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Articles Tagged with: Credit Analytics

COVID-19 and the Cloud

COVID-19 creates a need for analytics in real time

Regarding the COVID-19 pandemic, Warren Buffet has observed that we haven’t faced anything that quite resembles this problem” and the fallout is “still hard to evaluate. 

The pandemic has created unprecedented shock to economies and asset performance. The recent unemployment  data, although encouraging , has only added to the uncertaintyFurthermore, impact and recovery are unevenoften varying considerably from county to county and city to city. Consider: 

  1. COVID-19 cases and fatalities were initially concentrated in just a few cities and counties resulting in almost a total shutdown of these regions. 
  2. Certain sectors, such as travel and leisure, have been affected worse than others while other sectors such as oil and gas have additional issues  Regions with exposure to these sectors have higher unemployment rates even with fewer COVID-19 cases 
  3. Timing of reopening and recoveries have also varied due to regional and political factors. 

Regional employment, business activity, consumer spending and several other macro factors are changing in real time. This information is available through several non-traditional data sources. 

Legacy models are not working, and several known correlations are broken. 

Determining value and risk in this environment is requiring unprecedented quantities of analytics and on-demand computational bandwidth. 

COVID-19 in the Cloud

Need for on-demand computation and storage across the organization 

I don’t need a hard disk in my computer if I can get to the server faster… carrying around these non-connected computers is byzantine by comparison.” ~ Steve Jobs 


Front office, risk management, quants and model risk management – every aspect of the analytics ecosystem requires the ability to run large number of scenarios quickly. 

Portfolio managers need to recalibrate asset valuation, manage hedges and answer questions from senior management, all while looking for opportunities to find cheap assets. Risk managers are working closely with quants and portfolio managers to better understand the impact of this unprecedented environment on assets. Quants must not only support existing risk and valuation processes but also be able to run new estimations and explain model behavior as data streams in from variety of sources. 

These activities require several processors and large storage units to be stood up on-demand. Even in normal times infrastructure teams require at least 10 to 12 weeks to procure and deploy additional hardware. With most of the financial services world now working remotely, this time lag is further exaggerated.  

No individual firm maintains enough excess capacity to accommodate such a large and urgent need for data and computation. 

The work-from-home model has proven that we have sufficient internet bandwidth to enable the fast access required to host and use data on the cloud. 

Cloud is about how you do computing

“Cloud is about how you do computing, not where you do computing.” ~ Paul Maritz, CEO of VMware 


Cloud computing is now part of everyday vocabulary and powers even the most common consumer devices. However, financial services firms are still in early stages of evaluating and transitioning to a cloud-based computing environment. 

Cloud is the only way to procure the level of surge capacity required today. At RiskSpan we are computing an average of half-million additional scenarios per client on demand. Users don’t have the luxury to wait for an overnight batch process to react to changing market conditions. End users fire off a new scenario assuming that the hardware will scale up automagically. 

When searching Google’s large dataset or using Salesforce to run analytics we expect the hardware scaling to be limitless. Unfortunately, valuation and risk management software are typically built to run on a pre-defined hardware configuration.  

Cloud native applications, in contrast, are designed and built to leverage the on-demand scaling of a cloud platform. Valuation and risk management products offered as SaaS scale on-demand, managing the integration with cloud platforms. 

Financial services firms don’t need to take on the burden of rewriting their software to work on the cloud. Platforms such as RS Edge enable clients to plug their existing data, assumptions and models into a cloudnative platform. This enables them to get all the analytics they’ve always had—just faster and cheaper.  

Serverless access can also help companies provide access to their quant groups without incurring additional IT resource expense. 

A recent survey from Flexera shows that 30% of enterprises have increased their cloud usage significantly due to COVID-19.

COVID-19 in the Cloud

Cloud is cost effective 

In 2000, when my partner Ben Horowitz was CEO of the first cloud computing company, Loudcloud, the cost of a customer running a basic Internet application was approximately $150,000 a month.”  ~ Marc Andreessen, Co-founder of Netscape, Board Member of Facebook 


Cloud hardware is cost effective, primarily due to the on-demand nature of the pricing model. $250B asset manager uses RS Edge to run millions of scenarios for a 45minute period every day. Analysis is performed over a thousand servers at a cost of $500 per month. The same hardware if deployed for 24 hours would cost $27,000 per month 

Cloud is not free and can be a two-edged sword. The same on-demand aspect thaenables end users to spin up servers as needed, if not monitoredcan cause the cost of such servers to accumulate to undesirable levelsOne of the benefits of a cloud-native platform is built-on procedures to drop unused servers, which minimizes the risk of paying for unused bandwidth. 

And yes, Mr. Andreeseen’s basic application can be hosted today for less than $100 per month 

The same survey from Flexera shows that organizations plan to increase public cloud spending by 47% over the next 12 months. 

COVID-19 in the Cloud

Alternate data analysis

“The temptation to form premature theories upon insufficient data is the bane of our profession.” ~ Sir Arthur Conan Doyle, Sherlock Holmes.


Alternate data sources are not always easily accessible and available within analytic applications. The effort and time required to integrate them can be wasted if the usefulness of the information cannot be determined upfront. Timing of analyzing and applying the data is key. 

Machine learning techniques offer quick and robust ways of analyzing data. Tools to run these algorithms are not readily available on a desktop computer.  

Every major cloud platform provides a wealth of tools, algorithms and pre-trained models to integrate and analyze large and messy alternate datasets. 

Join fintova’s Gary Maier and me at 1 p.m. EDT on June 24th as we discuss other important factors to consider when performing analytics in the cloud. Register now.


