Housing And Transportation Affordability
- Indicator Description
- Related Strategies
- Transportation and Health Connection
- About the Data
- Moving Forward
The Housing and Transportation Affordability indicator measures the percentage of income that the average household spends on housing and transportation combined. Data come from the U.S. Department of Housing and Urban Development Location Affordability Index (LAI), version 2, which uses data on housing costs from the American Community Survey (ACS) and estimates transportation costs based on land use mix, commute patterns, and socioeconomic information.
- Complete Streets
- Expand public transportation
- Health impact assessment (HIA)
- Health performance metrics
- Integrate health and transportation planning
- Rural transit systems
Transportation and Health Connection
Housing costs are the single largest expense for most households. When combined with transportation costs, they account for approximately half of the average U.S. household budget. Combined housing and transportation costs strongly reflect aspects of the built environment. Those include density, land use mix, and overall accessibility, which influence public health through physical activity and access to basic amenities.
Although housing costs are regularly accounted for in location decisions, transportation costs often are not adequately considered when making decisions about where to live and work. Consequently, housing affordability indexes that do not account for transportation costs cannot provide an accurate assessment of the cost of housing choices.
Affordable housing is typically defined as housing that requires no more than 30% of a household’s income (U.S. Department of Housing and Urban Development, 2013), but this measure does not take into account the transportation costs associated with home locations. True affordability is related to the cost of housing and the cost of transportation from that location (Sustainable Cities Institute, 2012). The Center for Housing and Policy has found that the tradeoff in housing savings gained at the cost of transportation is eroding, with 77 cents being spent on transportation for every dollar spent on housing (Jewkes, Delgadillo, 2010).
Neighborhood and community characteristics, including relative housing and transportation costs, contribute to health disparities by racial/ethnic group, income level, and education level (Woolf, Braveman, 2011). Communities that are walkable and public transportation-friendly allow residents to access employment and amenities easily and effectively with less dependence on an automobile. This could result in not only saved time and money, but also in increased physical activity and reduced greenhouse gas emissions (Center for Neighborhood Technology). However, these communities also tend to have higher housing costs, thus potentially pushing lower-income residents to live where they are not able to reap the many benefits of accessible housing and transportation.
About the Data
The U.S. Department of Housing and Urban Development provides estimates of the affordability of different locations in the LAI. The LAI models costs of housing and transportation for each U.S. Census block group by using data from the ACS, the Census Longitudinal Employment-Household Dynamics program’s Origin-Destination Employment Statistics, and the Center for Neighborhood Technology’s AllTransit database. Data are downloadable from the LAI website, aggregated by core-based statistical area.
Housing and transportation costs are presented in relation to a number of different household types (e.g., typical regional household, dual-income families, low-income households, etc.). For the THT, data for the “regional typical” household, which is a household of average size with a median income for the region, are used for this indicator. Values for a given MSA are identified in the dataset by its core-based statistical area code in the LAI dataset.
Planners, policy makers, and advocates may leverage the LAI for regional planning, policy development, affordable housing siting, transportation planning, rural planning, and affordability awareness. Data derived from LAI can help public health practitioners connect the built environment factors that influence health outcomes. Evaluation of combined housing and transportation costs, rather than housing costs alone, also makes the indirect benefits of multimodal transportation and accessible housing more apparent. For instance, a household with a greater array of available transportation options could potentially reap financial savings through less automobile upkeep and fuel consumption. The savings accrued might also influence housing choice (Litman, 2013). A less auto dependent community could help decrease VMT, traffic congestion, and auto emissions associated with respiratory and heart diseases. More walkers and bikers may require improved design of roads and street crossings to help reduce motor vehicle, pedestrian, and bicyclist injuries.
Location Affordability Portal: About the Portal. http://www.locationaffordability.info/About.aspx
Jewkes MD, Delgadillo LM. Weaknesses of Housing Affordability Indices Used by Practitioners. Journal of Financial Counseling and Planning 2010:21:43-52. http://afcpe.org/assets/pdf/volume_21_issue_1/jewkes_delgadillo.pdf
Litman T. Transportation Affordability: Evaluation and Improvement Strategies; 2010. http://www.vtpi.org/affordability.pdf
Sustainable Cities Institute. Housing and Transportation Affordability; 2012. http://www.sustainablecitiesinstitute.org/topics/land-use-and-planning/housing-and-transportation-affordability
U.S. Department of Housing and Urban Development: Affordable Housing; 2013. http://www.hud.gov/offices/cpd/affordablehousing/
Woolf SH, Braveman P. Where Health Disparities Begin: the role of social and economic determinants – and why current policies may make matters worse. Health Affairs 2011;30:1859-9. http://www.ncbi.nlm.nih.gov/pubmed/21976326 †
* Indicates research that supports policies analyzed
† Indicates research that supports equity or vulnerable populations studied