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

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For this study a match was made if the project met all of the following criteria: Same housing program Same project name Same census tract Same number units Same state Same MSA Same placeThe matching process allowed for some flexibility when projects met thegeographic criteria but the name and number of units was close (off by up to 5 units)but not exact. Project matching resulted in the reduction of 43,946 units. SeeAppendix A-5 for results of the unduplication process.McClure (2006) used proprietary data from HUD for voucher units and LIHTCprojects and found about 10 percent of the projects and 16 percent of the units weremissing due to inability to match the addresses. So even if address data was to beavailable there would be substantial missing data which could be non-randomlydistributed at the census tract level.Duplication also occurs when vouchers are used by the residents in projectbasedsubsidized housing. It has been shown in a sample of early LIHTC projects that39 percent of LIHTC units had other federal subsidies, most in the form of Section 8(GAO, 1997). Abt, Associates (2003, 2004 and 2009) found vouchers being used in52
projects between 35.2 and 46.6 percent at the project level but the data does not reportthe number of units. A more recent statewide study in Florida found an average of 9.5percent of LIHTC units also had vouchers (Williamson et al, 2009). Individual MSA’sranged from a low of 1.1% to a high of 24.5%. This study is limited to one state at theMSA level and therefore cannot be generalized to the census tract level. It was notpossible to conduct a matching process for vouchers in this study because voucheraddresses were not available. Estimates could not be made because prior studiesshowed such a dramatic difference between MSA’s and there were no estimates at thecensus tract level.Data AccuracyAn analysis of the accuracy of the data was conducted by comparing thenumber of subsidized housing units in the census tract with the number of units andnumber of rental units in the tract. It was found that in 142 census tracts the number ofsubsidized units exceeded the total number of housing units and in 436 census tractsthe number of subsidized units exceeded the number of rental occupied units. Mostwere off by only a few units but in a few tracts the number of subsidized units was upto 12 times the total number of units in the tract. These were clearly errors in the data,perhaps due to the geocoding process.A review of the data for these tracts found that the total number of housingunits in the tract was often very small and that 70 percent of the subsidized units in53
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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
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 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: Unduplication of Subsidized UnitsDu
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 102 and 103: 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
Page 112 and 113: alter the perception of public hous
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VouchersVoucher type tracts are dom
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TABLE 6.1Subsidized Units by Cluste
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FIGURE 6.1Percent Census Tracts by
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FIGURE 6.5Census Tract Percent Rent
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Cluster - Concentration -PovertyCen
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strategies. The cluster map shows t
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FIGURE 6.9Map of Public Housing Uni
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Cluster 1: Voucher/No Subsidized Un
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considered moderately concentrated
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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
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REFERENCESAbt Associates, I. (2006)
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Briggs, X. d. S. (Ed.). (2005). The
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Deng, L. (2007). Comparing the effe
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Ellen, I. G., & Voicu, I. (2005). N
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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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