Anybody know who owns the loans?

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21 May 2010 | KYLE MROTEK | KENNETH BJURSTROM
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Lenders, investors, and insurers have certainly taken their losses in the
mortgage crisis. Now they are engaged in a new phase of the mortgage
crisis as they grapple with the question of who ends up with the bad
assets and how much of them—a veritable game of hot potato. Lenders
are facing repurchase requests on mortgages underlying securities sold to
investors. Mortgage guaranty insurers are going back over the loans they
insured and rescinding coverage due to alleged fraud and
misrepresentations. Financial guaranty insurers are trying to identify loans
underlying wrapped securities with breaches or misrepresentations and
requesting the mortgage-backed security transaction sponsor to repurchase, cure, or substitute the underlying suspect
mortgages (Ambac 10K p66).
For all the players, this raises issues of counterparty risk, impacting financial reporting and capital management decisions.
Deciding which assets get repurchased or rescinded for insurance could mean the difference between survival and
insolvency. Buyers and sellers face a complex game of quantifying the impact of this game of hot potato, making it
essential that the quantitative models they use to estimate the probability of resolution and severity of impact are as
accurate as possible.
For banks, this means getting a handle on the mortgages they are being requested to repurchase, to help them accurately
estimate earnings impacts. For investors and insurers, the modeling of mortgage remediation efforts will help them prioritize
their loan review process and assess financial reporting issues like establishing loss provisions.
These days, with the financial strength of banks, investors and insurers at stake, those who use the best methods to
optimally navigate decision making will be better off than those who resort to guesswork.
The status quo in remediation estimation
Typically, industry participants use so-called
aggregate and development methods to estimate
future remediation results. These approaches
rely on the experience of a group as a whole to
forecast future performance. Yet these methods
are crude at best. They do not account changes
in mix of the loans between those that have
already gone through the review process vs.
those that are still outstanding. And they don't
factor in detailed underlying (or innate) loan
characteristic differences from one portfolio to
the next.
Parties trying to estimate resolution need to
scrutinize each loan, using databases with
information about mortgages that have already
been considered and reviewed for repurchase or
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rejection. Using predictive models, analysts can perform statistical analysis on each loan already reviewed in order to
develop assumption ranges and confidence levels to estimate the probability and severity of loss for unresolved loans not
yet reviewed.
Passing the potato
Investors and insurers have some recourse as they contemplate possession of these hot-potato assets. Loans acquired by
investors contain representations and warranties that protect them from fraud and misrepresentation. For example, early
payment defaults is one warranty. The reasoning goes like this: If the borrower went delinquent early, then proper
underwriting wasn’t done. Other reps and warranties cover misrepresentations or fraud in the loan application, such as
false statements about income, occupancy, and appraisals.
Investors, in an effort to lessen their pain, are requesting that originators buy back the mortgages. Money center banks
have reported losses due to this phenomenon to the tune of billions of dollars. Lenders are setting aside establishing
repurchase reserves and consequently negatively impacting earnings.
The federal government is participating in the game of hot potato too. The FHFA has been increasing pressure to send
back loans that don't measure up to the representations and warranties.
1
Freddie Mac sent back $4.1 billion of single-
family mortgages to lenders in 2009, according to the agency's annual report. That compares with $1.8 billion in 2008.
2
Mortgage insurers are also rescinding coverage under the same premise: What they agreed to insure is not in fact what
they were given. Lenders have filed claims with insurers, asking for payment when the loans default. But on closer
inspection, the insurers are finding the information provided was either inaccurate or false, leading them to rescind
coverage. Until recently, rescission had been typically used for grossly inaccurate loan applications, running about 1% of
claims. Yet now 10% to 20% of claims are being rejected and in some instances much more. Nevertheless, mortgage
insurers could refine their estimates for rescission by implementing sophisticated predictive models.
Financial guaranty insurers—which insure a security that represents a pool of loans—are taking a similar approach. These
companies are in a similar predicament as investors and mortgage insurers. For these mortgage-backed securities,
financial guaranty insurers are asking lenders to repurchase misrepresented non-performing loans.
Digging deeper into data
All parties need a good handle on the assets they have on the books. Information about the failure rate of the loan
repurchase requests, rescissions, and putbacks can be used to calibrate a predictive model that can be used to estimate
future activity on a loan-by-loan basis, using loan characteristics. This approach is oftentimes used for pricing auto
insurance and other exposures. With ample data, it is relatively easy to calibrate and implement, and is superior to the
methods currently being used.
Often, examinations of loan pools are too simplistic. A bank may review a pool of 500 loans and repurchase, say, 35% of
them. Going forward, the bank might then use that aggregate number, with some back-of-the-envelope adjustments, as a
way to assign a future probability of repurchase on other loans. Yet say that first pool is based on vacation homes in
California, where unemployment now hovers around 12.5%.
3
If the bank were to apply the 35% number to a pool of
primary residence mortgages from Wisconsin, it would likely misestimate the likelihood of resolution, as the groups would
tend to have differing levels of resolution because they are grossly different groups
Using only aggregate data methods would be akin to grouping all drivers in one group to estimate premiums for automobile
insurance. For example, an analysis of using simple pure premium analysis may show a book of business where older cars
have higher claim frequencies relative to newer cars. Yet in reality, older cars tend to be driven by younger drivers, who
tend to generate higher claim frequencies. While the patterns for older cars and young drivers both look more prone to
claim, the poorer performance of the older autos might be mostly due to the young drivers. A robust model to determine
automobile insurance premium might take into consideration a variety of factors, such as age, car model, driving record
and credit scores, amongst others.
