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

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Author Date Data GeographyOakley 2008Van Zandt andMhatre 2009Deng 20091987-2000Four MSA's:Atlanta, LosAngeles,Chicago, andNew YorkCityType ofProgramLIHTC1987-2007 Dallas MSA LIHTC1990-2000Miami-DadeCountyLIHTCOutcomeMeasures Method FindingsPercent povertyin the census tractExtent ofconcentration ofsubsidizedhousing andassociation withrace, poverty,education andcrime at the zipcode levelNeighborhoodchange on eighthousing andeconomicindicatorsDescriptive statistics on thelocation of LIHTC units. Spatialcluster analysis (EDSA) usingthe Local Moran statistic whichidentifies multi-census tractcontiguous areas with similarconcentrations. A series of OLSmultiple regressions for eachgeographic area as well asspatial regression.Case study of one metropolitanregion, cluster analysis ofLIHTC units using LocalIndicators of Spatial Association(LISA), Moran's I and Gi* toindicate the extent of clustering.Analysis of exposure to socialand educational conditions.Compares matched pairs ofneighborhoods with and withoutLIHTC projects in low-poverty,moderate-poverty and highpovertyneighborhoods. Casestudies of three neighborhoods.Most neighborhood predictors includingpoverty and unemployment were notsignificant. The strongest predictors werethe location in a Qualified Census Tract(QCT) and other LIHTC projects nearby.The spatial distribution of LIHTC isgeographically more dispersed and not ashighly disadvantaged than previous sitebasedhousing projects but the trend istoward clustering of the LIHTC units overtime.There is significant spatial clustering ofLIHTC units in the region. Increasedspatial clustering is associated with higherminority populations (r=.483), higherpoverty levels (r=.359), lower householdincomes (r-.202), lower safety levels(r=.350) and lower educational quality(r=.263). Overall about half the units arehighly clustered, mostly in urban areas. Theother half are not clustered and located inareas with social and economic variablescomparable to regional averages.LIHTC neighborhoods have more positivechanges than matched pair neighborhoodswithout LIHTC. Low incomeneighborhoods were most likely to havepositive impacts.194
Galster, Tatianand Smith 19991991-1995BaltimoreCounty, MDVouchersPendall 2000 1998 Nationwide VouchersGuhathakurtaand Mushkatel 2002Kingsley,Johnson andPettit 20031980-1994 Phoenix, AZ Vouchers1993-200031 MSA'swhere HOPEVI projectswere locatedVouchersVOUCHERSProperty valuessurroundingsubsidizedhousing atvarious distancesPercent ofvouchers indistressed censustractsChange inhousing qualityover 14 yearperiod at variousdistance ringsfrom subsidizedhousingPoverty and raceat the census tractlevelRegression analysis of changesin property values of housessurrounding MTO and regularSection 8 movers locations priorto move and after move atvarious distances. Policyanalysis of negative communityreaction to MTO using focusgroups of homeowners at foursites.Comparison of the location ofvouchers with the location ofrentals and low-income renterhouseholds. Regressions used topredict the location in distressedareas.Used Housing ConditionEvaluation surveys conducted in1980 and 1994 to calculate thechange in housing quality within.25 and .5 miles of subsidizedhousing. Regression analysiscontrols for neighborhoodchange and householdcharacteristics of subsidizedhousing.Descriptive statistics on thespatial pattern of vouchers.Measures geographic clusteringof poverty and race using theIndex of Dissimilarity.Measures the change in tractpoverty over time due tohousehold moves.Negative spillover effects found when therewere clusters of Section 8 in small,vulnerable neighborhoods. Policyrecommendation to encourage or requirespatial deconcentration, possibly throughpayment of exception rents (exceed FMR)in non-impacted areas.The location of rentals is significant inpredicting where the poor will live ingeneral, but there does not appear to beadditional concentration effects due to thestatus of having a housing subsidy.Small negative spillover effect at .5 milesfrom subsidized housing. However, therewas a positive spillover if households wasfemale holding other factors constant.The scale of vouchers for relocation is smallenough not to impact the location pattern ofthe overall voucher program. Clusteringwas not the predominant pattern but wassignificant in a few neighborhoods in mostcities.195
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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
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that they are smaller scale, better
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Chapter 3METHODOLOGYWhile the conce
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Data AvailabilityA limitation in th
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just coming on line in the 1990’s
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with 1,500 to 12,000 the minimum an
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exclusion of these units is not pro
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Unduplication of Subsidized UnitsDu
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projects between 35.2 and 46.6 perc
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Resulting Data for AnalysisAs a res
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Lack of household level data will l
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TABLE 4.2Mean Subsidized Housing Un
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Measures of ConcentrationThree meas
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TABLE 4.6Subsidized Units as a Perc
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would have to be to be considered t
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mean of 82 subsidized units per tra
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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
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TABLE 4.12Correlation Matrix (page
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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
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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 -
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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
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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
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
Page 166 and 167: APPENDIX A.1Downloadable Databases
Page 168 and 169: APPENDIX A.4Missing DataPublicHousi
Page 170 and 171: APPENDIX A.6Subsidized Housing Unit
Page 172 and 173: APPENDIX A.8Subsidized Housing Unit
Page 174 and 175: APPENDIX A.9Demographics by Cluster
Page 176 and 177: MSAFIPS MSA Name Population Voucher
Page 186 and 187: MSA /PMSA Code MSA / PMSA Name Popu
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 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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