Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the U.S. Census Bureau hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according the amount of data that is brought in. Explore the U.S. Census Bureau using Kaggle and all of the data sources available through the U.S. Census Bureau organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Davide Cantelli on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Graph and download economic data for New Privately-Owned Housing Units Authorized in Permit-Issuing Places: Total Units (PERMIT) from Jan 1960 to Aug 2025 about headline figure, permits, buildings, new, private, housing, and USA.

Housing permit data is derived from reports and publications compiled by the U.S. Census Bureau's Residential Construction Statistics Division. Municipalities provide the U.S. Census Bureau with tabulations of the number of housing units authorized, according to types of structures. In the few cases where municipalities reported building permit approvals for some months but not for all 12 months, the U.S. Census Bureau estimates the total yearly number of units approved based on past years’ approvals.

Key information about US Building Permits

• United States Building Permits data was reported at 113,896.000 Unit in Aug 2025.
• This records a decrease from the previous number of 121,500.000 Unit for Jul 2025.
• US Building Permits data is updated monthly, averaging 103,905.000 Unit from Jan 1959 to Aug 2025, with 800 observations.
• The data reached an all-time high of 214,600.000 Unit in Jun 1972 and a record low of 36,280.000 Unit in Jan 2011.
• US Building Permits data remains active status in CEIC and is reported by CEIC Data.
• The data is categorized under World Trend Plus’s Global Economic Monitor – Table: Building Permits: Monthly.

View monthly updates and historical trends for US Building Permits. from United States. Source: Census Bureau. Track economic data with YCharts analytics.

Building Permits in the United States decreased to 1330 Thousand in August from 1362 Thousand in July of 2025. This dataset provides the latest reported value for - United States Building Permits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).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.

Graph and download economic data for New Private Housing Structures Authorized by Building Permits for Denver-Aurora-Lakewood, CO (MSA) (DENV708BPPRIVSA) from Jan 1988 to Aug 2025 about Denver, CO, permits, buildings, new, private, housing, and USA.

U.S. Census Bureau QuickFacts statistics for United States. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

HUDen is a raster of housing-unit density measured in housing units per square kilometer. The HUDen raster was generated using population and housing-unit count and data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. Generate the HUDen raster from the building points. We first converted the building points to a 30-m raster where the raster value is the sum of the housing-units-per-centroid attribute of all building centroids within each raster grid cell. We then generated a smoothed density raster using a three-step process: 1) calculate a 200-m radius moving-window sum of the 30-m housing-unit count raster; 2) calculate a 200-m radius moving- window sum of habitable land cover (in sq km), where habitable land cover is all land covers except open water and permanent-snow/ice; and 3) divide the smoothed housing-unit count raster by the smoothed habitable land cover raster to generate housing unit density in housing units/sq km. To produce the final integer version of the HUDen raster, we set values less than 0.1 HU/sq km to zero, values between 0.1 and 1.5 to a value of 1 HU/sq km, and rounded all other values to the nearest integer.

Graph and download economic data for New Privately Owned Housing Completions in the United States by Number of Units in Building, 20 or More Units (COMPDTA20UMQ) from Q1 1987 to Q2 2025 about privately owned, buildings, new, housing, and USA.

The Survey of Market Absorption of New Multifamily Units (SOMA) is sponsored by the Department of Housing and Urban Development (HUD) and administered by the U.S. Census Bureau. SOMA collects data for new residential construction of multifamily units, including apartment buildings and condominium buildings. SOMA provide information on amenities, rent/sales price levels, number of units, type of building, and the number of units taken off the market (absorbed).SOMA uses the Census Bureau's Survey of Construction (SOC) as its sampling base. Each month, a sample of residential buildings containing five or more units is selected for SOMA. The initial 3-month interview collects information on amenities, rent or sales price levels, number of units, type of building, and the number of units taken off the market (absorbed). Field representatives conduct subsequent interviews, if necessary, at 6, 9, and 12 months after completion.

City of Austin Open Data Terms of Use https://data.austintexas.gov/stories/s/ranj-cccq

Building, Electrical, Mechanical, and Plumbing Permits and Driveway/Sidewalk Permits issued by the City of Austin. Includes relevant details such as issue date, location, council district, expiration date, description of work, square footage, valuation, and units.

This dataset is compliant with the Building & Land Development Specification (BLDS) data standard.

