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Credit Risk Transfer: Front End Execution – Why Does It Matter?

This article was originally published on the GoRion blog.

Last month I described an overview of the activities of Credit Risk Transfer (CRT) as outlined from the Federal Finance Housing Agency (FHFA) guidance to Fannie Mae and Freddie Mac (the GSEs). This three-year-old program has shown great promise and success in creating a deeper residential credit investor segment and has enabled risk increments to be shifted from the GSEs and taxpayer to the private sector.

The FHFA issued an RFI to solicit feedback from stakeholders on proposals from the GSEs to adopt additional front-end credit risk transfer structures and to consider additional credit risk transfer policy issues. There is firm interest in this new and growing execution for risk transfer by investors who have confidence in the underwriting and servicing of mortgage loans through new and improved GSE standards.

In addition to the back-end industry appetite for CRT, there is also a growing focus to increase risk share at the front-end of the origination transaction. In particular, the mortgage industry and insurers (MIs) are interested in exploring risk sharing more actively on the front-end of the mortgage process. The MIs desire to participate in this new and growing market opportunity would increase their traditional coverage to much deeper levels than the standard 30% coverage.

Front-End Credit Risk Transfer

In 2016 FHFA expanded the GSE scorecards to include broadening the types of loans and risk transfer which included expanding to the front-end CRT. In addition to many prescriptive outlines on CRT, they also included wording such as “…Work with FHFA to conduct an analysis and assessment of front-end credit risk transfer transactions, including work to support a forthcoming FHFA Request for Input. Work with FHFA to engage key stakeholders and solicit their feedback. After conducting the necessary analysis and assessment, work with FHFA to take appropriate steps to continue front-end credit risk transfer transactions.”

Two additional ways to work with risk sharing on the front-end are using 1) recourse transactions and 2) deeper mortgage insurance.

Recourse Transaction

Recourse as a form of credit enhancement is not a new concept. In years past, some institutions would sell loans with recourse to the GSEs but it was usually determined to be capital intensive and not an efficient way of selling loans to the secondary markets. However, some of the non-depositories have found recourse to be an attractive way to sell loans to the GSEs.

To date from 2013 through December 2015, the GSE’s have executed 12 deals with recourse on $12.6 billion in UPB. The pricing and structures are very different and the transactions are not transparent. While this can be attractive to both parties if structured adequately, the transactions are not as scalable and each deal requires significant review and assessment. Arguments against recourse note this diminishes opportunities for the small to medium sized player who would like to participate in this new form of reduced g-fee structure and front-end CRT transaction.

Penny Mac shared their perspective on this activity at a recent CRT conference. They use the recourse structure with Fannie Mae and it leverages their capital structure and allows flexibility. Importantly, Penny Mac reminds us that both parties’ interests are aligned as there is skin in the game for quality originations.

Deeper Mortgage Insurance

The GSE model has a significant amount of counter-party risk with MIs through their standard business offerings. Through their charter, they require credit enhancements on loans of 80% or higher Loan to Value (LTV). This traditionally plays out to be 30% first loss coverage of such loans. For example, a 95% LTV loan is insured down to 65%. The mortgage insurers are integral to most of the GSE’ higher LTV books of business. Per the RFI, as of December 2015, the MI industry collectively has counter-party exposure of $185.5 billion, covering $724.5 billion of loans. So as a general course of business, this is already a risk they share of higher LTV lending without adding any additional exposure.

Through the crisis, the MIs were unable to pay dollar for dollar initial claims. This has caused hesitation on embracing a more robust model with more counter-party risk than the model of today. It is well documented that the MIs did pay a great deal of claims and buffered the GSEs by taking the first loss on billions of dollars before any losses were incurred by the GSEs. While much of that has been paid back, memories are long and this has generated pause as to how to value the insurance which is different than the back-end transactions. Today the MI industry is in much better shape through capital raises and increased standards directed from the GSEs and state regulators. (Our recent blog post on mortgage insurance haircuts explores this phenomenon in greater detail.)

FHFA instituted the PMIERs which required higher capital for the MI business transacted with the GSEs. The state regulators also increased the regulatory capital for the residential insurance sector and today the industry has strengthened their hand as a partner to the GSEs. In fact, the industry has new entrants who do not have the legacy books of losses which also adds new opportunities for the GSEs to expand the counter-party pools.

The MI companies can be a front-end model and play a more significant role in the risk share business by having deeper MI on the front-end (to 50% coverage) as a way of de-levering the GSE’s and ultimately, the taxpayers. And, like the GSE’s, MI’s may also participate in reinsurance markets to shed risk and balance out their own portfolios. Other market participants may also participate in this type of transaction and we will observe what opportunities avail themselves in the longer term. While nothing is ever black and white, there appear to be benefits to expanding the risk share efforts to the front-end of the business.

Benefits

1) Strong execution: Pricing and executing on mortgage risk, at the front of the origination will allow for options in a counter-cyclical volatile market.

2) Transparency: Moving risk metrics and pricing to the front-end will drive more front-end price transparency for mortgage credit risk.

3) Inclusive institutional partnering: Smaller entities may participate in a front-end risk share effort thereby creating opportunities outside of the largest financial institutions.

4) Inclusive borrower process: Front-end CRT may reach more borrowers and provide options as more institutions can take part of this opportunity.

5) Expands options for CRT in pilot phase: By driving the risk share to the front-end, the GSE’s reach their goals in de-risking their credit guarantee while providing a timely trade off of G-fee and MI pricing on the front-end of the transaction.

As part of the RFI response, the trade representing the MIs summarized principal benefits of front-end CRT as follows:

Increased CRT availability and market stability
Reduced first-loss holding risk
Beneficial stakeholder familiarity and equitable access
Increased transparency.

The full letter may be found at usmi.org.

In summary, whether it is recourse to a lending institution or participation in the front-end MI cost structure, pricing this risk at origination will continue to bring forward price discovery and transparency. This means the consumer and lender will be closer to the true credit costs of origination. With experience pricing and executing on CRT, it may become clearer where the differential cost of credit lies. The additional impact of driving more front-end CRT will be scalability and less process on the back-end for the GSEs. By leveraging the front-end model, GSEs will reach more borrowers and utilize a wider array of lending partners through this process.

As of November 8, we experienced a historic election which may take us in new directions. However, credit risk transfer is an option that may be used in the future regardless of GSE status, even if they 1) revert back to the old model with recap and release; 2) re-emerge after housing reform post legislation; or 3) remain in conservatorship and continue to be led by FHFA down this path.

**Footnote: All data was retrieved from the Federal Housing Finance Agency, FHFA, Single Family Credit Risk Transfer request for input, June 2016. More information may be found at FHFA.gov.

This is the second installment in a monthly Credit Risk Transfer (CRT) series on the GoRion blog. CRT is a significant accomplishment in bringing back private capital to the housing sector. This young effort, three years strong, has already shown promising investor appetite while discussions are underway to expand offerings to front-end risk share executions. My goal in this series is to share insights around CRT as it evolves with the private sector.

Validating Model Inputs: How Much Is Enough?

In some respects, the OCC 2011-12/SR 11-7 mandate to verify model inputs could not be any more straightforward: “Process verification … includes verifying that internal and external data inputs continue to be accurate, complete, consistent with model purpose and design, and of the highest quality available.” From a logical perspective, this requirement is unambiguous and non-controversial. After all, the reliability of a model’s outputs cannot be any better than the quality of its inputs. From a functional perspective, however, it raises practical questions around the amount of work that needs to be done in order to consider a particular input “verified.” Take the example of a Housing Price Index (HPI) input assumption. It could be that the modeler obtains the HPI assumption from the bank’s finance department, which purchases it from an analytics firm. What is the model validator’s responsibility? Is it sufficient to verify that the HPI input matches the data of the finance department that supplied it? If not, is it enough to verify that the finance department’s HPI data matches the data provided by its analytics vendor? If not, is it necessary to validate the analytics firm’s model for generating HPI assumptions? It depends. Just as model risk increases with greater model complexity, higher uncertainty about inputs and assumptions, broader use, and larger potential impact, input risk increases with increases in input complexity and uncertainty. The risk of any specific input also rises as model outputs become increasingly sensitive to it.

