Reverse Mortgages: What Homeowners (Don t) Know and How it Matters. by Thomas Davidoff, Patrick Gerhard, Thomas Post

Transcription

1 Reverse Mortgages: What Homeowners (Don t) Know and How it Matters by Thomas Davidoff, Patrick Gerhard, Thomas Post Discussion by Danilo Cavapozzi Ca Foscari University of Venice and Netspar Netspar Pension Day, Utrecht November 28, 2014

2 The paper in a nutshell This paper assesses the determinants of the knowledge of (HECM-type) reverse mortgages characteristics and how knowledge correlates with the willingness to take out a reverse mortage (RM). Data are drawn from an ad-hoc online survey of US homeowners aged 58 or over run through SurveyMonkey. RM knowledge is assessed by a set of 13 questions. Once RM knowledge questions are posed, a randomized subsample of respondents is provided with an explanation of the RM characteristics. Then, respondents are asked about their willingness to take a RM and its perceived complexity. [why willingess instead of actual demand?]

3 Knowledge of RM Previous experience with RM is a powerful determinant of knowledge. Having such experience increases the average knowledge by about 34%. It might be argued that when previous experience with RM is available, it drives the knowledge of this product and overwhelms the role of other determinants, such as financial literacy or knowing other people who have a RM. I would run separate regressions for those with and without past experience with RM to assess whether other factors become more relevant when such experience is lacking.

4 Willingness to take out a RM In general, how likely is it that you will be taking out a reverse mortgage (HECM)? What does this question mean when it is posed to those who already have a RM? Do results change if regressions are run on the subsample of those without previous RM experience? Those who already have a RM or are planning to take it out might have improved their RM knowledge. I am not clear how using RM good deal as dependent variable can overcome the reverse causality issue. (feedback effect instead of reverse causality?).

5 Randomized treatment control group strategy (1) Providing an explanation of the RM characteristics is not found to have any effect on the willingness to take it out. The effect (if any) should be more sizeable for those who do not have any past experience with RM and for those with lower RM knowledge. Group-specific regressions needed? Can the randomized assignment be used as an (exogenous) instrumental variable? Perceived complexity question is posed after the randomized experiment. Knowledge questions are posed before.

6 Randomized treatment control group strategy (2) Perceived complexity depends on the product characteristics but it might be affected by the knowledge of agents with respect to RM characteristics as well. Can perceived complexity of RMs be considered as an indicator of RM knowledge? Does the treatment have any significant effect on perceived complexity? If this is the case, what about dropping the knowldege indicator from the set of explanatory variables and use the randomized assignment to the treatment as an instrument for perceived complexity?

7 Further points The standard errors for the HECM penetration coefficient estimates are always huge. Does it point to collinearity with Other know w. RM? What about dropping one of these two regressors? Home value and household savings are never found to be significant predictors of knowledge and willingness to purchase a RM. Does functional form matter? What about using logs or dummies instead of levels? Does SurveyMonkey make available a sample representative of the overall US population on which customized sample selection requirements can be imposed? Careful comparison of sample characteristics with other surveys is needed.