Spatial Heterogeneity in Spillover Effects of Assisted and Unassisted Rental Housing

Abstract

Three new contributions are added to the literature on subsidized rental housing impacts on nearby property values: (1) a primary focus on the spatial heterogeneity of these effects that warrants caution regarding citywide results, (2) an analysis by zoning area, and (3) a comparison of impacts with unsubsidized apartments. An adjusted-interrupted time-series (difference-indifference) model is estimated with a comprehensive data set for Seattle, Washington (1987-1997. Contrary to not in my backyard (NIMBY) expectations, the predominant impact is an upgrading effect of lower-value areas. However, spillover effects are very sensitive to how data are pooled across space: the citywide upgrading effects are driven by poorer pockets adjacent to affluent areas with no or small effects in more diverse low-and medium-income areas. They only occur in single-family, not multifamily zones. The only negative effects were associated with vouchers in one of the affluent areas. Impacts of unsubsidized rentals are very similar to those of subsidized ones, suggesting an independent effect beyond subsidy status. These findings are explained with Seattle's dispersion and good neighbor policies, with gentrification pressures as a possible alternative explanation. Site visits confirmed the location of subsidized sites in lower-value areas and the higher maintenance quality of subsidized units compared to neighboring unsubsidized units.

Beyond anecdotal evidence: Do subsidized housing developments increase neighborhood crime?

“…1 However, the findings in previous studies examining whether subsidized housing has negative impacts on nearby property values have been inconsistent. Some researchers who found negative impacts of subsidized housing point to the influx of "undesirables" as the cause of neighborhood decline (Cummings & Landis, 1993;Lee et al, 1999), while others suggested that subsidized housing developments lead to neighborhood revitalization by eliminating disamenities in neighborhoods (Baum-Snow & Marion, 2009; Koschinsky, 2009;Schwartz et al, 2006). However, support for public housing developments eroded in the 1980s (Eriksen & Rosenthal, 2010).…”

“…The underlying concept of the AITSeDID model is comparing the levels and trends of crime rates in neighborhoods with a LIHTC site before and after it was developed with those in other neighborhoods across the city where a LIHTC site was not developed (Galster et al, 2002;Koschinsky, 2009;Santiago et al, 2003;Woo et al, 2015). Thus, the model clarifies the causal direction of impacts of LIHTC developments by projecting the pre-development level and trend of crime rates in the neighborhood into the post-development period, while accounting for changes in city-wide crime trends .…”

“…The vector of ring variables (R it ) captures the differentials in levels and trends of pre-and post-crime rates related to LIHTC developments by comparing control and impact areas (Galster et al, 2002;Koschinsky, 2009;Santiago et al, 2003;Woo et al, 2014). The fundamental concept of ring variables could be explained in terms of 1) crime rates in control/impact areas and 2) pre/post differentials in the levels and trends of crime rates.…”

The External Neighborhood Effects of Low-Income Housing Tax Credit Projects Built by Three Sectors

“…As previous studies have argued, the impacts of affordable housing projects can differ significantly in different neighborhood contexts (Freeman & Botein, 2002;Koschinsky, 2009). It is thus important to know where the LIHTC projects have been built in Santa Clara County.…”

“…Clearly, one could argue that there might be some selection bias in conducting the study in Santa Clara County, given the tight market environment within which the LIHTC projects have been built. For example, several existing studies have argued that affordable housing developments may be less likely to have negative impacts in tight housing markets than in declining markets (Koschinsky, 2009; Schwartz et al, 2006; Briggs et al, 1999). While this might be the case, it is also important to note that as a suburban metropolis, Santa Clara County, including its core city of San Jose, has a landscape that is largely dominated by low‐density developments.…”