Validating Model Inputs Best Practices

So how much validation of model inputs is enough? As with the management of other risks, the level of validation or control should be dictated by the magnitude or impact of the risk. Like so much else in model validation, no ‘one size fits all’ approach applies to determining the appropriate level of validation of model inputs and assumptions. In addition to cost/benefit considerations, model validators should consider at least four factors for mitigating the risk of input and assumption errors leading to inaccurate outputs.

Complexity of inputs
Manual manipulation of inputs from source system prior to input into model
Reliability of source system
Relative importance of the input to the model’s outputs (i.e., sensitivity)

Consideration 1: Complexity of Inputs

The greater the complexity of the model’s inputs and assumptions, the greater the risk of errors. For example, complex yield curves with multiple data points will be inherently subject to greater risk of inaccuracy than binary inputs such as “yes” and “no.” In general, the more complex an input is, the more scrutiny it requires and the “further back” a validator should look to verify its origin and reasonability.

Consideration 2: Manual Manipulation of Inputs from Source System Prior to Input into Model

Input data often requires modification from the source system to facilitate input into the model. More handling and manual modifications increase the likelihood of error. For example, if a position input is manually copied from Bloomberg and then subjected to a manual process of modification of format to enable uploading to the model, there is a greater likelihood of error than if the position input is extracted automatically via an API. The accuracy of the input should be verified in either case, but the more manual handling and manipulation of data that occurs, the more comprehensive the testing should be. In this example, more comprehensive testing would likely take the form of a larger sample size.

In addition, the controls over the processes to extract, transform, and load data from a source system into the model will impact the risk of error. More mature and effective controls, including automation and reconciliation, will decrease the likelihood of error and therefore likely require a lighter verification procedure.

Consideration 3: Reliability of Source Systems

More mature and stable source systems generally produce more consistently reliable results. Conversely, newer systems and those that have produced erroneous results increase the risk of error. The results of previous validation of inputs, from prior model validations or from third parties, including internal audit and compliance, can be used as an indicator of the reliability of information from source systems and the magnitude of input risk. The greater the number of issues identified, the greater the risk, and the more likely it is that a validator should seek to drill deeper into the fundamental sources of source data.

Consideration 4: Output Sensitivity to Inputs

No matter how reliable an input data’s source system is deemed to be, or the amount of manual manipulation to which an input is subjected, perhaps the most important consideration is the individual input’s power to affect the model’s outputs. Returning to our original example, if a 50 percent change in the HPI assumption has only a negligible impact on the model’s outputs, then a quick verification against the report supplied by the finance department may be sufficient. If, however, the model’s outputs are extremely sensitive to even small shifts in the HPI assumption, then additional testing is likely warranted—perhaps even to include a validation of the analytics vendor’s HPI model (along with all of its inputs).

A Cost-Effective Model Input Validation Strategy

When it comes to verifying model inputs, there is no theoretical limitation to the lengths to which a model validator can go. Model risk managers, who do not have unlimited time or budgets, would benefit from applying practical limits to validation procedures using a risk-based approach to determine the most cost-effective strategies to ensure that models are sufficiently validated. Applying the considerations listed above on a case-by-case basis will help validators appropriately define and scope model input reviews in a manner commensurate with appropriate risk management principles.

Performance Testing: Benchmarking Vs. Back-Testing

When someone asks you what a model validation is, what is the first thing you think of? If you are like most, then you would immediately think of performance metrics— those quantitative indicators that tell you not only if the model is working as intended, but also its performance and accuracy over time and compared to others. Performance testing is the core of any model validation and generally consists of the following components:

Benchmarking
Back-testing
Sensitivity Analysis
Stress Testing

Sensitivity analysis and stress testing, while critical to any model validation’s performance testing, will be covered by a future article. This post will focus on the relative virtues of benchmarking versus back-testing—seeking to define what each is, when and how each should be used, and how to make the best use of the results of each.

Benchmarking

Benchmarking is when the validator is providing a comparison of the model being validated to some other model or metric. The type of benchmark utilized will vary, like all model validation performance testing does, with the nature, use, and type of model being validated. Due to the performance information it provides, benchmarking should always be utilized in some form when a suitable benchmark can be found.

Choosing a Benchmark

Choosing what kind of benchmark to use within a model validation can sometimes be a very daunting task. Like all testing within a model validation, the kind of benchmark to use depends on the type of model being tested. Benchmarking takes many forms and may entail comparing the model’s outputs to:

The model’s previous version
An externally produced model
A model built by the validator
Other models and methodologies considered by the model developers, but not chosen
Industry best practice
Thresholds and expectations of the model’s performance

One of the most used benchmarking approaches is to compare a new model’s outputs to those of the version of the model it is replacing. It remains very common throughout the industry for models to be replaced due to a deterioration of performance, change in risk appetite, new regulatory guidance, need to capture new variables, or the availability of new sets of information. In these cases, it is important to not only document but also prove that the new model performs better and does not have the same issues that triggered the old model’s replacement.

Another common benchmarking approach compares the model’s outputs to those of an external “challenger” model (or one built by the validator) which functions with the same objective and data. This approach is likely to return more apt output comparisons than those generated by benchmarking against older versions that are likely to be out of date since the challenger model is developed and updated with the same data as the champion model.

Another benchmark set which could be used for model validation includes other models or methodologies reviewed by the model developers as possibilities for the model being validated but ultimately not used. Model developers as best practice should always list any alternative methodologies, theories, or data which were omitted from the model’s final version. Additionally, model validators should always leverage their experience and understanding of the current best practices throughout the industry, along with any analysis previously completed on similar models. Model validation should then take these alternatives and use them as benchmarks to the model being validated.

Model validators have multiple, distinct ways to incorporate benchmarking into their analysis. The use of the different types of benchmarking discussed here should be based on the type of model, its objective, and the validator’s best judgment. If a model cannot be reasonably benchmarked, then the validator should record why not and discuss the resulting limitations of the validation.

Back-Testing

Back-testing is used to measure model outcomes. Here, instead of measuring performance with a comparison, the validator is specifically measuring whether the model is both working as intended and is accurate. Back-testing can take many forms based on the model’s objective. As with benchmarking, back-testing should be a part of every full-scope model validation to the extent possible.

What Back-Tests to Perform

As a form of outcomes analysis, back-testing provides quantitative metrics which measure the performance of a model’s forecast, the accuracy of its estimates, or its ability to rank-order risk. For instance, if a model produces forecasts for a given variable, back-testing would involve comparing the model’s forecast values against actual outcomes, thus indicating its accuracy.

A related function of model back-testing evaluates the ability of a given model to adequately measure risk. This risk could take any of several forms, from the probability of a given borrower to default to the likelihood of a large loss during a given trading day. To back-test a model’s ability to capture risk exposure, it is important first to collect the right data. In order to back-test a probability of default model, for example, data would need to be collected containing cases where borrowers have actually defaulted in order to test the model’s predictions.

Back-testing models that assign borrowers to various risk levels necessitate some special considerations. Back-testing these and other models that seek to rank-order risk involves looking at the model’s performance history and examining its accuracy through its ability to rank and order the risk. This can involve analyzing both Type 1 (false positive) and Type 2 (false negative) statistical errors against the true positive and true negative rates for a given model. Common statistical tests used for this type of back-testing analysis include, but are not limited to, a Kolmogorov-Smirnov score (KS), a Brier score, or a Receiver Operating Characteristic (ROC).

Benchmarking vs Backtesting

Back-testing measures a model’s outcome and accuracy against real-world observations, while benchmarking measures those outcomes against those of other models or metrics. Some overlap exists when the benchmarking includes comparing how well different models’ outputs back-test against real-world observations and the chosen benchmark. This overlap sometimes leads people to mistakenly conclude that model validations can rely on just one method. In reality, however, back-testing and benchmarking should ideally be performed together in order to bring their individual benefits to bear in evaluating the model’s overall performance. The decision, optimally, should not be whether to create a benchmark or to perform back-testing. Rather, the decision should be what form both benchmarking and back-testing should take.

While benchmarking and back-testing are complementary exercises that should not be viewed as mutually exclusive, their outcomes sometimes appear to produce conflicting results. What should a model validator do, for example, if the model appears to back-test well against real-world observations but do not benchmark particularly well against similar model outputs? What about a model that returns results similar to those of other benchmark models but does not back-test well? In the first” scenario, the model owner can derive a measure of comfort from the knowledge that the model performs well in hindsight. But the owner also runs the very real risk of being “out on an island” if the model turns out to be wrong. The second scenario affords the comfort of company in the model’s projections. But what if the models are all wrong together?

