The Spatial Concentration of Subsidized Housing - Poverty & Race ...

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y subsidy type. The correlation between the absolute number and percent of totalunits is .96 at the census tract level, so it is possible to use the absolute number ratherthan the percent as a measure of concentration without significantly changing theresults. All variables were entered at equal weight.Only one case was excluded because the number of subsidized units in thistract (6,002 units) exceeded by two times the number in the next highest census tract.This one tract was causing the cluster analysis not to converge on a parsimonioussolution and was therefore excluded.The number of subsidized units at the census tract is highly skewed. There are8,253 census tracts with zero subsidized units and 14,732 tracts with under 10subsidized units. In order to incorporate this scale effect, the cluster analysis wasconducted using several cut-off points along the distribution of subsidized housingunits. Cut-points at 100 and 200 subsidized units in the census tract resulted in a 9-cluster solution that captured the detail of low (under 50 units), medium (50- 200units) and high (over 200 units) subsidized census tracts.A two-step cluster method was selected because this method is able to identifythe number of clusters that best fit the data rather than setting the number of clustersahead of time as in the k-means cluster method. Based on this a 9-cluster solution wasarrived at. A sensitivity analysis was conducted to test for the validity of the 9-clustersolution using greater or fewer clusters by varying the cut-off points. A 6-cluster90
solution, although parsimonious, led to too much diversity within voucher tracts andobscured the difference between tracts that also had site-based units from those thatdid not. Larger number of clusters (11, 15 and 17-cluster solutions) broke the data toofinely resulting in too few units while not changing the basic interpretation.Description of ClustersThe 9-cluster solution indicates the complexity of geographic distribution ofsubsidized housing types. The clusters are described here followed by an analysis bysubsidy type. Supporting data for this analysis can be found in Tables 6.1 – 6.4,Figures 6.1 – 6.6, and Appendix Table A.9. Policy recommendations for each clusterare described here and summarized in Table 6.5.Cluster 1: Vouchers/No Subsidized Housing – Very Low Concentration – Very LowPovertyThe first cluster (cluster 1) consists of the largest number of census tracts andcontains very few units of subsidized housing with an average of seven subsidizedunits per census tract. On the basis of number of neighborhoods this cluster is thelargest with 30,792 census tracts (60.2 percent). A subset of this cluster consisting of8,253 tracts (16.1 percent) has no subsidized housing units. Only one cluster type hasa mean poverty rate under the MSA-wide mean of 11.8 percent and that is cluster 191
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THE SPATIAL CONCENTRATION OF SUBSID
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I certify that I have read this dis
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TABLE OF CONTENTSLIST OF TABLES ...
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6.4 Summary of Cluster Analysis Res
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Table 5.2 Range of MSA Segregation
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ABSTRACTSubsidized housing has been
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Chapter 1INTRODUCTIONPublic housing
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Data on subsidized housing prior to
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subsidy programs in that rents are
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Chapter 2LITERATURE REVIEWA compreh
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from the nine matched neighborhood
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Overall, it is clear that there are
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limited the study to city vs. subur
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to the public housing, location adj
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hardship; and 2) public housing wea
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deconcentrated over time is whether
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deconcentration. In fact, a higher
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in the same neighborhood). On avera
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Concentration of Tenant-Based Subsi
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Wang, Varady and Wang (2008) studie
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from the vouchers. However, there w
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consisting of single family zones,
