FHA Finds Some Borrowers More Likely to Default on HECM Tax and Insurance

Borrowers in HECM default share some common characteristics, the Department of Housing and Urban Development reported to attendees of the National Reverse Mortgage Lenders Association conference in Boston last week.

Reporting on data compiled in recent months following Mortgagee Letter 2011-01, HUD’s Colin Cushman, director of portfolio analysis, shared some of the department’s findings.

The HUD research shows that borrowers who are likely to default are the younger borrowers, in the 62-65 age range. Single males are most likely to default while couples are least likely, and those with home values that are higher than the area median value are less likely to default.


Another significant observation noted by HUD is the probability of default increases over the first four years of the HECM loan, then declines over time. Additionally, those borrowers with high initial draws (as a percent of the initial principal limit) have a higher likelihood of default.

Regionally, some areas have much higher rates of default than others. In Florida and Texas, there is a higher incidence of default with respect to the number of loans than in other states. In California, default is less common. The difference, a HUD representative noted, may stem from tax rates that vary state to state.

The department also found that there is an even split between those who are in default for taxes only, those who are in default for insurance only, and those who are in default for missing payments on both tax and insurance.

Written by Elizabeth Ecker



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  • Texas and Florida may also be high because of high insurance rates or lack of available insurance coverage at all, in hurricane-prone areas.  I understand that homeowners getting dropped from insurance and then not being able to get coverage, other than force-placed, is a significant cause of insurance-related default.

  • The statistics from HUD beg some interesting questions that should spur some further research:
     – Why are the 1st four years of a reverse the most likely to see a technical default? Is it overspending the new cash flow in the absence of mortgage principal and interest payments?
    – Is there a generational difference (1st Wave Boomers vs Greatest Generation) in attitudes/expectations/behaviors at work here? 
    – Will we see “assigned risk” categories of borrowers as is done in auto insurance for teen drivers? This might be expressed as a lower principal limit for higher risk borrowers to allow the lender more potential headroom to  make draws for forced placement of insurance and payment of property taxes.

    The problem is that lenders will probabaly have to establish tactical financial underwriting protocols before we have a better grasp of the strategic nature of the problem.

  • What the article presents are some of the objective indicia of default traits Mr. Cushman presented related to nonpayment of taxes and insurance.  It would have been helpful had percentage of incidence also been presented.  It is not only important to know what are the most significant characteristics of default but also what percentage they are of the whole.  Perhaps that will come with time.
    One trait which jumps out and is easily identifiable during the origination process is “high initial draws (as a percent of the initial principal limit).”  That is NOT holistic or “right versus left brained;” it is a simple fact.  It is both logical and very intuitive for “financial types” (what a silly basis for discussion yet this is exactly the level of argument which many counseling executives have lowered their reasoning in excusing their irrational initiatives such as FIT scoring and its report).
    It is also very logical that in a significant population of US senior homeowners with mortgages, younger borrowers would have a greater tendency to default than older seniors.  There are huge differences in the overall outlook about debt and the level of responsibility one has to honor loan covenants in comparing the attitudes of younger and older borrowers.
    It is surprising how the counselor ran to anecdotal evidence about insurance rather than discussing the need for the financial risk assessment portion of counseling to be refocused so that its financial risk assessment segment takes into consideration the sex and marital status of counselees.  Lenders cannot decline loans based on sex or marital status but those same prohibitions do not apply to how the emphasis of financial risk assessment is applied to counselees. 
    It is matter of fact that the property taxes compared to values are higher in some states than others.  This is particularly true with Texas; prop 13 makes that overall ratio less so in California.  Since these are the two largest HECM origination states, the difference in the rate of default for taxes should be carefully examined.  This again is a very logical result of higher required tax payments compared to property values.

  • When they say that the probability of default is highest in the first four years, I wonder if that is being influenced by cohort effects.  In other words, many more borrowers in the last two or three years have taken lump sum draws, so their loans may be more likely to have gone into default than older loans.  I suspect it doesn’t really mean that the individual borrower’s risk of default decreases over time, but that, population-wise, the greatest proportion of defaults occurs in the first four years.  I’d like to see how these statistics were calculated.  Is this report available in published form anywhere?

  • Elizabeth,

    rmcounselor addresses some interesting issues in his/her last comment.My interpretation of what you wrote about what Colin said about the incidence of default for nonpayment in the first four years is that the rate of first time borrower incidence of default is highest in the first four years and tapers off from there.  The percentages by year following funding would be helpful.  What would be helpful to know is the percentage of notes which experienced  default by the fiscal year of origination.  Then it would help to see that percentage broken down into year in which the first default on a note occurred following funding.  It would be nice to know the average number of times a  defaulted borrowers defaults.  It would also be nice to know by year of origination how many notes are in default and what the average amount of those defaults are.The dream list could go on and on. 

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