Scenarios where benchmarking and back-testing do not produce complementary results are not common, but they do happen. In these situations, it becomes incumbent on model validators to determine whether back-testing results should trump benchmarking results (or vice-versa) or if they should simply temper one another. The course to take may be dictated by circumstances. For example, a model validator may conclude that macro-economic indicators are changing to the point that a model which back-tests favorably is not an advisable tool because it is not tuned to the expected forward-looking conditions. This could explain why a model that back-tests favorably remains a benchmarking outlier if the benchmark models are taking into account what the subject model is missing. On the other hand, there are scenarios where it is reasonable to conclude that back-testing results trump benchmarking results. After all, most firms would rather have an accurate model than one that lines up with all the others.

As seen in our discussion here, benchmarking and back-testing can sometimes produce distinct or similar metrics depending on the model being validated. While those differences or similarities can sometimes be significant, both benchmarking and back-testing provide critical complementary information about a model’s overall performance. So when approaching a model validation and determining its scope, your choice should be what form of benchmarking and back-testing needs to be done, rather than whether one needs to be performed versus the other.

The Real Reason Low Down Payment VA loans Don’t Default Like Comparable FHA Loans

On this Veterans Day, I was reminded of the Urban Institute’s 2014 article on VA loan performance and its explanation of why VA loans outperform FHA loans.¹ The article illustrated how VA loans outperformed comparable FHA loans despite controlling for key variables like FICO, income, and DTI. The article further explained the structural differences and similarities between the veterans program and FHA loans—similarities that include owner occupancy, loan size, and low down payments.

The analysis was well thought out and clearly showed how VA loans outperformed FHA. The article took great care to understand how FICOs, DTI, and income levels could impact default performance. The article further demonstrated VA outperformance wasn’t a recent or short-lived trend.

Its concluding rationale for superior performance was based on:

VA’s residual income test
VA’s loss mitigation efforts
Lender’s “skin in the game”
Lender concentration
Military culture

Two of these reasons assume VA’s internal policies made the difference. Two of the reasons assume lenders’ ability to self-regulate credit policy or capital had an influence. The final reason centered on veterans as a social group with differing values that contributed to the difference.

As someone who has spent his mortgage career between modeling credit, counterparty risks and managing credit underwriters, the lack of good analytical data or anecdotal evidence makes it hard to see how these reasons can account for VA’s strong relative default performance versus FHA. While I understand their rationale, I don’t see how it makes for a compelling explanation.

VA Internal Policies

The residual income test is a tertiary measure used by lenders to qualify borrowers. It is used after applying the traditional MTI (mortgage payment to income) and DTI (total debt to income) ratios. The test mandates that the borrowing veteran have a minimum net income after paying all mortgage and debt payments. But as a third-level underwriting test, it is hard to see how it could be the source of so much of default performance improvement.

The same goes for VA’s loss mitigation outreach efforts. It sounds good in the press, but it is just a secondary loss mitigation effort used in conjunction with the servicer’s own loss mitigation efforts. Perhaps it is responsible for some of the incremental improvement, but it’s hard to believe it accounts for much more.

Lenders’ Ability to Self-Regulate

“Skin in the Game” attempts to explain how lenders manage their retained credit risk when the VA insurance payment is insufficient to cover all loan losses.² The “Skin in the Game” theory holds when lenders have exposure to losses they will adjust their lending policies to reduce their risk. This translates into tighter credit policies, like floors on credit scores or ceilings on DTIs. Having worked for companies with strong credit cultures that nearly failed in the recent financial crisis, I find it hard to believe privately held mortgage bankers can manage this risk. Quite the opposite, my experience tells me: 1) lenders always underestimate their residual credit risks, and 2) pressure for volume, market share, and profits quickly overwhelm any attempts to maintain credit discipline.

The notion that lender concentration in the VA originations market somehow means that those lenders have more capital or the ability to earn more money also makes little sense. Historically, the largest source of origination revenue is the capitalized value of the MSRs created when the loan is securitized. Over the past several years Ginnie Mae MSR prices have collapsed. This severely limits the profit margins from VA loans. Trust me, the top lenders are not making it up on volume.

Effect of Military Culture on VA Loans

So, what’s left? Military culture. This I believe. And not just because it is the only reason left. As the son of a retired Army colonel and the brother of both a retired Navy captain and a retired Marine Corps lieutenant colonel, I think I understand what the military culture is.

My view of military culture isn’t that veterans are more disciplined or responsible than the rest of the American public. My view of military culture is that institutional, structural, and societal differences make the military personnel workforce different than that of the general public. How?

Job Security

Active duty military personnel are not subject to mass layoffs or reductions in force typical in the business world.
Poor performing military personnel are typically eased out of the military and not fired. This process of “getting passed over” spans several years and not weeks or months.
Most active duty military personnel have the flexibility to determine their exit strategy/retirement date, so they can defer when economic times are bad.

Retirement

Military personnel can retire with 50% pay of their base pay after 20 years of service.
This pension is received immediately upon retirement and is indexed to inflation.

Job Skills

A high percentage of retired military personnel re-enter the workforce and work for governmental agencies.
Military personnel typically leave active duty with more marketable skills than their similarly educated peers.
Active duty, retired, and former military personnel have jobs/careers/professions that are in higher demand than the rest of the American population.

Military Pay

Active and retired military pay is transparent, public, and socialized. The only variables are rank and years in service. The pay schedule provides for automatic pay increases based on the number of years in service and rank. Every two years you will get a pay increase. If you get promoted, you will get a pay raise. You know how much your boss makes. Female service-members make the same pay as their male counterparts.
Military pay is indexed to inflation.
In addition to their base pay, all military personnel are given a tax-free monthly housing allowance which is adjusted regionally.

Medical

All military families, active duty and retired, receive full medical care.
This health insurance has nearly no deductible or out-of-pocket expenses.
This means veterans don’t default due to catastrophic medical emergencies or have their credit capacity impacted by unpaid medical bills.

It is for these reasons that I believe VA borrowers default less frequently than FHA borrowers. The U.S. military is not a conscripted force, but rather an all-volunteer force. The structural programs offered by the U.S. Government provide the incentives necessary for people to remain in the military.

The Federal Government has designed a military force with low turnover and backed by an institutionalized social safety net to help recruit, retain, and reward its personnel. Lower default rates associated with the VA loan program are just the secondary benefits of a nation trying to keep its citizens safe.

So, on this Veterans Day, remember to thank a veteran for his service. But also, remember the efforts of the Federal Government to ease the difficulties of those protecting our nation.

[1] Housing Finance Policy Center Commentary, “VA Loans Outperform FHA Loans. Why. And What Can We Learn?”, Laura Goodman, Ellen Seidman, Jun Zhu. Urban Institute – July 16, 201

[2] VA insurance is a first loss guaranty like MI insurance. If the loss is greater than the insurance payment the mortgage servicer is responsible for the additional loss

4 Questions to Ask When Determining Model Validation Scope

Model risk management is a necessary undertaking for which model owners must prepare on a regular basis. Model risk managers frequently struggle to strike an appropriate cost-benefit balance in determining whether a model requires validation, how frequently a model needs to be validated, and how detailed subsequent and interim model validations need to be. The extent to which a model must be validated is a decision that affects many stakeholders in terms of both time and dollars. Everyone has an interest in knowing that models are reliable, but bringing the time and expense of a full model validation to bear on every model, every year is seldom warranted. What are the circumstances under which a limited-scope validation will do and what should that validation look like? We have identified four considerations that can inform your decision on whether a full-scope model validation is necessary:

What about the model has changed since the last full-scope validation?
How have market conditions changed since the last validation?
How mission-critical is the model?
How often have manual overrides of model output been necessary?

What Constitutes a Model Validation

Comprehensive model validations consist of three main components: conceptual soundness, ongoing monitoring and benchmarking, and outcomes analysis and back-testing.[1] A comprehensive validation encompassing all these areas is usually required when a model is first put into use. Any validation that does not fully address all three of these areas is by definition a limited-scope validation. ¹ Comprehensive validations on ‘black box’ models developed and maintained by third-party vendors are therefore problematic because the mathematical code and formulas are not typically available for review (in many cases a validator can only hypothesize the cause and effect relationships between the inputs and outputs based on a reading of the model’s documentation). Ideally, regular comprehensive validations are supplemented by limited-scope validations and outcomes analyses on an ongoing, interim basis to ensure that the model performs as expected.

