The number of U.S. home sales in the United States declined in 2023, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2023, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes are expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Only 15 percent of U.S. renters could afford to become homeowners and in metros with highly competitive housing markets such as Los Angeles, CA, and Urban Honolulu, HI, this share was below five percent. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 387,000 U.S. dollars in 2023 and was forecast to increase slightly until 2025. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Graph and download economic data for Housing Inventory: Active Listing Count in Boston-Cambridge-Newton, MA-NH (CBSA) (ACTLISCOU14460) from Jul 2016 to Feb 2025 about Boston, NH, MA, active listing, listing, and USA.

Graph and download economic data for Housing Inventory: Median Listing Price in Massachusetts (MEDLISPRIMA) from Jul 2016 to Feb 2025 about MA, listing, median, price, and USA.

United States Sold Above Asking: Townhouse: Wooster, OH data was reported at 100.000 % in May 2019. This records an increase from the previous number of 0.000 % for Mar 2019. United States Sold Above Asking: Townhouse: Wooster, OH data is updated monthly, averaging 0.000 % from May 2012 to May 2019, with 14 observations. The data reached an all-time high of 100.000 % in May 2019 and a record low of 0.000 % in Mar 2019. United States Sold Above Asking: Townhouse: Wooster, OH data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB021: Homes Sold Above Asking: by Metropolitan Areas.

United States Sold Above Asking: sa: All Residential: Knoxville, TN data was reported at 23.682 % in Jun 2020. This records an increase from the previous number of 22.537 % for May 2020. United States Sold Above Asking: sa: All Residential: Knoxville, TN data is updated monthly, averaging 15.320 % from Feb 2012 to Jun 2020, with 101 observations. The data reached an all-time high of 23.682 % in Jun 2020 and a record low of 8.108 % in May 2013. United States Sold Above Asking: sa: All Residential: Knoxville, TN data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB022: Homes Sold Above Asking: by Metropolitan Areas: Seasonally Adjusted.

The number of housing units in the United States has grown year-on-year and in 2023, there were approximately 145 million homes. That was an increase of about 1.3 percent from the previous year - the highest annual increase recorded in the past 15 years. Homeownership in the U.S. Most of the housing stock in the U.S. is owner-occupied, meaning that the person who owns the home uses it as a primary residence. Homeownership is an integral part of the American Dream, with about two in three Americans living in an owner-occupied home. For older generations, the homeownership rate is even higher, showing that buying a home is an important milestone in life. Housing transactions slowing down During the coronavirus pandemic, the U.S. experienced a housing market boom and witnessed an increase in the number of homes sold. Since 2020, when the market peaked, new homes transactions have slowed down and so have the sales of existing homes. That has affected the development of home prices, with several states across the country experiencing a decline in house prices.

Graph and download economic data for All-Transactions House Price Index for Boston, MA (MSAD) (ATNHPIUS14454Q) from Q3 1977 to Q4 2024 about Boston, MA, appraisers, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for All-Transactions House Price Index for Massachusetts (MASTHPI) from Q1 1975 to Q4 2024 about MA, appraisers, HPI, housing, price index, indexes, price, and USA.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

United States Homes Sold: sa: Single Family: Lexington, KY data was reported at 613.088 Unit th in Jul 2020. This records an increase from the previous number of 549.764 Unit th for Jun 2020. United States Homes Sold: sa: Single Family: Lexington, KY data is updated monthly, averaging 573.841 Unit th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 711.140 Unit th in Jan 2017 and a record low of 407.286 Unit th in Feb 2012. United States Homes Sold: sa: Single Family: Lexington, KY data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB018: Homes Sold: by Metropolitan Areas: Seasonally Adjusted.

This layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.This layer is symbolized to show the count and percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Forecast: Manufactured Homes (Mobile Homes) Sales in the US 2024 - 2028 Discover more data with ReportLinker!

Forecast: Electronic Shopping and Mail-Order Houses E-commerce Sales in the US 2024 - 2028 Discover more data with ReportLinker!

Graph and download economic data for All-Transactions House Price Index for Sacramento-Roseville-Folsom, CA (MSA) (ATNHPIUS40900Q) from Q2 1976 to Q4 2024 about Sacramento, appraisers, CA, HPI, housing, price index, indexes, price, and USA.

The number of home sales in the United States peaked in 2021 at almost seven million after steadily rising since 2018. Nevertheless, the market contracted in the following year, with transaction volumes falling to 4.8 million. Home sales remained muted in 2024, with a mild increase expected in 2025 and 2026. A major factor driving this trend is the unprecedented increase in mortgage interest rates due to high inflation. How have U.S. home prices developed over time? The average sales price of new homes has also been rising since 2011. Buyer confidence seems to have recovered after the property crash, which has increased demand for homes and also the prices sellers are demanding for homes. At the same time, the affordability of U.S. homes has decreased. Both the number of existing and newly built homes sold has declined since the housing market boom during the coronavirus pandemic. Challenges in housing supply The number of housing units in the U.S. rose steadily between 1975 and 2005 but has remained fairly stable since then. Construction increased notably in the 1990s and early 2000s, with the number of construction starts steadily rising, before plummeting amid the infamous housing market crash. Housing starts slowly started to pick up in 2011, mirroring the economic recovery. In 2022, the supply of newly built homes plummeted again, as supply chain challenges following the COVID-19 pandemic and tariffs on essential construction materials such as steel and lumber led to prices soaring.

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

Graph and download economic data for All-Transactions House Price Index for Colorado Springs, CO (MSA) (ATNHPIUS17820Q) from Q2 1979 to Q4 2024 about Colorado Springs, CO, appraisers, HPI, housing, price index, indexes, price, and USA.

United States Sold Above Asking: sa: Townhouse: Nassau County, NY data was reported at 50.000 % in Jun 2020. This records an increase from the previous number of 33.333 % for May 2020. United States Sold Above Asking: sa: Townhouse: Nassau County, NY data is updated monthly, averaging 10.870 % from Feb 2012 to Jun 2020, with 101 observations. The data reached an all-time high of 75.000 % in Apr 2018 and a record low of 0.000 % in Apr 2020. United States Sold Above Asking: sa: Townhouse: Nassau County, NY data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB022: Homes Sold Above Asking: by Metropolitan Areas: Seasonally Adjusted.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of housing units that are vacant. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B25004, B25002, B25003 (Not all lines of ACS tables B25002 and B25003 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 11, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Snapshot img

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