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Is Free Public Data Worth the Cost?

No such thing as a free lunch.

The world is full of free (and semi-free) datasets ripe for the picking. If it’s not going to cost you anything, why not supercharge your data and achieve clarity where once there was only darkness?

But is it really not going to cost you anything? What is the total cost of ownership for a public dataset, and what does it take to distill truly valuable insights from publicly available data? Setting aside the reliability of the public source (a topic for another blog post), free data is anything but free. Let us discuss both the power and the cost of working with public data.

To illustrate the point, we borrow from a classic RiskSpan example: anticipating losses to a portfolio of mortgage loans due to a hurricane—a salient example as we are in the early days of the 2020 hurricane season (and the National Oceanic and Atmospheric Administration (NOAA) predicts a busy one). In this example, you own a portfolio of loans and would like to understand the possible impacts to that portfolio (in terms of delinquencies, defaults, and losses) of a recent hurricane. You know this will likely require an external data source because you do not work for NOAA, your firm is new to owning loans in coastal areas, and you currently have no internal data for loans impacted by hurricanes.

Know the Data.

The first step in using external data is understanding your own data. This may seem like a simple task. But data, its source, its lineage, and its nuanced meaning can be difficult to communicate inside an organization. Unless you work with a dataset regularly (i.e., often), you should approach your own data as if it were provided by an external source. The goal is a full understanding of the data, the data’s meaning, and the data’s limitations, all of which should have a direct impact on the types of analysis you attempt.

Understanding the structure of your data and the limitations it puts on your analysis involves questions like:

  • What objects does your data track?
  • Do you have time series records for these objects?
  • Do you only have the most recent record? The most recent 12 records?
  • Do you have one record that tries to capture life-to-date information?

Understanding the meaning of each attribute captured in your data involves questions like:

  • What attributes are we tracking?
  • Which attributes are updated (monthly or quarterly) and which remain static?
  • What are the nuances in our categorical variables? How exactly did we assign the zero-balance code?
  • Is original balance the loan’s balance at mortgage origination, or the balance when we purchased the loan/pool?
  • Do our loss numbers include forgone interest?

These same types of questions also apply to understanding external data sources, but the answers are not always as readily available. Depending on the quality and availability of the documentation for a public dataset, this exercise may be as simple as just reading the data dictionary, or as labor intensive as generating analytics for individual attributes, such as mean, standard deviation, mode, or even histograms, to attempt to derive an attribute’s meaning directly from the delivered data. This is the not-free part of “free” data, and skipping this step can have negative consequences for the quality of analysis you can perform later.

Returning to our example, we require at least two external data sets:  

  1. where and when hurricanes have struck, and
  2. loan performance data for mortgages active in those areas at those times.

The obvious choice for loan performance data is the historical performance datasets from the GSEs (Fannie Mae and Freddie Mac). Providing monthly performance information and loss information for defaulted loans for a huge sample of mortgage loans over a 20-year period, these two datasets are perfect for our analysis. For hurricanes, some manual effort is required to extract date, severity, and location from NOAA maps like these (you could get really fancy and gather zip codes covered in the landfall area—which, by leaving out homes hundreds of miles away from expected landfall, would likely give you a much better view of what happens to loans actually impacted by a hurricane—but we will stick to state-level in this simple example).

Make new data your own.

So you’ve downloaded the historical datasets, you’ve read the data dictionaries cover-to-cover, you’ve studied historical NOAA maps, and you’ve interrogated your own data teams for the meaning of internal loan data. Now what? This is yet another cost of “free” data: after all your effort to understand and ingest the new data, all you have is another dataset. A clean, well-understood, well-documented (you’ve thoroughly documented it, haven’t you?) dataset, but a dataset nonetheless. Getting the insights you seek requires a separate effort to merge the old with the new. Let us look at a simplified flow for our hurricane example:

  • Subset the GSE data for active loans in hurricane-related states in the month prior to landfall. Extract information for these loans for 12 months after landfall.
  • Bucket the historical loans by the characteristics you use to bucket your own loans (LTV, FICO, delinquency status before landfall, etc.).
  • Derive delinquency and loss information for the buckets for the 12 months after the hurricane.
  • Apply the observed delinquency and loss information to your loan portfolio (bucketed using the same scheme you used for the historical loans).

And there you have it—not a model, but a grounded expectation of loan performance following a hurricane. You have stepped out of the darkness and into the data-driven light. And all using free (or “free”) data!