The wildland-urban interface (WUI) is the area where urban development occurs in close proximity to wildland vegetation. We generated WUI maps for the conterminous U.S. using building point locations (Carlson et al. 2022), offering higher spatial resolution compared to previously developed WUI maps based on U.S. Census Bureau housing density data (Radeloff et al., 2017). Building point locations were obtained from a Microsoft product released in 2018, which classified building footprints based on high-resolution satellite imagery. Maps were also based on wildland vegetation mapped by the 2016 National Land Cover Dataset (Yang et al., 2018). The mapping algorithm utilized definitions of the WUI from the U.S. Federal Register (USDA & USDI, 2001) and Radeloff et al. (2005). According to these definitions, two classes of WUI were identified: 1) the intermix, where there is at least 50% vegetation cover surrounding buildings, and 2) the interface, where buildings are within 2.4 km of a patch of vegetation at least 5 km2 in size that contains at least 75% vegetation. Both classes required a minimum building density of 6.17 buildings per km2. Maps of intermix and interface WUI were generated using a range of circular neighborhood sizes, based on radius distances from 100 – 1,500 m, to determine building density and vegetation cover on a pixel-by-pixel basis (Bar Massada et al., 2013). A composite map was also generated by combining the combined WUI maps (both interface and intermix WUI) for all neighborhood sizes, with field values indicating the radius distances for which pixels are included in the WUI classification. For each of the 6 neighborhood sizes, the data include rasters indicating the vegetation density threshold for intermix WUI, building density, the building density threshold, and the building-based WUI classification. Additional rasters are included indicating the vegetation proximity threshold for interface WUI and the combined WUI composite map. References: Bar Massada, A., S.I. Stewart, R.B. Hammer, M.H. Mockrin, and V.C. Radeloff. 2013. Using structure locations as a basis for mapping the wildland urban interface. Journal of Environmental Management 128:540–547; https://doi.org/10.1016/j.jenvman.2013.06.021 Carlson, A.R., Helmers, D.P., Hawbaker, T.J., Mockrin, M.H., Radeloff, V.S. 2022. The wildland-urban interface in the United States based on 125 million building locations. Ecological Applications. https://doi.org/10.1002/eap.2597 Radeloff, V. C., R. B. Hammer, S. I. Stewart, J. S. Fried, S. S. Holcomb, and J. F. McKeefry. 2005. The wildland-urban interface in the United States. Ecological Applications 15:799-805; https://doi.org/10.1890/04-1413 Radeloff, V. C., D.P. Helmers, H.A. Kramer, M.H. Mockrin, P.M. Alexandre, A. Bar Massada, V. Butsic, T.J. Hawbaker, S. Martinuzzi, A.D. Syphard, and S.I. Stewart. 2017. The 1990-2010 wildland-urban interface of the conterminous United States (2nd ed.) [Geospatial data]. Forest Service Research Data Archive; https://doi.org/10.2737/RDS-2015-0012-2. USDA and USDI. 2001. Urban wildland interface communities within vicinity of Federal lands that are at high risk from wildfire. Federal Register 66:751-777. Yang, L., S. Jin, P. Danielson, C. Homer, L. Gass, S.M. Bender, A. Case, C. Costello, J. Dewitz, J. Fry, M. Funk, B. Granneman, G.C. Liknes, M. Rigge, and G. Xian. 2018. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS Journal of Photogrammetry and Remote Sensing 146:108–123; https://doi.org/10.1016/j.isprsjprs.2018.09.006

Because the 2020 Census relationship files released by the Census Bureau did not include population or housing unit percentage allocations, alternative methodologies to allocate population and housing units between 2010 and 2020 Census tracts were tested. The methodology selected by DRU was to use building footprints and residential parcels at the 2020 Census block level to allocate population and housing units.

This dataset consists of residential building permits issued in the 7-county Twin Cities Metropolitan Area from 2009 through 2024.

The data were obtained through an annual survey sent to communities by the Metropolitan Council and from the U.S. Census Bureau. The data on returned surveys were verified by Metropolitan Council staff through various means. Data from the Residential Construction Branch of the Manufacturing and Construction Division of the U.S. Census Bureau were used if a community did not return the survey. Data may be periodically updated to reflect corrections.

This dataset contains monthly counts of single-family and multi-family building permits issued by Fulton County jurisdictions as reported by the U.S. Census Bureau. Included are 12-month rolling averages, which can be used to better visualize trends over times.

