Reverse mortgages through artificial intelligence: new opportunities for the actuaries

Springer Science and Business Media LLC - Tập 44 - Trang 23-35 - 2020
Emilia di Lorenzo1, Gabriella Piscopo1, Marilena Sibillo2, Roberto Tizzano1
1Department of Economic and Statistical Science, University of Naples Federico II, Naples, Italy
2Department of Economics and Statistics, University of Salerno, Fisciano, Salerno, Italy

Tóm tắt

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.

Tài liệu tham khảo

Aalbers, B.M.: Subprime cities and the twin crises. In: Aalbers, M.B. (ed.) Subprime cities: the political economy of mortgage markets. Wiley, Chichester (2012). https://doi.org/10.1002/9781444347456.ch

Alai, D.H., 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)

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

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)

Chen, H., Cox, S.H., Wang, S.S.: 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)

Chinloy, P., Cho, M., Megbolugbe, I.F.: Appraisals, transaction incentives, and smoothing. J. Real Estate Financ. Econ. 14(1), 89–111 (1997)

D’Amato, V., Di Lorenzo, E., Haberman, S., Sibillo, M., Tizzano, R.: Pension schemes versus real estate. Ann. Oper. Res. (2019). https://doi.org/10.1007/s10479-019-03241-y

De la Fuente Merencio, I., Navarro, E., Serna, G.: Estimating the no-negative-equity guarantee in reverse mortgages: international sensitivity analysis. In: Mili, M., Samaniego Medina, R., di Pietro, F. (eds.) New Methods in Fixed Income Modeling. Springer, Berlin (2018). https://doi.org/10.1007/978-3-319-95285-7_13

Doyle, D., Groendyke, C.: Using neural network to price and hedge variable annuities guarantees. Risks 7(1), 1 (2019)

EIPOA: EIOPA’s advice on the development of an EU Single Market for personal pension products (PPP) EIOPA-16/457 04 July 2016

Ferrario, A., Noll, A., Wuthrich, M.V.: Insights from inside neural networks. (2018). Available at SSRN: https://ssrn.com/abstract=3226852

Fornero, E., Rossi, M., Virzì Bramati, M.C.: Explaining why, right or wrong, (Italian) households do not like reverse mortage. J. Pension Econ. Financ. 2, 180–202 (2016)

Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning : Data Mining, Inference, and Prediction. Springer, New York (2009). https://doi.org/10.1007/978-0-387-84858-7

Fritsch, S., Guenther, F., Wright, M.N., Suling, M., Mueller, S.M.: Neuralnet: Training of Neural Networks. R package version1.44.2 (2019)

Gan, G.: Application of data clustering and machine learning in variable annuity valuations. Insur. Math. Econ. 53(3), 795–801 (2013)

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)

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)

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

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)

Lee, R., Carter, L.: Modeling and forecasting U.S. mortality. J. Am. Stat. Assoc. 87(419), 659–671 (1992)

Lennartz, C., Arundel, R., Ronald, R.: Younger adults and homeownership in Europe through the global financial crisis. Popul. Sp. Place 22, 823–835 (2016). https://doi.org/10.1002/psp.1961

Li, J.S.H., Hardy, M.R., Tan, K.S.: On pricing and hedging the no-negative equity guarantee in equity release mechanisms. J. Risk Insur. 77(2), 499–522 (2010)

Li, J., Kogure, A., Liu, J.: Multivariate risk-neutral pricing of reverse mortgages under the Bayesian framework. Risks 7(11), 1–12 (2019)

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

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

Nakajima, M., Telywcava, I.: Reverse mortgage loans: a quantitative analysis. J. Financ. 72(2), 911–950 (2017)

Nigri, A., Levantesi, S., Marino, M., Scognmiglio, S., Perla, F.: A deep learning integrated Lee–Carter model. Risks 7(1), 33 (2019)

Park, J., Sandberg, I.: Approximation and radial basis function networks. Neural Comput. 5, 305–316 (1993)

Petruscev, P.: Approximation by ridge functions and neural networks. SIAM J. Math. Anal. 30(1), 155–189 (1999)

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/

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)

Shao, A.W., 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)

Shao, D., Zhang, T., Mannar, K.: Time series forecasting on engineering systems using recurrent neural networks. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q.Z. (eds.) Advanced Data Mining and Applications. Springer International Publishing, Berlin (2016)

Valente, A., Gilardi, C.: Soluzioni finanziarie per la terza età. Cacucci Editore, Bari (2013)

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

Wang, L., Valdez, E.A., Piggot, J.: Securitization of longevity risk in reverse mortgage. N. Am. Actuar. J. 12, 345–371 (2008)

Wuthrich, M.V., Buser, C.: Data analytics for non-life insurance pricing. Swiss Finance Institute Research Paper (2019) - papers.ssrn.com

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)

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 44

23-35

Thông tin tác giả