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Neighborhood impact of foreclosure: A quantile regression approach

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  • Zhang, Lei
  • Leonard, Tammy

Abstract

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 250ft of the property and (3) in the 12months 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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  • Zhang, Lei & Leonard, Tammy, 2014. "Neighborhood impact of foreclosure: A quantile regression approach," Regional Science and Urban Economics, Elsevier, vol. 48(C), pages 133-143.
    • Handle: RePEc:eee:regeco:v:48:y:2014:i:c:p:133-143
    DOI: 10.1016/j.regsciurbeco.2014.06.004
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    Cited by:

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    2. Zhang, Lei & Yi, Yimin, 2018. "What contributes to the rising house prices in Beijing? A decomposition approach," Journal of Housing Economics, Elsevier, vol. 41(C), pages 72-84.
    3. Zhang, Lei, 2016. "Flood hazards impact on neighborhood house prices: A spatial quantile regression analysis," Regional Science and Urban Economics, Elsevier, vol. 60(C), pages 12-19.
    4. Anthony Yezer & Yishen Liu, 2017. "Can Differences Deceive? The Case of “Foreclosure Externalities"," Working Papers 2017-29, The George Washington University, Institute for International Economic Policy.
    5. McMillen, Daniel & Shimizu, Chihiro, 2017. "Decompositions of Spatially Varying Quantile Distribution Estimates: The Rise and Fall of Tokyo House Prices," HIT-REFINED Working Paper Series 74, Institute of Economic Research, Hitotsubashi University.
    6. Yishen Liu & Anthony M. Yezer, 2021. "Foreclosure Externalities: Have We Confused the Cure with the Disease?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 49(S2), pages 428-460, September.
    7. Xian F. Bak & Geoffrey J. D. Hewings, 2019. "The heterogeneous spatial impact of foreclosures on nearby property values," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 439-466, June.
    8. Bethany B. Cutts & Michael Minn, 2018. "Dead Grass: Foreclosure and the Production of Space in Maricopa County, Arizona," Urban Planning, Cogitatio Press, vol. 3(3), pages 16-25.
    9. McMillen, Daniel, 2015. "Conditionally parametric quantile regression for spatial data: An analysis of land values in early nineteenth century Chicago," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 28-38.
    10. Anthony Yezer, 2016. "Testing the Association between Foreclosure and Nearby House Values: Can Differences Deceive?," Working Papers 2016-29, The George Washington University, Institute for International Economic Policy.
    11. Alter, Adrian & Mahoney, Elizabeth M., 2021. "Local house-price vulnerability: Evidence from the U.S. and Canada," Journal of Housing Economics, Elsevier, vol. 54(C).
    12. Mandai, Yu & Nakabayashi, Masaki, 2018. "Stabilize the peasant economy: Governance of foreclosure by the shogunate," Journal of Policy Modeling, Elsevier, vol. 40(2), pages 305-327.
    13. Tammy Leonard & Nikhil Jha & Lei Zhang, 2017. "Neighborhood price externalities of foreclosure rehabilitation: an examination of the Neighborhood Stabilization Program," Empirical Economics, Springer, vol. 52(3), pages 955-975, May.
    14. António Almeida & Brian Garrod, 2017. "Insights from analysing tourist expenditure using quantile regression," Tourism Economics, , vol. 23(5), pages 1138-1145, August.
    15. Lei Zhang & Tammy Leonard & James C. Murdoch, 2016. "Time and distance heterogeneity in the neighborhood spillover effects of foreclosed properties," Housing Studies, Taylor & Francis Journals, vol. 31(2), pages 133-148, March.
    16. Kanayama, Yuki & Sadayuki, Taisuke, 2021. "What types of houses remain vacant? Evidence from a municipality in Tokyo, Japan," Journal of the Japanese and International Economies, Elsevier, vol. 62(C).
    17. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    18. Suzuki, Masatomo & Hino, Kimihiro & Muto, Sachio, 2022. "Negative externalities of long-term vacant homes: Evidence from Japan," Journal of Housing Economics, Elsevier, vol. 57(C).
    19. Han, Kwideok & Whitacre, Brian E., 2018. "Student Performance and School Size: A Two-stage Spatial Quantile Regression Approach to Evaluate Oklahoma High Schools," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266597, Southern Agricultural Economics Association.
    20. Luc Anselin, 2019. "Quantile local spatial autocorrelation," Letters in Spatial and Resource Sciences, Springer, vol. 12(2), pages 155-166, August.

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    More about this item

    Keywords

    Hedonic model; Quantile regression; Foreclosures; Spatial dependence;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis

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