Skip to main content Skip to site navigation

Healthy Housing Data Methods, Limitations, and References

General information

Median Rent

Methods

  • Data were obtained from U.S. Census Bureau’s 2010-2014 American Community Survey 5-year Estimates at American FactFinder.
  • Data were mapped using ArcGIS for Desktop and ArcGIS Online.
  • Data included all renter-occupied housing units.

Limitations

  • Approximately 295,000 households are selected to participate in the annual American Community Survey, or less than 1 percent of all households in the U.S. 
  • Data were estimated from survey responses.
  • Estimates have some chance of error.

References

Rent Burden

Methods

  • Data were obtained from U.S. Census Bureau’s 2010-2014 American Community Survey 5-year Estimates at American FactFinder.
  • Data were cleaned and aggregated to determine the percentage of rent burdened households on the census tract level.
  • Data were mapped using ArcGIS for Desktop and ArcGIS Online.
  • Data included all renter-occupied housing units.

Limitations

  • Approximately 295,000 households are selected to participate in the annual American Community Survey, or less than 1 percent of all households in the U.S. 
  • Data were estimated from survey responses.
  • Estimates have some chance of error.

References

Homeownership

Methods

  • Data were obtained from U.S. Census Bureau’s 2010-2014 American Community Survey 5-year Estimates at American FactFinder.
  • Data were mapped using ArcGIS for Desktop and ArcGIS Online.
  • Data included all occupied housing units.

Limitations

  • Approximately 295,000 households are selected to participate in the annual American Community Survey, or less than 1 percent of all households in the U.S. 
  • Data were estimated from survey responses.
  • Estimates have some chance of error.

References

Overcrowding

Methods

  • Data were obtained from U.S. Census Bureau’s 2010-2014 American Community Survey 5-year Estimates at American FactFinder.
  • Data were mapped using ArcGIS for Desktop and ArcGIS Online.
  • Data inlcluded all occupied housing units.

Limitations

  • Approximately 295,000 households are selected to participate in the annual American Community Survey, or less than 1 percent of all households in the U.S. 
  • Data were estimated from survey responses.
  • Estimates have some chance of error.

References

Displacement

Methods

  • Data were obtained from the UC Berkeley Urban Displacement Project.
  • Data were cleaned and aggregated.
  • Data were mapped using ArcGIS for Desktop and ArcGIS Online.

Limitations

  • Loss of low-income households was used as proxy for displacement in calculating the gentrification index.
  • The gentrification index is an estimate of neighborhoods’ displacement status and not expected to always be precise. There is some chance of error with all estimates.

References

Jobs-Housing Fit

Methods

  • Data used are from the UC Davis Center for Regional Change 2013 Regional Opportunity Index, which is a combination of Census-produced datasets including the Longitudinal Employer Dynamics (LEHD) Origin-Destination Employment Statistics Dataset (LODES), and the Workplace Area Characteristics file.
  • Housing data within the Regional Opportunities Index are calculated from 2009-2013 American Community Survey 5-year estimates.
  • Definitions for low wage are from the LODES dataset and affordable housing is defined as less than 30% of monthly income.
  • Data were mapped using ArcGIS for Desktop and ArcGIS Online.

Limitations

  • The Regional Opportunity Index defines employment as that covered by the Unemployment Insurance System and does not include self-employed workers.
  • The reference point for employment is April 1 of each year and therefore undercounts seasonal employment.

References

Affordable Housing

Methods

  • RHNA data was compiled from the Association of Bay Area Governments (2007-2014).
  • Very low income households are households earning 0-50% of the area median income and low income households earn 50-80% of the area median income.

Limitations

  • RHNA are based on U.S. Census population data and projections and are therefore limited by the number of people residing in a jurisdiction. Fluctuations in population growth could impact the actual number of housing units required to support every income category.

References

Commuting Patterns

Methods

  • Data were obtained from the Bay Area Council Economic Institute’s analysis of the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics Program data.
  • The Bay Area Council Economic Institute’s 2012 Labor Supply and Commute Patterns in San Mateo County included analysis of Bay Area workers’ place of work compared to their residence.

Limitations

  • The Longitudinal Employer-Household Dynamics Program data were compiled from state administrative data and combined with other U.S. Census Bureau data.
  • Data were based on estimates which have some chance of error.

References

Commands