Graph and download economic data for Housing Inventory Estimate: Vacant Housing Units in the United States (EVACANTUSQ176N) from Q2 2000 to Q1 2025 about vacancy, inventories, housing, and USA.

The number of vacant homes for rent in the United States increased for the third year in a row in 2024, after reaching a record low in 2021. In the fourth quarter of 2024, there were approximately *** million unoccupied housing units for rent.

The homeowner vacancy rate in the United States reached its lowest value in 2022, followed by an uptick in 2023. The rate shows what share of owner-occupied housing units were vacant and for sale. That figure peaked in 2008, when nearly three percent of homes were vacant, and gradually fell below one percent after the 2020 housing boom. Homeownership is a form of living arrangement where the owner of the inhabited property, whether apartment, house, or type of real estate, lives on the premises. Due to usually high costs associated with owning a property and perceived advantages or disadvantages associated with such a long-term investment, homeownership rates differ greatly around the world, based on both cultural and economic factors. In Europe, Romania is the country with the highest rate of homeownership, while the lowest homeownership rate was observed in Switzerland. Homeownership attitude in the U.S. Individuals may have very different opportunities or inclination to become homeowners based on nationality, age, financial status, social status, occupation, marital status, education or even ethnicity and whether one is local-born or foreign-born. In 2023, the homeownership rate among older Americans was higher than for younger Americans. In the U.S., homeownership is generally believed to be a good investment, in terms of security (no risk of eviction) and financial aspect (owning a valuable real estate property). In 2023, there were approximately 86 million owner-occupied housing units, a stark increase compared to four decades prior. Why is homeownership sentiment low? The housing market has been suffering chronic undersupply, leading to a surge in prices and eroding affordability. In 2023, the housing affordability index plummeted, reflecting the growing challenge that homeowners face when looking for property. Insufficient income, savings, and high home prices are some of the major obstacles that come in the way of a property purchase. Though affordability varied widely across different metros, just about 15 percent of U.S. renters could afford to buy the median priced home in their area.

A housing unit is vacant if no one is living in it at the time of the interview, unless its occupants are only temporarily absent. In addition, a vacant unit may be one which is entirely occupied by persons who have a usual residence elsewhere. New units not yet occupied are classified as vacant housing units if construction has reached a point where all exterior windows and doors are installed and final usable floors are in place. Vacant units are excluded if they are exposed to the elements, that is, if the roof, walls, windows, or doors no longer protect the interior from the elements, or if there is positive evidence (such as a sign on the house or block) that the unit is to be demolished or is condemned. Also excluded are quarters being used entirely for nonresidential purposes, such as a store or an office, or quarters used for the storage of business supplies or inventory, machinery, or agricultural products. Vacant sleeping rooms in lodging houses, transient accommodations, barracks, and other quarters not defined as housing units are not included in the statistics.

U.S. Vacant Homes: 25 years of historical data from 2000 to 2025.

Graph and download economic data for Housing Inventory Estimate: Vacant Housing Units Held Off the Market and Vacant for Other Reasons in the United States (EOTHUSQ176N) from Q2 2000 to Q1 2025 about vacancy, inventories, housing, and USA.

Beginning in 1990, year-round vacant mobile homes were included as part of the year-round vacant count of housing units. Year-round units are those intended for occupancy at any time of the year, even though they may not be in use the year round. In resort areas, a housing unit which is usually occupied on a year-round basis is considered a year-round unit. As indicated above, year-round units temporarily occupied by persons with usual residence elsewhere are included with year-round vacant units.

United States Number of Housing Unit: Vacant: Year Round: Other Reasons data was reported at 4,146.000 Unit th in Jun 2018. This records an increase from the previous number of 4,006.000 Unit th for Mar 2018. United States Number of Housing Unit: Vacant: Year Round: Other Reasons data is updated quarterly, averaging 2,142.500 Unit th from Mar 1965 (Median) to Jun 2018, with 214 observations. The data reached an all-time high of 4,146.000 Unit th in Jun 2018 and a record low of 931.000 Unit th in Dec 1970. United States Number of Housing Unit: Vacant: Year Round: Other Reasons data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EB011: Number of Housing Units. Series Remarks1. Data for 1979 Q1 to Q4 was revised to reflect changes made in 1980.2. Data for 1989 Q1 to Q4 was revised to include year-round vacant mobile homes.3. Data for 1993 Q1 to Q4 was revised based on the 1990 Census.4. Data for 2002 Q1 to Q4 was revised based on the 2000 Census.