Key Considerations for Model Validation

There is no ‘one size fits all’ question for determining how often a comprehensive validation is necessary, versus when a limited-scope review would be appropriate. Beyond the obvious time and cost considerations, model validation managers would benefit from asking themselves a minimum of four questions in making this determination:

Question 1: What about the model has changed since the last full-scope validation?

Many models layer economic assumptions on top of arithmetic equations. Most models consist of three principal components:

inputs (assumptions and data)
processing (underlying mathematics and code that transform inputs into estimates)
output reporting (processes that translate estimates into useful information)

Changes to either of the first two components are more likely to require a comprehensive validation than changes to the third component. A change that materially impacts how the model output is computed, either by changing the inputs that drive the calculation or by changing the calculations themselves, is more likely to merit a more comprehensive review than a change that merely affects how the model’s outputs are interpreted.

For example, say I have a model that assigns a credit rating to a bank’s counterparties on a 100-point scale. The requirements the bank establishes for the counterparty are driven by how the model rates the counterparty. Say, for example, that the bank lends to counterparties that score between 90 and 100 with no restrictions, between 80 and 89 with pledged collateral, between 70 and 79 with delivered collateral, and does not lend to counterparties scoring below a 70. Consider two possible changes to the model:

Changes in model calculations that result in what used to be a 65 now being a 79.
Changes in grading scale that result in a counterparty that receives a rating of 65 now being deemed creditworthy.

While the second change impacts the interpretation of model output and may require only a limited-scope validation to determine whether the amended grading scale is defensible, the first change is almost certain to require that the validator go deeper ‘under the hood’ for verification that the model is working as intended. Assuming that the inputs did not change, the first type of change may be the result of changes to assumptions (e.g., weighting schemes) or simply a revision to a perceived calculation error. The second is a change on the reporting component, where a comparison of the model’s forecasts to those of challenger models and back-testing with historical data may be sufficient for validation. Not every change that affects model outputs necessarily requires a full-scope validation. The insertion of recently updated economic forecasts into a recently validated model may require only a limited set of tests to demonstrate that changes in the model estimates are consistent with the new economic forecast inputs. The magnitude of the impact on output also matters. Altering several input parameters that results in a material change to model output is more likely to require a full validation.

Question 2: How have market conditions changed since the last validation?

Even models that do not change at all require periodic, full-scope validations because macroeconomic conditions or other external factors call one or more of the model’s underlying assumptions into question. The 2008 global financial crisis is a perfect example. Mortgage credit and prepayment models prior to 2008 were built on assumptions that appeared reasonable and plausible based on market observations prior to 2008. Statistical models based solely on historical data before, during, or after the crisis are likely to require full-scope validations as their underlying datasets are expanded to capture a more comprehensive array of observed economic scenarios. It doesn’t always have to be bad news in the economy to instigate model changes that require full-scope validations. The federal funds rate has been hovering near zero since the end of 2008. With a period of gradual and sustained recovery potentially on the horizon, many models are beginning to incorporate rising interest rates into their current forecasts. These foreseeable model adjustments will likely require more comprehensive validations geared toward verifying that model outputs are appropriately sensitive to the revised interest rate assumptions.

Question 3: How mission-critical is the model?

The more vital the model’s outputs are to financial statements or mission-critical business decisions, the greater the need for frequent and detailed third-party validations. Model risk is amplified when the model outputs inform reports that are provided to investors, regulators, or compliance authorities. Particular care should be given when deciding whether to partially validate models with such high-stake outputs. Models whose outputs are used for internal strategic planning are also important. That being said, some models are more critical to a bank’s long-term success than others. Ensuring the accuracy of the risk algorithms used for DFAST stress testing is more imperative than the accuracy of a model that predicts wait times in a customer service queue. Consequently, DFAST models, regardless of their complexity, are likely to require more frequent full-scope validations than models whose results likely undergo less scrutiny.

Question 4: How often have manual overrides of model output been necessary?

Another issue to consider revolves around the use of manual overrides to the model’s output. In cases where expert opinion is permitted to supersede the model outputs on a regular basis, more frequent full-scope validations may be necessary in order to determine whether the model is performing as intended. Counterparty credit scoring models, cited in our earlier example, are frequently subjected to manual overrides by human underwriters to account for new or other qualitative information that cannot be processed by the model. The decision of whether it is necessary to revise or re-estimate a model is frequently a function of how often such overrides are required and what the magnitude of these overrides tends to be. Models that frequently have their outputs overridden should be subjected to more frequent full-scope validations. And models that are revised as a result of numerous overrides should also likely be fully validated, particularly when the revision includes significant changes to input variables and their respective weightings.

Full or Partial Model Validation?

Model risk managers need to perform a delicate balancing act in order to ensure that an enterprise’s models are sufficiently validated while keeping to a budget and not overly burdening model owners. In many cases, limited-scope validations are the most efficient means to this end. Such validations allow for the continuous monitoring of model performance without bringing in a Ph.D. with a full team of experts to opine on a model whose conceptual approach, inputs, assumptions, and controls have not changed since its last full-scope validation. While gray areas abound and the question of full versus partial validation needs to be addressed on a case-by-case basis, the four basic considerations outlined above can inform and facilitate the decision. Incorporating these considerations into your model risk management policy will greatly simplify the decision of how detailed your next model validation needs to be. An informed decision to perform a partial model validation can ultimately save your business the time and expense required to execute a full model validation.

[1] In the United States, most model validations are governed by the following sets of guidelines: 1) OCC 2011-12 (institutions regulated by the OCC), and 2) FRB SR-11 (institutions regulated by the Federal Reserve). These guidelines are effectively identical to one another. Model validations at Government-sponsored enterprises, including Fannie Mae, Freddie Mac, and the Federal Home Loan Banks, are governed by Advisory Bulletin 2013-07, which, while different from the OCC and Fed guidance, shares many of the same underlying principles.

Single Family Rental Securitization Market

The Single Family Rental Market

The single family rental market has existed for decades as a thriving part of the U.S. housing market. Investment in single family homes for rental purposes has provided many opportunities for the American “mom and pop” investors to build and maintain wealth, prepare for retirement, and hold residual cash flow producing assets. According to the National Rental Home Council (NRHC) (“Single-Family Rental Primer”; Green Street Advisors, June 6, 2016) as of year-end 2015, the single-family rental market comprised approximately 13% (16 million detached single-family rentals) of all occupied housing and roughly 37% of the entire United States rental market.

Single-Family Rental Securitization Structure

Introduce the credit crisis of 2008. Limited credit for non-prime borrowers in combination with record setting delinquency and foreclosure rates prompted a significant reduction of housing prices. According to the S&P CoreLogic Case-Shiller U.S. National Home Price NSA Index, since the index’s launch in May 18, 2006 (initial index value = 184.38), national house prices had dropped 25% (index value = 138.5) by April 2012.

The market dynamic combination of low prices and post-crises rental demand along with highly restrictive mortgage credit qualifications alerted particular investors to an opportunity. Specific private institutional investors, mostly private equity firms, began acquiring large quantities of distressed single family homes. According to the working paper entitled “The Emerging Economic Geography of Single-Family Rental Securitization” by the Federal Reserve Bank of San Francisco (Fields, Kohli, Schafran; January 2016) the entrance of these “large institutional investors into their new role as ‘corporate landlords’ [represented] a paradigm shift for the single-family rental market.”

Not only did they rehabilitate the homes and rent them out to non-prime borrowers, they then in turn introduced these assets into the capital markets by pledging the collateral and rental receipts into publicly issued REIT’s as well as issuing single-family rental securitizations (SFR). The issuance of single family rental securitizations was a new concept utilizing an old vehicle, the issuance of a bankruptcy remote special purpose vehicle for the purpose of issuing debt via pledged collateral assets.

In this case, the collateral is generally a loan secured by a first priority mortgage (that was placed in an LP or LLC) backed by the pledging or sale of the underlying single family homes operated as rental properties (also normally placed in a previous LP or LLC). Not only did this provide a strong exit strategy for investors because it allowed them to obtain immediate capital, but they were also able to increase their leveraged return on equity.

When Did Single-Family Rental Securitization Begin?

