Who Wants Affordable Housing in Their Backyard? An Equilibrium Analysis of Low-Income Property Development

Diamond, Rebecca; McQuade, Timothy

doi:10.1086/701354

Cited by 182 publications

(95 citation statements)

References 29 publications

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“…A recent Stanford University study examined the impact of multifamily housing developments funded by the Low Income Housing Tax Credit on surrounding neighborhoods. Their results indicated that building more affordable housing units in low income areas would lead to a reduction in violent and property crime, an increase in the income of home buyers, and an increase in income diverse populations (Diamond & McQuade, 2019). These increases could potentially lead to more investment and community assets within these materially deprived areas.…”

Section: Future Directionsmentioning

confidence: 99%

Geospatial analysis of neighborhood deprivation index (NDI) for the United States by county

et al. 2020

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Little is known about the spatial clustering of neighborhood deprivation across the United States (US). Using data from the 2010 US Census Bureau, we created a neighborhood deprivation index (NDI: higher NDI indicates higher deprivation/ lower neighborhood socioeconomic status) for each county within the US County level scores were loaded into ArcGIS 10.5.1 where they were mapped and analyzed using Moran's I and Anselin Local Moran's I. Ultimately, NDI varies spatially across the US. The highest NDI scores were found in the Southeastern and Southwestern US states, and inland regions of Southern California. This information is critical for public health initiative development as planners may need to tailor the scale of their efforts based on the higher NDI neighborhoods of the county or geographic region with potentially greater chronic disease burden.

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Section: Future Directionsmentioning

confidence: 99%

Geospatial analysis of neighborhood deprivation index (NDI) for the United States by county

et al. 2020

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“…This makes it difficult to construct a forecast that would credibly match expectations of homebuyers. See Aliprantis and Hartley (2015) and Diamond and McQuade (2019) for related literature. We use homicide data for -2002for Chicago, and 2006-2007 for NYC and Philadelphia as our base period.…”

Section: City-level Crime Instrumentmentioning

confidence: 99%

Unlocking amenities: Estimating public good complementarity

Albouy

Christensen

Sarmiento-Barbieri

2020

Journal of Public Economics

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, for helpful comments and feedback. Yifang Zhang provided excellent research assistance. Data and replication files available at: https://github.com/uiuc-bdeep/Unlocking-Amenities. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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“…Freedman and Owens (2011) find a significant reduction of crime, measured at the county level, associated with a LIHTC development. Diamond and McQuade (2016) and Freedman and Owens (2011) also find a decline in crime associated with the placement of LIHTC developments.…”

Section: Crimementioning

confidence: 89%

Low-Income Housing Tax Credit Developments and Neighborhood Property Conditions

Edmiston

2018

SSRN Journal

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Public housing has long been a contentious issue for cities and regions. On one hand, there is an acute need for affordable housing in low-and moderate-income communities. But the massing of public or otherwise subsidized housing in disadvantaged neighborhoods has given rise to concerns that "public housing" has led to decay in these communities. The purpose of this paper is to use analytical tools to evaluate the "conventional wisdom" that lower-income housing developments lead to decay the lower-income communities in which they generally are placed. I use a highly unique dataset on property conditions for tens of thousands of individual land parcels and an approach for estimating count data models based on the Conway-Maxwell-Poisson (CMP) distribution. The CMP is useful for estimating models with underdispersed data, which is uncommon. Results suggest that while large rehab developments tend to enhance property conditions nearby, the effects of other types of developments generally are negative.

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Who Wants Affordable Housing in Their Backyard? An Equilibrium Analysis of Low-Income Property Development

Cited by 182 publications

References 29 publications

Geospatial analysis of neighborhood deprivation index (NDI) for the United States by county

Geospatial analysis of neighborhood deprivation index (NDI) for the United States by county

Unlocking amenities: Estimating public good complementarity

Low-Income Housing Tax Credit Developments and Neighborhood Property Conditions

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