Reverse Mortgages (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: longevity risk, as retirees’ life expectancy increases, interest rate risk, especially in the low-rate post-crisis period, property market risk, in the last stage of the current business cycle. The paper focuses on reverse mortgage contracts whose expiry is a function of the contractor’s life span and whose assets depend on the evolution of real estate market prices. A neural network procedure is employed in order to include a range of explanatory variables as part of the Reverse Mortgage evaluation algorithms.
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