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Housing vouchers as a means of poverty deconcentration and race desegregation: Patterns and factors of voucher recipients’ spatial concentration in Cleveland

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Abstract

Housing Choice Voucher Program is the single largest housing subsidy program in the USA with the goal of poverty deconcentration and race desegregation. This study aims to identify the presence and locations of voucher holders’ spatial concentration, and to investigate the factors associated with the location outcomes of voucher recipients in Cleveland from 2005 to 2009. Hotspot analysis indicated that voucher holders have clustered together and their concentrations have changed during the five years. Over time, concentration patterns spread out from the central city to suburbs. Spatial concentrations were significantly different by race. Regression analysis identified several factors associated with voucher recipients’ concentration, including race, availability of affordable housing, poverty rates, vacancy rates, and accessibility to public transportation. The spatial error model estimation and Geographically Weighted Regression account for spatial autocorrelation and spatial heterogeneity. Results from the study presented the limited potential of the voucher program since voucher holders are still clustered in specific neighborhoods, even though they tend to move in less poor neighborhoods over time.

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Notes

  1. These efforts had included organizing community associations, using pro-integrative mortgage incentives, even suing individuals, organizations, and suburban municipalities for alleged exclusionary practices. As a result, they succeeded in preventing blockbusting and white flight in some suburbs; however, having minimal impact on influencing municipality to adopt affirmative, pro-integrative policies in others.

  2. Voucher recipients are responsible for finding their housing with rents under FMRs that HUD has announced every year by regions and unit sizes.

  3. Clarence Perry (1929) introduced a concept of “neighborhood unit” as an ideal residential neighborhood with school, churches, and recreational areas. The neighborhood unit design allowed residents to walk no more than a quarter mile to reach these community facilities and discouraged unwanted through traffic.

  4. When using central city and suburbs dichotomy, only 21 % of the dwellings in suburbs have rents below the FMRs, compared with 45 % of dwellings in the city of Cleveland. Accordingly, twice as many voucher holders are located in the central city than the suburbs among total occupied housing units (3.9 vs. 1.7 %). However, when considering affordable housing units below the FMRs, voucher holders show a relatively even distribution between the central city and suburbs; 8.6 % in the central city and 8.3 % in the suburbs.

  5. Since the base map of this analysis is a shapefile, a contiguity-based option is selected to create a spatial weight matrix. There are also two different options in contiguity-based spatial weights: rook contiguity and queen contiguity weight matrix. A spatial weight matrix with queen contiguity criterion can include all neighborhoods that do not have a full boundary in common, while rook criterion often eliminates those neighborhoods which have a full boundary segment in common (Anselin 2005b). Thus, this analysis will employ the queen contiguity type as constructing a spatial weight matrix because the queen method can include neighborhoods where full boundaries are not in common.

  6. The east side of the Cleveland near down is abundant of affordable rental housing; over 60 % of rental housing is below FMRs. The regions that are overcrowded with affordable housing are overlapped with the regions that are abundant of low rent level, such as rent level below $400.

  7. There are several options to conduct GWR estimation: choosing kernel type, bandwidth, and type of significance test. An adaptive kernel, a corrected Akaike Information Criterion (AICc) minimization method, and a Monte Carlo significance test are adopted to conduct GWR. Also, the AICc method finds the bandwidth which minimizes the AICc value. The AICc method is recommended due to interaction between the bandwidth and the complexity of the model. Finally, Monte Carlo tests are utilized to determine the significance of the spatial variability in the local parameter estimates.

  8. Property Owners and Managers Survey (POMS) was conducted between November 1995 and June 1996 for the US Department of Housing and Urban Development (HUD) by the US Bureau of the Census.

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  1. Korea Research Institute for Human Settlements, 254 Simindaero, Gwanyang-dong, Dongan-gu, Anyang-si, Gyeonggi-do, South Korea

    Miseon Park

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Park, M. Housing vouchers as a means of poverty deconcentration and race desegregation: Patterns and factors of voucher recipients’ spatial concentration in Cleveland. J Hous and the Built Environ 28, 451–468 (2013). https://doi.org/10.1007/s10901-012-9319-0

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  • DOI: https://doi.org/10.1007/s10901-012-9319-0

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