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Reverse mortgages through artificial intelligence: new opportunities for the actuaries. (English) Zbl 1470.91226

Summary: 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.

MSC:

91G05 Actuarial mathematics
68T05 Learning and adaptive systems in artificial intelligence
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[1] Aalbers, BM; Aalbers, MB, Subprime cities and the twin crises, Subprime cities: the political economy of mortgage markets (2012), Chichester: Wiley, Chichester · doi:10.1002/9781444347456.ch
[2] Alai, DH; Chen, H.; Cho, D.; Hanewald, K.; Sherris, M., Developing equity release markets: risk analysis for reverse mortgage and home reversion, N. Am. Actuar. J., 18, 1, 217-241 (2014) · Zbl 1412.91028 · doi:10.1080/10920277.2014.882252
[3] Beltrametti, L.: House rich, cash poor. Come rendere liquida la ricchezza rappresentata dalla casa di abitazione” Quaderni dell’Osservatorio, Fondazione Cariplo n.26 (2017). http://www.fondazionecariplo.it/static/upload/hou/house-rich-cash-poor.pdf
[4] Cascione, C.M.: L’ipoteca inversa tra discipline nazionali e prassi applicative: esperienze a confronto. In: Il prestito vitalizio ipotecario (a cura di M. Lobuono). Gappichelli Editore, pp. 1-32 (2017)
[5] Chen, H.; Cox, SH; Wang, SS, Is the home equity conversion program in the United States sustainable? Evidence from pricing mortgage insurance premiums and non recourse provisions using the conditional Escher transform, Insur. Math. Econ., 46, 371-384 (2010) · Zbl 1231.91154 · doi:10.1016/j.insmatheco.2009.12.003
[6] Chinloy, P.; Cho, M.; Megbolugbe, IF, Appraisals, transaction incentives, and smoothing, J. Real Estate Financ. Econ., 14, 1, 89-111 (1997) · doi:10.1023/A:1007772018106
[7] D’Amato, V.; Di Lorenzo, E.; Haberman, S.; Sibillo, M.; Tizzano, R., Pension schemes versus real estate, Ann. Oper. Res. (2019) · Zbl 1481.91048 · doi:10.1007/s10479-019-03241-y
[8] De la Fuente Merencio, I.; Navarro, E.; Serna, G.; Mili, M.; Samaniego Medina, R.; di Pietro, F., Estimating the no-negative-equity guarantee in reverse mortgages: international sensitivity analysis, Methods in Fixed Income Modeling (2018), New: Springer, Berlin, New · doi:10.1007/978-3-319-95285-7_13
[9] Doyle, D.; Groendyke, C., Using neural network to price and hedge variable annuities guarantees, Risks, 7, 1, 1 (2019) · doi:10.3390/risks7010001
[10] EIPOA: EIOPA’s advice on the development of an EU Single Market for personal pension products (PPP) EIOPA-16/457 04 July 2016
[11] Ferrario, A., Noll, A., Wuthrich, M.V.: Insights from inside neural networks. (2018). Available at SSRN: https://ssrn.com/abstract=3226852
[12] Fornero, E.; Rossi, M.; Virzì Bramati, MC, Explaining why, right or wrong, (Italian) households do not like reverse mortage, J. Pension Econ. Financ., 2, 180-202 (2016) · doi:10.1017/S1474747215000013
[13] Friedman, J.; Hastie, T.; Tibshirani, R., The Elements of Statistical Learning : Data Mining, Inference, and Prediction (2009), New York: Springer, New York · Zbl 1273.62005 · doi:10.1007/978-0-387-84858-7
[14] Fritsch, S., Guenther, F., Wright, M.N., Suling, M., Mueller, S.M.: Neuralnet: Training of Neural Networks. R package version1.44.2 (2019)
[15] Gan, G., Application of data clustering and machine learning in variable annuity valuations, Insur. Math. Econ., 53, 3, 795-801 (2013) · Zbl 1290.91086 · doi:10.1016/j.insmatheco.2013.09.021
[16] Giordano, L., Siciliano, G.: Real-world and risk-neutral probabilities in the regulation on the transparency of structured products, Quaderni di Finanza, Consob, 74, agosto (2013)
[17] Hanewald, K.; Post, T.; Sherris, M., Portfolio choice in retirement: what is the optimal home equity reease product?”, J. Risk Insur., 83, 2, 421-446 (2016) · doi:10.1111/jori.12068