Chart of the Month: Tracking Mortgage Delinquency Against Non-traditional Economic Indicators by MSA

Tracking Mortgage Delinquency Against Non-traditional Economic Indicators by MSA 

Traditional economic indicators lack the timeliness and regional granularity necessary to track the impact of COVID-19 pandemic on communities across the country. Unemployment reports published by the Bureau of Labor Statistics, for example, tend to have latency issues and don’t cover all workers. As regional economies attempt to get back to a new “normal” RiskSpan has begun compiling non-traditional “alternative” data that can provide a more granular and real-time view of issues and trends. In past crises, traditional macro indicators such as home price indices and unemployment rates were sufficient to explain the trajectory of consumer credit. However, in the current crisis, mortgage delinquencies are deteriorating more rapidly with significant regional dispersion. Serious mortgage delinquencies in the New York metro region were around 1.1% by April 2009 vs 30 day delinquencies at 9.9% of UPB in April 2020.  

STACR loan–level data shows that nationwide 30–day delinquencies increased from 0.8% to 4.2% nationwide. In this chart we track the performance and state of employment of 5 large metros (MSA). 

May Chart of the Month


Indicators included in our Chart of the Month: 

Change in unemployment is the BLS measure computed from unemployment claims. Traditionally this indicator has been used to measure economic health of a region. BLS reporting typically lags by months and weeks. 

Air quality index is a measure we calculate using level PM2.5 reported by EPA’s AirNow database on a daily basis. This metric is a proxy of increased vehicular traffic in different regions. Using a nationwide network of monitoring sites, EPA has developed ambient air quality trends for particle pollution, also called Particulate Matter (PM). We compute the index as daily level of PM2.5 vs the average of the last 5 years.  For regions that are still under a shutdown air quality index should be less than 100 (e.g. New York at 75% vs Houston at 105%) 

Air pollution from traffic has increased in regions where businesses have opened in May ’20 (e.g. LA went up from 69% in April to 98% in May).  However, consumer spending has not always increased at the same level.  We look to proxies for hourly employment levels. 

New Daily COVID-19 Cases: This is a health crisis and managing the rate of new COVID-19 cases will drive decisions to open or close businesses. The chart reports the monthly peak in new cases using daily data from Opportunity Insight 

Hourly Employment and Hours Worked at small businesses is provided by Opportunity Insight using data from Homebase. Homebase is a company that provides virtual scheduling and time-tracking tools, focused on small businesses in sectors such as retail, restaurant, and leisure/accommodation. The chart shows change in level of hourly employment as compared to January 2020. We expect this is to be a leading indicator of employment levels for this sector of consumers. 


Sources of data: 

Freddie Mac’s (STACR) transaction database 

Opportunity Insight’s Recovery Tracker 

Bureau of Labor and Statistics (BLS)’ MSA level economic reports 

Environment Protection Agency (EPA)’s AirNow database. 


Webinar: MGIC Perspectives on CECL – Presented by MGIC with RiskSpan – Thank you

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Webinar: Managing Your Whole Loan Portfolio with Machine Learning

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Managing Your Whole Loan Portfolio with Machine Learning

Whole Loan Data Meets Predictive Analytics

  • Ingest whole loan data
  • Normalize data sets
  • Improve data quality
  • Analyze your historical data
  • Improve your predictive analytics 

Learn the Power of Machine Learning

DATA INTAKE — How to leverage machine learning to help streamline whole loan data prep

MANAGE DATA — Innovative ways to manage the differences in large data sets

DATA IMPROVEMENT — Easily clean your data to drive better predictive analytics


About The Hosts

LC Yarnelle

Director – RiskSpan

LC Yarnelle is a Director with experience in financial modeling, business operations, requirements gathering and process design. At RiskSpan, LC has worked on model validation and business process improvement/documentation projects. He also led the development of one of RiskSpan’s software offerings, and has led multiple development projects for clients, utilizing both Waterfall and Agile frameworks.  Prior to RiskSpan, LC was as an analyst at NVR Mortgage in the secondary marketing group in Reston, VA, where he was responsible for daily pricing, as well as on-going process improvement activities.  Before a career move into finance, LC was the director of operations and a minority owner of a small business in Fort Wayne, IN. He holds a BA from Wittenberg University, as well as an MBA from Ohio State University. 

Matt Steele

Senior Analyst – RiskSpan

LC Yarnelle is a Director with experience in financial modeling, business operations, requirements gathering and process design. At RiskSpan, LC has worked on model validation and business process improvement/documentation projects. He also led the development of one of RiskSpan’s software offerings, and has led multiple development projects for clients, utilizing both Waterfall and Agile frameworks.  Prior to RiskSpan, LC was as an analyst at NVR Mortgage in the secondary marketing group in Reston, VA, where he was responsible for daily pricing, as well as on-going process improvement activities.  Before a career move into finance, LC was the director of operations and a minority owner of a small business in Fort Wayne, IN. He holds a BA from Wittenberg University, as well as an MBA from Ohio State University. 



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. 

———————————– 

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


RiskSpan VQI: Current Underwriting Standards – March 2020

riskspan-VQI-report-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-VQI-report

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:

  1. 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
  2. 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
FICO less than 660
DTI greater than 45
adjustable rate share
cashout refinance
loan occupancy
one borrower loans

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

Copyright © structuredcreditinvestor.com 2007-2019.

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

Structured Credit Investor

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.  

Contact us to talk about how RiskSpan’s operational risk assessment tool can be customized to your servicing portfolio. 


   

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