The What, Where, and When of Place-Based Housing Policy’s Neighborhood Effects

Abstract

Ever-scarce affordable housing production resources, in addition to their primary function of providing housing for those in need, are increasingly enlisted for the dual goals of strengthening distressed communities and increasing access to higher opportunity neighborhoods. Information on spillovers can inform investment decisions over time and across communities. We leverage recent, high-quality research on neighborhood effects of Low-Income Housing Tax Credit (LIHTC) production, synthesizing evidence according to neighborhood context. We also summarize the evidence on project features moderating impacts of publicly subsidized, place-based rental housing, in general. We conclude that context matters. Producing LIHTC housing in distressed neighborhoods positively impacts the surrounding neighborhood—in terms of modest property value gains and increased safety. By contrast, higher opportunity neighborhoods experience small property value reductions, and no impacts on crime. Big questions remain, however, about impact heterogeneity—via tenant mix, property design, and ongoing property management, as examples—with the scarcity of systematic data representing one of the field’s largest constraints.

Keywords:

• Low-income housing
• neighborhood
• development/revitalization

Acknowledgments

We thank Carlota Melo for her excellent research assistance, and The Boston Foundation, the Massachusetts Department of Housing and Community Development, and the Citizens’ Housing and Planning Association for their generous support.

Notes

1. The federal administrator of the Low-Income Housing Tax Credit (LIHTC) program, the U.S. Department of the Treasury, has established project preferences and selection criteria for states administering credits. However, the states still lack federal guidance on how to further define those preferences, rank them against each other, or relate them to other local housing priorities (Khadduri, 2013). Meanwhile, state agencies have limited evidence of how allocation thresholds and preferences affect what is actually developed (Ellen, Horn, Kuai, Pazuniak, & Williams, 2015; Gustafson & Walker, 2002).

2. Whereas our focus is on place-based investments (as Owens considers people-focused policies in this issue), this distinction is somewhat artificial. Fifty percent of all LIHTC households also receive some form of rental assistance (O’Regan & Horn, 2013).

3. In a thoughtful review of this literature 10 years ago, Nguyen (2005) suggests that synthesizing evidence for a single housing program from across a number of different cities would further our understanding of neighborhood spillovers.

4. See Freeman and Botein (2002), Galster (2004), and Nguyen (2005) for detailed discussions of the methodological challenges of estimating neighborhood impacts and the limits of most early research in this area.

5. Several important studies of neighborhood spillovers in New York City include projects funded through a mix of programs, often including LIHTC but for which LIHTC-specific findings are not reported (Ellen & Voicu 2006; Lens, 2013a; Schill et al., 2002; Schwartz, Ellen, Voicu, & Schill 2006). They are included in the review of impacts from other housing programs.

6. Examining LIHTC projects across the entire city of Austin, Texas, Woo and Joh (2015) conclude from predevelopment crime rates that they are located in disadvantaged neighborhoods, but do not offer other characteristics of host neighborhoods.

7. (Baum-Snow & Marion, 2009; Freedman & MacGavock, 2015; Freedman & Owens, 2011)

8. (Deng, 2011a; Freeman, 2003; Freeman & Rohe, 2000; Funderburg & MacDonald, 2010; Ellen et al., 2009; Woo, Joh, & Van Zandt, 2015).

9. Given the information available in the reviewed studies, this categorization scheme is as much art as science. It fails to account for the multiplicity of neighborhood dimensions that are likely relevant to spillovers. Beyond poverty rates, other area-level features and trends may be equally important, such as the presence of local amenities such as transportation or the presence of a larger public revitalization effort. We also recognize that the definition of neighborhood and submarket types can yield contrasting spillover findings. Therefore, whereas this approach can move our understanding forward, we must take care in conclusions across contexts.