Similarly, loan-level predictive models take into account characteristics about each loan individually (as the name
suggests) and respond accordingly to differences in loans when estimating resolution.
Analysis: Data gathering
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Doc Type Mix A Mix B % Repo A % Repo B
Full 20% 70% 5% 5%
Low/No 80% 30% 65% 65%
Total 100% 100%
Avg 53% 23%
One way to tackle this game of hot potato is to set up a database with information about loans that have already been
considered and reviewed for repurchase, rescission, or putback. There also needs to be a field identifying the outcome of
the effort—in other words, whether the loan was repurchased. This data can more or less be developed only once a
thorough review of a subset of loans has been conducted and the outcome of the efforts have been evidenced.
For those loans not yet considered, a database should be set up with the same fields as relied on for the model calibration.
Information found to be helpful include documentation type, geography, borrower credit scores, occupancy type, presences
of I/O or negative amortization features, fixed vs adjustable interest rate and loan purpose.
Using predictive models, analysts can take the underlying data of loans that have already been reviewed. This calibration
helps shape a model that estimates probability of resolution based on loan-level characteristics. The model then can be
used to assign probability of resolution (success or failure) to each loan in the database of loans not already manually
reviewed. This explicit probability estimate can then be used in the loss reserving and forecasting exercise.
Models also need to consider outside factors such as changes in operations. The models being used by lenders,
investors, and insurers currently might rely on development of time from one date—in other words, origination or
documentation request date to the date of a trigger, such as repurchase request date. In their most arcane form these
methods assume no change in operational activities, despite the rise of a cottage industry focusing on loan scrutiny. Just
the hiring of staffs by investors and insurers means more loans likely will be sent back to the lenders.
Case study
Let’s look at an example where an aggregate model is
used without consideration for loan-level underwriting
characteristics. Assume an analyst was asked to estimate
the proportion of loans in Pool A that will be repurchased.
Of the loans in Pool A, 20% are categorized as “Full Doc”,
and 80% as “Low/No Doc”. Full Doc refers to the fact the
borrower supplied the mortgage originator with verifiable
documentation on assets and income. Low/No Doc refers to a borrower that did not. The analyst finds that other pools
(say, Pool B) in a similar economic, litigious and operational environment had repurchase rates of 23% (ie, 23% of the
loans were repurchased, and 77% were not repurchased). The analyst then goes on to apply this credit of 23% to Pool A.
In reality, the difference in the mix of Full to Low/No Doc is a key driver of repurchase likelihood. Pool B had a mix of Full
Doc to Low/No Doc of 70%/30%. A more exhaustive analysis finds that Full Doc loans had a repurchase rate of 5%
whereas Low/No Doc loans had a repurchase rate of 65%. The aggregate repurchase rate of 23% (ie, 70%*5% +
30%*65%) may be appropriate for Pool B, however, a repurchase rate of 53% may be more appropriate for Pool A. This
example highlights the weakness of an aggregate approach. In this example, a single factor (ie, documentation as the
independent factor) model was shown to be superior. In reality though, there are additional fields available such that a
multi-factor model should be considered.
Use predictive modeling
Lenders, investors and insurers would all benefit from using these kinds of methods to gauge exposure of their existing
mortgage portfolios or loans they insure. Current aggregate and development methods fail to do an adequate job of
estimating future activity. By using information about the failure rate of loan repurchase requests, rescissions and
putbacks, industry participants can calibrate a predictive model that can be used to estimate future activity on a
loan-by-loan basis based on loan characteristics and operational activity. This type of actuarial approach, often times used
for pricing auto insurance and other insurance exposures with a lot of data, is relatively easy to calibrate and implement.
The predictive modeling approach gives a better snapshot of exposure than the methods currently used.
Sidebar: How we got here
The market for mortgage-backed securities came in vogue in the 1990s, growing in popularity each year. It
ballooned over time, with gross issuances reaching nearly $2.2 trillion in 2005, up from $318 billion a decade
earlier. Banks and mortgage lenders granted loans to higher risk borrowers, often with little due diligence
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believing that the underlying collateral would support their decisions. Mortgage production extended its reach to
riskier groups of borrowers, approving subprime loans and so-called alt-A loans, which were mortgages that
required little documentation or no money down.
But borrowers also contributed to the frenzy, misrepresenting key underwriting information such as inflating
income and assets in order to make it look like they could afford the payments. Borrowers who speculated on
real estate fudged applications, saying they would live in the residence rather than rent it out, giving them
lower mortgage rates and mortgage insurance premiums.
All of these loans were pooled and sold off as mortgage-backed securities, giving investors a steady stream of
interest payments—so long as the mortgages stayed healthy. Mortgage guaranty insurers underwrote the loans
against default. Financial guaranty insurers insured securities against non-timely payments of principal and
interest. When the mortgages soured, both investors and insurers faced huge losses.
1 Saha-Bubna, Aparajita, "Repurchased Loans Putting Banks in Hole," Wall Street Journal (March 8, 2010).
2 Ibid.
3 Bureau of Labor Statistics, News Release (March 2010), http://www.bls.gov/news.release/laus.nr0.htm.
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Anybody know who owns the loans?

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