Extreme commuting

Summary: The share of workers age 16 or older who work outside of home that commute 90 minutes or more to work, one-way. Data for 2010 and 2015 represent five-year averages (e.g. 2011-2015).

Data Source(s): U.S. Census Bureau, 2010 and 2015 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries; Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2000 5% sample, 2010 and 2015 American Community Survey 5-year samples.

Universe: All workers age 16 or older who work outside of home.

Methods: The number and percentage of extreme commuters among workers age 16 or older who work outside of home was calculated by race/ethnicity, gender, nativity, ancestry, commute mode and poverty level for each year and geography. Private vehicles include cars, trucks, and motorcycles; public transportation includes buses, streetcars, subways/light rails, railroads, taxicabs, and ferryboats; walk or bike includes bicycles, walking, and other modes. See the methodology page for other relevant notes.

Notes:

  • Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.

  • Data for 2010 and 2015 represent 2006-2010 and 2011-2015 averages, respectively.

  • Workers are defined as people who reported working during the week prior to the survey (and they had to have worked outside of home to be included in the universe).

  • No data are available for other cities or towns or Census Designated Places for the by race/ethnicity, by gender, by nativity, by ancestry, and by poverty breakdowns, as they are based on the IPUMS microdata.

  • No data are reported if based on fewer than 100 individual (i.e. unweighted) survey respondents age 16 or older who work outside of home for the by race/ethnicity, by gender, by nativity, by ancestry, and by poverty breakdowns.

  • No data are reported if based on fewer than 100 people age 16 or older who work outside of home for the trend, ranking and map breakdowns.

Housing burden

Summary: The share of owner- and renter-occupied households that are cost-burdened (spending more than 30 percent of income on housing costs) and "severely" cost-burdened (more than 50 percent). Data for 2010 and 2015 represent five-year averages (e.g. 2011-2015).

Data Source(s): U.S. Census Bureau, 2010 and 2015 American Community Survey 5-year Summary Files; GeoLytics, Inc., 2000 Long Form in 2010 Boundaries; Integrated Public Use Microdata Series, IPUMS USA, University of Minnesota, www.ipums.org, 2000 5% sample, 2010 and 2015 American Community Survey 5-year samples.

Universe: Occupied households with housing costs, excluding non-traditional owner-occupied households (e.g. multi-unit structures and trailers).

Methods: The number and percentage of burdened and severely burdened households was calculated by tenure (owner vs. renter), race/ethnicity, gender, nativity, ancestry and poverty level for each year and geography. Housing costs for renters include contract rent as well as utilities while housing costs for owners includes most costs of owning a home such as mortage, insurance, utilities, real estate taxes and other costs. See the methodology page for other relevant notes.

Notes:

  • Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.

  • Demographic characteristics are based on those of the householder.

  • Household income is based on the year prior to the survey while housing costs are based on the survey year.

  • Data for 2010 and 2015 represent 2006-2010 and 2011-2015 averages, respectively.

  • No data are available for other cities or towns or Census Designated Places for the by race/ethnicity, by gender, by nativity, by ancestry, and by poverty breakdowns, as they are based on the IPUMS microdata.

  • No data are reported if based on fewer than 100 individual (i.e. unweighted) survey respondents age 16 or older who work outside of home for the by race/ethnicity, by gender, by nativity, by ancestry, and by poverty breakdowns.

  • No data are reported if based on fewer than 100 people age 16 or older who work outside of home for the trend, ranking and map breakdowns.

Market rent

Summary: Estimated monthly median market rent based on the Zillow Rent Index (ZRI) for all rental units. Values are for April 30th of each year and are not adjusted for inflation.

Data Source(s): Zillow Group, Inc., Zillow Rent Index (ZRI) for all rental units; U.S. Census Bureau, 2011, 2012, 2013, 2014, 2015, 2016, and 2017 American Community Survey 5-year Summary Files.

Universe: All rental housing units (multifamily, single-family residence and condo/co-op).

