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

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subsidized housing data and census neighborhood characteristics. The study testedthe effect of the presence or absence of subsidized housing (dichotomous variable) aswell as the number of assisted units (density). Propensity scores were used to matchneighborhoods with and without subsidized housing. Controls were added for movingbehavior by age and race. In addition, various interaction effects were studied.Results indicated that the relationship between assisted housing and neighborhood outmigrationwas a spurious one, “Once the relevant control variables are added to themodels, typically no relationship can be discerned” (p. 126). The author concludesthat the non-poor are likely to avoid neighborhoods with assisted housingdevelopments but that this is perhaps caused by other characteristics of theneighborhood and not the subsidized housing (p. 130).Three studies looked at the issue of “crowding out” which is the potentialreduction in unsubsidized units due to the construction of new units of subsidizedhousing. Murray (1999) studied public housing and other site-based housing andfound that there was no reduction with the construction of low-income housing butthere was a reduction with the construction of moderate-income housing. Sinai andWaldfogel (2005) found less “crowd out” in lower-income and high population areas.And Eriksen and Rosenthal (2007) studying LIHTC also found a difference betweenlow income and higher income areas with reductions in the higher income areas andactual increases in construction in lower-income areas as well as increased propertyvalues.10
Overall, it is clear that there are differential effects on the housing marketdepending on the location of the subsidized housing construction. Results have beenmixed, finding both positive and negative effects, and the effects are usually small.Unfortunately many of these studies use flawed methodologies which make the resultsunreliable. The focus is typically on only one housing market and each study looks atdifferent housing programs which make the results difficult to compare and generalize.The negative effect of public housing on property values may be limited to theolder and larger project-based public housing. A recent study by Bair and Fitzgerald(2005) indicates that the negative impacts of public housing may be overcome throughHOPE VI revitalization. An evaluation of six HOPE VI projects in Atlanta, Charlotte,Kansas City, Boston, Denver and Philadelphia found a statistically significant andlarge positive increase in property values of 8-10 percent per quarter mile from thesite.Subsidized Housing Impact on Racial SegregationEarly studies tended to focus on the impact of public housing on racialsegregation and whether subsidized housing was causing “white flight”. Gray andTursky (1986) found that families receiving rental assistance were highly concentratedin a small number of minority-concentrated neighborhoods. Between 50 and 85percent of family units were located in only 5 percent of the census tracts in each MSA(p. 249). Bickford and Massey (1991) found that projects for the elderly were11
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: from the nine matched neighborhood
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 and 44: consisting of single family zones,
Page 45 and 46: early-mid 1990’s consisting of pu
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
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mean of 82 subsidized units per tra
Page 82 and 83:
Subsidized Housing by Type and Pove
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TABLE 4.9Subsidized Units by Subsid
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unemployment rate (.427), less than
Page 88 and 89:
TABLE 4.12Correlation Matrix (page
Page 90 and 91:
TABLE 4.12Correlation Matrix (page
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spatial sensitivity because many di
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It is possible that these census tr
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only 8 MSA’s). The segregation in
Page 98 and 99:
The correlation between the subsidi
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developing strategies to deconcentr
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y subsidy type. The correlation bet
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with a poverty rate of 9.2 percent.
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The cluster is relatively small con
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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
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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
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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
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scale at which the impacts occur; 2
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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
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Author Date Data GeographyMurray 19
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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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