Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2023. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 117.5 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.

Mexico Housing Affordability rose 0.4points in 2019, compared to the previous year.

United States Housing Affordability Index: Fixed data was reported at 146.900 NA in Oct 2018. This records a decrease from the previous number of 147.400 NA for Sep 2018. United States Housing Affordability Index: Fixed data is updated monthly, averaging 127.900 NA from Jan 1989 (Median) to Oct 2018, with 357 observations. The data reached an all-time high of 212.800 NA in Jan 2013 and a record low of 97.600 NA in May 1989. United States Housing Affordability Index: Fixed data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

Turkey, Russia, Portugal, and Latvia were the countries with the highest house price-to-rent-ratio in the ranking in the second quarter of 2024. In all three countries, the ratio exceeded 160 index points, meaning that house price growth had outpaced rents by over 60 percent between 2015 and 2024. What does the house-price-to-rent ratio show? The house-price-to-rent-ratio measures the evolution of house prices compared to rents. It is generally calculated by dividing the median house price by the median annual rent. In this statistic, the values have been normalized with 100 equaling the 2015 ratio. Consequentially, a value under 100 means that rental rates have risen more than house prices. When all OECD countries are considered as a whole, the gap between house prices and rents was wider than in the Euro area. Measures of housing affordability The national house-price-to-rent ratio may not fully reflect the cost of housing in a particular country, as it does not capture the price variations that can exist between different regions. It also does not take into consideration the relationship between incomes and housing costs, which is measured by the house-price-to-income and household-rent-to-income ratios. Taking both these factors into account uncovers vast differences in housing affordability between different regions and different professions.

Global Housing Affordability by Country, 2023 Discover more data with ReportLinker!

The Housing Affordability Index value in the United States plummeted in 2022, surpassing the historical record of 107.1 index points in 2006. In 2024, the housing affordability index measured 98.1 index points, making it the second-worst year for homebuyers since the start of the observation period. What does the Housing Affordability Index mean? The Housing Affordability Index uses data provided by the National Association of Realtors (NAR). It measures whether a family earning the national median income can afford the monthly mortgage payments on a median-priced existing single-family home. An index value of 100 means that a family has exactly enough income to qualify for a mortgage on a home. The higher the index value, the more affordable a house is to a family. Key factors that drive the real estate market Income, house prices, and mortgage rates are some of the most important factors influencing homebuyer sentiment. When incomes increase, consumer power also increases. The median household income in the United States declined in 2022, affecting affordability. Additionally, mortgage interest rates have soared, adding to the financial burden of homebuyers. The sales price of existing single-family homes in the U.S. has increased year-on-year since 2011 and reached 389,000 U.S. dollars in 2023.

A commonly accepted threshold for affordable housing costs at the household level is 30% of a household's income. Accordingly, a household is considered cost burdened if it pays more than 30% of its income on housing. Households paying more than 50% are considered severely cost burdened. These thresholds apply to both homeowners and renters.

The Housing Affordability indicator only measures cost burden among the region's households, and not the supply of affordable housing. The directionality of cost burden trends can be impacted by changes in both income and housing supply. If lower income households are priced out of a county or the region, it would create a downward trend in cost burden, but would not reflect a positive trend for an inclusive housing market.

The Affordable Housing Market is projected to grow at 5.9% CAGR, reaching $75.95 Billion by 2029. Where is the industry heading next? Get the sample report now!

United States Housing Affordability Index: Monthly Principal and Interest Payment data was reported at 1,092.000 USD in Oct 2018. This records an increase from the previous number of 1,085.000 USD for Sep 2018. United States Housing Affordability Index: Monthly Principal and Interest Payment data is updated monthly, averaging 783.000 USD from Jan 1989 (Median) to Oct 2018, with 358 observations. The data reached an all-time high of 1,207.000 USD in Jul 2006 and a record low of 568.000 USD in Feb 1994. United States Housing Affordability Index: Monthly Principal and Interest Payment data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

The housing affordability measure illustrates the relationship between income and housing costs. A household that spends 30% or more of its collective monthly income to cover housing costs is considered to be “housing cost-burden[ed].”[1] Those spending between 30% and 49.9% of their monthly income are categorized as “moderately housing cost-burden[ed],” while those spending more than 50% are categorized as “severely housing cost-burden[ed].”[2]

How much a household spends on housing costs affects the household’s overall financial situation. More money spent on housing leaves less in the household budget for other needs, such as food, clothing, transportation, and medical care, as well as for incidental purchases and saving for the future.

The estimated housing costs as a percentage of household income are categorized by tenure: all households, those that own their housing unit, and those that rent their housing unit.