VITAL SIGNS INDICATOR
Housing Permits (LU3)

FULL MEASURE NAME
Permitted housing units

LAST UPDATED
February 2023

DESCRIPTION
Housing growth is measured in terms of the number of units that local jurisdictions permit throughout a given year. A permitted unit is a unit that a city or county has authorized for construction.

DATA SOURCE
California Housing Foundation/Construction Industry Research Board (CIRB) - https://www.cirbreport.org/
Construction Review report (1967-2022)

Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) - https://data.bayareametro.gov/Development/HCD-Annual-Progress-Report-Jurisdiction-Summary/nxbj-gfv7
Housing Permits Database (2014-2021)

Census Bureau Building Permit Survey - https://www2.census.gov/econ/bps/County/
Building permits by county (annual, monthly)

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Bay Area housing permits data by single/multi family come from the California Housing Foundation/Construction Industry Research Board (CIRB). Affordability breakdowns from 2014 to 2021 come from the Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) Housing Permits Database.

Single-family housing units include detached, semi-detached, row house and town house units. Row houses and town houses are included as single-family units when each unit is separated from the adjacent unit by an unbroken ground-to-roof party or fire wall. Condominiums are included as single-family units when they are of zero-lot-line or zero-property-line construction; when units are separated by an air space; or, when units are separated by an unbroken ground-to-roof party or fire wall. Multi-family housing includes duplexes, three-to-four-unit structures and apartment-type structures with five units or more. Multi-family also includes condominium units in structures of more than one living unit that do not meet the single-family housing definition.

Each multi-family unit is counted separately even though they may be in the same building. Total units is the sum of single-family and multi-family units. County data is available from 1967 whereas city data is available from 1990. City data is only available for incorporated cities and towns. All permits in unincorporated cities and towns are included under their respective county’s unincorporated total. Permit data is not available for years when the city or town was not incorporated.

Affordable housing is the total number of permitted units affordable to low and very low income households. Housing affordable to very low income households are households making below 50% of the area median income. Housing affordable to low income households are households making between 50% and 80% of the area median income. Housing affordable to moderate income households are households making below 80% and 120% of the area median income. Housing affordable to above moderate income households are households making above 120% of the area median income.

Permit data is missing for the following cities and years:
Clayton, 1990-2007
Lafayette, 1990-2007
Moraga, 1990-2007
Orinda, 1990-2007
San Ramon, 1990

Building permit data for metropolitan areas for each year is the sum of non-seasonally adjusted monthly estimates from the Census Building Permit Survey. The Bay Area values are the sum of the San Francisco-Oakland-Hayward MSA and the San Jose-Sunnyvale-Santa Clara MSA. The counties included in these areas are: San Francisco, Marin, Contra Costa, Alameda, San Mateo, Santa Clara, and San Benito.

Permit values reflect the number of units permitted in each respective year. Note that the data columns come from difference sources. The columns (SFunits, MFunits, TOTALunits, SF_Share and MF_Share) are sourced from CIRB. The columns (VeryLowunits, Lowunits, Moderateunits, AboveModerateunits, VeryLow_Share, Low_Share, Moderate_Share, AboveModerate_Share, Affordableunits and Affordableunits_Share) are sourced from the ABAG Housing Permits Database. Due to the slightly different methodologies that exist within each of those datasets, the total units from each of the two sources might not be consistent with each other.

As shown, three different data sources are used for this analysis of housing permits issued in the Bay Area. Data from the Construction Industry Research Board (CIRB) represents the best available data source for examining housing permits issued over time in cities and counties across the Bay Area, dating back to 1967. In recent years, Annual Progress Report (APR) data collected by the California Department of Housing and Community Development has been available for analyzing housing permits issued by affordability levels. Since CIRB data is only available for California jurisdictions, the U.S. Census Bureau provides the best data source for comparing housing permits issued across different metropolitan areas. Notably, annual permit totals for the Bay Area differ across these three data sources, reflecting the limitations of needing to use different data sources for different purposes.

Graph and download economic data for Retail Sales: Building Materials and Garden Equipment and Supplies Dealers (MRTSSM444USS) from Jan 1992 to Jul 2025 about garden, materials, supplies, buildings, retail trade, equipment, sales, retail, and USA.

U.S. Census Bureau QuickFacts statistics for District of Columbia. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.