Methods: An extract of the monthly ZRI for all rental units (multifamily, single-family residence and condo/co-op) at the 2010 census tract level of geography was obtained from Zillow Group, Inc. The ZRI is "a smoothed measure of the median estimated market rate rent across a given region and housing type." Median "market" rent reflects the median rent of units up for rent at a given point in time, and not necessarily the median rent of all renter-occupied units. In places where rents are on the rise, median market rent will be higher the median rent paid by all renters and vice-versa. The underlying data used to derive the ZRI are the particular set of housing units for rent in a geographic area over at a given point in time. Because this sample of rental units may not reflect the overall rental housing stock in terms of types of units for rent, which could bias the estimate of median market rent, the methodology behind the ZRI is designed to adjust for differences between the sample of units for rent and the overall rental stock to yeild an index of median market rent that is unnaffected by the mix of homes in each sample used. For example, imagine a neighborhood that is mostly comprised of multifamily apartments, but for some reason most of the units up for rent at a given point in time are single-family units. Since single-family units tend to be more expensive, this could bias the estimate of median market rent upward if no adjustments were are made. A "smoothed measure" simply means that the ZRI for any given month is a three-month average to smooth the volitility in the estimates that can occur from one month to the next. ZRI estimates for April 30 of each year from 2011 through 2017 were selected for all census tracts in the 9-county Bay Area, and merged with data on the number of renter-occupied housing units from the 2011 through 2017 American Communty Survey (ACS) 5-year summary files (i.e. eight different 5-year summary files). ZRI values were estimated for geographies other than census tracts in each year by taking a weighted average of the tract-level values, using the number of renter-occupied housing units from the ACS 5-year summary file for the corresponding year as weight. For example, to estimate the ZRI for the large cities in 2015, a weighted average of the ZRI values for all tracts contained in each large city was calculated, using the number of renter-occupied housing units from the 2015 5-year summary file (which reflects a 2011-2015 average) as weight. To aggregate from census tracts to sub-counties (CPUMAs) and census-defined places (large cities, other cities or towns, and Census Designated Places), we used geographic crosswalks that were created by assigning each 2010 tract to the CPUMA and census-defined place containing the plurality of its 2010 population (from SF1 of the 2010 Census) by census block. All estimates were derived based on the same underlying 2010 census tract level data file for consistency. However, of this, estimates for geographies such as cities and counties will not align perfectly with the official ZRI estimates from Zillow, Inc., which can be accessed here. For more information on the methodology used by Zillow to develop the ZRI, see here. See the methodology page for other relevant notes.

Notes:

  • Values are for April 30th of each year and are not adjusted for inflation.
  • Data are only reported for census-defined places (large cities, other cities or towns and Census Designated Places) for which at least three census tracts were assigned in the geographic crosswalks noted above, or for which one or two tracts were assigned but the vast majority (at least 80 percent) of the tract population(s) fell within the census-defined place based on 2010 block level population counts.

Affordable housing production

Summary: Housing unit permits approved to meet housing needs by income level based on the Regional Housing Needs Assessment conducted by the Association of Bay Area Governments.

Data Source(s): Association of Bay Area Governments (ABAG), 7-year Regional Housing Needs Assessments.

Universe: Total number of housing unit permits needed, by income level of tenants, to meet affordable housing needs in each projection period (1999-2006, 2007-2014, 2015-2023).

Methods: Regional Housing Need Allocation (RHNA) is the state-mandated process to identify the total number of housing units, by affordability level, that each jurisdiction must accommodate in its Housing Element. As part of this process, the California Department of Housing and Community Development (HCD) identifies the total housing need for the San Francisco Bay Area for an eight-year period (in the current cycle, from 2015-2023). Data by income are based on affordability for households falling in different ranges of Area Median Income (AMI) designated by ABAG for each Regional Housing Needs Assessment period (1999-2006, 2007-2014, 2015-2023). "Very-low income" is defined as income between zero and 50 percent of AMI, "low income" is between 50 and 80 percent of AMI, "moderate income" is between 80 and 120 percent of AMI and "above-moderate income" is 120 percent of AMI or higher. Data on the projected of housing units needed by affordability level and the number for which permits were issued were collected for the two historic RHNA periods of 1999-2006 and 2007-2014, and the current period of 2015-2023. For the current period, the number of permits issues is as of the end of 2017. The number of housing unit permits issues and their percentage of total housing units needed was collected for each of the three RHNA periods for jurisdictions reporting in the Bay Area (i.e. counties and cities). The data were aggregated across counties to derive regional totals for the Five- and Nine-county Bay Area regions. See the methodology page for other relevant notes.

