Last Spring, Moody’s Investors Service raised the alarm about generous repayment plans for federally guaranteed student loans, putting nearly $40 billion of bonds backed by these loans under review for possible downgrades.

The reviews were shelved when Moody’s subsequently decided to conduct a wholesale review of its criteria for rating Federal Family Education Loan Program (FFELP) securitization on July 9, 2015, leaving 58 deals in ratings purgatory.

The purgatory continues, but now it has been extended to an even wider universe of deals.

On June 14, the rating agency published its update methodology to takes into account heavy borrower usage of Income Based Repayment (IBR), which slows down the rate of repayment, sometimes to as long as 25 years from the standard 10. This means that bonds backed by these loans may not pay o at their nal maturity. e uncertainty is a problem for investors who had planned to put their money to work elsewhere. Failure to pay o at maturity is also an event of default.

But, aer extensive feedback from market participants, Moody’s has also revised its methodology in ways that benet the bonds. It assumes that more borrowers will voluntarily prepay the loans. It also added new prepayment assumptions that account for government reimbursements of principal and accrued interest on a loan if the borrower dies or becomes disabled.

The new prepayment assumptions also account for potential loan forgiveness for borrowers who enroll in IBR, although this is likely to have very little impact on the speed at which bonds are repaid. at’s because borrowers in IBR must make 300 qualifying payments before the forgiveness kicks in. So the earliest that loans can be forgiven in this program is 2034.

Debash Chatterjee, managing director of Moody’s structured nance group, said that the revised methodology is “less punitive” than originally proposed.

Nevertheless it has resulted in a much broader universe of bonds being put under ratings review.

Moody's took rating actions on 403 tranches of bonds backed by FFELP loans as well as bonds backed by a mix of FFELP and private student loans that do not benet from a government guarantee.

It placed 266 tranches in 141 transactions ($44.9 billion) on review for downgrade, 89 tranches in 59 transactions ($3.1 billion) on review for upgrade and 45 tranches in 34 transactions ($2.8 billion) on review with direction uncertain. Moody's also conrmed the ratings on three tranches ($1.5 billion).

In addition, 101 tranches ($30.7 billion) previously placed on review for downgrade remain on review for downgrade and four tranches ($1.4 billion) previously placed on review for upgrade will remain on review for upgrade.

The rating agency will start releasing its rating actions shortly on a rolling basis and expects to complete the actions within six months. In some cases the ratings migrations on these bonds, which are normally rated ‘Aaa,’ could be large.

When concerns about slower repayments emerged last year, it roiled the normally staid market for FFELP bonds last year. e ratings reviews (Fitch Ratings is also taking actions) and subsequent sello of FFELP bonds spurred servicers into action.

Navient, the largest sponsor of FFELP SLABS, has been able to extend the legal nal maturity dates for $6.8 billion of bonds that it sponsors since December 2015. is is considered to be the cleanest solution to extension risk; the challenge is that it requires rating agency and bondholder approval.

Other servicers have called bonds and provided other kinds of support for tranches at risk of failing to pay o at their nal maturities. Moody’s has said that, while such support can be helpful, it is optional and the willingness of a sponsor to provide such support may change and is not a given.

Reaction to the new methodology has been muted. Analysts at Deutsche Bank wrote in a June report that it was "encouraging" to see that Moody's took market feedback into account. But the analysts are concerned that the new approach, which uses 28 dierent scenarios, will be dicult and time consuming to model.

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