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

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comparable. 5 In spite of these drawbacks, the data was useful in this study in teasingout resident perceptions of their subsidized housing. But due to the lack ofcomprehensive data at a small geographic level this study was not able to discussconcentration effects.Rohe and Freeman (2001) studied race and poverty concentration using anational sample of MSA’s that was limited to areas that were tracted in 1980 whichsignificantly reduced the number and type of MSA’s included. The strongest predictorof location for all subsidized housing types except elderly housing was the housingvalue of owner-occupied units in 1980. Other significant variables includedpercentage African Americans and percent poor, with African Americans particularlystrong in predicting the location of LIHTC units.The most comprehensive national study was conducted by Newman andSchnare (1997) with data they compiled on 6 million subsidized housing units in the5 Coulibaly (1998, p. 46) and Shlay & King (1995, p. 499) have noted the inaccuraciesof self-report in the AHS and the inability to break out data by subsidy type. AHS alsohas limited use for small area (census tract level) analysis due to confidentiality rules.The AHS reports a total of 5,368,000 units in 1999 while the number of federallysubsidized units reported in HUD administrative databases in 2000 totaled 4,975,157units. The difference between the two estimates in 1999/2000 was 392,843 units. Thedifference between the two sources in 1993 was only 77,161.32
early-mid 1990’s consisting of public housing, Section 202, Section 236, Rural Rental(515), LIHTC, State programs, and vouchers. The goal of this study was to comparethe spatial distribution of various types of subsidized housing programs and to assessthe concentration in high poverty and “highly impacted underclass” tracts. The studyfound that approximately 6 percent of all housing units and 20 percent of all rentalunits were subsidized in the early 1990’s (p. 708).Most, but not all subsidized housing units were located in poor-qualityneighborhoods. Public housing was highly concentrated in high poverty areas whilevouchers were more decentralized; however there was little evidence that the voucherprogram encouraged moves to middle and upper income neighborhoods (p. 728).Elderly housing was much more decentralized than family projects. Multivariateanalysis with controls for housing market factors confirmed the results of thedescriptive analysis. The authors concluded that project-based assistance appears todo little to improve the quality of the neighborhood and that public housing inparticular made things worse (p. 726). As with all correlations, the authors cautionthat association does not necessarily mean causation. “Theoretically it is possible thatpublic housing is the cause of a neighborhood’s decline” but that it wasn’t likely to beresponsible for bringing about the decline of the whole tract due to the low number ofunits and that it was more likely that the neighborhood decline was already inprogress” (p. 727).33
Page 1 and 2: THE SPATIAL CONCENTRATION OF SUBSID
Page 3 and 4: I certify that I have read this dis
Page 5 and 6: TABLE OF CONTENTSLIST OF TABLES ...
Page 7 and 8: 6.4 Summary of Cluster Analysis Res
Page 9 and 10: Table 5.2 Range of MSA Segregation
Page 11 and 12: ABSTRACTSubsidized housing has been
Page 13 and 14: Chapter 1INTRODUCTIONPublic housing
Page 15 and 16: Data on subsidized housing prior to
Page 17 and 18: subsidy programs in that rents are
Page 19 and 20: Chapter 2LITERATURE REVIEWA compreh
Page 21 and 22: from the nine matched neighborhood
Page 23 and 24: Overall, it is clear that there are
Page 25 and 26: limited the study to city vs. subur
Page 27 and 28: to the public housing, location adj
Page 29 and 30: hardship; and 2) public housing wea
Page 31 and 32: deconcentrated over time is whether
Page 33 and 34: deconcentration. In fact, a higher
Page 35 and 36: in the same neighborhood). On avera
Page 37 and 38: Concentration of Tenant-Based Subsi
Page 39 and 40: Wang, Varady and Wang (2008) studie
Page 41 and 42: from the vouchers. However, there w
Page 43: consisting of single family zones,
Page 47 and 48: Recent studies of individual housin
Page 49 and 50: 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
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y subsidy type. The correlation bet
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
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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
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tracts than other subsidy types it
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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
Page 152 and 153:
Harris, L. E. (1999). A home is mor
Page 154 and 155:
Johnson, M. P. (2006). Single-perio
Page 156 and 157:
Lee, B. A., Reardon, S. F., Firebau
Page 158 and 159:
Nguyen, M. T. (2005). Does Affordab
Page 160 and 161:
implementing eight consent decrees.
Page 162 and 163:
Schwartz, A. (1999). New York City
Page 164 and 165:
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
Page 178 and 179:
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
MSA /PMSA Code MSA / PMSA Name Popu
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Author Date Data GeographyGalster a
Page 198 and 199:
Author Date Data GeographyHolloway,
Page 200 and 201:
Author Date Data GeographyType ofPr
Page 202 and 203:
Author Date Data GeographyMurray 19
Page 204 and 205:
Author Date Data GeographyLee 20081
Page 206 and 207:
Author Date Data GeographyOakley 20
Page 208 and 209:
Devine, Gray,Rubin andTaghavi (HUD)
Page 210 and 211:
Carlson,Haveman,Kaplan andWolfe 200
Page 212 and 213:
Newman andSchnare 1997Rohe andFreem
Page 214:
Galster andZobel 1998Freeman andBot
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