Hyperbole aside, nothing about our example analysis is easy, but it plainly illustrates the power and cost of publicly available data. The power is obvious in our example: without the external data, we have no basis for generating an expectation of losses after a hurricane. While we should be wary of the impacts of factors not captured by our datasets (like the amount and effectiveness of government intervention after each storm – which does vary widely), the historical precedent we find by averaging many storms can form the basis for a robust and defensible expectation. Even if your firm has had experience with loans in hurricane-impacted areas, expanding the sample size through this exercise bolsters confidence in the outcomes. Generally speaking, the use of public data can provide grounded expectations where there had been only anecdotes.

But this power does come at a price—a price that should be appreciated and factored into the decision whether to use external data in the first place. What is worse than not knowing what to expect after a hurricane? Having an expectation based on bad or misunderstood data. Failing to account for the effort required to ingest and use free data can lead to bad analysis and the temptation to cut corners. The effort required in our example is significant: the GSE data is huge, complicated, and will melt your laptop’s RAM if you are not careful. Turning NOAA PDF maps into usable data is not a trivial task, especially if you want to go deeper than the state level. Understanding your own data can be a challenge. Applying an appropriate bucketing to the loans can make or break the analysis. Not all public datasets present these same challenges, but all public datasets present costs. There simply is no such thing as a free lunch. The returns on free data frequently justify these costs. But they should be understood before unwittingly incurring them.


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

Within weeks of the March 11th declaration of COVID-19 as a global pandemic by the World Health Organization, rating agencies were downgrading businesses across virtually every sector of the economy. Not surprisingly, these downgrades were felt most acutely by businesses that one would reasonably expect to be directly harmed by the ensuing shutdowns, including travel and hospitality firms and retail stores. But the downgrades also hit food companies and other areas of the economy that tend to be more recession resistant. 

An accompanying spike in credit spreads was even quicker to materialize. Royal Caribbean’s and Marriott’s credit spreads tripled essentially overnight, while those of other large companies increased by twofold or more. 

But then something interesting happened. Almost as quickly as they had risen, most of these spreads began retreating to more normal levels. By mid-June, most spreads were at or lower than where they were prior to the pandemic declaration. 

What business reason could plausibly explain this? The pandemic is ongoing and aggregate demand for these companies’ products does not appear to have rebounded in any material way. People are not suddenly flocking back to Marriott’s hotels or Wynn’s resorts.    

The story is indeed one of increased demand. But rather than demand for the companies’ productswe’re seeing an upswing in demand for these companies’ debt. What could be driving this demand? 

Enter the Federal Reserve. On March 23rd, The Fed announced that its Secondary Market Corporate Credit Facility (SMCCF) would begin purchasing investment-grade corporate bonds in the secondary market, first through ETFs and directly in a later phase. 

And poof! Instant demand. And instant price stabilization. All the Fed had to do was announce that it would begin buying bonds (it hasn’t actually started buying yet) for demand to rush back in, push prices up and drive credit spreads down.  

To illustrate how quickly spreads reacted to the Fed’s announcement, we tracked seven of the top 20 companies listed by S&P across different industries from early March through mid-June. The chart below plots swap spreads for a single bond (with approximately five years to maturity) from each of the following companies: 

  • Royal Caribbean Cruises (RCL)
  • BMW 
  • The TJX Companies (which includes discount retailers TJ Maxx, Marshalls, and HomeGoods, among others) 
  • Marriott 
  • Wynn Resorts 
  • Kraft Foods 
  • Ford Motor Company

We sourced the underlying data for these charts from two RiskSpan partners: S&P, which provided the timing of the downgrades, and Refinitiv, which provided time-series spread data.  

The companies we selected don’t cover every industry, of course, but they cover a decent breadth. Incredibly, with the lone exception of Royal Caribbean, swap spreads for every one of these companies are either better than or at the same level as where they were pre-pandemic. 

As alluded to above, this recovery cannot be attributed to some miraculous improvement in the underlying economic environment. Literally the only thing that changed was the Fed’s announcement that it would start buying bonds. The fact that Royal Caribbean’s spreads have not fully recovered seems to suggest that the perceived weakness in demand for cruises in the foreseeable future remains strong enough to overwhelm any buoying effect of the impending SMCCF investment. For all the remaining companies, the Fed’s announcement appears to be doing the trick. 

We view this as clear and compelling evidence that the Federal Reserve in achieving its intended result of stabilizing asset prices, which in turn should help ease corporate credit.


