Making a Case for Deep Coverage Mortgage Insurance

Making a Case for Deep Coverage Mortgage Insurance

The US housing finance system remains unchanged since 2008, when the Federal Housing Finance Authority (FHFA) placed Fannie Mae and Freddie Mac (the GSEs) into conservatorship. As a result, the GSEs continue to present a significant risk exposure to the taxpayers. Deep Coverage Mortgage Insurance can play a significant role in bringing additional private capital to housing finance, reducing taxpayer risk before the loans arrive at the GSEs.

“Deep Coverage Mortgage Insurance” refers to extending the use of mortgage insurance, both by using deeper coverage on loans that currently require mortgage insurance (loans with less than a 20% down payment) and by using MI on loans that do not currently require it. There are many obvious benefits to this approach, including:

  • Deeper coverage is compatible with the mortgage origination and servicing “plumbing”
  • Mortgage originators and servicers already have systems in place and know how to use mortgage insurance
  • Competition among 7 mortgage insurance companies brings an immediate increase in competition for pricing mortgage credit risk
  • Mortgage insurance pricing is transparent and available to all lenders, regardless of size
  • Deeper coverage brings an immediate decrease in taxpayer risk, putting substantial additional private capital in front of taxpayers before the GSEs purchase the loans

One of the challenges to greater use of mortgage insurance is the GSEs, in addition to setting their own Guaranty Fees (GFees) and Loan-Level Price Adjustments (LLPAs), also exert considerable control over mortgage insurance pricing via the financial requirements of the Private Mortgage Insurer Eligibility Requirements (PMIERS). In order for Deep Coverage Mortgage Insurance to be a better execution for lenders, the additional MI premium needs to be offset by a reduction in GFees and LLPAs. With thumbs on both sides of the scale, the GSEs are in full control of the economics that determine whether Deep Coverage Mortgage Insurance will become an option for lenders and future housing finance.

Given the limited insight into GSE capital and pricing, we have been building our own analysis to determine appropriate pricing for Deep Coverage Mortgage Insurance, both for insurance providers and for GFee reductions by the GSEs to reflect properly the reduction in their risk. An important component of our analysis compares Deep Coverage Mortgage Insurance to the structured credit risk transfer (CRT) deals the GSEs recently developed.

Primary mortgage insurance operates at the loan level and typically limits losses on a given loan to a fixed percentage of the loan amount, unpaid interest, and foreclosure expenses. For example, standard GSE coverage on a 90% loan-to-value (LTV) loan is 25%. That means the mortgage insurer pays for losses up to 25% of the total amount due the lender. If the property value is less than the remaining 75%, the lender (or the GSE that bought the loan) bears the remaining loss. In a CRT deal, the coverage operates at the pool level, absorbing 100% of the losses beyond some threshold level until an aggregate loss limit is reached. In a typical CRT structure, that loss limit might be around 5% of the total amount of the loans in the pool.

Using methods created by Andrew Davidson & Co., we compared the effectiveness and cost of Deep Coverage Mortgage Insurance to the Freddie Mac STACR 2015-DNA2 transaction. We found that market-implied pricing is equivalent to insurance capitalized to a 97% expected shortfall earning a 16% return. We found counterparty risk, measured as Excess Expected Shortfall under simplifying assumptions, to be very low, relative to the risk transferred away from the GSEs. We recently published these findings in the Journal of Structured Finance (JSF).

On its face, our finding would suggest the primary mortgage insurance is covering 5 times (25% vs. 5%) the risk of the CRT deal. The reality is more complex. The primary MI would only cover 25% of the total amount of the pool if every loan in the entire pool defaulted. While that is theoretically possible, it is such a low probability that nobody would use that as an expected result, even in a severe stress. If 20% of the loans default, which would correspond to a somewhat more severe stress than the recent crisis, then the primary MI would cover 5% of the total value of the pool, just like the CRT structure. Deeper coverage, say to 40%, would cover 8% of the total value. Determining the economic equivalence between MI and CRT deals requires estimation of the range of possible outcomes and their probabilities and comparing the level of losses covered by each form.

Andrew Davidson & Co. developed a set of models that enable just such a comparison. It actually has two approaches to generating scenarios and likelihoods. The first approach is the well-known Monte Carlo method, the equivalent of rolling dice many, many times to generate a large number of possible outcomes. The second approach uses a fixed set of outcomes and assigns probabilities to each, relying on a variation of the same Vasicek model that underlies Basel bank capital rules.

The end result of this analysis, which we present in more detail in our JSF article, is a powerful method of comparing the economics of a variety of ways to absorb mortgage credit risk. Using the pricing from an actual Freddie Mac CRT deal from 2015, we found that Deep Coverage Mortgage Insurance can provide equivalent protection for a similar price. As important, MI can do this in a way that’s fully compatible with CRT structures, making broader use of MI an attractive, complementary, rather than competing form of putting private capital in front of the taxpayers. Along with all the other benefits of Deep Coverage Mortgage Insurance, we think this makes a compelling case for deepening and broadening the use of MI.

View our JSF article, Making a Case for Deep Coverage Mortgage Insurance!

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Ted Durant - Former MGIC VP Model Development & Portfolio Analytics

Ted Durant had been with MGIC for over 25 years and served as Vice President of Model Development & Portfolio Analytics. As the nation's largest provider of private mortgage insurance, MGIC has a considerable interest in statistical analysis and forecasting of home prices, prepayment and default risk. The analytic services group at MGIC provides those tools both to internal users and externally to MGIC's clients. In addition to his work at MGIC, Ted has experience in oil & gas exploration, real estate development, chocolate manufacturing, and bicycle manufacturing and retailing. Ted earned his MBA in Real Estate from the Wharton School at the University of Pennsylvania, his BA in Economics from Carleton College in Northfield, Minnesota, and he grew up on the south shore of Massachusetts Bay. He now lives with his wife and two daughters in Milwaukee, where he serves on the board of directors of the Center for Communication, Hearing and Deafness and enjoys riding bicycles year round.

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