Neighborhood Stabilization Program

Joice, Paul

doi:10.2139/ssrn.1808957

Cited by 17 publications

(13 citation statements)

References 5 publications

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“…However, the literature has largely only examined mean foreclosure effects, while the policy focus has remained on lower income neighborhoods, which presumably are more likely to suffer significant decline as a result of high foreclosure rates. For example, the federal Neighborhood Stabilization Program, which has been used in many areas to rehabilitate foreclosed properties, has been directed only to low-and middle-income neighborhoods, with an emphasis on very low-income neighborhoods (Joice, 2011). Evidence regarding the heterogeneity in price externalities of foreclosures across high-, middle-and low-income neighborhoods is lacking.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood impact of foreclosure: A quantile regression approach

Zhang

Leonard²

2014

Regional Science and Urban Economics

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This paper uses quantile regression, while accounting for spatial autocorrelation, to examine the simultaneous space-time impact of foreclosures on neighborhood property values. We find that negative price externalities associated with neighborhood foreclosures are greatest (1) among lower-priced homes, (2) within 250 feet of the property and (3) in the 12 months following a foreclosure auction. By using quantile regression, we are able to also investigate changes in the distribution of house prices associated with varying levels of neighborhood foreclosures.

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Section: Introductionmentioning

confidence: 99%

Neighborhood impact of foreclosure: A quantile regression approach

Zhang

Leonard²

2014

Regional Science and Urban Economics

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“…This research can also inform future efforts to mitigate and prevent mortgage defaults. The primary place‐based U.S. response to rising foreclosure rates has been the Neighborhood Stabilization Program, which provided municipalities with funds to purchase and dispose of vacant, previously foreclosed, or abandoned properties in neighborhoods where these houses are concentrated (Joice ) . Its policy rationale lies in combating spillover effects from concentrated foreclosed and vacant properties, which are associated with higher violent crime rates and lower housing costs (Immergluck and Smith , ).…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Foreclosure, Inequality, and the Difference That Place Makes

Webb

Brown

2016

Rural Sociology

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While the Great Recession and the associated rise in foreclosures significantly impacted households across the United States, default rates in rural areas and rural‐urban foreclosure differences have failed to attract substantial research attention. To expand the scale and scope of the foreclosure literature, this article examines place‐based differences in estimated foreclosure rates across U.S. counties, which are classified as urban, suburban, micropolitan, or rural. Defaults are considered both overall and for each county classification, and are related to income distribution and inequality, homeownership, adjudication of default, place‐based factors including amenity scores and proximity to urban areas, and geographic region. The article finds a complex relationship among these variables, with inequality itself negatively associated with default rates and lower‐middle−income households positively related to foreclosure. Further, while proximity to urban areas is positively related to foreclosure rates among nonurban counties, natural amenities are related to lower default rates in these areas. The article concludes by considering policy implications, and recommends expanding foreclosure mitigation and prevention strategies to nonurban places.

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“…Regardless of the activities pursued at the local level, the NSP enabling legislation required that the use of funds be geographically targeted toward areas with the greatest need. (For discussion of targeting requirements and risk scores see, Joice, . )…”

Section: Overview Of the Nsp Program And Local Challengesmentioning

confidence: 99%