Descriptive Title: Proportion of renter households whose gross rent is 50% or more of their household income

Geographic Unit of Analysis: Census tract

Proportion of renter households whose gross rent* is 50% or more of their household income (2005-2009)
Neighborhood%MOE**
Bayview/Hunter's Point 30% 6%
Bernal Heights 20% 4%
Castro/Upper Market 14% 3%
Chinatown 23% 4%
Excelsior 29% 7%
Financial District/South Beach 26% 8%
Glen Park
Golden Gate Park NA NA
Haight Ashbury 19% 4%
Hayes Valley
Inner Richmond 20% 3%
Inner Sunset 17% 3%
Japantown
Lakeshore 28% 5%
Lincoln Park
Lone Mountain/USF
Marina 13% 3%
McLaren Park
Mission 17% 2%
Mission Bay
Nob Hill 18% 3%
Noe Valley 14% 3%
North Beach 18% 4%
Oceanview/Merced/Ingleside
Outer Mission 18% 5%
Outer Richmond 17% 3%
Pacific Heights 12% 3%
Portola
Potrero Hill 18% 6%
Presidio 23% 13%
Presidio Heights 15% 5%
Russian Hill 14% 3%
San Francisco 20% 1%
Seacliff *** ***
South of Market 18% 3%
Sunset/Parkside
Tenderloin
Treasure Island 15% 8%
Twin Peaks *** ***
Visitacion Valley 31% 8%
West of Twin Peaks 19% 7%
Western Addition 24% 3%

Why Is This An Indicator Of Health and Sustainability?

High housing costs relative to the income of an individual or household result in one or more outcomes with adverse health consequences: spending a high proportion of income on housing, living in overcrowded conditions, accepting lower cost substandard housing, moving to an area where housing costs are lower, or becoming homeless. Spending a high proportion of income on rent or a mortgage means fewer resources for food, heating, transportation, health care, and child care. Overcrowded housing condition can increase the risks for infectious disease, noise, and fires. Lower cost housing is often substandard with exposure to waste and sewage, physical hazards, mold spores, poorly maintained paint, cockroach antigens, old carpeting, inadequate heating and ventilation, exposed heating sources and wiring, and broken windows. Moving away can result in the loss of job, increased transportation costs, difficult school transitions, and the loss of health protective social networks. For additional information on the connections between housing and health, visit: The Case for Housing Impacts Assessment by SFDPH, Program on Health Equity and Sustainability. Accessed online on October 19, 2006: http://www.SustainableSF.org/etc/004_HIAR-May2004.pdf

Interpretation and Geographic Equity Analysis

The above map illustrates the percentage of households that spend 50% or more of their income on rent at the census tract level. The table provides the data aggregated at the neighborhood level. As the map demonstrates, there are many areas in San Francisco where 24%-65% of the population pays half or more of their income to rent. In the following neighborhoods, 25% or more of the population spends at least half of their income on rent:

  • Financial District (26%)
  • Downtown/Civic Center (27%)
  • Lakeshore (28%)
  • Excelsior (29%)
  • Ocean View (29%)
  • Bayview (30%)
  • Visitacion Valley (31%)

Households that spend more than 50% of their income on their homes are classified by the National Low Income Housing Coalition as severely cost-burdened. 

Methods

Data on proportion of household income spend on gross rent was obtained from the 2005-2009 American Community Survey (ACS). Gross rent is calculated by summing the contract rent and the estimated average monthly cost of utilities and fuels. The number of renters who pay 50% or more of their income to gross rent was extracted and divided by the total number of renting households.

The American Community Survey (ACS) is a sample survey, and thus, data are estimates rather than counts. Estimates have accompanying margins of error that indicate the span of values that the true value could fall within. Margins of error should be subtracted from and added to the value to determine the range of possible values. If the margin of error is too big relative to the value, data are not shown because they are statistically unstable. A coefficient of variation of 30% was used to determine statistical instability.

Limitations

For many census tracts in San Francisco, the percent of households whose gross rents was 50% of last year’s income could not be calculated with the appropriate level of statistical significance needed for this analysis; therefore, no estimate or comparison to the rest of the city could be made. Statistical instability generally results when the population of interest is too small. For this indicator, tracts or neighborhoods with unstable values likely have either: a) very small populations overall, b) relatively few renters living there (i.e. high home ownership), or c) few people with unaffordably high rents.

Data Source

American Community Survey (ACS), 5-year Estimates, 2005-2009.

Map and table created by San Francisco Department of Public Health, Environmental Health Section using ArcGIS software.

Map data is presented at the level of the census tract, which was calculated by assigning census block data to census tracts based on spatial location. The map also includes planning neighborhood names, in the vicinity of their corresponding census tracts.

Table data is presented by planning neighborhood. Planning neighborhoods are larger geographic areas than census tracts. SFDPH chose to use the San Francisco Planning Department's census tract neighborhood assignments to calculate neighborhood values. This assignment method relies on a 'centroids within' methodology to convert census tracts to geographic mean center points. Census tracts are assigned to planning neighborhoods based on the spatial location of those geographic mean center points and neighborhood totals are calculated for the table. In a few case, certain census tracts were redesignated to different neighborhoods based on knowledge of the population dispersion in the tract.

Detailed information regarding census data, geographic units of analysis, their definitions, and their boundaries can be found at the following links:

Interactive boundaries map

http://sfindicatorproject.org/resources/data_map_method