Throughout the period of analysis, the percentage of housing cost-burdened renter households in Champaign County was higher than the percentage of housing cost-burdened homeowner households in Champaign County. All three categories saw year-to-year fluctuations between 2005 and 2023, and none of the three show a consistent trend. However, all three categories were estimated to have a lower percentage of housing cost-burdened households in 2023 than in 2005.

Data on estimated housing costs as a percentage of monthly income was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Housing Tenure.

[1] Schwarz, M. and E. Watson. (2008). Who can afford to live in a home?: A look at data from the 2006 American Community Survey. U.S. Census Bureau.

[2] Ibid.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (22 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (30 September 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using data.census.gov; (10 June 2021).;U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; 16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25106; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Graph and download economic data for Housing Affordability Index (Fixed) (FIXHAI) from Jan 2024 to Jan 2025 about fixed, housing, indexes, and USA.

The house price to income index in Europe declined in almost all European countries in 2023, indicating that income grew faster than house prices. Portugal, Luxembourg, and the Netherlands led the house price to income index ranking in 2023, with values exceeding 125 index points. Romania, Bulgaria, and Finland were on the other side of the spectrum, with less than 100 index points. The house price to income ratio is an indicator for the development of housing affordability across OECD countries and is calculated as the nominal house prices divided by nominal disposable income per head, with 2015 chosen as a base year. A ratio higher than 100 means that the nominal house price growth since 2015 has outpaced the nominal disposable income growth, and housing is therefore comparatively less affordable. In 2023, the OECD average stood at 117.4 index points.

United States Housing Affordability Index: Qualifying Income data was reported at 52,320.000 USD in Sep 2018. This records a decrease from the previous number of 53,904.000 USD for Aug 2018. United States Housing Affordability Index: Qualifying Income data is updated monthly, averaging 37,488.000 USD from Jan 1989 (Median) to Sep 2018, with 357 observations. The data reached an all-time high of 57,936.000 USD in Jul 2006 and a record low of 27,264.000 USD in Feb 1994. United States Housing Affordability Index: Qualifying Income data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

As of the third quarter of 2024, the housing affordability index in South Korea stood at around 61 points. The third quarter of 2022 saw the first drop in figures since 2020. The lower the index value, the more affordable a home is for a median-income household. Apartments in South Korea Average apartment prices in South Korea had risen for almost a decade before 2022. The country's popularity worldwide helped it become an economic powerhouse, attracting young workers from the countryside to large cities in hopes of taking part in or benefiting from this growth. As such, apartments are an attractive option for cities, optimizing space as they become more crowded. In terms of financing a new home, the leasehold deposit system exists as an alternative to traditional monthly rentals in Korea. Jeonse and leasehold deposits Jeonse is a leasehold deposit system in Korea where, instead of paying rent monthly, people pay a large deposit equivalent to a share of a property's value. In exchange, the person receives the right to reside in the property as a tenant for a limited amount of time. Granted, the up-front cost is high, and it is common to receive loans from banks to pay for the leasehold deposit. It had traditionally been a popular method as you could live in a housing unit without worrying about rent for one or two years. However, this system is slowly being phased out as interest rates, the large up-front cost, and cases of jeonse fraud have turned people away.

This web map shows the potential relationship between Esri's housing affordability index (HAI) and median household income in the United States for CBSA metropolitan and micropolitan statistical areas. It also shows Total Household Growth between 2010-2018. The data can be used to determine the impact between income earned in relationship to housing affordability. For example;Which areas experience very affordable housing and earn high median household incomesWhich areas experience affordable housing but earn relatively low median household incomesWhich areas experience the least affordable housing but earn relatively high median household incomesWhich areas experience the least affordable housing along and below US median household incomes

This data sets out the percentage of residents of the Cambridge housing sub-region who are unable to afford housing, based on contemporary income data and housing costs, broken down into percentage for 1, 2 and 3 bedroom homes.

The data comes from the housing sub-region's Strategic Housing Market Assessment, or SHMA, which is updated regularly. The data provided in this open data set comes from: *SHMA 2013, based on 2011/12 data *SHMA 2012, based on 2009/10 data *SHMA 2010, based on 2008/9 data *SHMA 2009, based on mostly 2007/8 data

The data is all published in chapters of our strategic housing market assessment which are used as part of our calculations around the need for affordable housing, particularly where we need to work out the proportion of people unlikely to be able to afford housing via the private market (owned or rented) and thus potentially in need of "sub market" or affordable housing.

