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What can we learn about mortgage supply from online data?

Author

Listed:
  • Agnese Carella

    (Bank of Italy)

  • Federica Ciocchetta

    (Bank of Italy)

  • Valentina Michelangeli

    (Bank of Italy)

  • Federico Maria Signoretti

    (Bank of Italy)

Abstract

We exploit a novel dataset on mortgages offered by banks through Italy’s main online mortgage broker, which works with banks representing over 80 per cent of mortgages granted, to gain an up-to-date assessment of loan supply conditions. Characteristics of mortgages are reported for about 85,000 borrower-contract profiles, constant over time, available at the beginning of each month starting from March 2018. We document that riskier applications, characterized by high loan-to-value ratios and long maturity, are, on average, offered by a smaller number of banks that charge higher interest rates. Online banks tend to provide better price conditions than traditional intermediaries. We use the online rates offered to nowcast bank-level official (MIR) interest rate statistics, available only several weeks later. By using both regression analysis and machine learning algorithms, we show that the rates offered have significant predictive content for fixed-rate contracts, also after controlling for time-varying demand conditions, market reference rates, and unobserved time-invariant bank characteristics. Machine learning algorithms provide further improvements over regression models in out of sample predictions.

Suggested Citation

  • Agnese Carella & Federica Ciocchetta & Valentina Michelangeli & Federico Maria Signoretti, 2020. "What can we learn about mortgage supply from online data?," Questioni di Economia e Finanza (Occasional Papers) 583, Bank of Italy, Economic Research and International Relations Area.
    • Handle: RePEc:bdi:opques:qef_583_20
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    References listed on IDEAS

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    1. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    2. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    3. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    4. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    5. Goldfarb, Avi & Greenstein, Shane M. & Tucker, Catherine E. (ed.), 2015. "Economic Analysis of the Digital Economy," National Bureau of Economic Research Books, University of Chicago Press, number 9780226206981, December.
    6. Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.
    7. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    8. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    9. Petra Gerlach-Kristen & Seán Lyons, 2018. "Determinants of mortgage arrears in Europe: evidence from household microdata," International Journal of Housing Policy, Taylor & Francis Journals, vol. 18(4), pages 545-567, October.
    10. Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
    11. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    12. Petra Gerlach-Kristen & Seán Lyons, 2018. "Determinants of mortgage arrears in Europe: evidence from household microdata," European Journal of Housing Policy, Taylor and Francis Journals, vol. 18(4), pages 545-567, October.
    13. John Whitley & Richard Windram & Prudence Cox, 2004. "An empirical model of household arrears," Bank of England working papers 214, Bank of England.
    14. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2019. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 249-273, National Bureau of Economic Research, Inc.
    15. Valentina Michelangeli & Enrico Sette, 2016. "How does bank capital affect the supply of mortgages? Evidence from a randomized experiment," Temi di discussione (Economic working papers) 1051, Bank of Italy, Economic Research and International Relations Area.
    16. Jorge Guzman & Scott Stern, 2016. "Nowcasting and Placecasting Entrepreneurial Quality and Performance," NBER Chapters, in: Measuring Entrepreneurial Businesses: Current Knowledge and Challenges, pages 63-109, National Bureau of Economic Research, Inc.
    17. Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.
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    Cited by:

    1. Agnese Carella & Valentina Michelangeli, 2021. "Information or persuasion in the mortgage market: the role of brand names," Temi di discussione (Economic working papers) 1340, Bank of Italy, Economic Research and International Relations Area.
    2. Ferrari, Alessandro & Loseto, Marco, 2023. "Liquidity constraints and demand for maturity the case of mortgages," Working Paper Series 2859, European Central Bank.

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    More about this item

    Keywords

    mortgage; experimental data; risk-taking; nowcasting;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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