10. Deng (2011a) and Woo and Joh (2015) also afforded impact evidence in only one neighborhood context. Finally, the three studies focusing on projects in QCT or QCT-like neighborhoods afforded evidence exclusively in distressed neighborhoods (Baum-Snow & Marion, 2009; Freedman & MacGovack, 2015; Freedman & Owens, 2011).

11. Diamond and McQuade (2015) organize their analyses and discussion by high-income tracts (the top two income quartiles, Q3 and Q4) and low-income tracts (the bottom two income quartiles, Q1 and Q2). They conduct separate analyses on all four quartiles (except for the outcome of crime). We assigned findings from Q1 to the low-income category (findings from Q1 tracts with high minority concentrations were categorized as distressed); findings from Q2 tracts were categorized as moderate poverty; and the findings from Q3 and Q4 were categorized as high opportunity. Both Freeman (2003) and Freeman and Rohe (2000) identify five neighborhood strata based on the likelihood of receiving a project. We used information on neighborhoods in each stratum to assign findings across strata to our neighborhood categories.

12. Freeman and Botein (2002) provide a fuller discussion of the theories of why subsidized housing is expected to affect surrounding neighborhoods. Additional mechanisms are included in the Moderating Neighborhood Effects section discussion of project features moderating neighborhood impacts.

13. The population growth from subsidized housing may not be entirely positive. Based on their study of dispersed public housing, Santiago, Galster, and Tatian (2001) conclude that the negative externalities of adding more low-income households to a poverty-concentrated neighborhood could trump any potentially positive externalities from rehabilitation of a distressed property.

14. For discussions of these mechanisms see Ellen (2008); Ellen et al. (2007); Freeman and Botein (2002); Schwartz et al. (2006).

15. A fuzzy match is one where not all criteria are required to be the same to match two observations. This is often used when handling large administrative data sets as it allows probable matches to be made in the presence of missing or incorrect data.

16. Lens (2013a) provides a thorough discussion of the theoretical explanations from the planning and criminology traditions on how and why crime and subsidized housing (including both place based and tenant based) may be linked in U.S. cities. This review of theory is about why LIHTC should increase safety, but there are equally important theories and evidence examining crime increases from subsidized development. In fact, much of the early research in this area focused on crime rates in public housing and explanatory factors of project physical characteristics and design, socioeconomic features of subsidized communities, and policing. Santiago, Galster, and Pettit (2003) review theories and evidence related to public housing, in particular, including the breakdown of social cohesion and resulting lure of crime-prone individuals to the neighborhood.

17. For example, to the extent that an LIHTC project is part of a larger community revitalization initiative, a strengthened local economy may yield additional public resources for public schools (via an improved tax base). This may attract higher-income in-movers willing to invest in local public schools (Khadduri, Schwartz, & Turnham, 2008). At the same time, school-centered revitalization efforts can attract or retain children who might otherwise attend private or charter schools beyond neighborhood boundaries.

18. The next section includes a discussion of recent evidence on the role of tenant mix, the quality of project designs and materials, and ongoing property management.

19. Diamond and McQuade’s investigation of crime is based on 127 LIHTC sites in the cities of Chicago, San Francisco, and San Diego, and crime statistics over 7- to 15-year periods. Because of the small sample size they cut the data only by high/low income and high/low minority tracts.

20. When applying the definition of moderate-poverty neighborhoods enlisted here (10–29% poverty rate), the tipping point literature suggests that a subset of neighborhoods are vulnerable to even small increases in concentrated poverty resulting from affordable housing production. That is, those that are above the 20% poverty rate threshold could see meaningful increases in negative outcomes such as crime and school leaving as a result of marginal increases in poverty rates. Moderate-poverty neighborhoods at the lower end of the poverty rate distribution (say, 10–20%) could experience meaningful declines in neighborhood housing values with each marginal increase in poverty rate. As discussed above, the recent literature does not suggest consistent or sizable increases in neighborhood poverty rates from the production of LIHTC projects.

