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Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand

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  • Branislav Saxa

Abstract

This paper examines the usefulness of Google Trends data for forecasting mortgage lending in the Czech Republic. While the official monthly statistics on mortgage lending come with a publication lag of one month, the data on how often people search for mortgage-related terms on the internet are available without any lag on a weekly basis. Growth in searches for mortgages and growth in mortgages actually provided are strongly correlated. The lag between these two growth rates is two months. Evaluation of out-of-sample forecasts shows that internet search data improve mortgage lending predictions significantly. In addition to forecasting performance evaluation, an experimental indicator of restrictively tight mortgage credit standards and conditions is proposed. Nowadays many countries run bank lending surveys to monitor the tightness of bank lending standards and conditions. The proposed indicator represents a complementary tool to such a survey.

Suggested Citation

  • Branislav Saxa, 2014. "Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand," Working Papers 2014/14, Czech National Bank.
    • Handle: RePEc:cnb:wpaper:2014/14
    as

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    File URL: https://www.cnb.cz/export/sites/cnb/en/economic-research/.galleries/research_publications/cnb_wp/cnbwp_2014_14.pdf
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    References listed on IDEAS

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    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. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    3. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Al-Nasseri, Alya & Menla Ali, Faek & Tucker, Allan, 2021. "Investor sentiment and the dispersion of stock returns: Evidence based on the social network of investors," International Review of Financial Analysis, Elsevier, vol. 78(C).
    2. Caporale, Guglielmo Maria & Menla Ali, Faek & Spagnolo, Fabio & Spagnolo, Nicola, 2022. "Cross-border portfolio flows and news media coverage," Journal of International Money and Finance, Elsevier, vol. 126(C).
    3. repec:prg:jnlpep:v:preprint:id:690:p:1-23 is not listed on IDEAS
    4. 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.
    5. repec:men:wpaper:58_2015 is not listed on IDEAS
    6. Jaroslav Bukovina & Matus Marticek, 2016. "Sentiment and Bitcoin Volatility," MENDELU Working Papers in Business and Economics 2016-58, Mendel University in Brno, Faculty of Business and Economics.
    7. Jaroslav Bukovina, 2017. "The attention of a society towards corporate brand name and its determinants within the information-rich economy," MENDELU Working Papers in Business and Economics 2017-71, Mendel University in Brno, Faculty of Business and Economics.
    8. Vilma Deltuvaitė & Svatopluk Kapounek & Petr Koráb, 2019. "Impact of Behavioural Attention on the Households Foreign Currency Savings as a Response to the External Macroeconomic Shocks," Prague Economic Papers, Prague University of Economics and Business, vol. 2019(2), pages 155-177.
    9. Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.

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    More about this item

    Keywords

    Credit demand; credit standards and conditions; credit supply; forecast evaluation; forecasting; Google econometrics; Internet search data; mortgage; smoothing;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E51 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Money Supply; Credit; Money Multipliers

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