Housing Inventory Estimate: Vacant Housing Units for Rent in the United States was 3538.00000 Thous. of Units in January of 2025, according to the United States Federal Reserve. Historically, Housing Inventory Estimate: Vacant Housing Units for Rent in the United States reached a record high of 4625.00000 in July of 2009 and a record low of 2491.00000 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory Estimate: Vacant Housing Units for Rent in the United States - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for Home Vacancy Rate for the United States (USHVAC) from 1986 to 2024 about vacancy, housing, rate, and USA.

This map compares housing units by three different types: owner-occupied, renter-occupied, or vacant. Only the type with the largest count of housing units receives a color on the map.This pattern is shown by states, counties, and tracts throughout the entire US. This data comes from the most recent 5-year American Community Survey from the Census Bureau (ACS). This data comes from this current-year ACS layer from the ArcGIS Living Atlas of the World.Each year, the data within this map is updated to reflect the newest ACS data, keeping this map up-to-date.This map helps us answer different questions such as:Are renters or home-owners more prevalent in cities? Suburbs? Rural areas?Where are vacant housing units? This question can help pinpoint blight within cities.How many housing units are within different areas?

This layer shows vacant housing by type (for rent/sale, vacation home, etc.). This is shown by tract, county, and state boundaries. 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 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.

The AHS is the largest, regular national housing sample survey in the United States. The U.S. Census Bureau conducts the AHS to obtain up-to-date housing statistics for the Department of Housing and Urban Development (HUD). The most recent AHS was conducted in 2013 and the next AHS will be conducted in 2015. The AHS national survey was conducted annually from 1973-1981 and biennially (every two years) from 1983 - 2015. Metropolitan area surveys have been conducted annually or biennially since 1974.

Housing Inventory Estimate: Vacant Housing Units in the United States was 15571.00000 Thous. of Units in January of 2025, according to the United States Federal Reserve. Historically, Housing Inventory Estimate: Vacant Housing Units in the United States reached a record high of 19137.00000 in January of 2009 and a record low of 13268.00000 in October of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory Estimate: Vacant Housing Units in the United States - last updated from the United States Federal Reserve on July of 2025.

United States Number of Housing Unit: Vacant: Year Round: Held Off Market data was reported at 7,467.000 Unit th in Sep 2018. This records a decrease from the previous number of 7,548.000 Unit th for Jun 2018. United States Number of Housing Unit: Vacant: Year Round: Held Off Market data is updated quarterly, averaging 4,691.000 Unit th from Mar 1965 (Median) to Sep 2018, with 215 observations. The data reached an all-time high of 7,700.000 Unit th in Jun 2014 and a record low of 1,764.000 Unit th in Jun 1965. United States Number of Housing Unit: Vacant: Year Round: Held Off Market data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB011: Number of Housing Units. Series Remarks Data for 1979 Q1 to Q4 was revised to reflect changes made in 1980. Data for 1989 Q1 to Q4 was revised to include year-round vacant mobile homes. Data for 1993 Q1 to Q4 was revised based on the 1990 Census. Data for 2002 Q1 to Q4 was revised based on the 2000 Census.

The layer was compiled from the U.S. Census Bureau’s 2018 Planning Database (PDB), a database that assembles a range of housing, demographic, socioeconomic, and census operational data. The data is from the 2012 – 2016 American Community Survey 5-Year Estimates. The purpose of the data is for 2020 Census planning purposes.

Source: 2018 PDB, U.S. Census Bureau

Effective Date: June 2018

Last Update: January 2020

Update Cycle: Generally, annually as needed. 2018 PDB is vintage used for 2020 Census planning purposes by Nation and County.

U.S. Vacant Rental Homes: 25 years of historical data from 2000 to 2025.

Included in this category are units held for occasional use, temporarily occupied by persons with usual residence elsewhere, and vacant for other reasons.

The AHS is the largest, regular national housing sample survey in the United States. The U.S. Census Bureau conducts the AHS to obtain up-to-date housing statistics for the Department of Housing and Urban Development (HUD). The AHS contains a wealth of information that can be used by professionals in nearly every field for planning, decisionmaking, market research, or various kinds of program development. It gives you data on apartments, single-family homes, mobile homes, vacant homes, family composition, income, housing and neighborhood quality, housing costs, equipment, fuels, size of housing unit, and recent movers.

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.