The number of housing units in the United States has grown year-on-year and in 2024, there were approximately *** million homes. That was an increase of about one percent from the previous year. 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 *** in ***** 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.

Following a period of stagnation over most of the 2010s, the number of owner occupied housing units in the United States started to grow in 2017. In 2023, there were over 86 million owner-occupied homes. Owner-occupied housing is where the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing. Homeownership sentiment in the U.S. Though homeownership is still a cornerstone of the American dream, an increasing share of people see themselves as lifelong renters. Millennials have been notoriously late to enter the housing market, with one in four reporting that they would probably continue to always rent in the future, a 2022 survey found. In 2017, just five years before that, this share stood at about 13 percent. How many renter households are there? Renter households are roughly half as few as owner-occupied households in the U.S. In 2023, the number of renter occupied housing units amounted to almost 45 million. Climbing on the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home.

Graph and download economic data for Housing Inventory Estimate: Total Housing Units in the United States (ETOTALUSQ176N) from Q2 2000 to Q1 2025 about inventories, housing, and USA.

Housing Starts in the United States increased to 1321 Thousand units in June from 1263 Thousand units in May of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Housing Inventory Estimate: Occupied Housing Units in the United States (EOCCUSQ176N) from Q2 2000 to Q1 2025 about inventories, housing, and USA.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show value of owner-occupied housing units by Georgia House in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website. Naming conventions: Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)Suffixes:NoneChange over two periods_eEstimate from most recent ACS_mMargin of Error from most recent ACS_00Decennial 2000 Attributes:SumLevelSummary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)GEOIDCensus tract Federal Information Processing Series (FIPS) code NAMEName of geographic unitPlanning_RegionPlanning region designation for ARC purposesAcresTotal area within the tract (in acres)SqMiTotal area within the tract (in square miles)CountyCounty identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)CountyNameCounty NameOwnOcc_e# Owner-occupied housing units, 2017OwnOcc_m# Owner-occupied housing units, 2017 (MOE)ValLt50k_e# Owner-occupied units valued less than $50,000, 2017ValLt50k_m# Owner-occupied units valued less than $50,000, 2017 (MOE)pValLt50k_e% Owner-occupied units valued less than $50,000, 2017pValLt50k_m% Owner-occupied units valued less than $50,000, 2017 (MOE)Val50_100k_e# Owner-occupied units valued $50,000 to $99,999, 2017Val50_100k_m# Owner-occupied units valued $50,000 to $99,999, 2017 (MOE)pVal50_100k_e% Owner-occupied units valued $50,000 to $99,999, 2017pVal50_100k_m% Owner-occupied units valued $50,000 to $99,999, 2017 (MOE)Val100_150k_e# Owner-occupied units valued $100,000 to $149,999, 2017Val100_150k_m# Owner-occupied units valued $100,000 to $149,999, 2017 (MOE)pVal100_150k_e% Owner-occupied units valued $100,000 to $149,999, 2017pVal100_150k_m% Owner-occupied units valued $100,000 to $149,999, 2017 (MOE)Val150_200k_e# Owner-occupied units valued $150,000 to $199,999, 2017Val150_200k_m# Owner-occupied units valued $150,000 to $199,999, 2017 (MOE)pVal150_200k_e% Owner-occupied units valued $150,000 to $199,999, 2017pVal150_200k_m% Owner-occupied units valued $150,000 to $199,999, 2017 (MOE)Val200_300k_e# Owner-occupied units valued $200,000 to $299,999, 2017Val200_300k_m# Owner-occupied units valued $200,000 to $299,999, 2017 (MOE)pVal200_300k_e% Owner-occupied units valued $200,000 to $299,999, 2017pVal200_300k_m% Owner-occupied units valued $200,000 to $299,999, 2017 (MOE)Val300_500k_e# Owner-occupied units valued $300,000 to $499,999, 2017Val300_500k_m# Owner-occupied units valued $300,000 to $499,999, 2017 (MOE)pVal300_500k_e% Owner-occupied units valued $300,000 to $499,999, 2017pVal300_500k_m% Owner-occupied units valued $300,000 to $499,999, 2017 (MOE)Val500k_1m_e# Owner-occupied units valued $500,000 to $999,999, 2017Val500k_1m_m# Owner-occupied units valued $500,000 to $999,999, 2017 (MOE)pVal500k_1m_e% Owner-occupied units valued $500,000 to $999,999, 2017pVal500k_1m_m% Owner-occupied units valued $500,000 to $999,999, 2017 (MOE)Val1mP_e# Owner-occupied units valued $1,000,000 or more, 2017Val1mP_m# Owner-occupied units valued $1,000,000 or more, 2017 (MOE)pVal1mP_e% Owner-occupied units valued $1,000,000 or more, 2017pVal1mP_m% Owner-occupied units valued $1,000,000 or more, 2017 (MOE)ValLt100k_e# Owner-occupied units valued less than $100,000, 2017ValLt100k_m# Owner-occupied units valued less than $100,000, 2017 (MOE)pValLt100k_e% Owner-occupied units valued less than $100,000, 2017pValLt100k_m% Owner-occupied units valued less than $100,000, 2017 (MOE)Val100_300k_e# Owner-occupied units valued $100,000 to $299,999, 2017Val100_300k_m# Owner-occupied units valued $100,000 to $299,999, 2017 (MOE)pVal100_300k_e% Owner-occupied units valued $100,000 to $299,999, 2017pVal100_300k_m% Owner-occupied units valued $100,000 to $299,999, 2017 (MOE)Val300kPlus_e# Owner-occupied units valued $300,000 or more, 2017Val300kPlus_m# Owner-occupied units valued $300,000 or more, 2017 (MOE)pVal300kPlus_e% Owner-occupied units valued $300,000 or more, 2017pVal300kPlus_m% Owner-occupied units valued $300,000 or more, 2017 (MOE)mMedHUValue_eMedian value of owner-occupied unit (dollars), 2017mMedHUValue_mMedian value of owner-occupied unit (dollars), 2017 (MOE)last_edited_dateLast date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2013-2017 For additional information, please visit the Census ACS website.

