September 30 Webinar: Machine Learning in Model Validation
Consulting Partner, Risk and Regulatory Compliance Strategic Initiative, Tata Consulting
Consistent & Transparent Forbearance Reporting Needed in the PLS Market
There is justified concern within the investor community regarding the residential mortgage loans currently in forbearance and their ultimate resolution. Although most of the 4M loans in forbearance are in securities backed by the Federal Government (Fannie Mae, Freddie Mac or Ginnie Mae), approximately 400,000 loans currently in forbearance represent collateral that backs private-label residential mortgage-backed securities (PLS). The PLS market operates without clear, articulated standards for forbearance programs and lacks the reporting practices that exist in Agency markets. This leads to disparate practices for granting forbearance to borrowers and a broad range of investor reporting by different servicers. COVID-19 has highlighted the need for transparent, consistent reporting of forbearance data to investors to support a more efficient PLS market.
Inconsistent investor reporting leaves too much for interpretation. It creates investor angst while making it harder to understand the credit risk associated with underlying mortgage loans. RiskSpan performed an analysis of 2,542 PLS deals (U.S. only) for which loan-level foreclosure metrics are available. The data shows that approximately 78% of loans reported to be in forbearance were backing deals originated between 2005-2008 (“Legacy Bonds”). As you would expect, new issue PLS has a smaller percentage of loans reported to be in forbearance.
Not all loans in forbearance will perform the same and it is critical for investors to receive transparent reporting of underlying collateral within their PLS portfolio in forbearance. These are unchartered times and, unlike historic observations of borrowers requesting forbearance, many loans presently in forbearance are still current on their mortgage payments. In these cases, they have elected to join a forbearance program in case they need it at some future point. Improved forbearance reporting will help investors better understand if borrowers will eventually need to defer payments, modify loan terms, or default leading to foreclosure or sale of the property.
In practice, servicers have followed GSE guidance when conducting forbearance reviews and approval. However, without specific guidance, servicers are working with inconsistent policies and procedures developed on a company-by-company basis to support the COVID forbearance process. For example, borrowers can be forborne for 12-months according to FHFA guidance. Some servicers have elected to take a more conservative approach and are providing forbearance in 3-month increments with extensions possible once a borrower confirms they remain financially impacted by the COVID pandemic.
Servicers have the data that investors want to analyze. Inconsistent practices in the reporting of COVID forbearances by servicers and trustees has resulted in forbearance data being unavailable on certain transactions. This means investors are not able to get a clear picture of the financial health of borrowers in transactions. In some cases, trustees are not reporting forbearance information to investors which makes it nearly impossible to obtain a reliable credit assessment of the underlying collateral.
The PLS market has attempted to identify best practices for monthly loan-level reporting to properly assess the risk of loans where forbearance has been granted. Unfortunately, the current market crisis has highlighted that not all market participants have adopted the best practices and there are not clear advantages for issuers and servicers to provide clear, transparent forbearance reporting. At a minimum, RiskSpan recommends that the following forbearance data elements be reported by servicers for PLS transactions:
- Last Payment Date: The last contractual payment date for a loan (i.e. the loan’s “paid- through date”).
- Loss Mitigation Type: A code indicating the type of loss mitigation the servicer is pursuing with the borrower, loan, or property.
- Forbearance Plan Start Date: The start date when either a) no payment or b) a payment amount less than the contractual obligation has been granted to the borrower.
- Forbearance Plan Scheduled End Date: The date on which a Forbearance Plan is scheduled to end.
- Forbearance Exit – Reason Code: The reason provided by the borrower for exiting a forbearance plan.
- Forbearance Extension Requested: Flag indicating the borrower has requested one or more forbearance extensions.
- Repayment Plan Start Date: The start date for when a borrower has agreed to make monthly mortgage payments greater than the contractual installment in an effort to repay amounts due during a Forbearance Plan.
- Repayment Plan Scheduled End Date: The date at which a Repayment Plan is scheduled to end.
- Repayment Plan Violation Date: The date when the borrower ceased complying with the terms of a defined repayment plan.
The COVID pandemic has highlighted monthly reporting weaknesses by servicers in PLS transactions. Based on investor discussions, additional information is needed to accurately assess the financial health of the underlying collateral. Market participants should take the lessons learned from the current crisis to re-examine prior attempts to define monthly reporting best practices. This includes working with industry groups and regulators to implement consistent, transparent reporting policies and procedures that provide investors with improved forbearance data.
Chart of the Month: Not Just the Economy — Asset Demand Drives Prices
Within weeks of the March 11th declaration of COVID-19 as a global pandemic by the World Health Organization, rating agencies were downgrading businesses across virtually every sector of the economy. Not surprisingly, these downgrades were felt most acutely by businesses that one would reasonably expect to be directly harmed by the ensuing shutdowns, including travel and hospitality firms and retail stores. But the downgrades also hit food companies and other areas of the economy that tend to be more recession resistant.
An accompanying spike in credit spreads was even quicker to materialize. Royal Caribbean’s and Marriott’s credit spreads tripled essentially overnight, while those of other large companies increased by twofold or more.
