This research led the Department of Housing and Urban Development (HUD) to rethink the way they use their Location Affordability Index (LAI). From Josh Geyer, Office of Environment and Energy, High-Performance Buildings Team, U.S. Department of Housing and Urban Development: "Dr. Ganning’s thorough and convincing analysis of the strengths and weaknesses of the Location Affordability Index led HUD to rethink using Census block groups as the geographical unit of analysis. As a result, Version 3 of the Location Affordability Index (published in April 2019) was generated at the Census tract level, addressing multiple data and methodological problems identified by Dr. Ganning in her 2017 article."
In late 2013, the Department of Housing and Urban Development (HUD) launched the Location Affordability Index (LAI) portal. Their dataset uses models to estimate the median amount households spend on housing and transportation at the block group level, and calculates “H + T Affordability,” the percent of household income spent on these items. In our previous research, we analyzed 81 shrinking cities to determine how location affordability differs across various neighborhoods. Our results suggest that households in declining neighborhoods, as compared to stable or redeveloping neighborhoods, face the greatest H + T affordability challenges in shrinking cities. Furthermore, in declining neighborhoods, virtually all of the additional affordability challenges encountered can be accounted for by differences in transportation affordability rather than housing. Since there is virtually no research to either validate or suggest bias in the LAI data, and a declining neighborhood in a shrinking city presents both a relatively common yet entirely dissimilar context to the norm, we feel that this data should be carefully calibrated to, and tested for, this setting.
Our project objectives include the completion of a literature review to establish hypotheses regarding reasonable household budget maximums (in terms of percent of income) and elasticities for transportation, and a comparison of those hypotheses to existing conditions as given by the LAI data for shrinking cities. We will use these hypotheses to construct a survey, to be sent to households in a subset of our sample of 81 cities, to determine whether the LAI data accurately estimate transportation costs for declining neighborhoods in shrinking cities. Our survey results will be supplemented by data from the U.S. Census Bureau regarding means of transportation to work, neighborhood demographics, and neighborhood socioeconomic conditions. We will conduct qualitative field work to better assess how households cope with unaffordable transportation costs, in terms of either selecting budgetary trade-offs, or in finding lower cost transportation solutions that are not adequately reflected in the LAI modeling process. Finally, we will synthesize this research to articulate a series of recommendations for improving the LAI data for shrinking cities contexts, and for informing local transportation policy to improve livability in declining neighborhoods.