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Thursday, February 16, 2012

Withdrawing, Confidently

As structured finance deals wind down and the asset pools grow smaller, the situation often arises that the effectiveness of outstanding tranche ratings – previously based on a portfolio-level diversification – can hinge on the performance of one or two bonds. The problem is compounded, of course, in that many of the models work best for large, diverse, portfolios and often break down when the portfolios become arbitrarily small.

The question then becomes, if a rated tranche can just as easily be rated AAA or D, what does one do?

This tricky situation, now part skill, part luck, calls into question the predictive content of highly sophisticated ratings models when the outcome is really not a model-driven result, but simply a short-term occurrence (e.g., a payoff or a default) or the lack of an occurrence, in a credit-default swap environment.

Moody’s and S&P suffered severe blushes in January when a well-structured CDO, backed heavily by other CDOs and RMBS (including substantial subprime, yes subprime), paid off in full ­– with their outstanding ratings on all tranches having been in the CC to CCC range. What was interesting was that both ratings agencies had visited this deal as recently as June of last year.
From our conversation with analysts at one of the agencies, what happened here was simply that as the deal was winding down, the manager was able to sell the few remaining assets at prices high enough to pay down all the notes, rendering irrelevant the Monte Carlo default simulation trials being run by the raters. In other words, the model let them down.

In an interesting, perhaps prudent decision, S&P took a different course in an announcement they made earlier today, entitled “S&P Takes Various Ratings Actions on 30 U.S. RMBS Deals.” As certain deals dwindled down, compromising the predictive content of their ratings, they chose to simply withdraw the ratings.
“We subsequently withdrew our ratings on certain affected classes that are backed by a pool with a small number of remaining loans. If any of the remaining loans in these pools default, the resulting loss could have a greater effect on the pool's performance than if the pool consisted of a larger number of loans. Because this performance volatility may have an adverse affect on our outstanding ratings, we withdrew our ratings on the related transactions.”
While it may cause frustration to note holders to see the ratings withdrawn, it augurs well that a rating agency is able and willing to say that it cannot have confidence in the outcome, and therefore chooses to withdraw its rating rather than have investors rely, perhaps falsely, on a rating in which it does not have confidence.

Friday, February 3, 2012

Analysis of The Shortcomings of Statistical Sampling in the Mortgage Loan Due Diligence Process

This is a popular litigation-related piece on our website we thought we'd share through this post (pdf version available here) - enjoy the read.



Introduction

Financial institutions, when assembling mortgage pools for the purpose of inclusion in residential mortgage-backed securities (RMBS), often hire independent analytical companies, like Clayton Holdings LLC (“Clayton”), to perform due diligence on the loans and flag any that are problematic.

Leading up to the financial downturn, Clayton reviewed mortgages for its clients - investment and commercial banks and lending platforms, including those of Bear Stearns, Barclays, Bank of America, C-Bass, Countrywide, Credit Suisse, Citigroup, Deutsche Bank, Doral, Ellington, Freddie Mac, Greenwich, Goldman, HSBC, JP Morgan, Lehman, Merrill Lynch, Morgan Stanley, Nomura, Société Générale, UBS and Washington Mutual (the “Issuers”). As such Clayton was purportedly one of the larger due diligence companies that analyzed whether these loans met specifications like loan-to-value ratios, credit scores and the income levels of borrowers.

Clayton describes, in the presentation it provided to the Financial Crisis Inquiry Commission (“FCIC”), the results of its review of a total of 911,039 mortgage loans between Q1 2006 and Q2 2007 1. As can be seen from the chart, of the loans shown to Clayton, Clayton determined approximately 72% of them to be in compliance, and 28% of them to be out of compliance with the standards tested, or “non-conforming.”

Upon determining that a loan failed to meet its guidelines, an Issuer (i.e., Clayton’s client) would have the ability to exercise their contractual right in “putting back” these non-conforming loans to the mortgage lenders – New Century, Fremont, Countrywide, Decision One Mortgage – rather than include them in securitizations.