United States Housing Affordability Index: Mortgage Rate data was reported at 4.770 % in Sep 2018. This records a decrease from the previous number of 4.780 % for Aug 2018. United States Housing Affordability Index: Mortgage Rate data is updated monthly, averaging 6.470 % from Jan 1989 (Median) to Sep 2018, with 357 observations. The data reached an all-time high of 10.590 % in Jun 1989 and a record low of 3.430 % in Dec 2012. United States Housing Affordability Index: Mortgage Rate data remains active status in CEIC and is reported by National Association of Realtors. The data is categorized under Global Database’s United States – Table US.EB018: Housing Affordability Index.

The Housing Affordability Data System (HADS) is a set of housing unit level datasets that measures the affordability of housing units and the housing cost burdens of households, relative to area median incomes, poverty level incomes, and Fair Market Rents. The purpose of these datasets is to provide housing analysts with consistent measures of affordability and burdens over a long period. The datasets are based on the American Housing Survey (AHS) national files from 1985 through 2005 and the metropolitan files for 2002 and 2004. Users can link records in HADS files to AHS records, allowing access to all of the AHS variables. Housing-level variables include information on the number of rooms in the housing unit, the year the unit was built, whether it was occupied or vacant, whether the unit was rented or owned, whether it was a single family or multiunit structure, the number of units in the building, the current market value of the unit, and measures of relative housing costs. The dataset also includes variables describing the number of people living in the household, household income, and the type of residential area (e.g., urban or suburban).

Affordability ratios calculated by dividing house prices for existing dwellings, by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.

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Real Estate Market Size 2025-2029

The real estate market size is forecast to increase by USD 1,258.6 billion at a CAGR of 5.6% between 2024 and 2029.

The market is experiencing significant shifts and innovations, with both residential and commercial sectors adapting to new trends and challenges. In the commercial realm, e-commerce growth is driving the demand for logistics and distribution centers, while virtual reality technology is revolutionizing property viewings. Europe's commercial real estate sector is witnessing a rise in smart city development, incorporating LED lighting and data centers to enhance sustainability and efficiency. In the residential sector, wellness real estate is gaining popularity, focusing on health and well-being. Real estate software and advertising services are essential tools for asset management, streamlining operations, and reaching potential buyers. Regulatory uncertainty remains a challenge, but innovation in construction technologies, such as generators and renewable energy solutions, is helping mitigate risks.

What will be the Size of the Real Estate Market During the Forecast Period?

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The market continues to exhibit strong activity, driven by rising population growth and increasing demand for personal household space. Both residential and commercial sectors have experienced a rebound in home sales and leasing activity. The trend towards live-streaming rooms and remote work has further fueled demand for housing and commercial real estate. Economic conditions and local market dynamics influence the direction of the market, with interest rates playing a significant role in investment decisions. Fully furnished, semi-furnished, and unfurnished properties, as well as rental properties, remain popular options for buyers and tenants. Offline transactions continue to dominate, but online transactions are gaining traction.
The market encompasses a diverse range of assets, including land, improvements, buildings, fixtures, roads, structures, utility systems, and undeveloped property. Vacant land and undeveloped property present opportunities for investors, while the construction and development of new housing and commercial projects contribute to the market's overall growth.

How is this Real Estate Industry segmented and which is the largest segment?

The 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.

Type

  Residential
  Commercial
  Industrial


Business Segment

  Rental
  Sales


Manufacturing Type

  New construction
  Renovation and redevelopment
  Land development


Geography

  APAC

    China
    India
    Japan
    South Korea


  North America

    Canada
    US


  Europe

    Germany
    UK


  South America

    Brazil


  Middle East and Africa

By Type Insights

The residential segment is estimated to witness significant growth during the forecast period.

The market encompasses the buying and selling of properties designed for dwelling purposes, including buildings, single-family homes, apartments, townhouses, and more. Factors fueling growth in this sector include the increasing homeownership rate among millennials and urbanization trends. The Asia Pacific region, specifically China, dominates the market due to escalating homeownership rates. In India, the demand for affordable housing is a major driver, with initiatives like Pradhan Mantri Awas Yojana (PMAY) spurring the development of affordable housing projects catering to the needs of lower and middle-income groups. The commercial real estate segment, consisting of office buildings, shopping malls, hotels, and other commercial properties, is also experiencing growth.

Furthermore, economic and local market conditions, interest rates, and investment opportunities in fully furnished, semi-furnished, unfurnished properties, and rental properties influence the market dynamics. Technological integration, infrastructure development, and construction projects further shape the real estate landscape. Key sectors like transportation, logistics, agriculture, and the e-commerce sector also impact the market.

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The Residential segment was valued at USD 1440.30 billion in 2019 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 64% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The Asia Pacific region holds the largest share of The market, dr