Notes: 

  • No data are available for California as a whole, sub-counties, and Census Designated Places.

Gentrification risk

Summary: The share of low-income households (household income < $60,000) living in neighborhoods classified according to the UC Berkeley Urban Displacement Project’s gentrification/displacement typology. Exclusive neighborhoods are moderate- to high-income with low or declining shares of low-income households. Race/ethnicity is based on the race of the householder; with the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.

Data Source(s): UC Berkeley Urban Displacement Project, Gentrification and Displacement Census Tract Typology for the nine-county Bay Area (udp_2017results.csv); U.S. Census Bureau, 2015 American Community Survey 5-year Summary File.

Universe: All households with income below $60,000 per year.

Methods: Gentrification risk categories at the 2010 census tract level were constructed using data from the Urban Displacement Project (UDP) Gentrification and Displacement Census Tract Typology for the nine-county Bay Area. The typology was created to better understand and predict where gentrification and displacement is happening and will likely occur in the future, and is based on a wide variety of data sources covering the years 1990 through 2015. Maps of the typology are available on the Urban Displacement Project's website, here, and the methodology can be found here. While the typology includes eight different types of census tracts, they were consolidated into four categories for the gentrification risk indicator on the Atlas, as follows: "gentrifying" includes typology categories of Displacement of Low Income Households / Ongoing Gentrification (Low Income) and Advanced Gentrification (Moderate to High Income); "at risk" includes the typology category At Risk of Gentrification (Low Income); "stable" includes the typology categories of Not Losing Low-Income Households (Low Income) and Not Losing Low-Income Households (Moderate to High Income); "exclusive" includes the typology categories of At Risk of Exclusion (Moderate to High Income), Displacement of Low-Income Households - Ongoing Exclusion (Moderate to High Income) and Advanced Exclusion (Moderate to High Income). The tract level gentrification risk data were merged with tract-level data on the number of households by race/ethnicity and annual income level from the 2015 American Community Survey 5-year Summary File, which reflects a 2011-2015 average. Low-income households were defined as those with annual income under $60,000, and the number of such households by race/ethnicity were summed up to higher-level Atlas geographies for each of the four gentrification risk categories. To aggregate from census tracts to sub-counties (CPUMAs) and census-defined places (large cities, other cities or towns, and Census Designated Places), we used geographic crosswalks that were created by assigning each 2010 tract to the CPUMA and census-defined place containing the plurality of its 2010 population (from SF1 of the 2010 Census) by census block. See the methodology page for other relevant notes.

Notes:

  • Race/ethnicity is based on the householder; with the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.
  • The analysis is restricted to tracts with an assigned gentrification/displacement type by the UC Berkeley typology, excluding college towns.
  • Low-income households are defined as those with an annual household income of less than $60,000. 
  • No data are reported if based on fewer than 100 low-income households.
  • Data are only reported for census-defined places (large cities, other cities or towns and Census Designated Places) for which at least three census tracts were assigned in the geographic crosswalks noted above, or for which one or two tracts were assigned but the vast majority (at least 80 percent) of the tract population(s) fell within the census-defined place based on 2010 block level population counts.
  • No data are available for California as a whole.

Neighborhood opportunity

Summary: The share of the population living in neighborhoods with different neighborhood resource levels based on the California Fair Housing Task Force opportunity maps created by the Haas Institute at UC Berkeley. High segregation and poverty neighborhoods are those with poverty rates of at least 30 percent, with high levels of racial segregation and high shares of people-of-color households. Resource levels are based on a comprehensive index of opportunity. 

