Is HUD Affordable Housing Really Affordable?

This project will determine whether the U.S Department of Housing and Urban Development (HUD) rental assistance programs provide affordable housing when transportation costs are factored in. Under these programs, participants can pay no more than 40 percent of their adjusted income toward housing rent. We can assume, therefore, that housing costs alone are affordable for households participating in HUD rental assistance programs. But is the combination of housing and transportation affordable? HUD has no way of knowing since transportation costs fall outside its purview and regulations. This study will build on the work of the Center for Neighborhood Technology (CNT)…

This project will determine whether the U.S Department of Housing and Urban Development (HUD) rental assistance programs provide affordable housing when transportation costs are factored in.  Under these programs, participants can pay no more than 40 percent of their adjusted income toward housing rent.  We can assume, therefore, that housing costs alone are affordable for households participating in HUD rental assistance programs.  But is the combination of housing and transportation affordable?  HUD has no way of knowing since transportation costs fall outside its purview and regulations.

This study will build on the work of the Center for Neighborhood Technology (CNT) with their Housing + Transportation (H+T) Affordability Index.  We will at least initially accept CNTs guideline that the sum of H+T should be no more than 45 percent of household income for housing affordability, and that transportation costs alone should be no more than CNTs because they are based on better data and better methodology.

The models will be based on pooled travel datasets for 12 regions: Austin, Boston, Denver, Eugene, Houston, Kansas City, Los Angeles, Minneapolis-St. Paul, Portland, Sacramento, Salt Lake City, and Seattle, three of which are new datasets that we will compile (Denver, Los Angeles, and Minneapolis-St. Paul). Complete and consistent datasets are already available for nine of the 12 regions. Partial datasets are available for the remaining three. Such datasets are extremely hard to acquire and construct, as they require XY coordinates for households and trips and parcel-level land use data.

Our models will account for all the so-called D variables found to affect travel and vehicle ownership in the peer-reviewed literature.  The Ds are development density, land use diversity, street design, destination accessibility, and distance to transit.  They have been shown to affect household travel decisions in more than 200 peer-reviewed studies (see the meta-analysis by Ewing and Cervero 2010).

With the new models in hand, we will geo-locate more than 34,000 rental housing assistance properties in HUDs Multifamily Portfolio Dataset and then apply the new transportation cost models to typical low-income households living at these locations to determine whether their transportation costs are more or less than 15 percent of household income.