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early-mid 1990’s consisting of pu
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Recent studies of individual housin
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One of the criticisms of the HOPE V
Page 51 and 52: that they are smaller scale, better
Page 53 and 54: Chapter 3METHODOLOGYWhile the conce
Page 55 and 56: Data AvailabilityA limitation in th
Page 57 and 58: just coming on line in the 1990’s
Page 59 and 60: with 1,500 to 12,000 the minimum an
Page 61 and 62: exclusion of these units is not pro
Page 63 and 64: Unduplication of Subsidized UnitsDu
Page 65: projects between 35.2 and 46.6 perc
Page 68 and 69: Resulting Data for AnalysisAs a res
Page 70 and 71: Lack of household level data will l
Page 72 and 73: TABLE 4.2Mean Subsidized Housing Un
Page 74 and 75: Measures of ConcentrationThree meas
Page 76 and 77: TABLE 4.6Subsidized Units as a Perc
Page 78 and 79: would have to be to be considered t
Page 80 and 81: mean of 82 subsidized units per tra
Page 82 and 83: Subsidized Housing by Type and Pove
Page 84 and 85: TABLE 4.9Subsidized Units by Subsid
Page 86 and 87: unemployment rate (.427), less than
Page 88 and 89: TABLE 4.12Correlation Matrix (page
Page 90 and 91: TABLE 4.12Correlation Matrix (page
Page 92 and 93: spatial sensitivity because many di
Page 94 and 95: It is possible that these census tr
Page 96 and 97: only 8 MSA’s). The segregation in
Page 98 and 99: The correlation between the subsidi
Page 100 and 101: developing strategies to deconcentr
Page 104 and 105: with a poverty rate of 9.2 percent.
Page 106 and 107: The cluster is relatively small con
Page 108 and 109: Cluster 7: Other Site-Based Units -
Page 110 and 111: the only cluster that had a signifi
Page 112 and 113: alter the perception of public hous
Page 114 and 115: VouchersVoucher type tracts are dom
Page 116 and 117: TABLE 6.1Subsidized Units by Cluste
Page 118 and 119: FIGURE 6.1Percent Census Tracts by
Page 120 and 121: FIGURE 6.5Census Tract Percent Rent
Page 122 and 123: Cluster - Concentration -PovertyCen
Page 124 and 125: strategies. The cluster map shows t
Page 126 and 127: FIGURE 6.9Map of Public Housing Uni
Page 128 and 129: Cluster 1: Voucher/No Subsidized Un
Page 130 and 131: considered moderately concentrated
Page 132 and 133: Cluster Analysis ResultsThe cluster
Page 134 and 135: tracts than other subsidy types it
Page 136 and 137: ecommend efforts to reduce the leve
Page 138 and 139: scale at which the impacts occur; 2
Page 140 and 141: units. Given the high cost of these
Page 142 and 143: REFERENCESAbt Associates, I. (2006)
Page 144 and 145: Briggs, X. d. S. (Ed.). (2005). The
Page 146 and 147: Deng, L. (2007). Comparing the effe
Page 148 and 149: Ellen, I. G., & Voicu, I. (2005). N
Page 150 and 151: Galster, G. C. (2005). Consequences
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Harris, L. E. (1999). A home is mor
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Johnson, M. P. (2006). Single-perio
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Lee, B. A., Reardon, S. F., Firebau
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Nguyen, M. T. (2005). Does Affordab
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implementing eight consent decrees.
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Schwartz, A. (1999). New York City
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Varady, D. P., & Walker, C. C. (200
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APPENDIX A.1Downloadable Databases
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APPENDIX A.4Missing DataPublicHousi
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APPENDIX A.6Subsidized Housing Unit
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APPENDIX A.8Subsidized Housing Unit
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APPENDIX A.9Demographics by Cluster
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MSAFIPS MSA Name Population Voucher
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MSA /PMSA Code MSA / PMSA Name Popu
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Author Date Data GeographyGalster a
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Author Date Data GeographyHolloway,
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Author Date Data GeographyType ofPr
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Author Date Data GeographyMurray 19
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Author Date Data GeographyLee 20081
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Author Date Data GeographyOakley 20
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Devine, Gray,Rubin andTaghavi (HUD)
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Carlson,Haveman,Kaplan andWolfe 200
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Newman andSchnare 1997Rohe andFreem
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Galster andZobel 1998Freeman andBot
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