Data Source(s): California Fair Housing Task Force 2019 Opportunity Maps; U.S. Census Bureau, 2015 American Community Survey 5-year Summary File.

Universe: All people.

Methods: Data on neighborhood opportunity from 2019 version of the California Fair Housing Task Force Opportunity Mapping project, drawing on data from a range of years between 2010 and 2018, was downloaded from the California State Treasurer website. The data, which are at the 2010 census tract level of geography, were merged with tract-level population data from the 2015 American Community Survey 5-year Summary File, which reflects a 2011-2015 average. To briefly summarize the neighborhood opportunity methodology, tract level opportunity scores were derived relative to the 9-county Bay Area region using an index based on three domains of indicators: the health and environment domain (e.g. air pollution concentrations, drinking water contaminants, toxic releases from polluting facilities, pesticides, traffic density); the education domain (e.g. math and reading proficiency, student poverty, and high school graduation rates); and the economics domain (e.g. employment rates, job proximity, median home values, poverty rates, and adult educational attainment). The top 20 percent of tracts in terms of the opportunity score were assigned to the "highest resource" category and the next 20 percent were assigned to the "high resource" category. For the remaining tracts, a filtering approach was used to assign those identified as having high levels of poverty and racial segregation into a "high segregation & poverty" category. Finally, the remaining tracts in the region were divided evenly into the “moderate resource” and “low Resource” categories based on their index scores. Tract-level counts of people by race/ethnicity and nativity were summed up to higher-level Atlas geographies for each of the five neighborhood opportunity categories. To aggregate from census tracts to sub-counties (CPUMAs) and census-defined places (large cities, other cities or towns, and Census Designated Places), we used geographic crosswalks that were created by assigning each 2010 tract to the CPUMA and census-defined place containing the plurality of its 2010 population (from SF1 of the 2010 Census) by census block. See the methodology page for other relevant notes.

Notes: 

  • For the by race/ethncity and ranking breakdowns, Latinos include people of Hispanic origin of any race and all other groups exclude people of Hispanic origin.
  • For the by nativity breakdown, with the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.
  • Resource levels are based on a comprehensive index of opportunity based on data spanning a range of years between 2010 and 2018; counts of people by race/ethnicity and nativity reflect a 2011-2015 average.
  • High segregation and poverty neighborhoods are those with poverty rates of at least 30 percent, with high levels of racial segregation and high shares of people-of-color households.
  • No data are reported if based on fewer than 100 people.
  • Data are only reported for census-defined places (large cities, other cities or towns and Census Designated Places) for which at least three census tracts were assigned in the geographic crosswalks noted above, or for which one or two tracts were assigned but the vast majority (at least 80 percent) of the tract population(s) fell within the census-defined place based on 2010 block level population counts.
  • No data are available for California as a whole.

Business ownership

Summary: The number of firms per 100 persons in the labor force ages 16 or older and growth in the number of firms. Firms are classified by race/ethnicity and gender based on the self-identification of the majority owner. With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity. 

Data Source(s): U.S. Census Bureau, 2007 and 2012 Survey of Business Owners, 2009 and 2014 American Community Survey 5-year Summary Files.

Universe: Firms include all nonfarm businesses filing Internal Revenue Service tax forms as individual proprietorships, partnerships, or any type of corporation, and with receipts of $1,000 or more.

