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Measuring foreclosure impact mitigation: Evidence from the Neighborhood Stabilization Program in Chicago

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  • Bak, Xian F.
  • Hewings, Geoffrey J.D.

Abstract

The Neighborhood Stabilization Program (NSP) is a $7 billion nationwide government program that was established to reduce the negative impacts of the housing crisis in foreclosure-concentrated neighborhoods. NSP rehabilitations aim to bring foreclosed and abandoned properties back to productive use. Very few quantitative studies have evaluated NSP and provided policy suggestions for future stabilization. Furthermore, there is some ambiguity about the channels through which foreclosures influence neighboring properties. This study fills the gap in the literature by evaluating the effects of NSP acquisition and rehabilitation in terms of the impact on elevating neighboring property values. In addition, it provides evidence that disamenity effects are a source of the negative impacts of foreclosures on their neighbors. Using a 2008–2014 repeated cross-section dataset for housing sales in the city of Chicago, the difference-in-differences estimates reveal that the average sales prices of homes within 0.1 miles of the NSP projects increased by 14.3% and these effects do not appear until the completion of the rehabilitation. Furthermore, large program effects are found for normal homes but not for foreclosure-related homes. The results vary under different contexts of NSP implementation, but the analytical approach presented in this study is reproducible for NSP studies in other regions.

Suggested Citation

  • Bak, Xian F. & Hewings, Geoffrey J.D., 2017. "Measuring foreclosure impact mitigation: Evidence from the Neighborhood Stabilization Program in Chicago," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 38-56.
  • Handle: RePEc:eee:regeco:v:63:y:2017:i:c:p:38-56
    DOI: 10.1016/j.regsciurbeco.2016.12.002
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    3. Hu, Lirong & He, Shenjing & Han, Zixuan & Xiao, He & Su, Shiliang & Weng, Min & Cai, Zhongliang, 2019. "Monitoring housing rental prices based on social media:An integrated approach of machine-learning algorithms and hedonic modeling to inform equitable housing policies," Land Use Policy, Elsevier, vol. 82(C), pages 657-673.

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