The regulatory bodies, and the media, have concentrated heavily on the sizeable portions of non-conforming loans, and the lowering of underwriting standards throughout this period; but for this analysis, we concentrate on a more illuminating aspect of the way in which non-conforming loans ultimately found their way into the securitized RMBS pools.

There are at least two ways that non-conforming loans can find their way into the securitizations:
  • First, the Issuer may choose to waive the loan back into the pool, despite its being originally rejected by Clayton.
  • Second, a more overwhelming mechanism, is to not show the loan to Clayton.


The Intricacies of Loan Sampling

Importantly, it seems to have been common practice for Issuers to show only a sample of the loans to Clayton. A sample risks being unreflective of the population of loans, but random sampling can provide an effective statistical approximation under very strict conditions. It can be a cheaper process and, if the sample is well chosen, can accurately reflect the pool.

The objective of sampling is satisfied if the randomly-selected sample is sufficiently large, and is deemed to be in order. Alternatively, if the sample fails to meet expectations, the entire portfolio ought to be revisited. However, in the mortgage due diligence process the samples were often deemed to be problematic – they resulted in an average of 28% of loans failing their criteria. Importantly, the samples were then adjusted, as we understand it, but the original portfolios were not: the Issuers would only put back certain non-conforming loans from that sample.

In this case, the resulting sample, after throwing out certain non-conforming loans, fails to accurately depict the remaining portfolio of loans it was chosen to represent.



How the Sampling Process Worked, and Difficulties Therewith

Former President and COO of Clayton, D. Keith Johnson, explained to the FCIC committee, during their hearing of September 2010, that in the 2004 to 2006 time period, sample sizes went down to the region of two to three percent2. As the sample size decreases, which it did, the effect of the sampling process alone begins to undermine the effectiveness of the due diligence process.

The media have focused their attentions on what happened to the 28% non-conforming loans – the slices in red in the associated charts. Indeed, many of these non-conforming loans, approximately 39%, were not “kicked out” or put back to the mortgage lenders, but were “waived” back in to the to-be-securitized portfolio. This 39% is substantial, and a factor worthy of the media’s attentions.

But the game-changing fact is not among these 28% non-conformers, or the 39% of them which remained in the securitized pool. These are only part of a sample, and when the sample becomes insignificantly small, its overall contribution to the portfolio as a whole is rendered less meaningful. Rather, it is more prudent to consider the composition of the pool as a whole.

For illustrative purposes, let us assume that the sample loans shown to Clayton represented 3% of the pools, on the higher end of those referred to in the abovementioned Johnson hearing. Let us conservatively assume that the sample provided to Clayton was truly randomly selected.3

For a pool of 10,000 loans, Clayton would have been presented with approximately 300 loans, or 3%.

As we can see from the analysis performed, the effect of “throwing out” 61% of all non-conforming loans is marginal: the pool’s overall composition decreased only from 28% non-conforming to 27.67% non-conforming thanks to the due diligence process. Even had the Issuers returned all 84 non-conforming loans, the overall portfolio would not have been greatly altered –non-conforming loans would have declined from 28% to 27.16%.

When a random sample is tampered with, the final product, by definition, no longer represents the original pool. Here, the sample reflects that ultimately 89% of the pool is conforming and 11% are non-conforming (11% = 28% x 39%). But given the reality of the situation, with the original pool remaining status quo, in fact 27.76%, not 11%, of the overall pool was non-conforming, even after the put backs administered as part of the due diligence process.

A well-selected random sample can effectively capture the characteristics of a pool under certain conditions. But an altered sample seldom accurately reflects the original pool.



1 http://fcic-static.law.stanford.edu/cdn_media/fcic-testimony/2010-0923-Clayton-All-Trending-Report.pdf
2 http://fcic.law.stanford.edu/resource/interviews#J
3 course, the sample sizes used and the percentages rejected by Clayton will differ from Issuer to Issuer. So too will the waiver rate.