Housing Units by Type by U.S. Postal ZIP Code from the 2020 Decennial Census

Physical Housing Characteristics For Occupied Housing Units Report based on US Census and American Community Survey Data.

Table contains count and percentage of housing units in the county that were built before 1980. Data are presented at county, city, zip code and census tract level. Data are presented for zip codes (ZCTAs) fully within the county. Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-year estimates, Table B25034; data accessed on July 20, 2022 from https://api.census.gov. The 2020 Decennial geographies are used for data summarization.METADATA:notes (String): Lists table title, notes, sourcesgeolevel (String): Level of geographyGEOID (Numeric): Geography IDNAME (String): Name of geographyHU_total (Numeric): Total housing unitsHU_before1980 (Numeric): Number of housing units built before 1980pct_before1980 (Numeric): Percent of housing units built before 1980

US Census American Community Survey (ACS) 2020, 5-year estimates of the key housing characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 406 fields for the variable groups H01: Housing occupancy (universe: total housing units, table X25, 3 fields); H02: Units in structure (universe: total housing units, table X25, 11 fields); H03: Population in occupied housing units by tenure by units in structure (universe: total population in occupied housing units, table X25, 13 fields); H04: Year structure built (universe: total housing units, table X25, 15 fields); H05: Rooms (universe: total housing units, table X25, 18 fields); H06: Bedrooms (universe: total housing units, table X25, 21 fields); H07: Housing tenure by race of householder (universe: occupied housing units, table X25, 51 fields); H08: Total population in occupied housing units by tenure (universe: total population in occupied housing units, table X25, 3 fields); H09: Vacancy status (universe: vacant housing units, table X25, 8 fields); H10: Occupied housing units by race of householder (universe: occupied housing units, table X25, 8 fields); H11: Year householder moved into unit (universe: occupied housing units, table X25, 18 fields); H12: Vehicles available (universe: occupied housing units, table X25, 18 fields); H13: Housing heating fuel (universe: occupied housing units, table X25, 10 fields); H14: Selected housing characteristics (universe: occupied housing units, table X25, 9 fields); H15: Occupants per room (universe: occupied housing units, table X25, 13 fields); H16: Housing value (universe: owner-occupied units, table X25, 32 fields); H17: Price asked for vacant for sale only, and sold not occupied housing units (universe: vacant for sale only, and sold not occupied housing units, table X25, 28 fields); H18: Mortgage status (universe: owner-occupied units, table X25, 10 fields); H19: Selected monthly owner costs, SMOC (universe: owner-occupied housing units with or without a mortgage, table X25, 45 fields); H20: Selected monthly owner costs as a percentage of household income, SMOCAPI (universe: owner-occupied housing units with or without a mortgage, table X25, 26 fields); H21: Contract rent distribution and rent asked distribution in dollars (universe: renter-occupied housing units paying cash rent and vacant, for rent, and rented not occupied housing units, table X25, 7 fields); H22: Gross rent (universe: occupied units paying rent, table X25, 28 fields), and; X23: Gross rent as percentage of household income (universe: occupied units paying rent, table X25, 11 fields). The US Census geodemographic data are based on the 2020 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

Housing Units by Type by U.S. Postal ZIP Code from the Series 13 Regional Growth Forecast

This layer shows housing units broken down by owner occupied and renter occupied in Tempe Zip Codes.Data is from US Census American Community Survey (ACS) 5-year estimates.To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online).A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2016-2020ACS Table(s): S2502 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data table downloaded and joined with Zip Code boundaries in the City of Tempe.Date of Census update: March 17, 2022National Figures: data.census.gov

This map uses a two-color thematic shading to emphasize where areas experience the least to the most affordable housing across the US. This web map is part of the How Affordable is the American Dream story map.