But then something interesting happened. Almost as quickly as they had risen, most of these spreads began retreating to more normal levels. By mid-June, most spreads were at or lower than where they were prior to the pandemic declaration.
What business reason could plausibly explain this? The pandemic is ongoing and aggregate demand for these companies’ products does not appear to have rebounded in any material way. People are not suddenly flocking back to Marriott’s hotels or Wynn’s resorts.
The story is indeed one of increased demand. But rather than demand for the companies’ products, we’re seeing an upswing in demand for these companies’ debt. What could be driving this demand?
Enter the Federal Reserve. On March 23rd, The Fed announced that its Secondary Market Corporate Credit Facility (SMCCF) would begin purchasing investment-grade corporate bonds in the secondary market, first through ETFs and directly in a later phase.
And poof! Instant demand. And instant price stabilization. All the Fed had to do was announce that it would begin buying bonds (it hasn’t actually started buying yet) for demand to rush back in, push prices up and drive credit spreads down.
To illustrate how quickly spreads reacted to the Fed’s announcement, we tracked seven of the top 20 companies listed by S&P across different industries from early March through mid-June. The chart below plots swap spreads for a single bond (with approximately five years to maturity) from each of the following companies:
- Royal Caribbean Cruises (RCL)
- BMW
- The TJX Companies (which includes discount retailers TJ Maxx, Marshalls, and HomeGoods, among others)
- Marriott
- Wynn Resorts
- Kraft Foods
- Ford Motor Company
We sourced the underlying data for these charts from two RiskSpan partners: S&P, which provided the timing of the downgrades, and Refinitiv, which provided time-series spread data.
The companies we selected don’t cover every industry, of course, but they cover a decent breadth. Incredibly, with the lone exception of Royal Caribbean, swap spreads for every one of these companies are either better than or at the same level as where they were pre-pandemic.
As alluded to above, this recovery cannot be attributed to some miraculous improvement in the underlying economic environment. Literally the only thing that changed was the Fed’s announcement that it would start buying bonds. The fact that Royal Caribbean’s spreads have not fully recovered seems to suggest that the perceived weakness in demand for cruises in the foreseeable future remains strong enough to overwhelm any buoying effect of the impending SMCCF investment. For all the remaining companies, the Fed’s announcement appears to be doing the trick.
We view this as clear and compelling evidence that the Federal Reserve in achieving its intended result of stabilizing asset prices, which in turn should help ease corporate credit.
Webinar: Data Analytics and Modeling in the Cloud – June 24th
On Wednesday, June 24th, at 1:00 PM EDT, join Suhrud Dagli, RiskSpan’s co-founder and chief innovator, and Gary Maier, managing principal of Fintova for a free RiskSpan webinar.
Suhrud and Gary will contrast the pros and cons of analytic solutions native to leading cloud platforms, as well as tips for ensuring data security and managing costs.
Click here to register for the webinar.
Changes to Loss Models…and How to Validate Them
So you’re updating all your modeling assumptions. Don’t forget about governance.
Modelers have now been grappling with how COVID-19 should affect assumptions and forecasts for nearly two months. This exercise is raising at least as many questions as it is answering.
No credit model (perhaps no model at all) is immune. Among the latest examples are mortgage servicers having to confront how to bring their forbearance and loss models into alignment with new realities.
These new realities are requiring servicers to model unprecedented macroeconomic conditions in a new and changing regulatory environment. The generous mortgage forbearance provisions ushered in by March’s CARES Act are not tantamount to loan forgiveness. But servicers probably shouldn’t count on reimbursement of their forbearance advances until loan liquidation (irrespective of what form the payoff takes).
The ramifications of these costs and how servicers should modeling them is a central topic to be addressed in a Mortgage Bankers Association webinar on Wednesday, May 13, “Modeling Forbearance Losses in the COVID-19 world” (free for MBA members). RiskSpan CEO Bernadette Kogler will lead a panel consisting of Faith Schwartz, Suhrud Dagli, and Morgan Snyder in a discussion of the forbearance’s regulatory implications, the limitations of existing models, and best practices for modeling forbearance-related advances, losses, and operational costs.
Models, of course, are only as good as their underlying data and assumptions. When it comes to forbearance modeling, those assumptions obviously have a lot to do with unemployment, but also with the forbearance take-up rate layered on top of more conventional assumptions around rates of delinquency, cures, modifications, and bankruptcies.
The unique nature of this crisis requires modelers to expand their horizons in search of applicable data. For example, GSE data showing how delinquencies trend in rising unemployment scenarios might need to be supplemented by data from Greek or other European crises to better simulate extraordinarily high unemployment rates. Expense and liquidation timing assumptions will likely require looking at GSE and private-label data from the 2008 crisis. Having reliable assumptions around these is critically important because liquidity issues associated with servicing advances are often more an issue of timing than of anything else.
Model adjustments of the magnitude necessary to align them with current conditions almost certainly qualify as “material changes” and present a unique set of challenges to model validators. In addition to confronting an expanded workload brought on by having to re-validate models that might have been validated as recently as a few months ago, validators must also effectively challenge the new assumptions themselves. This will likely prove challenging absent historical context.
RiskSpan’s David Andrukonis will address many of these challenges—particularly as they relate to CECL modeling—as he participates in a free webinar, “Model Risk Management and the Impacts of COVID-19,” sponsored by the Risk Management Association. Perhaps fittingly, this webinar will run concurrent with the MBA webinar discussed above.
As is always the case, the smoothness of these model-change validations will depend on the lengths to which modelers are willing to go to thoroughly document their justifications for the new assumptions. This becomes particularly important when introducing assumptions that significantly differ from those that have been used previously. While it will not be difficult to defend the need for changes, justifying the individual changes themselves will prove more challenging. To this end, meticulously documenting every step of feature selection during the modeling process is critical not only in getting to a reliable model but also in ensuring an efficient validation process.
Documenting what they’re doing and why they’re doing it is no modeler’s favorite part of the job—particularly when operating in crisis mode and just trying to stand up a workable solution as quickly as possible. But applying assumptions that have never been used before always attracts increased scrutiny. Modelers will need to get into the habit of memorializing not only the decisions made regarding data and assumptions, but also the other options considered, and why the other considered options were ultimately passed over.
Documenting this decision-making process is far easier at the time it happens, while the details are fresh in a modeler’s mind, than several months down the road when people inevitably start probing.
Invest in the “ounce of prevention” now. You’ll thank yourself when model validation comes knocking.
April Chart of the Month: COVID-19 Impact on Junior Bond Spreads
Our chart of the month presents data illustrating what has already been acutely felt by mezzanine and other subordinate bond investors – a sharp rise in spreads across all sectors coinciding with the imposition of pandemic-related lockdowns in the United States and around the world.
Spreads on aircraft leases had already begun widening by the start of March as travel was slowing dramatically well before widespread government-imposed shutdowns began hitting other parts of the economy. Spreads on aircraft bonds soared to 1,800 basis points at the end of March and 2,400 basis points on April 25th.
Aircraft differed from most other sectors in its spreads continued to widen throughout April. Spreads in most other sectors began reverting closer to normal in April after experiencing the March market shock. Another notable exception to this pattern were timeshare spreads, which also continued widening during April, reaching a level on April 25th four times where they were on March 2nd.
It is not surprising to see bonds associated with the travel sector of the economy react in this way. Other sectors that did not rebound during April included student, equipment, floor plan and commercial loans.
Widening spreads naturally correspond with price declines over the same period.
The spreads in this chart were computed using TRACE data enhanced by RiskSpan’s Market Color application.
Market dislocations like these are compelling an increasing number of portfolio managers to begin marking their portfolios to model rather than to market. Join us on Thursday at 1:00 p.m. for the webinar, “Valuing Hard-to-Value Bonds” for a lively discussion on some of the ramifications of this change.
RiskSpan VQI: Current Underwriting Standards – March 2020
The RiskSpan Vintage Quality Index (“VQI”) indicates that we are entering the current economic downturn with a cohort of mortgages that were far more conservatively originated than the mortgages in the years leading up to the 2008 crisis. The VQI dropped three points for mortgages originated during March to finish the first quarter of 2020 at 87.77. This reflects generally tight underwriting standards leading into the COVID-19 crisis, though not nearly as tight as what was witnessed in the years immediately following the housing finance crisis.
The VQI climbed slightly during the first two months of the year—evidencing a mild loosening in underwriting standards—peaking at just over 90 in February, before dropping to its current level in March. The following chart illustrates the historical trend of risk layering that contributes to the VQI and how that layering has evolved over time. Mortgages with one borrower—now accounting for more than 50 percent of originations—remain a consistent and important driver of the index and continued to climb during Q1. High-DTI loans, which edged higher in Q1, continue to drive the index today but not nearly to the degree they did in the years leading up to the 2008 crisis.
RiskSpan introduced the VQI in 2015 as a way of quantifying the underwriting environment of a particular vintage of mortgage originations. The idea is to provide credit modelers a way of controlling for a particular vintage’s underwriting standards, which tend to shift over time. The VQI is a function of the average number of risk layers associated with a loan originated during a given month. It is computed using:
- The loan-level historical data released by the GSEs in support of Credit Risk Transfer initiatives (CRT data) for months prior to December 2005, and
- Loan-level disclosure data supporting MBS issuances through today.
The value is then normalized to assign January 1, 2003 an index value of 100. The peak of the index, a value of 139 in December 2007, indicates that loans issued in that month had an average risk layer factor 39% greater (i.e., loans issued that month were 39% riskier) than loans originated during 2003. In other words, lower VQI values indicate tighter underwriting standards (and vice-versa).
Build-Up of VQI
The following chart illustrates how each of the following risk layers contributes to the overall VQI:
- Loans with low credit scores (FICO scores below 660)
- Loans with high loan-to-value ratios (over 80 percent)
- Loans with subordinate liens
- Loans with only one borrower
- Cash-out refinance loans
- Loans secured by multi-unit properties
- Loans secured by investment properties
- Loans with high debt-to-income ratios (over 45%)
- Loans underwritten based on reduced documentation
- Adjustable rate loans
