The majority of the U.S. housing stock was between 42 and 51 years old as of 2021. According to the source, the median year was 1979, meaning that the median house age was 42 years. Housing construction in the U.S. plummeted between 2005 and 2010 and has since been slow to recover.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Jun 2025 about median and USA.

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.

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.

The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller the level of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.

Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.

Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock

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Population: Total Population (B01003)

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Households: Total number of households (B25003)

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OwnHH: Total number of owner-occupied households (B25003)

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RentHH: Total number of renter-occupied households (B25003)

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TotalU: Total number of housing units (B25001)

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VacantU: Total number of vacant units (B25004)

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SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)

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ForSale: Total number of units currently for sale (B25004)

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ForRent: Total number of units currently for rent (B25004)

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MedianHI: Median Household Income (B25119)

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OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

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POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)

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OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

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POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)

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OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

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POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)

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RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

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PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)

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RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

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PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)

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RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

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PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)

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Poverty: Population below poverty level. (B17001)

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PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)

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MYearBuilt: Median structure year of construction. (B25035)

US Census Bureau American Community Survey 2013-2017 Estimates in the Keys the Valley Region for Population, Households, Tenure, Cost Burden, Poverty, and Age of Housing Stock.The American Community Survey (ACS) is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. Because the ACS is based on a sample, rather than all housing units and people, ACS estimates have a degree of uncertainty associated with them, called sampling error. In general, the larger the sample, the smaller thelevel of sampling error. Data associated with a small town will have a greater degree of error than data associated with an entire county. To help users understand the impact of sampling error on data reliability, the Census Bureau provides a “margin of error” for each published ACS estimate. The margin of error, combined with the ACS estimate, give users a range of values within which the actual “real-world” value is likely to fall.Single-year and multiyear estimates from the ACS are all “period” estimates derived from a sample collected over a period of time, as opposed to “point-in-time” estimates such as those from past decennial censuses. For example, the 2000 Census “long form” sampled the resident U.S. population as of April 1, 2000. The estimates here were derived from a sample collected over time from 2013-2017.Data Dictionary - Population, Households, Tenure, Cost Burden, Poverty, Age of Housing Stock· Population: Total Population (B01003)· Households: Total number of households (B25003)· OwnHH: Total number of owner-occupied households (B25003)· RentHH: Total number of renter-occupied households (B25003)· TotalU: Total number of housing units (B25001)· VacantU: Total number of vacant units (B25004)· SeasRecOcU: Total number of Seasonal/Recreational/Occasionally Occupied Units (B25004)· ForSale: Total number of units currently for sale (B25004)· ForRent: Total number of units currently for rent (B25004)· MedianHI: Median Household Income (B25119)· OwnHH3049: Total number of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· POwnHH3049: Percentage of owner-occupied households spending 30-49% of their income on housing costs. (B25095)· OwnHH50: Total number of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· POwnHH50: Percentage of severely cost-burdened owner-occupied households – those spending 50% or more of their income on housing costs. (B25095)· OwnHH_CB: Total number of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· POwnHH_CB: Percentage of owner-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25095)· RenHH3049: Total number of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· PRenHH3049: Percentage of renter-occupied households spending 30-49% of their income on housing costs. (B25070)· RenHH50: Total number of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· PRenHH50: Percentage of severely cost-burdened renter-occupied households – those spending 50% or more of their income on housing costs. (B25070)· RenHH_CB: Total number of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· PRenHH_CB: Percentage of renter-occupied, cost-burdened households - those who spend 30% or more of their income on housing costs. (B25070)· Poverty: Population below poverty level. (B17001)· PPoverty: Percentage of population below poverty level. Note poverty status (above or below) is not determined for the entire population. (B17001)· MYearBuilt: Median structure year of construction. (B25035)

