Who Buys Foreclosed Homes? How Neighborhood Characteristics Influence Real Estate-Owned Home Sales to Investors and Households

McMillan, Andrew; Chakraborty, Arnab

doi:10.1080/10511482.2016.1163277

Cited by 12 publications

(5 citation statements)

References 25 publications

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“…Overall, these findings demonstrate high investor activity among foreclosed properties in neighborhoods with lower shares of whites, consistent with prior studies (Ellen et al 2015;Herbert et al 2013;McMillan and Chakraborty 2016;Pfeiffer and Molina 2013). The results, however, do not indicate that corporate investors targeted hard-hit neighborhoods in Boston with greater shares of whites in an effort to advance gentrification.…”

Section: Postforeclosure Buyerssupporting

confidence: 87%

Racialized Recovery: Postforeclosure Pathways in Boston Neighborhoods

Hwang

2019

City & Community

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Following the Great Recession, homeownership rates declined precipitously, raising concerns for the stability and well–being of neighborhoods. While many studies document shifts in household constraints, this article draws from foreclosure records from 2006 to 2011, subsequent transactions, tax exemption filings, and maintenance data in Boston, Massachusetts to show how the foreclosure crisis altered the landscape of ownership and unfolded differentially across hard–hit neighborhoods. Results from logistic regression analyses show that corporate investors were more likely to purchase foreclosures in predominantly black hard–hit neighborhoods, while owner–occupants were more likely to purchase foreclosures in hard–hit mixed–ethnoracial neighborhoods with substantial shares of non–Hispanic/Latinx whites. Relative to other foreclosure buyers, corporations were more likely to resell previously foreclosed properties to other investors and have reported maintenance issues against them. The findings have implications for further disadvantages for hard–hit black neighborhoods and highlight how the housing crisis exacerbated neighborhood inequality by race and ethnicity.

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Section: Postforeclosure Buyerssupporting

confidence: 87%

Racialized Recovery: Postforeclosure Pathways in Boston Neighborhoods

Hwang

2019

City & Community

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“…This is neither realistic nor desirable. The effects of housing and household diversity are many, from encouraging aging in place to decreasing time on market (for the latter, see McMillan & Chakraborty, 2016). Second, and echoed by Renne, Tolford, Hamidi, and Ewing (2016), the housing costs included in the LAI have both temporal and spatial resolution issues and do not reflect the cost of entry for homeowners (HUD employee, personal communication, September 27, 2016).…”

Section: Background and Reviewmentioning

confidence: 99%

It’s Good but Is It Right? An Under-the-Hood View of the Location Affordability Index

Ganning

2017

Housing Policy Debate

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In 2012, the U.S. Department of Housing and Urban Development (HUD) released the Location Affordability Index (LAI) as an online portal and downloadable data set. The LAI has elevated the U.S. conversation on affordability to include transportation and access to opportunities, and has been used in state and federal programming, by researchers, and by private households. However, although some researchers have noted concerns with and potential limitations of the data, none has provided practitioners and researchers with an under-the-hood view of the data, analysis of its reliability or validity, or its conceptual limitations. This article recommends methodological improvements dealing with issues of variable construction, aggregation, and modeling. A recreation of the LAI at the census-tract level suggests the LAI overestimates both costs and cost burden, but especially among renters, and especially in metropolitan areas. On the transportation side, model recreation requires partnership and resourcing to both gain access to restricted data and to develop a reliable database on transit supply and use.In 2012, the U.S. Department of Housing and Urban Development (HUD) released the Location Affordability Index (LAI) as an online portal and downloadable data set. HUD adopted and revised the LAI from its origin as the Center for Neighborhood Technology's Housing and Transportation (H+T) Affordability Index. This database seeks to inform households and public agencies of the costs of living in various places, changing the discussion about affordability from one focused solely on housing to one inclusive of transportation costs. Numerous scholars have analyzed the LAI estimates, resulting in a special issue of Housing Policy Debate as well as other publications. These publications have addressed housing subsidies, mortgage risk, and racial disparities in costs, among other things. However, these publications have not provided practitioners and researchers with an under-the-hood view of the data, analysis of its reliability or validity, or its conceptual limitations. As this article shows, some elements of the LAI introduce either considerable error or the potential for considerable error. Many of those issues, based in variable construction, aggregation, and modeling decisions, can be fixed. Others-notably, data on transit use-are more problematic. If left unchecked, such error could misinform decisions regarding household location or public policy issues in housing or transportation-potentially costing private households or public agencies considerably. This article uses data from across the state of Ohio, intending it as an illustrative case study of the national implications of the conceptual and technical bounds of the LAI. ARTICLE HISTORY

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“…A multilevel mixed-effects model accounts for the correlation of observations within geographic levels/clusters. McMillan and Chakraborty (2016), Chasco and Le Gallo (2013), and Pfeiffer and Molina (2013) are but a few of the recent housing studies that use this type of model. In this study, the dependent variable (i.e., log of the house sales price) is measured at the individual house level, and the data are clustered by census tract (neighborhood socioeconomic data) and high school district levels (school quality data).…”

Section: Methodsmentioning

confidence: 99%

Assessing the Effect of Mansionization on Suburban Single-Family House Sales Prices

Charles

2019

Journal of Planning Education and Research

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Using observed single-family house sales in the inner-ring suburbs of Chicago from 2010 through 2017, this paper uses a multilevel mixed-effects model with crossed random effects to estimate the effect that millennium mansions—new, large single-family houses—have on the sales prices of nearby single-family houses. Controlling for property, sale timing, and surrounding neighborhood socioeconomic characteristics, the study finds that mansionization is associated with an increase in the sales prices of neighboring houses. Long-term residents of a neighborhood undergoing mansionization should not fear a decrease in their house values; however, decreases in neighborhood affordability may result in exclusionary displacement.

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Who Buys Foreclosed Homes? How Neighborhood Characteristics Influence Real Estate-Owned Home Sales to Investors and Households

Cited by 12 publications

References 25 publications

Racialized Recovery: Postforeclosure Pathways in Boston Neighborhoods

Racialized Recovery: Postforeclosure Pathways in Boston Neighborhoods

It’s Good but Is It Right? An Under-the-Hood View of the Location Affordability Index

Assessing the Effect of Mansionization on Suburban Single-Family House Sales Prices

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