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Mortgage default risk: New evidence from internet search queries

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  • Chauvet, Marcelle
  • Gabriel, Stuart
  • Lutz, Chandler

Abstract

We use Google search query data to develop a broad-based and real-time index of mortgage default risk. Unlike established indicators, our Mortgage Default Risk Index (MDRI) directly reflects households’concerns regarding their risk of mortgage default. The MDRI predicts housing returns, mortgage delinquency indicators, and subprime credit default swaps. These results persist both in- and out-of-sample and at multiple data frequencies. Together, research findings suggest internet search queries yield valuable new insights into household mortgage default risk.

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  • Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
    • Handle: RePEc:eee:juecon:v:96:y:2016:i:c:p:91-111
    DOI: 10.1016/j.jue.2016.08.004
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Aron, Janine & Muellbauer, John, 2016. "“Modelling and forecasting mortgage delinquency and foreclosure in the UK.”," Journal of Urban Economics, Elsevier, vol. 94(C), pages 32-53.
    3. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    4. Elliot Anenberg & Edward Kung, 2014. "Estimates of the Size and Source of Price Declines Due to Nearby Foreclosures," American Economic Review, American Economic Association, vol. 104(8), pages 2527-2551, August.
    5. John C. Driscoll & Aart C. Kraay, 1998. "Consistent Covariance Matrix Estimation With Spatially Dependent Panel Data," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 549-560, November.
    6. Jonathan H. Wright, 2012. "What does Monetary Policy do to Long‐term Interest Rates at the Zero Lower Bound?," Economic Journal, Royal Economic Society, vol. 122(564), pages 447-466, November.
    7. Ronel Elul & Nicholas S. Souleles & Souphala Chomsisengphet & Dennis Glennon & Robert Hunt, 2010. "What "Triggers" Mortgage Default?," American Economic Review, American Economic Association, vol. 100(2), pages 490-494, May.
    8. Greg Tkacz, 2013. "Predicting Recessions in Real-Time: Mining Google Trends and Electronic Payments Data for Clues," C.D. Howe Institute Commentary, C.D. Howe Institute, issue 387, September.
    9. Case, Karl E & Shiller, Robert J, 1989. "The Efficiency of the Market for Single-Family Homes," American Economic Review, American Economic Association, vol. 79(1), pages 125-137, March.
    10. Gerardi, Kristopher & Rosenblatt, Eric & Willen, Paul S. & Yao, Vincent, 2015. "Foreclosure externalities: New evidence," Journal of Urban Economics, Elsevier, vol. 87(C), pages 42-56.
    11. Longstaff, Francis A., 2010. "The subprime credit crisis and contagion in financial markets," Journal of Financial Economics, Elsevier, vol. 97(3), pages 436-450, September.
    12. Crocker H. Liu & Adam Nowak & Stuart Rosenthal, 2014. "Bubbles, Post-Crash Dynamics, and the Housing Market," Working Papers 14-18, Department of Economics, West Virginia University.
    13. Foote, Christopher L. & Gerardi, Kristopher & Willen, Paul S., 2008. "Negative equity and foreclosure: Theory and evidence," Journal of Urban Economics, Elsevier, vol. 64(2), pages 234-245, September.
    14. Lambie-Hanson, Lauren, 2015. "When does delinquency result in neglect? Mortgage distress and property maintenance," Journal of Urban Economics, Elsevier, vol. 90(C), pages 1-16.
    15. Chan, Sewin & Gedal, Michael & Been, Vicki & Haughwout, Andrew, 2013. "The role of neighborhood characteristics in mortgage default risk: Evidence from New York City," Journal of Housing Economics, Elsevier, vol. 22(2), pages 100-118.
    16. Bricker, Jesse & Bucks, Brian, 2016. "Negative home equity, economic insecurity, and household mobility over the Great Recession," Journal of Urban Economics, Elsevier, vol. 91(C), pages 1-12.
    17. Scott Baker & Andrey Fradkin, 2011. "What Drives Job Search? Evidence from Google Search Data," Discussion Papers 10-020, Stanford Institute for Economic Policy Research.
    18. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    19. Jennifer L. Castle & Nicholas W.P. Fawcett & David F. Hendry, 2009. "Nowcasting Is Not Just Contemporaneous Forecasting," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210(1), pages 71-89, October.
    20. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    21. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    22. Mondria, Jordi & Wu, Thomas & Zhang, Yi, 2010. "The determinants of international investment and attention allocation: Using internet search query data," Journal of International Economics, Elsevier, vol. 82(1), pages 85-95, September.
    23. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    24. Lawrence R. Cordell & Yilin Huang & Meredith Williams, 2011. "Collateral damage: Sizing and assessing the subprime CDO crisis," Working Papers 11-30, Federal Reserve Bank of Philadelphia.
    25. Gyourko, Joseph & Tracy, Joseph, 2014. "Reconciling theory and empirics on the role of unemployment in mortgage default," Journal of Urban Economics, Elsevier, vol. 80(C), pages 87-96.
    26. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    27. Eli Beracha & M. Babajide Wintoki, 2013. "Forecasting Residential Real Estate Price Changes from Online Search Activity," Journal of Real Estate Research, American Real Estate Society, vol. 35(3), pages 283-312.
    28. Lutz Kilian, 1998. "Small-Sample Confidence Intervals For Impulse Response Functions," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 218-230, May.
    29. Neil Bhutta & Jane K. Dokko & Hui Shan, 2010. "The depth of negative equity and mortgage default decisions," Finance and Economics Discussion Series 2010-35, Board of Governors of the Federal Reserve System (U.S.).
    30. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    31. Gregory DeCoster & William Strange, 2012. "Developers, Herding, and Overbuilding," The Journal of Real Estate Finance and Economics, Springer, vol. 44(1), pages 7-35, January.
    32. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
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    5. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2018. "Mortgage Default Risks and High-Frequency Predictability of the US Housing Market: A Reconsideration," Working Papers 201875, University of Pretoria, Department of Economics.
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    More about this item

    Keywords

    Mortgage default risk;

    JEL classification:

    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • G02 - Financial Economics - - General - - - Behavioral Finance: Underlying Principles
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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