Between 1968 and 2023, there had been over six million housing unit completions more than new households formed in the United States. That means that throughout that period the number of homes and apartments completed increased at a faster pace than the number of households, indicating no deficit. However, if only completions of single-family homes were considered, there was a housing deficit. From 1969 to 2023, there were roughly 16 million less single-family homes completed than new households were formed. Those figures do not include the number of housing units demolished, and therefore do not reflect the exact housing shortage, as some of those homes completed might not exist anymore due to demolitions or natural disasters.

Graph and download economic data for New Privately-Owned Housing Units Under Construction: Total Units (UNDCONTSA) from Jan 1970 to Jun 2025 about construction, new, private, housing, and USA.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads.Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. For a deep dive into the data model including every specific metric, see the ACS 2017-2021 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e21Estimate from 2017-21 ACS_m21Margin of Error from 2017-21 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_21Change, 2010-21 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLine (buffer)BeltLine Study (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Planning Unit STV (3 NPUs merged to a single geographic unit within City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)City of Atlanta Neighborhood Statistical Areas E02E06 (2 NSAs merged to single geographic unit within City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)SPARCC = Strong, Prosperous And Resilient Communities ChallengeState of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)WFF = Westside Future Fund (subarea of City of Atlanta)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2017-2021). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2017-2021Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://garc.maps.arcgis.com/sharing/rest/content/items/34b9adfdcc294788ba9c70bf433bd4c1/data

Total Housing Inventory in the United States decreased to 1530 Thousands in June from 1540 Thousands in May of 2025. This dataset includes a chart with historical data for the United States Total Housing Inventory.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show age, type, vacancy rates, and owner/renter tenure of housing units by Georgia House in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

Attributes and definitions available below under "Attributes" section and in Infrastructure Manifest (due to text box constraints, attributes cannot be displayed here).