The first securitization transaction was issued in November 2013 by Invitation Homes (IH, 2013-1), a subsidiary of the Blackstone Group BX. As of July 2016, 32 single-borrower (26) and multi-borrower (six) SFR transactions have been issued. The table below provides a list of all SFR single and multi- borrower securitization transactions rated as of July 2016.¹²³

Table: SFR Securitization Transactions Rated as of July 2016

Interestingly, the current inventory owned as well as securitized is only approximately 1 to 2% of the overall market. Also of particular interest is the recent consolidation of institutions active in this market and the introduction of new participants. American Homes 4 Rent (AM4R) acquired Beazer Rental Homes in July 2014 and Colony American Homes (Colony) merged with Starwood Waypoint Residential Trust (SWAY) in January 2016. Subsequent to the Colony and SWAY merger, this newly formed company issued its own SFR securitization in June 2016 of approximately 3,600 properties with a loan balance of $536 million (CSH, 2016-1). Moreover introducing themselves into the SFR securitization market was Home Partners of America (formerly Hyperion Homes, Inc.), which issued its first single-family rental securitization earlier this year (approximately $654mm, property count of 2,232).

Single-Family Rental Securitization Market Outlook

The question remains, is the SFR securitization market here to stay? On the one hand, issuance still appears to be strong; however, SFRs could be an efficient market’s response to the market dislocation of 2008, the effects of which may now appear to be fading away. At a minimum this type of securitization demonstrates the effectiveness of the capital markets in moving quickly to fill the gaps left by the bursting of the housing bubble.

[1] Source: Kroll Bond Rating Agency, Inc. (KBRA)

[2] Source: www.businesswire.com

[3] Source: Yahoo Finance

Sample Size Requirements for CECL Modeling

Part One of a Two-Part Series on CECL Data Requirements

With CECL implementation looming, many bankers are questioning whether they have enough internal loan data for CECL modeling. Ensuring your data is sufficient is a critical first step in meeting the CECL requirements, as you will need to find and obtain relevant third-party data if it isn’t. This article explains in plain English how to calculate statistically sufficient sample sizes to determine whether third-party data is required. More importantly, it shows modeling techniques that reduce the required sample size. Investing in the right modeling approach could ultimately save you the time and expense of obtaining third-party data.

CECL Data Requirements: Sample Size for a Single Homogenous Pool

Exhibit 1: Required Sample Size

Let’s first consider the sample required for a single pool of nearly identical loans. In the case of a uniform pool of loans — with the same FICO, loan-to-value (LTV) ratio, loan age, etc. — there is a straightforward formula to calculate the sample size we need to estimate the pool’s default rate, shown in Exhibit 1.¹ As the formula shows, the sample size depends on several variables, some of which must be estimated:

Materiality Threshold and Confidence Level: Suppose you have a $1 billion loan portfolio and you determine that, from a financial statement materiality standpoint, your ALLL estimate needs to be reliable to within +/- $2.5 million. Statistically, we would say that we need to be 95% confident that our loss reserve estimate is within an error margin of +/- $2.5 million of the true figure. The wider our materiality thresholds and lower our required confidence levels, the smaller the sample size we need.
Loss Severity: As your average loss severity increases, you need a greater sample size to achieve the same error margin and confidence level. For example, if your average loss severity is 0%, you will estimate zero losses regardless of your default rates. Theoretically, you don’t even need to perform the exercise of estimating default rates, and your required sample size is zero. On the opposite end, if your average loss severity is 100%, every dollar of defaulted balance translates into a dollar of loss, so you can least afford to misestimate default rates. Your required sample size will therefore be great.
Default Rates: Your preliminary estimate of default rate, based on your available sample, also affects the sample size you will require. (Of course, if you lack any internal sample, you already know you need to obtain third-party data for CECL modeling.) Holding dollar error margin constant, you need fewer loans for low default-rate populations.

Example: Suppose we have originated a pool of low-risk commercial real estate loans. We have historical observations for 500 such loans, of which 495 paid off and five defaulted, so our preliminary default rate estimate is 1%. Of the five defaults, loss severity averaged 25% of original principal balance. We deem ALLL estimate errors within 0.25% of the relevant principal balance to be immaterial. Is our internal sample of 500 loans enough for CECL modeling purposes, or do we need to obtain proxy data? Simply apply the formula from Exhibit 1: In this case, our internal sample of 500 loans is more than enough to give us a statistical confidence interval that is narrower than our materiality thresholds. We do not need proxy data to inform our CECL model in this case.

CECL Data Requirements: Sample Size Across an Asset Class

If we have an asset class with loans of varying credit risk characteristics, one way to determine the needed sample is just to carve up the portfolio into many buckets of loans with like-risk characteristics, determine the number of loans needed for each bucket on a standalone basis per the formula above, and then sum these amounts. The problem with this approach – assuming our concern is to avoid material ALLL errors at the asset class level – is that it will dramatically overstate the aggregate number of loans required. A better approach, which still involves segregating the portfolio into risk buckets, is to assign varying margins of error across the buckets in a way that minimizes the aggregate sample required while maintaining a proportional portfolio mix and keeping the aggregate margin of error within the aggregate materiality threshold. A tool like Solver within Microsoft Excel can perform this optimization task with precision. The resulting error margins (as a percentage of each bucket’s default rate estimates) are much wider than they would be on a standalone basis for buckets with low frequencies and slightly narrower for buckets with high default frequencies. Even at its most optimized, though, the total number of loans needed to estimate the default rates of multiple like-risk buckets will skyrocket as the number of key credit risk variables increases. A superior approach to bucketing is loan-level modeling, which treats the entire asset class as one sample but estimates loan-specific default rates according to the individual risk characteristics of each loan.

Loan-Level Modeling

Suppose within a particular asset class, FICO is the only factor that affects default rates, and we segregate loans into four FICO buckets based on credit performance. (Assume for simplicity that each bucket holds an equal number of loans.) The buckets’ default rates range from 1% to 7%. As before, average loss severity is 25% and our materiality threshold is 0.25% of principal balance. Whether with a bucketing approach or loan-level modeling, either way we need a sample of about 5,000 loans total across the asset class. (We calculate the sample required for bucketing with Solver as described above and calculate the sample required for loan-level modeling with an iterative approach described below.) Now suppose we discover that loan age is another key performance driver. We want to incorporate this into our model because an accurate ALLL minimizes earnings volatility and thereby minimizes excessive capital buffers. We create four loan age buckets, leaving us now with 4 × 4 = 16 buckets (again, assume the buckets hold equal loan count). With four categories each of two variables, we would need around 9,000 loans for loan-level modeling but 20,000 loans for a bucketing approach, with around 1,300 in each bucket. (These are ballpark estimates that assume that your loan-level model has been properly constructed and fit the data reasonably well. Your estimates will vary somewhat with the default rates and loss severities of your available sample. Also, while this article deals with loan count sufficiency, we have noted previously that the same dataset must also cover a sufficient timespan, whether you are using loan-level modeling or bucketing.) Finally, suppose we include a third variable, perhaps stage in the economic cycle, LTV, Debt Service Coverage Ratio, or something else.

Exhibit 2: Loan-Level Modeling Yields Greater Insight from Smaller Samples

Again assume we segregate loans into four categories based on this third variable. Now we have 4^3= 64 equal-sized buckets. With loan-level modeling we need around 12,000 loans. With bucketing we need around 100,000 loans, an average of around 1,600 per bucket. As the graph shows in Exhibit 2, a bucketing approach forces us to choose between less insight and an astronomical sample size requirement. As we increase the number of variables used to forecast credit losses, the sample needed for loan-level modeling increases slightly, but the sample needed for bucketing explodes. This points to loan-level modeling as the best solution because well-performing CECL models incorporate many variables. (Another benefit of loan-level credit models, one that is of particular interest to investors, is that the granular intelligence they provide can facilitate better loan screening and pricing decisions.)

CECL Data Requirements: Sample Size for Loan-Level Modeling

Determining the sample size needed for loan-level modeling is an iterative process based on the standard errors reported in the model output of a statistical software package. After estimating and running a model on your existing sample, convert the error margin of each default rate (1.96 × the standard error of the default rate estimate to generate a 95% confidence interval) into an error margin of dollars lost by multiplying the default rate error margin by loss severity and the relevant principal balance. Next, sum each dollar error margin to determine whether the aggregate dollar error margin is within the materiality threshold, and adjust the sample size up or down as necessary. The second part in our series on CECL data requirements will lay out the data fields that should be collected and preserved to support CECL modeling.