CityPropStats provides aggregated property statistics for 795 cities and towns (i.e., Metropolitan and Micropolitan statistical areas) in the conterminous United States. These statistics include sum, mean, median, Gini index and entropy of residential floor space, cadastral parcel size, floor-area ratio, and property value, approximately for the reference year 2020, aggregated by building construction year in decadal steps (cumulative and incremental) from 1910 to 2020.Cumulative statistics: CBSA_Property_Statistics_1910-2020_cumulative.csvDecadal time slices statistics: CBSA_Property_Statistics_1910-2020_decadal_slices.csvData source: Zillow Transaction and Assessment Dataset (ZTRAX), provided to University of Colorado Boulder via a data share agreement (2016-2023).CityPropStats is a supplementary dataset to:Ortman S.G., et al. (accepted): "Changes in Agglomeration and Productivity are Poor Predictors of Inequality Across the Archaeological Record". Proceedings of the National Academy of Sciences (2025).Column description:cbsa_idCBSA GEOIDcbsa_nameFull namecbsa_typeCBSA type (metro vs micropolitan statistical area)year_fromEarliest year for selection interval of properties based on their construction yearyear_toLatest year for selection interval of properties based on their construction yearcbsa_popCBSA population or population change (US Census)tot_res_propsTotal residential propertiestot_res_area_sqkmTotal indoor area of residential properties in sqkmavg_res_area_sqmAverage indoor area of residential properties in sqmmedian_res_area_sqmMedian indoor area of residential properties in sqmq25_res_area_sqm25th percentile of indoor area of residential properties in sqmq75_res_area_sqm75th percentile of indoor area of residential properties in sqmgini_res_areaGini index of residential property indoor areatot_prop_value_usdTotal residential property value in USDmedian_prop_value_usdMedian residential property value in USDq25_prop_value_usd25th percentile of residential property values in USDq75_prop_value_usd75th percentile of residential property values in USDgini_prop_valueGini index of residential property valuestot_lot_area_sqkmTotal lot (cadastral parcel) area in sqkmavg_lot_area_sqmMean lot area in sqmmedian_lot_area_sqmMedian lot area in sqmq25_lot_area_sqm25th percentile of lot area in sqmq75_lot_area_sqm75th percentile of lot area in sqmgini_lot_areaGini index of lot areaavg_farMean floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesmedian_farMedian floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesq25_far25th percentile of floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesq75_far75th percentile of floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesentropy_res_areaShannon entropy of the indoor area of residential properties, based on propertiesentropy_prop_valueShannon entropy of the property value of residential properties, based on propertiesentropy_lot_areaShannon entropy of the lot size of residential properties, based on propertiesarea_completenessRatio of properties with a valid indoor area attribute [0,1]value_completenessRatio of properties with a valid property value attribute [0,1]lotsize_completenessRatio of properties with a valid indoor area, property value, and lot size attribute [0,1]area_value_completenessRatio of properties with a valid lot size attribute [0,1]area_value_lotsize_completenessRatio of properties with both a valid indoor area and property value attribute [0,1]

The Roofing Contractors industy has enjoyed an uptick in demand because of several factors through the end of 2025. A gain in severe weather events, like tornadoes and hurricanes, led to significant roof damage across residential and commercial properties, increasing the need for emergency repairs and replacements. However, roofing contractors faced supply chain constraints and high material costs alongside raised revenues. Meanwhile, M&A activity has noticeably reshaped the industry, allowing larger contractors to expand their reach and access new markets. Overall, revenue has climbed at a CAGR of 2.2% to an estimated $76.4 billion through the end of 2025, including a projected 0.8% gain in 2025 alone. The industry's current state is strongly influenced by the aging housing stock, with re-roofing accounting for 80.0% of the industry's demand. The high median age of US homes has led many property owners to invest in maintaining and upgrading their existing properties, increasing demand for roof repairs and replacements. Demand from commercial units for roofing contractors has grown steadily, driven by economic expansion and infrastructure initiatives. Yet, labor challenges specific to this sector have resulted in delays and operational disruptions. Higher costs for materials like asphalt shingles, membranes and insulation have strained profit. Through the end of 2030, the roofing industry's performance will be strengthened by growing demand for metal roofing because of its durability, energy efficiency and sustainability. Persistent labor shortages are expected, although these may be managed by adopting progressive strategies and leveraging technological advancements. The industry sees promising potential in the growing influence of smart technologies, with IoT sensors, drones and AI-powered analytics offering significant improvements to efficiency, performance and cost reduction. As these technologies promote early detection of issues and safer remote inspections, roofing contractors will be well-equipped to meet future demands. However, to fully capitalize on these trends, contractors must prioritize scalability, workforce training and the integration of smart technologies. Industry revenue will rise at a CAGR of 2.5% to an expected $86.2 billion through the end of 2030.