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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.

Reverse mortages (RM) provide an attractive way to increase retirement incomes and to face the needs of health care for elderly people. The RM market is exposed to a number of risks: (1) longevity risk, as retirees’ life expectancy increases, (2) interest rate risk, especially in the low-rate post- crisis period, (3) property market risk, in the last stage of the current business cycle. We measure the overall risk for an insurer with a book of RM contracts. We also evaluate the optimal demand for RM in a retiree’s investment portfolio, taking into account diversification with respect to other asset classes..Numerical results are shown and suggestions to further developments of the market are offered.