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 has 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. 


Edge: PIW and Prepayments

Inspection waivers have been available on agency-backed mortgages since 2017, but in this era of social distancing, the convenience of forgoing an inspection looks set to become an important feature in mortgage origination. In this post, we compare prepayments on loans with and without inspections.

Broadly, FNMA allows inspection waivers on purchase single-family mortgages up to 80% LTV, and no cash-out refi with up to 90% LTV (75% if the refi is an investment property). Inspection waivers are available on cash-out refis for primary residences with LTV up to 70%, and investment properties with LTV up to 60%.

Inspection waivers were first introduced in mid-2017. In 2018, the proportion of loans with inspection waivers held steady around 6% but started a steady uptick in the middle of 2019, long before the pandemic made social distancing a must.[1]

In the current environment, market participants should expect a further uptick in loans with waivers as refis increase and as the GSEs consider relaxing restrictions around qualifying loans. In short, PIW will start to become a key factor in loan origination. Given this, we examine the different behavior between loans with waivers and loans with inspections.

In the chart below, we show prepayment speeds on 30yr borrowers with “generic” mortgages,[2] with and without waivers. When 100bp in the money, “generic” loans with a waiver paid a full 15 CPR faster than loans with an inspection appraisal. Additionally, the waiver S-curve is steeper. Waiver loans that are 50-75bp in the money outpaced appraised houses by 20 CPR.

Next, we look at PIW by origination channel. For retail origination, loans with waivers paid only 10-15 CPR faster than loans with inspections (first graph). In contrast, correspondent loans with a waiver paid 15-20 CPR faster versus loans with an inspection (second graph).

We also looked at loan purpose. Purchase loans with a waiver paid only 10 CPR faster than comparable loans purchase loans with an inspection (first graph), whereas refi loans paid 25 CPR faster when 50-75bp in the money.

PIW and Prepayments in RS Edge

We also examined servicer-specific behavior for PIW. We saw both a difference in the proportional volume of waivers, with some originators producing a heavy concentration of waivers, as well as a difference in speeds. The details are lengthy, please contact us on how to run this query in the Edge platform.

In summary, loans with inspection waivers pay faster than loans without waivers, but the differentials vary greatly by channel and loan purpose. With property inspection waivers rising as a percentage of overall origination, these differences will begin to play a larger role in forming overall prepayment expectations.

If you interested in seeing variations on this theme, contact us. Using RS Edge, we can examine any loan characteristic and generate a S-curve, aging curve, or time series.


[1] Refi loans almost entirely drove this uptick in waivers, see RiskSpan for a breakdown of refi loans with waivers.

[2] For this query, we searched for loans delivered to 30yr deliverable pools with loan balance greater than $225k, FICO greater than 700, and LTV below 80%.


What The FHFA’s Forbearance Announcement Means for Agency Prepayments

On Tuesday, the market received a modicum of clarity around Agency prepayments amid the uncertainty of COVID-19, when the FHFA released new guidelines for mortgage borrowers currently in forbearance or on repayment plans who wish to refinance or buy a new home.

Borrowers that use forbearance will most likely opt for a forbearance deferment, which delays the missed P&I until the loan matures. The FHFA announcement temporarily declares that borrowers are eligible to refinance three months after their forbearance ends and they have made three consecutive payments under their repayment plan, payment deferral option, or loan modification.”

With the share of mortgage loans in forbearance accelerating to over 8 percent, according to the MBA, and retail mortgage interest rates remaining at historically low levels, the FHFA’s announcement potentially expands the universe of mortgages in Agency securities eligible for refi. However, mortgage rates must be sufficiently low as to make economic sense to refinance both the unpaid principal balance of the loan and the deferred payments, which accrue at 0%. We estimate that a 6-month forbearance means that rates must be an additional 25bp lower to match the same payment savings as a borrower who doesn’t need to refinance the deferred payments.  In turn, this will slow refinancing on loans with a forbearance deferment versus loans without forbearance, when faced with the same refinancing incentive. This attenuated refi activity is on top of the three-payment delay after forbearance is over, which pushes the exercise of the call option out three months and lowers the probability of exercise. In total, loans in forbearance will both be slower and have better convexity than loans not in forbearance. 

Today’s FHFA release also extends Fannie’s and Freddie’s ability to purchase single-family mortgages currently in forbearance until at least August 31, 2020. 


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. 


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

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