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Detecting Mortgage Delinquencies

Author

Listed:
  • Askitas, Nikos

    (IZA)

  • Zimmermann, Klaus F.

    (University of Bonn)

Abstract

Economic hardship is strongly reflected by the housing market. It is the concern of much research, but its analysis is often obstructed by insufficient lagged data. This paper evaluates search intensity for "hardship letter" from Google Insights to detect ensuing mortgage delinquencies. Such searches locate documents which assist to write a successful loan modification request. Other relevant searches for "short sale", "REO" (as in Real Estate Owned) or "FHA" (as in Federal Housing Administration) are used to provide a comprehensive view of the housing market. Using data from the great recession and benchmarking them against data from the labor market, the paper demonstrates that internet activity captures socioeconomic phenomena in real time very well, with no interviewer effect at a high frequency. This suggests that the new data base should spur new housing research.

Suggested Citation

  • Askitas, Nikos & Zimmermann, Klaus F., 2011. "Detecting Mortgage Delinquencies," IZA Discussion Papers 5895, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp5895
    as

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    References listed on IDEAS

    as
    1. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    2. Nikos Askitas, 2011. "WEEKLYCLAIMS: Stata module to Get Weekly Initial Jobless Claims from the US Dept. of Labor," Statistical Software Components S457249, Boston College Department of Economics, revised 17 Jun 2012.
    3. Raven Molloy & Hui Shan, 2013. "The Postforeclosure Experience of U.S. Households," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 41(2), pages 225-254, June.
    4. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    5. Sanders, Anthony B. & Van Order, Robert, 2011. "Introduction to the special issue on housing and the credit crunch," Journal of Housing Economics, Elsevier, vol. 20(2), pages 66-67, June.
    6. Askitas, Nikos & Zimmermann, Klaus F., 2011. "Health and Well-Being in the Crisis," IZA Discussion Papers 5601, Institute of Labor Economics (IZA).
    7. Capozza, Dennis R. & Order, Robert Van, 2011. "The great surge in mortgage defaults 2006-2009: The comparative roles of economic conditions, underwriting and moral hazard," Journal of Housing Economics, Elsevier, vol. 20(2), pages 141-151, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute of Labor Economics (IZA), pages 206-206, November.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    3. Simon Oehler, 2019. "Developments in the residential mortgage market in Germany – what can Google data tell us?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Are post-crisis statistical initiatives completed?, volume 49, Bank for International Settlements.
    4. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    5. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    6. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
    7. Askitas, Nikos, 2015. "Trend-Spotting in the Housing Market," IZA Discussion Papers 9427, Institute of Labor Economics (IZA).
    8. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
    9. Guzi, Martin & de Pedraza, Pablo, 2013. "A Web Survey Analysis of the Subjective Well-being of Spanish Workers," IZA Discussion Papers 7618, Institute of Labor Economics (IZA).
    10. Chiara L. Comolli & Daniele Vignoli, 2019. "Spread-ing uncertainty, shrinking birth rates," Econometrics Working Papers Archive 2019_08, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".

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    More about this item

    Keywords

    housing; hardship letter; loan delinquency; mortgage; National Delinquency Survey (NDS); financial crisis; recession; bubble; Google Insights;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E65 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Studies of Particular Policy Episodes
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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