Welcome to The S-Curve
Now you will be able to receive the latest announcements, product updates, and our insights on the mortgage market in real time.
The name of the blog, the S-Curve, is a reflection of our logo and the central feature of our prepayment model. S-curves are seen in nature in many phenomenon, from population growth to prepayment and default models. Our first S-curve, in the early 1990s, used the arctangent function, then piece-wise linear functions, and evolved over time to be more complex and vary by FICO, loan size and LTV. This evolution encapsulates both the timeless nature of fundamental relationships and constant innovation to describe them better over time.
We hope you find the information useful and we look forward to your feedback.
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The Signal and The NoiseThoughts
Separating signal from noise is at the heart of what we do at AD&Co. One of the key tools we utilize for that purpose is a sophisticated set of model performance trigger reports. These monthly reports not only alert us to model drift but also point to possible causes for the drift.
In the mortgage market, changes in behavior on the part of both borrowers and lenders may create gaps between model projections and actual performance. Early analysis of these gaps provides rich information to our modelers based on which they can either identify temporary drifts in model performance or, more importantly, highlight fundamental changes in behavior that need to be captured in our models.
Every product type is monitored along the following four dimensions, with residuals defined as the monthly difference between projected and actual results:
Moving average: Are residuals deteriorating over time?
Trend: Are residuals consistently becoming more positive or negative?
Bias: Is the model persistently overpredicting or underpredicting actuals?
Magnitude: Are model misses large enough to matter?
Multiple types of triggers are useful for determining the seriousness of a breach as well as the type of adjustments that might be warranted in response. For example, a moving average warning might indicate a developing change in borrower behavior that may require either additional explanatory variables or more complex functions of existing variables to tighten the residuals. Alternatively, updated data may provide key information for refitting the model. A small but persistent bias, on the other hand, may be easily corrected with a simple tuning parameter adjustment.
These triggers also help us monitor performance at the factor level. Rather than simply observing, for example, how GNMA 15-year MBS are performing, we have triggers at different levels of credit score, LTV, note rate, and many other factors. This deep analysis, presented as an easy-to-use dashboard, quickly identifies possible causes of model drift.
In January, we will present a detailed review of our trigger methodology, and show how we use the reports to help set our modeling priorities in order to explain performance and, when necessary, modify our models to accommodate new behavioral patterns. For now, we will leave you with the following example of a segment of a trigger report for FHLMC MBS using LDM 2.2 with COVID tunings applied.
Happy Holidays!
Tom Parrent, Model Risk Management
October 2020 FHLMC Trigger Report
Trigger Type Bias Magnitude MovAvg Trend LoanType UPB FHLMC_30YR 2,085,272,108,356 ⬤ ⬤ ⬤ ⬤ Trigger Type Bias Magnitude MovAvg Trend LoanType Net Coupon UPB FHLMC_30YR 2.0 196,050,740,636 ⬤ ⬤ ⬤ ⬤ 2.5 253,743,515,938 ⬤ ⬤ ⬤ ⬤ 3.0 547,254,721,052 ⬤ ⬤ ⬤ ⬤ 3.5 498,363,756,722 ⬤ ⬤ ⬤ ⬤ 4.0 331,086,483,224 ⬤ ⬤ ⬤ ⬤ 4.5 152,994,012,086 ⬤ ⬤ ⬤ ⬤ 5.0 59,076,412,520 ⬤ ⬤ ⬤ ⬤ Legend Pass ⬤ Watch ⬤ Warning ⬤
The S-Curve Archives
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Thoughts
Summary
In 2021, Andrew Davidson & Co. Inc. (AD&Co) proposed a benchmark cohort approach to setting Ability-to-Repay (ATR) Qualified Mortgages (QM) standards. Successful benchmarks based on data are model-free and transparent, and the cohorts must perform consistently in comparison to one another and across time. Our original work used data through the early stages of the pandemic when non-performing loan percentages skyrocketed.
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ThoughtsHow Lowering Capital Costs Affects Higher-Risk Loans
Government-sponsored enterprises (or GSEs) are companies that provide guarantees and financing to originators through the mortgage secondary market. The size and resilience of the GSE secondary market maximizes diversification and liquidity which reduces financial risk and cost of capital. This benefit accrues to conforming borrowers through lower mortgage rates and resiliently available financing.
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ProductsThe release of Andrew Davidson & Co., Inc.’s (AD&Co) new generation of financial engineering tools marks a shift to a new reality; when the traditional benchmark for MBS valuation, the LIBOR/ Swap yield curve, becomes unavailable. Our recent Product Release email informed our readers about the change. In short, our users can:
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ThoughtsFHFA held a listening session for interested parties on its proposed rule on the GSE process for credit scores. The objective is making mortgage underwriting and pricing more accurate and more fair while balancing practical implementation by firms in the mortgage ecosystem. Along with many others, I had the opportunity to provide insights on this proposed rulemaking.
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ThoughtsIn our January 19th blog entitled, A More Equitable Lending System Will Not Be Created by Accident, we described the efforts it will take to overcome not just bias in lending today, but the systemic factors that have limited access to credit in the past and have created an unjust system.
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ThoughtsIn this short blog post I discuss some developments taking place in the flood insurance landscape in the US and look ahead at a few potential directions things could go. I suggest that universal catastrophic flood insurance coverage with a continuation of the introduction of risk-based pricing would be a significant improvement.
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ThoughtsIntroduction
The Government-Sponsored Enterprises (GSEs) entered conservatorship in September 2008. One could view the succeeding thirteen years as a journey back to financial stability with a refined operating model that looks more like a financial utility than a hedge fund. This business model is more compatible with a fair lending mission for a standard-setter that maintains secondary markets under an effective regulator. The GSEs remain the largest part of the housing finance backbone and a resilient funding source during economic stress.
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Thoughts
Around 75% of white American families were homeowners in the first quarter of 2020, according to data from the United States Census Bureau. However, only 44% of Black American families owned their homes at the same time.
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Thoughts
According to a report by the Research Institute for Housing America, climate change risk is rapidly increasing in the housing industry and will continue to demand more attention and regulation in the near future.
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Thoughts
Mortgage market participants are keenly aware that the Federal Reserve has been scaling back its UST and MBS purchases and factoring the outcomes of its actions on stakeholders across markets.