Why Accurate Loan Pool and MSR Cost Forecasting Requires Loan-by-Loan Analytics
When it comes to forecasting loan pool and MSR cash flows, the practice of creating “rep lines,” or cohorts, of loans with similar characteristics for analytical purposes has its roots in the Agency MBS market. One of the most attractive and efficient features of Agencies is the TBA market. This market allows originators and issuers to sell large pools of mortgages that have not even been originated yet. This is possible because all parties understand what these future loans will look like. All these loans will all have enough in common as to be effectively interchangeable with one another.
Institutions that perform the servicing on such loans may reasonably feel they can extend the TBA logic to their own analytics. Instead of analyzing a hundred similar loans individually, why not just lump them into one giant meta-loan? Sum the balances, weight-average the rates, terms, and other features, and you’re good to go.
Why the industry still resorts to loan cohorting when forecasting loan pool and MSR cash flows
The simplest explanation for cohort-level analytics lies in its simplicity. Rep lines amount to giant simplifying assumptions. They generate fewer technological constraints than a loan-by-loan approach does. Condensing an entire loan portfolio down to a manageable number of rows requires less computational capacity. This takes on added importance when dealing with on-premise software and servers. It also facilitates the process of assigning performance and cost assumptions.
What is more, as OAS modeling has evolved to dominate the loans and MSR landscape, the stratification approach necessary to run Monte Carlo and other simulations lends itself to cohorting. Lumping loans into like groups also greatly simplifies the process of computing hedging requirements.
Advantages of loan-level over cohorting when forecasting cash flows
Treating loan and MSR portfolios like TBA pools, however, has become increasingly problematic as these portfolios have grown more heterogeneous. Every individual loan has a story. Even loans that resemble each other in terms of rate, credit score, LTV, DTI, and documentation level have unique characteristics. Some of these characteristics – climate risk, for example – are not easy to bucket. Lumping similar loans into cohorts also runs the risk of underestimating tail risk. Extraordinarily high servicing/claims costs on just one or two outlier loans on a bid tape can be enough to adversely affect the yield of an entire deal.
Conversely, looking at each loan individually facilitates the analysis of portfolios with expanded credit boxes. Non-banks, which do not usually have the benefit of “knowing” their servicing customers through depository or other transactional relationships, are particularly reliant on loan-level data to understand individual borrower risks, particularly credit risks. Knowing the rate, LTV, and credit score of a bundled group of loans may be sufficient for estimating prepayment risk. But only a more granular, loan-level analysis can produce the credit analytics necessary to forecast reliably and granularly what a servicing portfolio is really going to cost in terms of collections, loss mitigation, and claims expenses.
Loan-level analysis also eliminates the reliance on stratification limitations. It facilitates portfolio composition analysis. Slicing and dicing techniques are much more simply applied to loans individually than to cohorts. Looking at individual loans also reduces the risk of overrides and lost visibility into convexity pockets.
Potential challenges and other considerations
So why hasn’t everyone jumped onto the loan-level bandwagon when forecasting loan pool and MSR cash flows? In short, it’s harder. Resistance to any new process can be expected when existing aggregation regimes appear to be working fine. Loan-level data management requires more diligence in automated processes. It also requires the data related to each individual loan to be subjected to QC and monitoring. Daily hedging and scenario runs tend to focus more on speed than on accuracy at the macro level. Some may question whether the benefits of such a granular, case-by-case analysis that identifying the most significant loan-level pickups requires actually justifies the cost of such a regime.
Rethink. Why now?
Notwithstanding these challenges, there has never been a better time for loan and MSR investors to abandon cohorting and fully embrace loan-level analytics when forecasting cash flows. The emergence of cloud-native technology and enhanced database and warehouse infrastructure along with the ability to outsource the hosting and computational requirements out to third parties creates practically limitless scalability.
The barriers between loan and MSR experts and IT professionals have never been lower. This, combined with the emergence of a big data culture in an increasing number of organizations, has brought the granular daily analysis promised by loan-level analytics tantalizingly within reach.
For a deeper dive into loan and MSR cost forecasting, view our webinar, “How Much Will That MSR Portfolio Really Cost You?”
Why Climate Risk Matters for Mortgage Loan & MSR Investors
The time has come for mortgage investors to start paying attention to climate risk.
Until recently, mortgage loan and MSR investors felt that they were largely insulated from climate risk. Notwithstanding the inherent risk natural hazard events pose to housing and the anticipated increased frequency of these events due to climate change, it seemed safe to assume that property insurers and other parties in higher loss position were bearing those risks.
In reality, these risks are often underinsured. And even in cases where property insurance is adequate, the fallout has the potential to hit investor cash flows in a variety of ways. Acute climate events like hurricanes create short-term delinquency and prepayment spikes in affected areas. Chronic risks such as sea level rise and increased wildfire risk can depress housing values in areas most susceptible to these events. Potential impacts to property insurance costs, utility costs (water and electricity in areas prone to excessive heat and drought, for example) and property taxes used to fund climate-mitigating infrastructure projects all contribute to uncertainty in loan and MSR modeling.