Methods: Data on the number of firms by firm type (firms with paid employees and sole proprietorships), industry, and race/ethnicity and gender of the proprietor was collected from the 2007 and 2012 Survey of Business Owners (SBO) for all Atlas geographies. To be consistent across breakdowns and cuts by race/ethnicity and gender, firm counts for all breakdowns were restricted to firms classifiable by race, gender, and veteran status. A single firm may be tabulated in more than one racial/ethnic group category. This can result because the sole owner was reported to be of more than one race, the majority owner was reported to be of more than one race, or a majority combination of owners was reported to be of more than one race. The denominator used to calculate the number of firms per 100 persons in the labor force age 16 or older by race/ethnicity and gender was merged in from the 2009 American Community Survey (ACS) 5-year summary file for the 2007 SBO data, and the 2014 ACS 5-year summary file for the 2012 SBO data. These two samples of the ACS summary file were chosen because the central year of each five-year pool aligns with the year of the SBO data (e.g. the central year of the 2014 5-year ACS, which covers years 2010-2014 is 2012). With the exception of Whites, all racial groups tablulated in the SBO include people of Hispanic origin who self-identify with that racial identity. Data on the number of people in the labor force age 16 or older by race/ethnicity and gender from the ACS follows the same racial classification. Estimates for small geographies and/or demographic groups are often not reported because the data does not meet SBO publication standards. Firm growth between 2007 and 2012 reflects the percentage change in the number of firms over the period. Due to the non-disclusure of some data points in the SBO at the county level, aggregating the data across counties to derive data for the Nine-county Bay Area region would result in incomplete firm counts and is therefore not reported. See the methodology page for other relevant notes.

Notes: 

  • With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.
  • Data for Asian or Pacific Islanders reflects only the Asian population (i.e. it excludes Pacific Islanders).
  • Data for the mixed/other racial/ethnic group only includes data persons identifying as other single race alone, not covered by the categories delineated by the surveys (and not mixed race).
  • No data on the number of firms per 100 workers (i.e. persons in the labor force age 16 or older) are reported if the calculated rate came out to more than 100 or if there are fewer than 1,000 workers in the denominator.
  • No data on the firm growth are reported if there are fewer than 30 firms in either year (2007 or 2012).
  • Total firm counts for all breakdowns are restricted to firms classifiable by race, gender, and veteran status.
  • No data are available for the Nine-county Bay Area region and sub-counties.

Business revenue

Summary: The average annual receipts per firm (in 2012 dollars) and growth in receipts per firm. Firms are classified by race/ethnicity and gender based on the self-identification of the majority owner. With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity. 

Data Source(s): U.S. Census Bureau, 2007 and 2012 Survey of Business Owners.

Universe: Firms include all nonfarm businesses filing Internal Revenue Service tax forms as individual proprietorships, partnerships, or any type of corporation, and with receipts of $1,000 or more.

Methods: Data on aggregate revenues and the number of firms by firm type (firms with paid employees and sole proprietorships), industry, and race/ethnicity and gender of the proprietor was collected from the 2007 and 2012 Survey of Business Owners (SBO) for all Atlas geographies. Average annual revenues per firm was calculated by dividing aggregate revenues by the number of firms, and values for 2007 were adjusted for inflation to reflect 2012 dollars (using the CPI-U from the U.S. Bureau of Labor Statistics). To be consistent across breakdowns and cuts by race/ethnicity and gender, revenues and firm counts for all breakdowns were restricted to firms classifiable by race, gender, and veteran status. A single firm may be tabulated in more than one racial/ethnic group category. This can result because the sole owner was reported to be of more than one race, the majority owner was reported to be of more than one race, or a majority combination of owners was reported to be of more than one race. Estimates for small geographies and/or demographic groups are often not reported because the data does not meet SBO publication standards. Growth in average annual revenue per firm between 2007 and 2012 reflects the real (i.e. inflation-adjusted) percentage change average annual revenue over the period. Due to the non-disclusure of some data points in the SBO at the county level, aggregating the data across counties to derive data for the Nine-county Bay Area region would result in incomplete firm counts and is therefore not reported. See the methodology page for other relevant notes.

Notes: 

  • With the exception of Whites, all racial groups include people of Hispanic origin who self-identify with that racial identity.
  • Data for Asian or Pacific Islanders reflects only the Asian population (i.e. it excludes Pacific Islanders).
  • Data for the mixed/other racial/ethnic group only includes data persons identifying as other single race alone, not covered by the categories delineated by the surveys (and not mixed race).
  • No data on revenue growth are reported if there are fewer than 30 firms in either year (2007 or 2012).
  • Revenues per firm for all breakdowns are restricted to firms classifiable by race, gender, and veteran status.
  • No data are available for the Nine-county Bay Area region and sub-counties.