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Reverse mortgages through artificial intelligence: new opportunities for the actuaries

Author

Listed:
  • Emilia Lorenzo

    (University of Naples Federico II)

  • Gabriella Piscopo

    (University of Naples Federico II)

  • Marilena Sibillo

    (University of Salerno)

  • Roberto Tizzano

    (University of Naples Federico II)

Abstract

In its basic structure, the reverse mortgage (RM) is a contract where a home owner borrows a part or the totality of the future liquidation value of his home at the time of his death. The risks that are borne by the lender are linked to the volatility of the real estate market, that is the house price risk, the financial market risk, that is the interest rate risk, and the uncertainty of the borrower’s lifetime, that is the longevity risk. The quantification of the future liquidation value and its valuation at the issue time is fundamental in the construction of the RM contract either in the perspective of the lender or in the one of the borrower. In the paper, we explore the use of neural networks to project the real estate market data; this approach allows to obtain a predictive analysis of the pricing process and indeed provides a dynamic pricing algorithm.

Suggested Citation

  • Emilia Lorenzo & Gabriella Piscopo & Marilena Sibillo & Roberto Tizzano, 2021. "Reverse mortgages through artificial intelligence: new opportunities for the actuaries," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 23-35, June.
    • Handle: RePEc:spr:decfin:v:44:y:2021:i:1:d:10.1007_s10203-020-00274-y
    DOI: 10.1007/s10203-020-00274-y
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    References listed on IDEAS

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    Cited by:

    1. Tsai, Pei-Hsuan & Wang, Ying-Wei & Chang, Wen-Chang, 2023. "Hybrid MADM-based study of key risk factors in house-for-pension reverse mortgage lending in Taiwan's banking industry," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    2. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
    3. Iván de la Fuente & Eliseo Navarro & Gregorio Serna, 2020. "Reverse Mortgage Risks. Time Evolution of VaR in Lump-Sum Solutions," Mathematics, MDPI, vol. 8(11), pages 1-17, November.
    4. de la Fuente, Iván & Navarro, Eliseo & Serna, Gregorio, 2023. "Proposal for calculating regulatory capital requirements for reverse mortgages," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).

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    More about this item

    Keywords

    Artificial intelligence; Neural network; Pension product; Real estate; Reverse mortgage;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • G1 - Financial Economics - - General Financial Markets
    • G5 - Financial Economics - - Household Finance
    • J1 - Labor and Demographic Economics - - Demographic Economics

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