Moreover, dismissing climate risk “because we are in fourth loss position” should be antithetical to any investor claiming to espouse ESG principles. After all, consider who is almost always in the first loan position – the borrower. Any mortgage investment strategy purporting to be ESG friendly must necessarily take borrower welfare into account. Dismissing climate risk because borrowers will bear most of the impact is hardly a socially responsible mindset. This is particularly true when a disproportionate number of borrowers prone to natural hazard risk are disadvantaged to begin with.
Hazard and flood insurers typically occupy the loss positions between borrowers and investors. Few tears are shed when insurers absorb losses. But society at large ultimately pays the price when losses invariably lead to higher premiums for everybody.
Evaluating Climate Exposure
For these and other reasons, natural hazards pose a systemic risk to the entire housing system. For mortgage loan and MSR investors, it raises a host of questions. Among them:
- What percentage of the loans in my portfolio are susceptible to flood risk but uninsured because flood maps are out of date?
- How geographically concentrated is my portfolio? What percentage of my portfolio is at risk of being adversely impacted by just one or two extreme events?
- What would the true valuation of my servicing portfolio be if climate risk were factored into the modeling?
- What will the regulatory landscape look like in coming years? To what extent will I be required to disclose the extent to which my portfolio is exposed to climate risk? Will I even know how to compute it, and if so, what will it mean for my balance sheet?
Incorporating Climate Data into Investment Decision Making
Forward-thinking mortgage servicers are at the forefront of efforts to get their arms around the necessary data and analytics. Once servicers have acquired a portfolio, they assess and triage their loans to identify which properties are at greatest risk. Servicers also contemplate how to work with borrowers to mitigate their risk.
For investors seeking to purchase MSR portfolios, climate assessment is making its way into the due diligence process. This helps would-be investors ensure that they are not falling victim to adverse selection. As investors increasingly do this, climate assessment will eventually make its way further upstream, into appraisal and underwriting processes.
Reliably modeling climate risk first requires getting a handle on how frequently natural hazard events are likely to occur and how severe they are likely to be.
In a recent virtual industrial roundtable co-hosted by RiskSpan and Housing Finance Strategies, representatives of Freddie Mac, Mr. Cooper, and Verisk Analytics (a leading data and analytics firm that models a wide range of natural and man-made perils) gathered to discuss why understanding climate risk should be top of mind for mortgage investors and introduced a framework for approaching it.
Building the Framework
The framework begins by identifying the specific hazards relevant to individual properties, building simulated catalogs of thousands of years’ worth of simulated events, computing likely events simulating damage based on property construction and calculating likely losses. These forecasted property losses are then factored into mortgage performance scenarios and used to model default risk, prepayment speeds and home price impacts.
Responsibility to Borrowers
One member of the panel, Kurt Johnson, CRO of mega-servicer Mr. Cooper, spoke specifically of the operational complexities presented by climate risk. He cited as one example the need to speak daily with borrowers as catastrophic events are increasingly impacting borrowers in ways for which they were not adequately prepared. He also referred to the increasing number of borrowers incurring flood damage in areas that do not require flood insurance and spoke to how critical it is for servicers to know how many of their borrowers are in a similar position.
Johnson likened the concept of credit risk layering to climate risk exposure. The risk of one event happening on the heels of another event can cause the second event to be more devastating than it would have been had it occurred in a vacuum. As an example, he mentioned how the spike in delinquencies at the beginning of the covid pandemic was twice as large among borrowers who had just recovered from Hurricane Harvey 15 months earlier than it was among borrowers who had not been affected by the storm. He spoke of the responsibility he feels as a servicer to educate borrowers about what they can do to protect their properties in adverse scenarios.
Recent Edge Platform Updates
Edge Platform Updates
MSR Engine
The Platform’s extensive library of available MSR analytic outputs has been expanded to include Effective Recapture Rate and other Income and Expense fields.
Base servicing cost inputs for MSR assumptions have also been enhanced.
LOANS
The ETL tool for loan onboarding has been further enhanced with machine learning capabilities.
New fields for querying options and enhanced segmentation have been added. And SOFRWalSpread and SOFRSpotSpread are now captured in static analysis output.
HISTORICAL PERFORMANCE
Special Eligibility Program fields have been added to Fannie and Freddie pool data outputs along with a complementing SpecialProgram100 filter
Fannie and Freddie datasets now include CBR and CPR metrics (previously only available for Ginnies).
New support has been added for saving CoreLogic LLD queries with complement filters.
Enhanced historical date-based queries in Edge Perspective (e.g., option to run and save queries with relative factor dates rather than specifically coded date.
