns. Real estate syndication and property management software facilitate efficient property ownership and management. Real estate loans, property insurance, and urban planning are essential components of the housing market, ensuring the development of affordable housing and addressing the needs of the middle class and upper middle class. Property disputes, property tax assessments, and real estate litigation are ongoing challenges, requiring careful attention from stakeholders.
Property search engines streamline the process of finding the perfect property, from studio apartments to luxury homes. Real estate auctions, land banking, and nano apartments are innovative solutions in the market, while property flipping and short sales provide opportunities for savvy investors. Urban growth and community development are key trends, with a focus on sustainable, planned cities and the integration of technology, such as real estate blockchain, into the industry. Developers secure building permits, review inspection reports, and manage escrow accounts during real estate transactions. Key services include contract negotiation, dispute resolution, and tailored investment strategies for portfolio management. Financial aspects cover tax implications, estate planning, retirement planning, taxdeferred exchanges, capital gains, tax deductions, and maintaining positive cash flow for sustained returns.

How is this Residential Real Estate Industry segmented?

The residential real estate industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Mode Of Booking

  Sales
  Rental or lease


Type

  Apartments and condominiums
  Landed houses and villas


Location

  Urban
  Suburban
  Rural


End-user

  Mid-range housing
  Affordable housing
  Luxury housing


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    UK


  APAC

    Australia
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Mode Of Booking Insights

The sales segment is estimated to witness significant growth during the forecast period. The sales segment dominates the global residential real estate market and will continue to dominate during the forecast period. The sales segment includes the sale of any property that is majorly used for residential purposes, such as single-family homes, condos, cooperatives, duplexes, townhouses, and multifamily residences. With the growing population and urbanization, the demand for homes is also increasing, which is the major factor driving the growth of the sales segment. Moreover, real estate firms work with developers to sel

Housing Index in Malaysia decreased to 224.20 Index in the fourth quarter of 2024 from 228.30 Index in the third quarter of 2024. This dataset provides - Malaysia House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States - Contributions to Percent Change in the Chain-Type Price Index for GDP by Industry: Finance, Insurance, Real Estate, Rental, and Leasing: Finance and Insurance was 0.50000 Percentage Points in October of 2024, according to the United States Federal Reserve. Historically, United States - Contributions to Percent Change in the Chain-Type Price Index for GDP by Industry: Finance, Insurance, Real Estate, Rental, and Leasing: Finance and Insurance reached a record high of 0.87000 in October of 2009 and a record low of -1.61000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Contributions to Percent Change in the Chain-Type Price Index for GDP by Industry: Finance, Insurance, Real Estate, Rental, and Leasing: Finance and Insurance - last updated from the United States Federal Reserve on June of 2025.

Graph and download economic data for Contributions to Percent Change in the Chain-Type Price Index for GDP by Industry: Finance, Insurance, Real Estate, Rental, and Leasing: Real Estate and Rental and Leasing (CPGDPPIRL) from Q2 2005 to Q4 2024 about contributions, financing, leases, chained, insurance, rent, real estate, private industries, percent, private, industry, GDP, price index, indexes, price, and USA.

Graph and download economic data for Contributions to Percent Change in the Chain-Type Price Index for GDP by Industry: Finance, Insurance, Real Estate, Rental, and Leasing: Finance and Insurance (CPGDPPIFI) from Q2 2005 to Q1 2025 about contributions, financing, leases, chained, insurance, rent, real estate, private industries, percent, private, industry, GDP, price index, indexes, price, and USA.

Graph and download economic data for Personal Consumption Expenditures: Services Excluding Energy and Housing (Chain-Type Price Index) (PC001260Q) from Q2 1959 to Q1 2025 about Supercore, chained, energy, PCE, consumption expenditures, consumption, personal, services, housing, inflation, GDP, price index, indexes, price, and USA.

Vietnam GDP: Year to Date: Service: Real Estate Activities data was reported at 185,890.500 VND bn in Sep 2018. This records an increase from the previous number of 116,090.107 VND bn for Jun 2018. Vietnam GDP: Year to Date: Service: Real Estate Activities data is updated quarterly, averaging 129,393.553 VND bn from Dec 2012 (Median) to Sep 2018, with 24 observations. The data reached an all-time high of 243,946.000 VND bn in Dec 2017 and a record low of 46,780.630 VND bn in Mar 2013. Vietnam GDP: Year to Date: Service: Real Estate Activities data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.A006: Gross Domestic Product: By Industry: Current Price: Quarterly.

Revenue for the Residential Real Estate industry in China is expected to decrease at a CAGR of 9.8% over the five years through 2025. This trend includes an expected decrease of 9.6% in the current year.Since August 2020, the People's Bank of China and the China Banking and Insurance Regulatory Commission have proposed three debt indicators for real estate development and management companies through which the company's financial health can be rated. This new policy has exacerbated the company's debt pressure, making it unable to repay old debts by borrowing new debt. Some real estate companies faced a liquidity crisis.In 2022, the city's lockdown and laying-off caused by COVID-19 epidemic led to the pressure of delaying the delivery of houses. The industry's newly constructed and completed areas decreased significantly throughout the year. In addition, the epidemic has impacted sales in the industry, and some sales offices have been forced to close temporarily. In 2022, the residential sales area decreased by 26.8%, and the residential sales decreased by 31.2%.Industry revenue will recover at an annualized 0.7% over the five years through 2030. Over the next five years, the industry's drag on GDP will weaken, and industry growth will stabilize. However, high housing prices have become a major social problem in China. Under the measures on the principle that residential real estate is used for living, not speculation, the financial attributes of real estate will gradually weaken, and housing prices will tend to stabilize.

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

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

The statistic shows the gross domestic product (GDP) of the United States from 1987 to 2024, with projections up until 2030. The gross domestic product of the United States in 2024 amounted to around 29.18 trillion U.S. dollars. The United States and the economy The United States’ economy is by far the largest in the world; a status which can be determined by several key factors, one being gross domestic product: A look at the GDP of the main industrialized and emerging countries shows a significant difference between US GDP and the GDP of China, the runner-up in the ranking, as well as the followers Japan, Germany and France. Interestingly, it is assumed that China will have surpassed the States in terms of GDP by 2030, but for now, the United States is among the leading countries in almost all other relevant rankings and statistics, trade and employment for example. See the U.S. GDP growth rate here. Just like in other countries, the American economy suffered a severe setback when the economic crisis occurred in 2008. The American economy entered a recession caused by the collapsing real estate market and increasing unemployment. Despite this, the standard of living is considered quite high; life expectancy in the United States has been continually increasing slightly over the past decade, the unemployment rate in the United States has been steadily recovering and decreasing since the crisis, and the Big Mac Index, which represents the global prices for a Big Mac, a popular indicator for the purchasing power of an economy, shows that the United States’ purchasing power in particular is only slightly lower than that of the euro area.

We use multivariate unobserved components models to estimate trend and cyclical components in gross domestic product (GDP), credit volumes, and house prices for the USA and the five largest European economies. With the exception of Germany, we find large and long cycles in credit and house prices, which are highly correlated with a medium-term component in GDP cycles. Differences across countries in the length and size of cycles appear to be related to the properties of national housing markets. The precision of pseudo real-time estimates of credit and house price cycles is roughly comparable to that of GDP cycles.