Document Type

Article

Publication Date

9-1-2023

Rights

In Copyright

Abstract

Prior to recent rule changes, which are still being deliberated as of this writing, Department of Housing and Urban Development (HUD) grantee communities charged with Affirmatively Furthering Fair Housing (AFFH) have been required to perform regular analyses of impediments (AIs) that identify barriers to fair housing in their territories. A central element of the AI is the delineation of racially or ethnically concentrated areas of poverty (R/ECAPs). Traditionally, grantees identify R/ECAPs using data for their jurisdictions only, ignoring surrounding communities. Doing so provides local decision-makers with knowledge about spaces in their territories where housing security might be relatively problematic, and where residents tend to be isolated from wealth-building opportunities. However, this piecemeal, jurisdiction-by-jurisdiction approach arguably reinforces, rather than challenges, the processes that produce residential segregation and concentrated poverty. This paper offers an example of how such an outcome might occur using information drawn from the most recent (February 2020) attempt at producing a countywide, “regional” AI in Erie County, NY, home to the city of Buffalo. The distribution of R/ECAPs calculated for that AI on a jurisdiction-by-jurisdiction basis are compared to R/ECAP distributions generated by spatial analyses which ignore municipal boundaries and operate on the entire study area. The thought exercise reveals that, while the regional geographies of R/ECAPs change depending on how boundaries are defined, the geographies of HUD funding are fixed, thereby disincentivizing grantees from pooling resources in ways that could contribute to cooperative regional solutions. The article concludes by exploring the policy implications of these findings.

Publication Title

Middle States Geographer

First Page

78

Last Page

91

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