Esri’s Housing Affordability Index (HAI) is a powerful tool to analyze local real estate markets. Esri’s housing affordability index measures the financial ability of a typical household to purchase an existing home in an area. A HAI of 100 represents an area that on average has sufficient household income to qualify for a loan on a home valued at the median home price. An index greater than 100 suggests homes are easily afforded by the average area resident. A HAI less than 100 suggests that homes are less affordable. The housing affordability index is not applicable in areas with no households or in predominantly rental markets . Esri’s home value estimates cover owner-occupied homes only. For a full demographic analysis of US growth refer to Esri's Trending in 2017: The Selectivity of Growth.

The pop-up is configured to show the following 2017 demographics for each County and ZIP Code:

Total Households 2010-17 Annual Pop Change Median Age Percent Owner-Occupied Housing Units Median Household Income Median Home Value Housing Affordability Index Share of Income to Mortgage

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 Zip Code Tabulation Area 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.

Housing Units by Type by U.S. Postal ZIP Code from the 2000 Decennial Census

This dataset identifies demographic and housing estimates including sex and age, race and housing units by zip code tabulation areas within the United States. This dataset resulted from the American Community Survey (ACS) conducted from 2010 through 2014. JSL enriched this dataset with Latitude and Longitude information and with the map information about the land and water area of zip code tabulation areas.

Summary Tape File 3B contains sample data weighted to represent the total population. The collection also contains 100-percent counts and unweighted sample counts for total persons and total housing units. Additional population and housing variables include items such as age, ancestry, disability, citizenship, education, income, marital status, race, sex, travel time to work, rent, tenure, value of housing unit, number of vehicles, and monthly owner costs. The collection includes 178 population tables and 99 housing tables. Data are provided by 5-digit ZIP code within each state, including county portions of the ZIP codes.

Housing Units by Type by U.S. Postal ZIP Code from the SANDAG v23 Release Population and Housing Estimates

Housing Units by Type by U.S. Postal ZIP Code from the SANDAG Vintage 23.1 Population and Housing Estimates

Master Area Reference Files (MARFs) link geographic areas with their respective numeric codes. This data collection is a five-digit ZIP-code equivalency file created for the 1980 Census of Population and Housing. The data contain geographic items from Summary Tape Files 1A and 3A, as well as total population and housing unit counts. This equivalency file was created to allow users to prepare additional data summaries relevant to ZIP-code areas. The file enables users to equate detailed record files having ZIP codes with census geographic units. This national file is hierarchically sequenced by geographic area.

This feature service outlines relationships between Zip Code Tabulation Areas (ZCTAs) used to denote Small Area Fair Market Rents (SAFMRs) and the Fair Market Rents (FMRs) calculated for Metropolitan Statistical Areas (MSAs) and County geographies. Small Area Fair Market Rents (SAFMRs) are FMRs calculated for ZIP Codes within Metropolitan Areas. Small Area FMRs are required to be used to set Section 8 Housing Choice Voucher payment standards in areas designated by HUD (available here). Other Housing Agencies operating in non-designated metropolitan areas may opt-in to the use of Small Area FMRs. Furthermore, Small Area FMRs may be used as the basis for setting Exception Payment Standards – PHAs may set exception payment standards up to 110 percent of the Small Area FMR. PHAs administering Public Housing units may use Small Area FMRs as an alternative to metropolitan area-wide FMRs when calculating Flat Rents.

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 affordability data (housing costs relative to income) by zip code 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. 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:

ZIP = Zip code (text)

ZIP_dbl = Zip code (numeric)

Total_Population_2010 = Total Population, 2010 Census

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

HousUnits_MonthOwnerCosts_toInc = #, Housing units for which Selected Monthly Owner Costs as % of income are computed

Sel_Mo_Own_Costs_30pct_of_Incom = #, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household income

Pct_Sel_Mo_Own_Costs_30pct_Inc = %, Selected Monthly Owner Costs (SMOCAPI) are 30% or more of household income

HousUnits_Compute_RentPctIncome = #, Housing units for which Gross rent as a percentage of income is computed

Rent_Pct_of_Inc_More30Pct = #, Gross rent as a percentage of household income (GRAPI) is 30% or more

PctRent_PctIncome_More30Pct = %, Gross rent as a percentage of household income (GRAPI) is 30% or more

HousUnits_OwnRent_Compute = #, Housing units for which SMOCAPI or GRAPI are computed

HousCosts_Units_30pctMore_Inc = #, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income

PctHousCost_30pctMore_Income = %, Housing costs (GRAPI or SMOCAPI) are 30% or more of household income

last_edited_date = Last date the feature was edited by ARC

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

Date: 2011-2015

Master Area Reference Files (MARFs) link geographic areas with their respective numeric codes. This data collection is a five-digit ZIP-code equivalency file created for the 1980 Census of Population and Housing. The data contain geographic items from Summary Tape Files 1A and 3A, as well as total population and housing unit counts. This equivalency file was created to allow users to prepare additional data summaries relevant to ZIP-code areas. The file enables users to equate detailed record files having ZIP codes with census geographic units. This national file is hierarchically sequenced by geographic area. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08323.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

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 characteristics including vacancy, age of structure, type of structure, and owner/renter occpancy, by zip code 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. 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:

ZIP = Zip code (text)

ZIP_dbl = Zip code (numeric)

Total_Population_2010 = Total Population, 2010 Census

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

Total_housing_units = Total housing units

Occupied_housing_units = #, Occupied housing units

Percent_Occupied_housing_units = %, Occupied housing units

Vacant_housing_units = #, Vacant housing units

Percent_Vacant_housing_units = %, Vacant housing units

Homeowner_vacancy_rate = Homeowner vacancy rate

Rental_vacancy_rate = Rental vacancy rate

One_unit_detatched_housing_unit = #, 1-unit detached housing units

Percent_1Unit_Detached = %, 1-unit detached housing units

Housing_units_built_since_2000 = #, Housing units built since 2000

Pct_Units_Built_Since_2000 = %, Housing units built since 2000

Units_Built_1980_to_1999 = #, Housing units built 1980 to 1999

Pct_Units_Built_1980_to_1999 = %, Housing units built 1980 to 1999

Units_Built_1979_or_Earlier = #, Housing units built 1979 or earlier

Pct_Units_Built_1979_or_Earlier = %, Housing units built 1979 or earlier

Owner_occupied_housing_units = Housing Tenure: #, Owner occupied housing units

Pct_Owner_Occ_HousUnits = Housing Tenure: %, Owner occupied housing units

Renter_occupied_housing_units = Housing Tenure: #, Renter occupied housing units

Pct_Renter_Occ_Units = Housing Tenure: %, Renter occupied housing units

last_edited_date = Last date the feature was edited by ARC

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

Date: 2011-2015

Credits

U.S. Census Bureau, Atlanta Regional Commission

This data collection relates ZIP codes to counties, to standard metropolitan statistical areas (SMSAs), and, in New England, to minor civil divisions (MCDs). The relationships between ZIP codes and other geographical units are based on 1979 boundaries, and changes since that time are not reflected. The Census Bureau used various sources to determine ZIP code-county or ZIP code-MCD relationships. In the cases where the sources were confusing or contradictory as to the geographical boundaries of a ZIP code, multiple ZIP-code records (each representing the territory contained in that ZIP-code area) were included in the data file. As a result, the file tends to overstate the ZIP code-county or ZIP code-MCD crossovers. The file is organized by ZIP code and is a byproduct of data used to administer the 1980 Census. Variables include ZIP codes, post office names, FIPS state and county codes, county or MCD names, and SMSA codes.

This statistic shows the share of zip codes where all homes were unaffordable in selected markets in the United States in 2019. In 2019, 100 percent of homes in **** percent of San Francisco zip codes were unaffordable for buyers earning the local median income.