[1] https://onlinecourses.science.psu.edu/stat506/node/11

Mortgage Insurance and Loss Severity: Causes and Effects of Mortgage Insurance Shortfalls

Mortgage Insurance and Loss Severity

This blog post is the first in a two-part series about Mortgage Insurance and Loss Severity. During the implementation of RiskSpan’s Credit Model, which enables users to estimate loan-level default, prepayment, and loss severity based on loan-level credit characteristics and macroeconomic forecasts, our team explored the many variables that affect loss severity. This series will highlight what our team discovered about Mortgage Insurance and loss severity, enabling banks to use this GSE data to benchmark their own MI recovery rates and help estimate their credit risk from MI shortfalls. RiskSpan reviewed the historical performance of Mortgage Insurers providing loan loss benefits between 1999 and 2015. Our analysis centered on Borrower and Lender-Paid Mortgage Insurance (referred to collectively as MI in this post) in Freddie Mac’s Single Family Loan-Level Dataset. Similar data is available from Fannie Mae, however, we’ve limited our initial analysis to Freddie Mac as its data more clearly reports the recovery amounts coming from Mortgage Insurers.

Mortgage Insurance Benefit Options

Exhibit 1: Mortgage Insurance Percentage Option Benefit Calculation

Mortgage Insurance Benefit = Calculated Losses x MI Percent Coverage Calculated Losses include:

UPB at time of default
Unpaid Interest
Other costs, such as attorney and statutory fees, taxes, insurance, and property maintenance.

Mortgage insurance protects investors against the event a borrower defaults. Mortgage Insurers have many options in resolving MI claims and determining the expected benefit, the amount the insurer pays in the event of a defaulted loan. The primary claim option is the Percentage Option, where the loan loss is multiplied by the MI percentage, as shown in Exhibit 1. Freddie Mac’s dataset includes the MI percentage and several loss fields, as well as other loan characteristics necessary to calculate the loss amount for each loan. The Mortgage Insurer will elect to use other claim options if they result in a lower claim than the Percentage Option. For example, if the Calculated Losses less the Net Proceeds from the liquidation of the property (i.e., net losses) are less than the Mortgage Insurance Benefit via the Percentage Option, the Mortgage Insurer can select to reimburse the net losses. Mortgage insurers can choose to acquire the property, known as the Acquisition Option. The mortgage insurer acquires the property after paying the full amount of the Calculated Losses on the loan to the investor. There were no instances in the data of Mortgage Insurers exercising the Acquisition Option after 2006.

Causes of Mortgage Insurance Shortfalls

Freddie Mac’s loan- level dataset allows us to examine loans with MI, which experienced default and sustained losses. We find that there are cases in which mortgages with MI coverage are not receiving their expected MI benefits after liquidation. These occurrences can be explained by servicing and business factors not provided in the data, for example: Cancellation: Mortgage Insurance may be cancelled either by non-payment of MI premium to the insurer or loan reaching a certain CLTV threshold. Per the Homeowners Protection Act of 1998, servicers automatically terminate private mortgage insurance (PMI) once the principal balance of the mortgage reaches 78% of the original value or if the borrower asks for MI to be cancelled once mark-to-market loan-to-value is below 80%. Denial: Mortgage Insurers may deny a claim for multiple factors, such as

not filing a Notice of Default with the Mortgage Insurer within MI policy guideline’s time frame,
not submitting the claim within a timely period after the liquidation event,
inability to transfer title, or
not providing the necessary claim documentation, usually from underwriting, from loan origination to Mortgage Insurers at time of claim.

Rescission: Mortgage Insurers will rescind an MI claim but will refund the MI premiums to the servicer. Rescission of claims are usually linked to the original underwriting of the loan and might be caused by multiple factors, such as

underwriting Negligence by the lender,
third-party fraud, or
misrepresentation of the Borrower.

Curtailment: Mortgage Insurers will partially reimburse the filed claim if the expenses are outside of their MI policy scope. Examples of curtailment to MI claims include

excess interest, taxes, and insurance expenses beyond coverage provision of the Master Policy. Most current MI policies do not have these restrictions,
non-covered expenses such as costs associated with physical damage to the property, tax penalties, etc., and
delays in reaching foreclosure in a timely manner.

Receivership: During the mortgage crisis, several of the Mortgage Insurers (for instance Triad, PMI, and RMIC) became insolvent and the state insurance regulators placed them into receivership. For the loans that were insured by Mortgage Insurers in receivership, claims are currently being partially paid (at around 50% of the expected benefit) with the unpaid benefit being deferred. This unpaid benefit runs the risk of not being paid. These factors are evident in the data and our analysis as follows: Cancellations: The Freddie Mac dataset does not provide the MI in force at the time of default, so we cannot identify cases of cancellation. These cases would show up as an instance of no MI payment. Denials & Rescissions: Our analysis excludes any loans that were repurchased by the lender, which would likely exclude most instances of MI rescission and denial. In instances where the Mortgage Insurer found sufficient case to rescind or deny, Freddie Mac would most likely find sufficient evidence for a lender repurchase as well. Curtailments: The analysis includes the impact of MI curtailment. Receivership: The analysis includes the impact of Mortgage Insurers going into receivership.

Shortfalls of Expected Mortgage Insurance Recoveries

In the exhibits below, we provide the calculated MI Haircut Rate by Vintage Year and by Disposition Year for the loans in our analysis. We define the MI Haircut Rate as the shortfall between our calculated expected MI proceeds and the actual MI proceeds reported in the dataset. The shortfall in MI recoveries is separated into two categories: MI Gap and No MI Payment.

MI Gap represents instances where some actual MI proceeds exist, but they are less than our calculated expected amount. The shortfall in actual MI benefit could be due to either Curtailment or partial payment due to Receivership.
No MI Payment represents instances where there was no MI recovery associated with loans that experienced losses and had MI at origination. No payment could be due to Rescission, Cancellation, Denial, or Receivership.

For purposes of this analysis, the Severity Rate represented below does not include the portion of the loss outside of the MI scope. For example, in 2001, average severity rate was 30%, but only 19% was eligible to be offset by MI. This was done in order to give a better understanding of the MI haircut’s effect on the Severity Rate. Exhibit 1: Mortgage Insurance Haircut Rate by Vintage Years We can observe an MI Haircut Rate averaging at 19.50% for vintages 1999 to 2011 with higher haircuts for the distressed vintages 2003 to 2008 at 23.50%. Exhibit 2: Mortgage Insurance Haircut Rate by Disposition Year Our analysis shows the MI Haircut Rate prior to 2008 on average was 6.5% and steadily increased to an average of 25% from 2009 thru 2014. We will explain below. Exhibit 3: Mortgage Insurance Haircut Rate and Expense to Delinquent UPB Percentage by Months Non-Performing In this analysis, we observe the MI Haircut Rate steadily increased by the number of months between when a loan was first classified as non-performing and when a loan liquidated. This increase can be explained by increased curtailments tied to expenses that increase over time, such as expenses associated with physical damage of the property, tax penalties, delinquent interest, insurance and taxes outside the coverage period, and excessive maintenance or attorney fees. Interest, taxes, and insurance typically constitute 85% of all loss expenses. This analysis of mortgage insurance is an exploratory post into what causes the shortfall in MI claims and how those shortfalls can affect loss severity. RiskSpan will be addressing a series of topics related to Mortgage Insurance and loss severity. In our next post we will address how banks can use this GSE data to benchmark their own MI recovery rates and help estimate their credit risk from MI shortfalls.

What CECL Means To Investors

Recent updates to U.S. GAAP will dramatically change the way financial institutions incorporate credit risk into their financial statements. The new method is called the Current Expected Credit Loss (CECL) model and will take effect over the next few years. For many institutions, CECL will mean a one-time reduction in book equity and lower stated earnings during periods of portfolio growth. These reductions occur because CECL implicitly double-counts credit risk from the time of loan origination, as we will meticulously demonstrate. But for investors, will the accounting change alter the value of your shares?

Three Distinct Measures of Value

To answer this question well, we need to parse three distinct measures of value:

1. Book Value: This is total shareholders’ equity as reported in financial reports like 10-Ks and annual reports prepared in accordance with U.S. GAAP.

2. Current Market Value (also known as Market Cap): Current share price multiplied by the number of outstanding shares. This is the market’s collective opinion of the value of your institution. It could be very similar to, or quite different from, book value, and may change from minute to minute.