[18] Institute and Faculty of Actuaries: Lifetime Mortgage. A good and appropriate investment for life companies with annuity liabilities?”. May 2014. https://www.actuaries.org.uk/system/…/lifetime-mortgage.pdf
[19] Ji, M.; Hardy, M.; Siu-Hang Li, J., A semi-Markov multiple state model for reverse mortgage terminations, Ann. Actuar. Sci., 6, 235-257 (2012) · doi:10.1017/S1748499512000061
[20] Lee, R.; Carter, L., Modeling and forecasting U.S. mortality, J. Am. Stat. Assoc., 87, 419, 659-671 (1992) · Zbl 1351.62186
[21] Lennartz, C.; Arundel, R.; Ronald, R., Younger adults and homeownership in Europe through the global financial crisis, Popul. Sp. Place, 22, 823-835 (2016) · doi:10.1002/psp.1961
[22] Li, JSH; Hardy, MR; Tan, KS, On pricing and hedging the no-negative equity guarantee in equity release mechanisms, J. Risk Insur., 77, 2, 499-522 (2010)
[23] Li, J.; Kogure, A.; Liu, J., Multivariate risk-neutral pricing of reverse mortgages under the Bayesian framework, Risks, 7, 11, 1-12 (2019)
[24] Merton, R.C., Lai, R.N.: On an efficient design of the reverse mortgage: structure, marketing and funding. November 2016. https://www.aeaweb.org/conference/2017/…/paper/3hsNdR4f
[25] Mudrazija, S., Butrica, A.B.: Homeownership, Social Insurance, and Old-age Security in the United States and Europe, Center for Retirement Research at Boston College (2017) http://crr.bc.edu
[26] Nakajima, M.; Telywcava, I., Reverse mortgage loans: a quantitative analysis, J. Financ., 72, 2, 911-950 (2017) · doi:10.1111/jofi.12489
[27] Nigri, A.; Levantesi, S.; Marino, M.; Scognmiglio, S.; Perla, F., A deep learning integrated Lee-Carter model, Risks, 7, 1, 33 (2019) · doi:10.3390/risks7010033
[28] Park, J.; Sandberg, I., Approximation and radial basis function networks, Neural Comput., 5, 305-316 (1993) · doi:10.1162/neco.1993.5.2.305
[29] Petruscev, P., Approximation by ridge functions and neural networks, SIAM J. Math. Anal., 30, 1, 155-189 (1999) · Zbl 0927.41006 · doi:10.1137/S0036141097322959
[30] Phang, S.-Y.: Asia Pathways—A blog of the Asian Development Bank Institute. Retrieved 9 Feb 2016, from Monetizing housing for retirement in Singapore: http://www.asiapathways-adbi.org/2015/10/monetizing-housing-for-retirement-in-singapore/
[31] Scanlon, K.; Lunde, J.; Whitehead, C., Mortgage product innovation in advanced economies: more choice, more risk, Int. J. Hous. Policy, 8, 2, 1-30 (2008)
[32] Shao, AW; Hanewal, K.; Sherris, M., Reverse mortgage pricing and risk analysis allowing for idiosyncratic home price risk and longevity risk, Insur. Math. Econ., 63, 76-90 (2015) · Zbl 1348.91179 · doi:10.1016/j.insmatheco.2015.03.026
[33] Shao, D.; Zhang, T.; Mannar, K.; Li, J.; Li, X.; Wang, S.; Li, J.; Sheng, QZ, Time series forecasting on engineering systems using recurrent neural networks, Advanced Data Mining and Applications (2016), Berlin: Springer International Publishing, Berlin
[34] Valente, A.; Gilardi, C., Soluzioni finanziarie per la terza età (2013), Bari: Cacucci Editore, Bari
[35] Wang, L.: Analysis of non-steady time-series forecast for economy based on ARMA model. J. Wuhan Univ. Technol. 1 (2004). http://en.cnki.com.cn/Journal_en/C-C000-JTKJ-2004-01.htm
[36] Wang, L.; Valdez, EA; Piggot, J., Securitization of longevity risk in reverse mortgage, N. Am. Actuar. J., 12, 345-371 (2008) · Zbl 1481.91189 · doi:10.1080/10920277.2008.10597529
[37] Wuthrich, M.V., Buser, C.: Data analytics for non-life insurance pricing. Swiss Finance Institute Research Paper (2019) - papers.ssrn.com
[38] Yu, L.; Jiao, C.; Xin, H.; Wang, Y.; Wang, K., Prediction on house price based on deep learning, Int. J. Comput. Inf. Eng., 12, 2, 90-99 (2018)
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