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Graph and download economic data for New Privately-Owned Housing Units Started: Single-Family Units (HOUST1F) from Jan 1959 to Jun 2025 about housing starts, privately owned, 1-unit structures, family, housing, and USA.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Funding for this project provided by: USDA Forest Service, Fire and Aviation Management. Funding also provided by USDA Forest Service, Fire Modeling Institute, which is part of the Rocky Mountain Research Station, Fire, Fuel and Smoke Science Program. Work on dataset development was primarily completed by the USDA Forest Service, Fire Modeling Institute. Some salary was provided by FMI through an ORISE agreement under the U.S. Department of Energy (DE-SC0014664).Author information:Melissa R. JaffePyrologix, LLChttps://orcid.org/0009-0002-8623-407XJoe H. ScottPyrologix, LLChttps://orcid.org/0009-0008-3246-1190Michael N. CallahanPyrologix, LLChttps://orcid.org/0009-0009-4937-5405Gregory K. DillonUSDA Forest Service, Rocky Mountain Research Stationhttps://orcid.org/0009-0006-6304-650XEva C. KarauUSDA Forest Service, Rocky Mountain Research Stationhttps://orcid.org/0009-0009-6776-9387Mitchell T. LazarzUSDA Forest Service, Rocky Mountain Research Stationhttps://orcid.org/0000-0002-4558-4949

Graph and download economic data for Private Nonfarm New Housing Units, Constant Dollars for United States (A02257USA382NNBR) from 1946 to 1963 about nonfarm, new, private, housing, real, and USA.

These data were developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable. .
For a deep dive into the data model including every specific metric, see the ACS 2018-2022 Data Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e22Estimate from 2018-22 ACS_m22Margin of Error from 2018-22 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_22Change, 2010-22 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2018-2022). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2018-2022Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/3b86ee614e614199ba66a3ff1ebfe3b5/about

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from American Community Survey 5-year estimates for 2011-2015 to show housing values for owner-occupied units, by state Senate district for the state of Georgia.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number. ACS data presented here represent combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2011-2015). Therefore, these data do not represent any one specific point in time or even one specific year. For further explanation of ACS estimates and methodology, click here.

Attributes:

DISTRICT = GA Senate District

POPULATION = District Population (2010 Census)

Total_Population_2011_2015_ACS = Total Population, 2011-2015 American Community Survey (ACS)

last_edited_date = Last date feature was edited by ARC

profile_url = Web address of ARC district profile

OwnOcc_units_valued_less_100k = #, Owner occupied housing units valued less than $100,000

Pct_OwnOcc_units_val_less_100k = %, Owner occupied housing units valued less than $100,000

OwnOcc_units_valued_100k_300k = #, Owner occupied housing units valued $100,000-$299,999

Pct_OwnOcc_units_val_100k_300k = %, Owner occupied housing units valued $100,000-$299,999

OwnOcc_units_valued_300k_more = #, Owner occupied housing units valued $300,000 or more

Pct_OwnOcc_units_val_300k_more = %, Owner occupied housing units valued $300,000 or more

Median_value_own_occ_units = Median value, owner occupied housing units

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2011-2015

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

Credits

U.S. Census Bureau, Atlanta Regional Commission

Graph and download economic data for Housing Inventory Estimate: Vacant Housing Units for Sale in the United States (ESALEUSQ176N) from Q2 2000 to Q1 2025 about vacancy, inventories, sales, housing, and USA.

Graph and download economic data for New Privately-Owned Housing Units Completed: Single-Family Units (COMPU1USA) from Jan 1968 to Jun 2025 about 1-unit structures, family, new, private, housing, and USA.

According to the U.S. Census Bureau estimates, 39.3 percent of all occupied housing units had three bedrooms in 2020. The second most popular type of housing was two-bedroom homes, accounting for 25.7 percent of housing stock.