3. Intrinsic Value (also known as Fundamental Value or True Value): The price that a rational investor with perfect knowledge of an institution’s characteristics would be willing to pay for its shares. It is by comparing an estimate of intrinsic value versus current market value that we deem a stock over- or under-priced. Investors with a long-term interest in a company should be concerned with its intrinsic or true value.

How Does an Accounting Change Affect Each Measure of Value?

Accounting standards govern financial statements, which investors then interpret. An informed, rational investor will “look through” any accounting quirk that distorts the true economics of an enterprise. Book value, therefore, is the only measure of value that an accounting change directly affects.

An accounting change may indirectly affect the true value of a company if costly regulations kick in as a result of a lower book value or if the operational cost of complying with the new standard is cumbersome. These are some of the risks to fundamental value from CECL, which we discuss later, along with potential mitigants.

Key Feature of CECL: Double-Counting Credit Risk

The single-most important thing for investors to understand about CECL is that it double-counts the credit risk of loans in a way that artificially reduces stated earnings and the book values of assets and equity at the time a loan is originated. It is not the intent of CECL to double-count credit risk, but it has that effect, as noted by no less authorities than the two members of the Financial Accounting Standards Board (FASB) who dissented from the rule. (CECL was adopted by a 5-2 vote.)

Consider this simple example of CECL accounting: A bank makes a loan with an original principal balance of $100. CECL requires the bank to recognize an expense equal to the present value of expected credit losses[i] and to record a credit allowance that reduces net assets by this same amount. Suppose we immediately reserve our $100 loan down to a net book value of $99 and book a $1 expense. Why did we even make the loan? Why did we spend $100 on something our accountant says is worth $99? Is lending for suckers?

Intuitively, consider that to make a loan of $100 is to buy a financial asset for a price of $100. If other banks would have made the same loan at the same interest rate (which is to say, they would have paid the same price for the same asset), then our loan’s original principal balance was equal to its fair market value at the time of origination. It is critical to understand that an asset’s fair market value is the price which market participants would pay after considering all of the asset’s risks, including credit risk. Thus, any further allowance for credit risk below the original principal balance is a double-counting of credit risk.

Here’s the underlying math: Suppose the $100 loan is a one-year loan, with a single principal and interest payment due at maturity. If the note rate is 5%, the contractual cash flow is $105 next year. This $105 is the most we can receive; we receive it if no default occurs. What is the present value of the $105 we hope to receive? One way to determine it is to discount the full $105 amount by a discount rate that reflects the risk of nonpayment. We established that 5% is the rate of return that banks are requiring of borrowers presenting similar credit risk, so an easy present value calculation is to discount next year’s contractual $105 cash flow by the 5% contractual interest rate, i.e., $105 / (1 + 5%) = $100. Alternatively, we could reduce the contractual cash flow of $105 by some estimate of credit risk. Say we estimate that if we made many loans like this one, we would collect an average of $104 per loan. Our expected future cash flow, then, is $104. If we take the market value of $100 for this loan as an anchor point, then the market’s required rate of return for expected cash flows must be 4%. ($104 / (1 + 4%) = $100.) It is only sensible that the market requires a lower rate of return on cash flows with greater certainty of collection.

What the CECL standard does is require banks to discount the lower expected cash flows at the higher contractual rate (or to use non-discounting techniques that have the same effect). This would be like discounting $104 at 5% and calculating a fair market value for the asset of $104 / (1 + 5%) ≈ $99. This (CECL’s) method double-counts credit risk by $1. The graph below shows the proper relationship between cash flow forecasts and discount rates when performing present value calculations, and shows how CECL plots off the line.

Proper Valuation Combinations (—)

FASB Vice Chairman James Kroeker and Board member Lawrence Smith described the double-counting issue in their dissent to the standards update: “When performing a present value calculation of future cash flows, it is inappropriate to reflect credit risk in both the expected future cash flows and the discount rate because doing so effectively double counts the reflection of credit risk in that present value calculation. If estimates of future cash flows reflect the risk of nonpayment, then the discount rate should be closer to risk-free. If estimates of future cash flows are based on contractual amounts (and thus do not reflect a nonpayment risk), the discount rate should be higher to reflect assumptions about future defaults.” Ultimately, the revised standard “results in financial reporting that does not faithfully reflect the economics of lending activities.”[ii]

The Accounting Standards Update notes two tangential counterpoints to Kroeker and Smith’s dissent. The first point is that banks would find alternative methods challenging, which may be true but is irrelevant to the question of whether CECL faithfully reflects true economics. The second point is that the valuation principles Kroeker and Smith lay out are for fair value estimates, whereas the accounting standard is not intended to produce fair value estimates. This concedes the only point we are trying to make, which is that the accounting treatment deviates (downwardly, in this case) from the fundamental and market value that an investor should care about.

How CECL Affects Each Measure of Value

As noted previously, the direct consequences of CECL will hit book value. Rating agency Fitch estimates that the initial implementation of CECL would shave 25 to 50 bps off the aggregate tangible common equity ratio of US banks if applied in today’s economy. The ongoing impact of CECL will be less dramatic because the annual impact to stated earnings is just the year-over-year change in CECL. Still, a growing portfolio would likely add to its CECL reserve every year.[iii]

There are many indirect consequences of CECL that may affect market and true value:

1. Leverage: The combination of lower book values from CECL with regulations that limit leverage on the basis of book value could force some banks to issue equity or retain earnings to de-leverage their balance sheet. Consider these points:

a. There is a strong argument to be made to regulators that the capital requirements that pre-dated CECL, if not adjusted for the more conservative asset calculations of CECL, will have become more conservative de facto than they were meant to be. There is no indication that regulators are considering such an adjustment, however. A joint statement on CECL from the major regulators tells financial institutions to “[plan] for the potential impact of the new accounting standard on capital.”[iv]

b. Withholding a dividend payment does not automatically reduce a firm’s true value. If the enterprise can put retained earnings to profitable use, the dollar that wasn’t paid out to investors this year can appreciate into a larger payment later.

c. The deeper threat to value (across all three measures) comes if regulations force a permanent de-leveraging of the balance sheet. This action would shift the capital mix away from tax-advantaged debt and toward equity, increase the after-tax cost of capital and decrease earnings and cash flow per share, all else equal.

Because banks face the shift to CECL together, however, they may be able to pass greater capital costs on to their borrowers in the form of higher fees or higher interest rates.

d. Banks can help themselves in a variety of ways. The more accurate a bank’s loss forecasts prove to be, the more stable its loss reserve will be, and the less likely regulators are to require additional capital buffers. Management can also disclose whether their existing capital buffers are sufficient to absorb the projected impact of CECL without altering capital plans. Conceivably, management could elect to account for its loans under the fair value option to avoid CECL’s double-counting bias, but this would introduce market volatility to stated earnings which could prompt its own capital buffers.

2. Investor Perception of Credit Risk: Investors’ perception of the credit risk a bank faces affects its market value. If an increase in credit allowance due to CECL causes investors to worry that a bank faces greater credit risk than they previously understood, the bank’s market value will fall on this reassessment. On the other hand, if investors have independently assessed the credit risk borne by an institution, a mere change in accounting treatment will not affect their view. An institution’s true value comes from the cash flows that a perfectly informed investor would expect. Unless CECL changes the kinds of loans an institution makes or the securities it purchases, its true credit risk has not changed, and nothing the accounting statements say can change that.

3. Actual Changes in Credit Risk: Some banks may react to CECL by shifting their portfolio mix toward shorter duration or less credit risky investments, in an effort to mitigate CECL’s impact on their book value. If underwriting unique and risky credits was a core competency of these banks, and they shift toward safer assets with which they have no special advantage, this change could hurt their market and fundamental value.

4. Volatility: ABA argues that the inherent inaccuracies of forecasts over long time horizons will increase the volatility of the loss reserve under CECL.[vi] Keefe, Bruyette & Woods (KBW) goes the other way, writing that CECL should reduce the cyclicality of stated earnings.[vii] KBW’s point can loosely be understood by considering that long-term averages are more stable than short-term averages, and short-term averages drive existing loss reserves. Certainly, if up-front CECL estimates are accurate, even major swings in charge-offs can be absorbed without a change in the reserve as long as the pattern of charge-offs evolves as expected. While cash flow volatility would hurt fundamental value, the concern from volatility of stated earnings is that it could exacerbate capital buffers required by regulators.

