In 2017, HUD added an neighborhood description question to HUD’s 2017 American Housing Survey (AHS) asking respondents whether they considered their neighborhood to be “urban,” “suburban,” or “rural.” The AHS is the nation’s most detailed housing survey and HUD obtained responses to the neighborhood description question from nearly 76,000 households, including approximately 2,150 households in each of 25 large metropolitan areas.
HUD had four principal reasons for adding the neighborhood description question to the 2017 AHS. First, HUD sought to replicate the main findings of another similar survey conducted by the real estate company Trulia. Second, HUD wanted to provide empirical evidence showing how existing federal definitions of urban and rural align with people’s description of their neighborhood. Third, HUD wanted to understand the extent to which existing federal definitions of urban and rural obscure the stylized fact that half of Americans live in a suburban setting. Finally, HUD wanted to provide data to help inform discussions around the next generation of Federal definitions, including the Census Bureau’s 2020 Urban Areas and OMB’s 2023 Core-based Statistical Areas.
To date, HUD has published two products from this data. First, HUD created a series of summary tables summarizing some basic results. Second, HUD and coauthors created the Urbanization Perceptions Small Area Index, which classified each census tract as urban, suburban, or rural based on the 2017 AHS data.
The Microsoft Excel workbook below contains 24 tables presenting cross-tabulations of existing federal definitions of urban and rural with respondent’s neighborhood description.
Urbanization Perceptions Small Area Index
Data: Download UPSAI zipped file (zip, 4953 KB)
Working Paper: Download UPSAI working paper (docx, 83 KB)
In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This paper illustrated how the AHS “neighborhood description” data was used to create the Urbanization Perceptions Small Area Index (UPSAI). To create the UPSAI, we first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. We then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. Our approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike.