COVID-19 Archives

Chart of the Month: Not Just the Economy — Asset Demand Drives Prices

Within weeks of the March 11^th 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 23^rd, 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

Credit Spreads React to Fed More than Downgrades

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.

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.

Estimating Credit Losses in the COVID-19 Pandemic

In the years of calm economic expansion before CECL adoption, institutions carefully tuned the macroeconomic forecasting approaches and macro-conditioned credit models they must defend under the new standard. Now, seemingly an hour before public entities are to record (and support) their first macro-conditioned credit losses, a disease with no cure and no vaccine sweeps the globe and darkens whole sectors of the economy. Truth is stranger than fiction.

Institutions Need New Scenarios and Model Adjustments, and Fast

Institutions must overhaul their projection capabilities to withstand audit scrutiny and with only nominal relief in CECL deadlines.

Faced with this unprecedented crisis, many institutions will need to find new sources of macroeconomic scenarios. Business-as-usual scenarios that seemed sensible a few months ago now appear wildly optimistic.

Credit and prepay models built on data prior to February 2020 – the models that institutions have spent so much time and effort validating – must now be rethought entirely.

Institutions may not have a great deal of time to make the necessary adjustments. While the Coronavirus Aid, Relief, and Economic Security (CARES) Act (in Section 4014, Optional Temporary Relief from Current Expected Credit Losses) allows banks and credit unions a brief delay in CECL adoption, RiskSpan’s public bank clients are adopting as scheduled. One reason is the short and uncertain length of the delay, which expires either on 12/31/2020 or when the national coronavirus emergency is declared over, whichever comes first. The national emergency could be declared over at any time, and indeed we hope the national emergency does end soon. Another reason is that, as Grant Thornton has noted, eligible entities that defer adoption will need to retrospectively restate their year-to-date results when they adopt ASU 2016-13. Ultimately, the “relief” is anti-relief.

The revised CECL approaches that institutions race into production will need to withstand the inspection not only of the normal sets of eyes, but many other senior stakeholders. Scrutiny on credit accounting will be more intense than ever in light of COVID-19.

Finally, to converge on a new macroeconomic scenario and model adjustments, institutions will be prompted by auditors and senior management to run their portfolio many times under many different combinations of approaches. As you can imagine, the volume of runs hitting RiskSpan’s Allowance Suite has spiked this month, with institutions running many different scenarios, and institutions with available-for-sale bond portfolios sending more impaired bonds than anticipated. The physics of pulling off so many runs in such a short time are impossible for teams and systems not set up for that scale.

How RiskSpan is Helping Institutions Overcome These Challenges

RiskSpan helps clients solve the new credit accounting rules for loans, held-to-maturity debt securities, and available-for-sale debt securities. As we all navigate these unique and evolving times, let us share how we incorporate the impact of COVID-19 into the allowances we generate. The toolbox includes new macroeconomic scenarios that reflect COVID-19, adjustments to credit and prepay models, an ability to selectively bypass models with user-defined scenarios, and even – sparingly – support for the dreaded “Q-factor” or management qualitative adjustment.

COVID-19-Driven Macroeconomic scenarios

RiskSpan partners with S&P Global Market Intelligence (“Market Intelligence”), employing their CECL model within our Allowance Suite. Each quarter, we apply a new macroeconomic forecast from the S&P Global Ratings team of economists (“S&P Global Economics”). We feed this forecast to all credit models in our platform to influence projections of default and severity and ultimately allowance. S&P Global Economics recent research has focused significantly on coronavirus, including the global and US economic impact of containment and mitigation measures and the recovery timeline. When the credit models take in this bearish outlook for the 3/31/2020 runs, they will return higher defaults and severities compared to prior quarters when the macroeconomic forecast was benign, which in turn will drive higher allowances. Auditors, examiners, and investors will rightly expect to see this.

MODEL ENHANCEMENTS AND TUNINGS

RiskSpan advises clients to apply model enhancements or adjustments as follows:

C&I loans, Corporate Bonds, Bank Loans, CLOs

Corporate & Industrial (C&I) loans often carry internal risk ratings that are ‘through-the-cycle’ evaluations of the default risk and highly independent of cyclical changes in creditworthiness. Corporate bonds carry public credit ratings that are designed to represent forward-looking opinions about the creditworthiness of issuers and obligations, known to be relatively stable throughout a cycle. (Note: higher ratings have been consistently more stable than lower ratings).

During upswings (downturns), an obligor’s point-in-time or short-term default rates will fall (rise) as the economic environment changes, and credit expectations may be better (worse) than implied by stable credit indicators and associated long-term default frequencies.

To appropriately reflect the impact of COVID-19 on allowances, most of our clients are now applying industry-specific Point-in-Time (PIT) adjustments, based on Market Intelligence’s Probability of Default Model Market Signals (PDMS). These PIT signals, which use recent, industry-specific trading activity, are used as a guide to form limited adjustments to stable (or in some cases lagging) internal risk ratings of commercial loans and the current credit ratings of corporate bonds. (Adjustments are for the purpose of the CECL model only.)

Because these adjusted risk ratings are key inputs to Market Intelligence’s CECL Model that we apply to these asset classes, Market Intelligence’s PDMS can influence allowances. Since economic conditions impact certain industry sectors (e.g., airlines, oil and gas, retail) in different ways, the industry-specific notches tend to vary by industry – some positive, some neutral, some negative. Consequently, in a diversified portfolio, we would not ordinarily expect a directional bias to the overall allowance, even though the allowance by industry will be refined. But this assumes a normal economic climate. During a major market downturn like we experienced in the runup to March 31, 2020, notches were negative across almost all industries, and we saw higher allowances as a result. Given the environment, this result is to be expected.

Resi Loans and RMBS

Even if we forecast the macroeconomy exactly right, the models of how borrowers perform given different macroeconomic patterns were built on prior decades of experience. Some of the macroeconomic twists and turns that this crisis will unleash will take different shapes than the last crisis.

For example, a model built on the past two decades of data can only extrapolate what borrowers will do if unemployment goes to 20%; the historical data doesn’t contain such a stress. Even if the macroeconomic patterns do resemble prior crises, policy response will be different, and so will borrower behavior. And then after some recovery period, we expect borrower behavior to fall back into its classic grooves. For these reasons, we recommend model tunings that, all else equal, boost or dampen delinquencies, defaults and recoveries during a time-limited recovery window to account for the near-term impacts of COVID-19. We help clients quantify these model tunings by back-testing model projections against experience from recent weeks.

In the past month, we have observed slower prepays from housing turnover because social distancing has blocked on-site walkthroughs and therefore home sales. Refinance applications, however, continue to roll in (as expected in this low rate environment), and the refi market is adopting property inspection waivers and remote notarization to work through the demand. As noted under Credit Model Tuning above, we help clients quantify and apply prepay model tunings that act in the short-term and can phase out across the long-term forecast horizon.

ABS

Conventionally, ABS research departments form expected-case projections for underlying collateral by averaging the historical default, severity, and prepay behavior of the issuer. Because CECL calls for expected-case projections, RiskSpan’s bond research team has applied the same approach to generate ABS collateral projections for clients.

ABS researchers identify stress scenarios by applying multiples or standard deviations to the historical averages. In the current climate, the expected case is a stress case. Therefore, RiskSpan has refined its methodology to apply our stress scenarios as our expected scenarios in times – like now – when the S&P Global Economics baseline macroeconomic forecasts show stress.

MODEL OVERRIDES/USER-DEFINED SCENARIOS

Where clients have their own views of how loans or bonds will perform, we have always empowered them to bypass models and define their own projections of default, severity, and prepayment.

Resi Loans and RMBS

For resi loans and RMBS collateral, we have rolled out new “build-a-curve” functionality that allows clients to use our platform to create their own default and severity paths by stipulating drivers such as:

Peak unemployment rate,
How long the peak will last and where unemployment rate will settle,
Share of those unemployed who will roll delinquent,
Length of external forbearance timelines, and
Share of loans that roll delinquent that will eventually default.

“Q-Factors”

At many institutions, we have seen “Q-factors” (“qualitative” management adjustments on top of modeled allowance results) go from all but forbidden before this crisis to all but required during it. This is because the macroeconomic scenarios now being fed into credit models is beyond the data upon which any vendor or proprietary models were built.

For example, unemployment rate gradually rose to 10% during the great recession. Many scenarios institutions are now considering call for unemployment rate to spike suddenly to 20% or more. Models can only extrapolate (beyond their known sample) to project obligor performance under these scenarios—there is nothing else they can do. But we know that such extrapolations are unlikely to be exactly right. This creates a strong argument to allow, or even encourage, management adjustments to model results. We are advising many clients to do just that, drawing on available data from natural disasters.

Throughput

As important as these quantitative refinements are, performing multiple runs to better understand the range of possible allowance results is equally important to meeting auditor expectations. Whereas before some institutions would use month-end allowances from a month before quarter-end because of tight reporting deadlines, now such institutions are running again at quarter-end, under a very tight timeline, to meet auditor demands for up-to-the-minute analysis. Whereas previously many institutions would run one macroeconomic scenario, now – at the prompting of auditors or their own management – they are running multiple. Institutions that previously did not apply Market Intelligence’s PDMS to their commercial loans and corporate bonds are now running with and without it to evaluate the difference. The dimensionality quickly explodes from one run per quarter to two, ten, or twenty. RiskSpan is happy to offer its platform to clients to support such throughput.

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We will be exploring these topics in greater detail in a webinar on May 28th, 2020 at 1:00 p.m. EDT. You can join us by registering here. I can also be reached directly at dandrukonis@riskspan.com.

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.

Modeling Delinquency Deluge

RiskSpan’s CEO Bernadette Kogler recently spoke with Simon Boughey of Structured Credit Investor (SCI) to discuss COVIDー19’s impact on the mortgage market & securitizations of mortgage assets. Simon’s article has been republished here with their permission.

Wednesday 8 April 2020 17:45 London/ 12.45 New York/ 01.45 (+ 1 day) Tokyo

Mortgage market advisers and consultants are struggling to find any models that work for the current crisis, but they are telling clients that they should prepare for a worst case scenario in mortgage market and securitizations of mortgage assets.

“Our clients are modeling a range of scenarios but are preparing themselves for the worst case including sustained levels of unemployment. Hopefully it won’t be that bad, but they need to prepare themselves,” says Bernadette Kogler, Chief Executive Officer of RiskSpan, a Washington, DC-based analytics and modeling firm which has particular expertise in mortgage markets.

RiskSpan clients include firms prominent in the mortgage securitization industry, such as lenders and servicers like Wells Fargo and Flagstar, as well as Fannie Mae and Freddie Mac. It also has clients on the buy-side, such as Barings, Northern Trust and Fidelity.

Both buy-side and sell-side clients are struggling to assess what the economic devastation of the last two weeks, with more to come, will mean for the MBS markets.

The “worst case” could be very bleak indeed. Economists at the Federal Reserve Bank of St Louis have predicted that the dislocation elicited by COVID-19 could cause 47M job losses in the US. This translates to an unemployment rate of 32% – comfortably worse than the rate of 25% recorded in the Great Depression of 1930-33.

Other economists are not quite so pessimistic, but Kogler agrees and she is advising clients to prepare for an unemployment rate of 30% in the worst affected regions of the USA. Las Vegas, Nevada, for example, is particularly exposed to the collapse of the hospitality industry, while Texas has been hit with a double whammy of a Coronavirus lockdown and a precipitous decline of oil and gas prices.

Metropolitan Las Vegas has a population of over 2.5M while the state of Texas is home to over 12.5M people.

An unemployment rate of 30% could lead to a mortgage delinquency rate of around 30%. Data provided by the Bureau of Labor shows that the correlation between unemployment and mortgage delinquency is very high – virtually 1:1. So, for example, both unemployment and mortgage delinquency peaked at around 10% in the Great Recession.

mortgage delinquency rate and unemployment rare

At the moment, a delinquency rate of 10% looks a lot better than what might be seen in a few months from now. Of course, foreclosure rates will be substantially lower than delinquencies, but if delinquencies do hit 30% foreclosures might be as high as 30%. The effect on the MBS market, both agency and non-agency, of delinquency rates of this magnitude is hard to over-estimate.

Kogler suggests that around 1M Federal Housing Authority (FHA) loans could be affected by unemployment levels like that.

The GSEs, of course, offer largely guaranteed debt to capital markets investors in the TBA market, so their position could become particularly painful.

On January 23, when COVID-19 was still something to be not too bothered about, Federal Housing Finance Authority (FHFA) director Mark Calabria gave a speech to National Association of Homebuilders and reminded his audience that Fannie Mae and Freddie Mac had a leverage ratio of 300 to 1.

“Given their risks and financial position, even in a modest downturn, Fannie and Freddie will fail,” he said.

Part of the problem in modeling for a disaster of this proportion is that there are still many unknowns. Though the Federal Reserve has intervened with a stimulus package, but no-one knows how much it will continue to do, or can do, as the crisis persists.

Certain areas of the mortgage industry are still without any Federal aid. Mortgage originators and servicers hope to receive some backing, but nothing has been divulged as yet.

Models based on natural disasters provide no firm clue about this crisis will unfold. In disasters of that kind, insurance companies intervene at some juncture, distorting the appropriateness of disaster-based models for the COVID-19 world.

“No models are sufficient. Predictive models are based on historical data, and to the extent that we have not seen anything like this before they are not going to work,” says Kogler.

Simon Boughey

08/04/2020 17:45:18

This article was published in Structured Credit Investor on 08 April 2020.

Impact of COVID-19 on Mortgage Originator Liquidity

Over the past few years, mortgage origination has shifted away from depository institutions and into the hands of independent mortgage banking entities. This transition has led to concerns over the ability of non-bank lenders to weather adverse market conditions.

Non-bank lenders employ large amounts of leverage. They rely heavily on warehouse lines of credit to finance their operations, while their balance sheet assets are mostly comprised of unclosed loan-locks in the pipeline, hedge instruments, funded whole–loan inventory available for sale, and mortgage servicing rights. Growing concern over the relative illiquidity of MSRs in particular, combined with the exposure of non-bank lenders to Government–backed servicing assets, prompted Ginnie Mae to announce plans to perform stress-testing on their non-bank issuers in 2019. The COVID-19 pandemic, however, appears poised to deliver a real-world scenario far worse than any stress test—one whose market disruption causes a disastrous liquidity crunch for a large sector of the mortgage origination market.

As the pandemic began to unfold and equity markets went into freefall, flight-to-quality trades pushed yields on Treasuries to record lows. Mortgage-backed securities significantly underperformed their treasury benchmarks, causing the Fed to re-ignite its MBS purchase program on March 19th and inject additional liquidity into the RMBS market.

This move by the Fed had an unintended consequence, however. As mortgage rates dropped, TBA passthroughs1 rallied significantly. Because TBAs are a widely used hedge instrument for mortgage originators, this rally triggered widespread margin calls on originators who sell TBAs short to the broker dealer community via lines of credit. Typically, this strategy is considered prudent for lenders and carries little risk, as originators are simply offsetting their interest rate exposure on their locked pipeline by selling a deliverable hedge instrument. However, the sudden spike in unemployment created by the pandemic has generated considerable uncertainty around whether pipeline loans will actually be able to close. Traditional pull–through models are rendered useless, as they do not contemplate huge numbers of mortgage applicants losing their jobs within a short period of locking their loans.

The sharp uptick in expected pipeline fallout coupled with heavy hedge losses and increased volatility in the TBA market has created the perfect storm for a liquidity crisis. Within ten days of the Fed’s announcement, the Mortgage Bankers Association was sounding the alarm to regulators about the extreme pressure that mortgage originators, particularly non-bank institutions, faced in the wake of excessive TBA margin calls, and the risk of widespread defaults that would likely result unless swift action were taken.

Even with loans that make it past the closing table and into an originator’s available-for-sale warehouse, it’s not exactly business as usual in this environment. Broker-dealer balance sheets are “heavier” than usual because their network of end-account customers who typically invest in mortgage–related securities are looking to reduce their exposure to the sector. This is causing bid-ask spreads in the TBA market to widen and specified pool pay-ups to collapse, both of which cut into profit margins for originators and further strain liquidity.

Lenders that sell their originated mortgages via the agency cash window programs are not immune, either. Cash executions are rumored to be softening relative to MBS executions. Agency capital requirements related to the actual/actual remittance requirements for servicers who use the cash window are leading to higher model–implied guarantee fees. This is creating a disconnect between TBA hedges and cash window prices for closed whole loans, which also adds pressure to expected profit margins. Originators who sell to correspondent buyers face similar challenges as correspondent buyers price in additional margin to help offset extreme market volatility and the anticipation of increased future borrower credit risk.

Adding to the immediate concern about non-bank mortgage originator liquidity is the current dysfunction in the mortgage servicing rights market. Mortgage servicers in scheduled/scheduled remittance programs are on the hook to advance principal and interest payments on the mortgage, as well as tax and insurance payments when a borrower misses a payment. These advances can quickly become capital intensive, a particular concern for non-bank servicers.

As the pandemic unfolded, MSR buyers quickly evaporated as government agencies have scrambled to provide relief to borrowers facing hardship. Similar relief, however, has not been offered to mortgage servicers. With co-issue counterparties and correspondent lenders softening bids or pausing MSR purchases altogether, originators that typically sell loans servicing–released are now forced to either 1) sell at heavily discounted prices to the relatively small handful of remaining buyers, or 2) retain the asset. Both options adversely impact profit margins and exacerbate existing liquidity concerns. For originators that choose to retain servicing, the prospect of servicer advance requirements and borrower forbearance pose additional long-term capital and liquidity issues.

Not coincidentally, MSR valuations are falling. Non-bank lenders holding these assets will be at risk of breaching net-worth, leverage, and liquidity covenants for warehouse lines, MSR financing facilities, and TBA lines of credit. Breaching these covenants would have a devastating effect on the lenders’ ability to do business.

Every crisis is unique, but things are beginning to feel eerily reminiscent of 2008. During that crisis, RiskSpan leveraged its deep familiarity with mortgage credit to help capital markets participants navigate unprecedented market conditions. Today, we are assisting our mortgage originator clients with modeling the impact of COVID-19 across several areas, including:

Balance Sheet and Liquidity Stress Testing
Servicing Advance Scenario Modeling and Analysis
Alternative Pipeline Hedge Coverage Models
MSR and Whole Loan Valuations

Contact us for a free consultation.

Modeling Credit and the Impact of COVID-19

Notwithstanding action taken at every level of government (including emergency measures taken by the Federal Reserve) to attempt to limit the economic fallout of the COVID-19 pandemic, markets remain highly volatile. How loans and structured credit are modeled needs to be modified (and quickly) to reflect the emerging expectations, moral hazard, and risks from the current crisis.

In the consumer finance market, existing models and data for the primary, secondary, and tertiary markets are strong, but these have a high probability of performing poorly as they’re based on historic data that doesn’t reflect the current crisis. To address this issue, RiskSpan has created a top–down framework that incorporates data from select historical events as well as a user-defined view of macro-economic forecasts.

A Framework for Modeling Mortgage Credit in COVID-19

Compile data from past catastrophes

The basis of our approach continues to be data-driven as historic events can serve as data points to inform analysis for the current crisis. Relevant catastrophes to look upon include:

Natural disasters, including Hurricane Katrina and the impact on regional economies
The Great Recession and its impact on certain borrowers
The federal government’s response to the Great Recession
The Great Depression
Federal Reserve Board stress tests

These events can inform some part of the modeling framework for key performance drivers, including: 1) unemployment and short-term delinquencies, 2) government relief programs, 3) default and foreclosure, and 4) home price changes and losses to investors.

UNEMPLOYMENT AND MORTGAGE DELINQUENCIES

The impact of unemployment on mortgage delinquencies will be severe. As the graph below shows, the relationship between unemployment and delinquencies is highly correlated—a nearly 1:1 relationship.

We further expect that unemployment and subsequent delinquencies will correlate to regional, state, or business sector unemployment. Certain industries are more susceptible to COVID-19 related disruption stemming from the decline of consumer demand, state and federal orders, and international government actions that affect tourism. Further, state–level executive orders and COVID-19 responses have been inconsistent—some state orders are more severe than others. This will lead to a corresponding impact at the state versus federal level and highlights the importance of taking geo-specific macroeconomic factors into account.

Economic forecasts of unemployment related to the current crisis vary widely so we look to past levels to inform possible boundaries. During the Great Recession, nearly 9 million people lost their jobs within one year leading to an unemployment rate of 10%, according to the BLS. In contrast, new unemployment claims spiked on March 26, 2020, to 3.28 million according to the Labor Department, and then to an astounding 6.6 million today. These figures far exceed the previous high of 665,000 claims during the Great Recession. The Great Depression can also serve as a benchmark when unemployment peaked at 24.9% in 1933. This can be particularly relevant for certain geographies or industries. The Federal Reserve 2020 Severely Adverse Scenario, with unemployment peaking at 10% in 2021, suddenly looks more akin to a base or optimistic scenario.

GOVERNMENT RELIEF AND DELINQUENCIES

The national scope of COVID-19 is forcing governments and mortgage guarantors into nationwide mortgage forbearance, foreclosure moratoriums, and government relief programs. Regardless of type or reason, mortgage non-payment will result in a peak in delinquencies which may remain elevated through a forbearance period.

We can look to natural disasters, like New Orleans and Puerto Rico hurricanes and the Houston floods, to find dramatic and immediate spikes in delinquencies. However, these natural disasters did not result in a corresponding spike in serious mortgages delinquencies or defaults. In these examples, forbearance and moratorium programs provided relief to borrowers until insurance companies paid claims.

SERIOUSLY DELINQUENT AND DEFAULT

As the table below shows, the Great Recession produced multiyear elevated delinquency and default rates with delinquencies spiking at near 10% in 2010. Peak default rates for some private investor programs exceeded 40%.

However, mortgage defaults from the Great Recession included the impact of aggressively expanded underwriting with rampant and unfettered fraud in the form of subprime and NINA (No Income No Assets) mortgage programs. The historical trend of RiskSpan’s Vintage Quality Index reflects the degree to which underwriting guidelines have generally tightened and steadied over the past decade.

Efforts to reduce default rates during the 2008 financial crisis were further hampered by initially slow government responses and uncoordinated efforts between investors and federal and state agencies. In the current crisis, we can expect government responses to COVID-19 to be immediate, aggressive, and coordinated. The U.S. federal government has already enacted relief legislation, recognizing that forbearance, loan modifications, and moratoriums are all proven tools to reduce mortgage delinquencies and severities. Unlike in 2008, however, the mortgages impacted by the current crisis are primarily federally insured and likely skewed towards low–income and low–FICO borrowers. Because federally insured mortgages tend to find their way into Ginnie Mae MBS, emerging issues relating to advances on those securities may require new and unproven programs.

HOME PRICES

The residential real estate market was strong prior to the pandemic. Home sales in February rose 6.5% to 5.77 million, according to the National Association of Realtors, and median home prices rose 8.0% year-over-year. As COVID-19 spreads, new sales activity is already coming to a halt, yet the impact on HPA is uncertain. A review of natural disasters, such as the Houston and Louisiana hurricanes, shows little negative long–term impact on HPA after the events. By comparison, the Great Recession produced nationwide declines that did not begin to rebound until 2012 (see below).

Supporting the argument of a short-term impact on HPA are the recent strength of the U.S. economy and continued discipline in credit lending standards. Further, there is also strong generational demand for housing during a nationwide housing shortage. Arguments for a less optimistic view are based on the potential for a longer-than–expected national economic shutdown and structural impacts to the economy, employment, and industries even after the pandemic ends.

Summary

The aphorism “things work until they don’t” is commonly used to explain financial markets and behaviors. The COVID-19 crisis is simply the latest manifestation of this reality. Risk managers and finance executives have to decide whether to rely on current models built on historical events and data – the models that got you here – or to start rethinking and retesting hypotheses and assumptions to manage and quantify new risks.

Email us at info@riskspan.com to talk about how RiskSpan can help and click here to see RS Edge in action.

Managing Operational and Credit Risk in Mortgage Servicing Portfolios During the COVID-19 Crisis

Tomorrow (April 1st) is the due date of the first significant wave of mortgage payments since the Coronavirus began disrupting the economy. The operational impact of COVID-19 on mortgage bankers—and servicers in particular—has been swift and dramatic. It will not soon subside. Its financial impact remains on the horizon but is likely to be felt over a more extended period.

Whereas borrower inquiries related to the Coronavirus accounted for zero percent of servicer call volume as recently as March 16th, within a week they have spiked to more than 25 percent of inquiries at one servicer. Another servicer reported receiving over 20,000 calls relating to forbearance relief during the same period.

We are officially in a new world. The next several months appear to hold chaos, disruption, and potentially devastating losses for mortgage servicers. When delinquencies associated with April 1st payments start to hit, the financial impact—felt primarily through P&I, T&I, and corporate advances, additional collection and compliance costs, and the loss of servicing fee income simply because fewer payments are being made—has the potential to linger considerably longer than the liquidity and funding crisis currently rocking financial markets.

Having a roadmap for navigating impending financial, credit, and operational dilemmas has never been more important.

Market dislocations created by the speed and seriousness of COVID-19 are constraining (and will continue to constrain) servicers’ tools for responding to and resolving a forthcoming tsunami of delinquencies, foreclosures and REOs. The ability of servicers to manage through this will be further complicated by external factors that will dictate when and how servicers will be able to manage their businesses. These are likely to include various forms of government intervention, such as payment holidays, mandatory forbearance, foreclosure moratoriums, and modification programs. While protecting borrowers, these programs will also add layers of complexity into servicer compliance operations.

In addition to introducing new sets of moral hazard issues for the servicing of mortgages, increases in delinquencies and illiquidity of trading markets will seize the trading markets for servicing portfolios, limiting mortgage bankers’ access to cash. Investors, guarantors, and insurers will increase their oversight into servicer operations to minimize their losses.

One Solution

RiskSpan has been working with its mortgage banking clients to construct a modeling framework for assessing, quantifying, and managing COVID-19 risk to servicing operations and income statements. The framework covers the full lifecycle of a servicing asset and is designed to forecast each of the following under several defined stress scenarios:

Principal and interest advances
Escrow (T&I) advances
Corporate advances to cover foreclosure, liquidations and REO expenditures
Financing and capital implications of delinquent and defaulted loans
Repurchases, denials, and rescissions
Compensatory fees and curtailments

In addition to projecting these financial costs, the modeling framework forecasts the incremental operational costs associated with servicing a portfolio with increasing shares of delinquencies, defaults, bankruptcies, liquidations, and REOs—including all the incremental personnel, compliance and other costs associated with servicing a portfolio that was prime at acquisition but is suddenly beginning to take on subprime characteristics.

Understanding the Impact of Federal Reserve Emergency Rate Cuts

Disruptions to the U.S. and global economy brought about by COVID-19 have prompted the Federal Reserve to take a number of emergency measures. These include twice cutting the federal funds rate (to near zero), resuming its purchase of securities, and temporarily relaxing regulatory capital and liquidity requirements (among several other things).

Although the Fed’s actions take many forms, few things capture investors’ attention in the way emergency rate cuts do. Predicting how financial markets will respond to these cuts is a complicated undertaking. To help investors analyze how these events have affected markets historically, RiskSpan has developed a tool to help investors visualize how various market indices, commodities, currencies and bond yields have reacted to emergency Fed rate cuts in the wake of various market shocks.

Analyzing events in this way enables investors to more effectively manage their portfolio risk by monitoring market–moving events and identifying response patterns. We analyze a range of past market events to formulate scenarios for RiskSpan’s RiskDynamics market risk service.

Every crisis is unique, of course. But the Fed’s interest rate cuts this month are specifically reminiscent of seven actions it has taken in response to past economic threats, including the Russian Ruble crisis (2014), the bursting of the dot-com bubble (2000), the September 11th attacks (2001), and the subprime mortgage/Lehman Brothers collapse (2008).

The chart below compares the response of the S&P 500 to the Lehman collapse and COVID-19 and how long it takes the ensuing Fed rate cut to affect the market. The similarity in the shape of these two curves is quite striking. It also reflects the time required for Congress to pass stimulus following Fed action.

The tool displays the performance of several markets across three asset classes in response to each of the seven Fed cuts. In this version we have included stocks, rates and commodities. The two interactive charts specifically help to visualize the following:

Performance of asset classes from 20 days before through 60 days following each rate cut.
Performance indexed to the event date—helping to illustrate market conditions leading up to the rate cut and its subsequent impact.
Daily returns enabling a cross-sector, cross-market comparisons to each rate cut.

Additional patterns also emerge when looking at how markets have responded to these seven prior cuts:

Equity market collapses tend to stall, but the recovery (if any) is slow.
The volatility index stabilizes, but it takes time to mean revert.
Treasury bonds generally perform better than other asset classes. Long–dated bonds don’t perform as well.
Crude oil continues to sell off in most cases.

We are continually expanding the list of asset classes and events covered by the tool. Our data science team is also working some interesting analytics for publication.

We welcome your feedback and requests for additional analysis. Please contact us to discuss further.

¹ In 2011-12, the market saw significant differences in buyout behavior, for example Bank of America was slow to buy out delinquent loans.

² On Bloomberg, the delinquency states 90 days onward are compressed into a single 90+ state.

Visualizing a CMBS Portfolio’s Exposure to COVID-19

he economic impact of the Coronavirus outbreak is all but certain to be felt by CMBS investors. The only real uncertainty surrounds when missed rent payments will begin, what industries are likely to feel them most acutely, and—more to the point—how your portfolio aligns with these eventualities.

The dashboard below—created using RS Edge and Tableau—displays a stylized example compiling small random excerpts from several CMBS portfolios. While business disruptions have not (yet) lasted long enough to be reflected in CMBS default rates, visualizing portfolios in this way provides a powerful tool for zeroing in on where problems are most likely to emerge.

The maps at the top of the dashboard juxtapose the portfolio’s geographic concentration with states where COVID-19 prevalence is highest. Investors are able to drill down not only into individual states but into individual NAICS-defined industries that the loans in their deals cover.

At each level of analysis (overall, by state, or by industry) the dashboard not only reports total exposure in UPB but also important risk metrics around the portfolio’s DSCR and LTV, thus enabling investors to quickly visualize how much cushion the underlying loans have to absorb missed rent payments before the deals begin to experience losses.

The real value of visualizations like these, of course, is the limitlessness of their flexibility and their applicability to any market sector.

We sincerely desire to be helpful during these unprecedented market conditions. Our teams are actively helping clients to manage through them. Whether you are looking for historical context, market analysis or just a conversation with folks who have been through several market cycles, we are here to provide support. Please contact us to talk about what we can do for you.

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How RiskSpan is Helping Institutions Overcome These Challenges

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April Chart of the Month: COVID-19 Impact on Junior Bond Spreads

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Impact of COVID-19 on Mortgage Originator Liquidity

Modeling Credit and the Impact of COVID-19

A Framework for Modeling Mortgage Credit in COVID-19

Compile data from past catastrophes

UNEMPLOYMENT AND MORTGAGE DELINQUENCIES

GOVERNMENT RELIEF AND DELINQUENCIES

SERIOUSLY DELINQUENT AND DEFAULT

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Summary

Managing Operational and Credit Risk in Mortgage Servicing Portfolios During the COVID-19 Crisis

Understanding the Impact of Federal Reserve Emergency Rate Cuts

Visualizing a CMBS Portfolio’s Exposure to COVID-19

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