The past decade has seen a surge of interest in the walkable neighborhood, motivated by environmental, health, economic, and communitarian goals. We take stock of this literature by linking together the various strands of research in which the "walkable neighborhood" is a primary concern. We organize the literature into three broad categories: measurement, criticism, and tests of the benefits of walkable neighborhoods. The latter category involves three primary claims. We find that claims about social impacts are the weakest in terms of research support, in part, because there continues to be a problem of self-selection and an inability to assign causality.

Social science research, public and private sector decisions, and allocations of federal resources often rely on data from the American Community Survey (ACS). However, this critical data source has high uncertainty in some of its most frequently used estimates. Using 2006-2010 ACS median household income estimates at the census tract scale as a test case, we explore spatial and nonspatial patterns in ACS estimate quality. We find that spatial patterns of uncertainty in the northern United States differ from those in the southern United States, and they are also different in suburbs than in urban cores. In both cases, uncertainty is lower in the former than the latter. In addition, uncertainty is higher in areas with lower incomes. We use a series of multivariate spatial regression models to describe the patterns of association between uncertainty in estimates and economic, demographic, and geographic factors, controlling for the number of responses. We find that these demographic and geographic patterns in estimate quality persist even after we account for the number of responses. Our results indicate that data quality varies across places, making cross-sectional analysis both within and across regions less reliable. Finally, we present advice for data users and potential solutions to the challenges identified.

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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.