21. “In the medium-quality market, the expected impacts are more ambiguous. If we assume subsidized housing is built at the medium-quality level, the impact of the physical attributes should be neutral. The perceived impacts of the occupants themselves are likely to be negative, particularly if the occupants are of a different race, unless the occupants are deemed to be part of the deserving poor, such as the elders. In such a case, where, for example, an elderly development is built in a middle-niche market, there should be no effect on property values that could be attributed specifically to subsidized housing” (Freeman & Botein, 2002, p. 362).

22. As noted above, due to sample size issues, Diamond and McQuade (2015) do not examine crime effects in moderate-poverty areas.

23. One prior literature review, including earlier studies, also came to this conclusion (Ellen, 2008).

24. Among the several federal programs examined, Ellen et al. (2007) find that in the case of Section 8 New Construction/Substantial Rehab program projects in New York City, increasing the scale in distressed neighborhoods results in larger property value declines. However, this finding is distinct from the overall pattern of increasing gains from larger projects and results from a program no longer producing meaningful numbers of new projects.

25. The average LIHTC project (including preservation projects) for units placed in service over the period from 1995 to 2013 is 77 units, and only 24% of projects were over 100 units.

26. Moreover, the average LIHTC project (including preservation projects) for units placed in service over the period from 1995 to 2013 is 77 units, and only 24% of projects were over 100 units.

27. Deng (2011b) also finds that housing authorities can have positive impacts.

28. Albright, Dericksen, and Massey (2013) include a careful qualitative component to explain the benign effects they observe of a large LIHTC development in a suburban, majority-white, middle-class neighborhood. Their multiple control group time series quasi-experiment found no detrimental effects on three primary outcomes: crime rates, property values, and property taxes. Whereas their analyses cannot assess the discrete role of project design, they conclude that design matters. The project was physically and esthetically similar to those in surrounding subdivisions through the use of cul-de-sac designs, spatial layouts, and materials that were roughly similar to those in nearby homes.

29. Denver Housing Authority’s management of its dispersed public housing program demonstrates that proactive and hands-on management is not exclusive to the private sector. In fact, housing authorities may be compelled by the distinctly negative public perception of public housing to double down on management. Unfortunately, the limited resources public housing agencies have long faced for maintenance have fed, in part, into the sweepingly negative perception of public housing that exists today (Goetz, 2013).

30. We did not review the small body of studies considering supportive (Galster et. al, 2000; Galster et. al, 2002; Gaslter, Tatian & Pettit, 2004).

31. Jill Khadduri (2004) stressed the importance of examining occupancy patterns in LIHTC projects to understand their neighborhood impacts because of the range of tenant incomes possible in the LIHTC program. Whereas tax credit developments can be home to many households with incomes close to the upper end of the tax credit limit, other projects may be occupied entirely by the poor—particularly where they are rehabilitating preexisting subsidized housing projects.

32. Two studies consider how tenant characteristics moderate neighborhood population changes, in terms of income composition and racial transition (Freeman, 2003; Freeman & Rohe, 2000). Although they are also hampered by the same data constraints, these studies suggest that tenant characteristics are of only limited relevance to individual neighborhood dynamics. That is, the nature of racial transition is largely the same whether a subsidized project focuses on families or the elderly (Freeman & Rohe, 2000). Tenant racial characteristics do not play any role in the class-selective outmigration from neighborhoods with affordable housing developments (Freeman, 2003).

33. An outstanding facet of neighborhood context is the level of urbanicity—more research is needed on relative effects across suburban and urban settings.

34. See McClure (2015) for a wider discussion of the importance of data for future research on assisted housing for the poor.

35. The Housing and Economic Recovery Act of 2008 requires state agencies administering the LIHTC program to submit demographic and economic data on LIHTC tenants to HUD. HUD’s Office of Policy Development and Research is working with states to develop a process for compiling, transmitting, and releasing the data (Hollar, 2014).