5. Transparency: All else equal, investors prefer a company whose risks are more fully and clearly disclosed. KBW reasons that the increased transparency required by CECL will have a favorable impact on financial stock prices.[viii]

6. Comparability Hindered: CECL allows management to choose from a range of modeling techniques and even to choose the macroeconomic assumptions that influence its loss reserve, so long as the forecast is defensible and used firm-wide. Given this flexibility, two identical portfolios could show different loss reserves based on the conservatism or aggressiveness of management. This situation will make peer comparisons impossible unless disclosures are adequate and investors put in the work to interpret them. Management can help investors understand, for example, if its loss reserve is larger because its economic forecast is more conservative, as opposed to because its portfolio is riskier.

7. Operational Costs: Complying with CECL requires data capacity and modeling resources that could increase operational costs. The American Bankers Association notes that such costs could be “huge.”[ix] Management can advise stakeholders whether it expects CECL to raise its operational costs materially. If compliance costs are material, they will affect all measures of value to the extent that they cannot be passed on to borrowers. As noted earlier, the fact that all US financial institutions face the shift to CECL together increases the likelihood of their being able to pass costs on to borrowers.

8. Better Intelligence: Conceivably, the enhancements to data collection and credit modeling required by CECL could improve banks’ ability to price loans and screen credit risks. These effects would increase all three measures of value.

Conclusion

CECL is likely to reduce the book value of most financial institutions. If regulators limit leverage because of lower book equity or the operational costs of CECL are material, and these costs cannot be transferred on to borrowers, then market values and fundamental values will also sag. If banks react to the standard by pulling back from the kinds of loans that have been their core competency, this, too, will hurt fundamental value. On the positive side, the required investment in credit risk modeling offers the opportunity for banks to better screen and price their loans.

Bank management can provide disclosures to analysts and investors to help them understand any changes to the bank’s loan profile, fee and interest income, capital structure and operational costs. Additionally, by optimizing the accuracy of its loss forecasts, management can contain the volatility of its CECL estimate and minimize the likelihood of facing further limitations on leverage.

[i] The term “expected loss” can be confusing; it does not necessarily mean that default is likely. If you have a 1% chance of losing $100, then your “expected loss” is 1% × $100 = $1. As long as a loan is riskier than a Treasury, your expected loss is greater than zero.

[ii] FASB Accounting Standards Update 2016-13, p. 237 and p. 235 http://www.fasb.org/jsp/FASB/Document_C/DocumentPage?cid=1176168232528&acceptedDisclaimer=true

[iii] By the end of a loan’s life, all interest actually collected and credit losses realized have been reflected in book income, and associated loss reserves are released, so lifetime interest income and credit losses are the same under any standard.

[iv] Joint Statement on the New Accounting Standard on Financial Instruments – Credit Losses. https://www.federalreserve.gov/newsevents/press/bcreg/bcreg20160617b1.pdf

Modigliani, Franco and Miller, Merton H. (1963) Corporate Income Taxes and the Cost of Capital: A Correction. The American Economic Review, Vol. 53, No. 3, pp. 433-443. https://www.jstor.org/stable/1809167?seq=1#page_scan_tab_contents

[vi] Gullette, Mike. (2016) FASB’s Current Expected Credit Loss Model for Credit Loss Accounting (CECL). American Bankers Association.

[vii] Kleinhanzl, Brian, et al. FASB is About to Accelerate Loan Loss Recognition for the Financial Industry. Keefe, Bruyette & Woods.

[viii] Kleinhanzl, Brian, et al, p. 1.

[ix] Gullette, Mike. (2016), p. 4.

Managing Model Risk and Model Validation

Over the course of several hundred model validations we have observed a number of recurring themes and challenges that appear to be common to almost every model risk management department. At one time or another, every model risk manager will puzzle over questions around whether an application is a model, whether a full-scope validation is necessary, how to deal with challenges surrounding “black box” third-party vendor models, and how to elicit assistance from model owners. This series of blog posts aims to address these and other related questions with what we’ve learned while helping our clients think through these issues.

Part 1: Model Validation: Is this Spreadsheet a Model

As model validators, we frequently find ourselves in the middle of debates between spreadsheet owners and enterprise risk managers over the question of whether a particular computing tool rises to the level of a “model.” To the uninitiated, the semantic question, “Is this spreadsheet a model?” may appear to be largely academic and inconsequential. But its ramifications are significant, and getting the answer right is of critical importance to model owners, to enterprise risk managers, and to regulators.

Part 2: Validating Vendor Models: Special Considerations

Many of the models we validate on behalf of our clients are developed and maintained by third-party vendors. These validations present a number of complexities that are less commonly encountered when validating “home-grown” models.

Notwithstanding these challenges, the OCC’s Supervisory Guidance on Model Risk Management (OCC 2011-12) specifies that “Vendor products should nevertheless be incorporated into a bank’s broader model risk management framework following the same principles as applied to in-house models, although the process may be somewhat modified.”

Part 3: Preparing for Model Validation: Ideas for Model Owners

Though not its intent, model validation can be disruptive to model owners and others seeking to carry out their day-to-day work. We have performed enough model validations over the past decade to have learned how cumbersome the process can be to business unit model owners and others we inconvenience with what at times must feel like an endless barrage of touch-point meetings, documentation requests and other questions relating to modeling inputs, outputs, and procedures.

Part 4: 4 Questions to Ask When Determining Model Scope

We have identified four considerations that can inform your decision on whether a full-scope model validation is necessary…

Part 5: Performance Testing: Benchmarking vs. Back-Testing

When someone asks you what a model validation is what is the first thing you think of? If you are like most, then you would immediately think of performance metrics— those quantitative indicators that tell you not only if the model is working as intended, but also its performance and accuracy over time and compared to others. Performance testing is the core of any model validation and generally consists of the following components:

Benchmarking
Back-testing
Sensitivity Analysis
Stress Testing

Part 6: Model Input Data Validation – How much is Enough?

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Front-End Credit Risk Transfer

Recourse Transaction

Deeper Mortgage Insurance

Benefits

Validating Model Inputs Best Practices

Consideration 1: Complexity of Inputs

Consideration 2: Manual Manipulation of Inputs from Source System Prior to Input into Model

Consideration 3: Reliability of Source Systems

Consideration 4: Output Sensitivity to Inputs

A Cost-Effective Model Input Validation Strategy

Benchmarking

Choosing a Benchmark

Back-Testing

What Back-Tests to Perform

Benchmarking vs Backtesting

VA Internal Policies

Lenders’ Ability to Self-Regulate

Effect of Military Culture on VA Loans

Key Considerations for Model Validation

Question 1: What about the model has changed since the last full-scope validation?

Question 2: How have market conditions changed since the last validation?

Question 3: How mission-critical is the model?

Question 4: How often have manual overrides of model output been necessary?

Full or Partial Model Validation?

The Single Family Rental Market

Single-Family Rental Securitization Structure

When Did Single-Family Rental Securitization Begin?

Table: SFR Securitization Transactions Rated as of July 2016

Single-Family Rental Securitization Market Outlook

Part One of a Two-Part Series on CECL Data Requirements

CECL Data Requirements: Sample Size for a Single Homogenous Pool

Exhibit 1: Required Sample Size

CECL Data Requirements: Sample Size Across an Asset Class

Loan-Level Modeling

Exhibit 2: Loan-Level Modeling Yields Greater Insight from Smaller Samples

CECL Data Requirements: Sample Size for Loan-Level Modeling

Mortgage Insurance and Loss Severity

Mortgage Insurance Benefit Options

Exhibit 1: Mortgage Insurance Percentage Option Benefit Calculation

Causes of Mortgage Insurance Shortfalls

Shortfalls of Expected Mortgage Insurance Recoveries

Three Distinct Measures of Value

How Does an Accounting Change Affect Each Measure of Value?

Key Feature of CECL: Double-Counting Credit Risk

Proper Valuation Combinations (—)

How CECL Affects Each Measure of Value

Conclusion

Part 2: Validating Vendor Models: Special Considerations

Part 3: Preparing for Model Validation: Ideas for Model Owners

Part 4: 4 Questions to Ask When Determining Model Scope

Part 5: Performance Testing: Benchmarking vs. Back-Testing

Part 6: Model Input Data Validation – How much is Enough?

Company

Products

Security & Compliance