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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Combatting the Effects of Algorithmic BiasThoughtsHomeownership is the largest source of wealth accumulation and inter-generational wealth transfer for the working and middle class. However, the history of racial discrimination (it was actually legal to discriminate by race in housing until the Fair Housing Act of 1968), suggests that we have a continuing responsibility to ensure fair access to housing and housing finance.
The homeownership rate for white Americans has averaged 70-75% over the last 25 years but only 40-50% for black Americans. In fact, the gap has widened over this period.
What is Equitable Housing Finance, and how do we make progress towards it?
The issues of economic opportunity, and geographic and housing inequality, are long-standing and varied. But as practitioners in mortgage risk and analytics, we focus on data, assessing risk and equal access to mortgage credit. Households of moderate means generally use credit to buy their first home so we must consider credit access and the quantitative process carefully, especially in the context of artificial intelligence and unintentionally biased algorithms.
The premise is simple: Use the same comprehensive set of financial data for everyone and apply it fairly.
Going beyond credit scores
Most people know about credit scores, which serve as the principal metric used for credit decisioning. What if it turns out that credit scores don’t reflect all relevant consumer financial data? What if this data gap has grown over time, and what if it’s larger for targeted groups like minorities and low-income families?
To the degree that mortgage decisioning models omit relevant data, they become less accurate. To the degree that such omissions are concentrated among certain groups, these models will contain algorithmic bias.
Consumer credit scores were created in the 1950s, and the Equal Credit Opportunity Act of 1974 ensured they could not include discriminatory information. The FICO formulation commonly used for mortgage credit today was built about 2004 and it correlates well to the likelihood of short-term delinquency.
However, financial data is now available that is materially relevant to consumer credit performance, but is not included in credit scores. This data is generally more significant for renters and underserved populations, those with smaller traditional financial footprints. Such indicators include consumer credit card balances, telecom/utility payment data, and free cash flow from bank accounts.
The mortgage ecosystem is beginning to work towards using expanded consumer financial data. AD&Co is acquiring this data and improving our analytics make mortgage decisioning both more accurate and more fair.
Working through public policy
Leveraging new data, advancing national standards, and broadly implementing improved decisioning are not automatic. Most mortgage lending is federally connected (GSEs, FHA/VA, banks), and compliance standards are universally applied. This occurs in part because the mortgage market contains inherent information asymmetries and social externalities around fairness and stability. The confluence of finance and policy leads us to combine our analytic efforts with actively engaging with federal counter-parties and in the policy debate. This includes focusing on how to integrate new data sources into mortgage decisioning on a national scale as a means to improve accuracy and fairness.
The S-Curve Archives
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ThoughtsHomeownership is the largest source of wealth accumulation and inter-generational wealth transfer for the working and middle class. However, the history of racial discrimination (it was actually legal to discriminate by race in housing until the Fair Housing Act of 1968), suggests that we have a continuing responsibility to ensure fair access to housing and housing finance.
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ThoughtsDear Friends,
As Andrew Davidson & Co., Inc. (AD&Co) reaches its 30-year milestone, I reflect on two seemingly contradictory ideas: Firms need experience to guide clients through difficult times but sometimes it is necessary to discard past practices to achieve breakthroughs.
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ThoughtsFor many people, having accessible transportation (a car, for example) is necessary. Most U.S. people live in areas without adequate public transportation and require vehicles to access jobs, healthcare, and groceries.
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Thoughts
As interest rates rise and fewer loans with refinancing incentive remain, other factors are primed to play a larger role in determining prepayment speeds in the coming months (and perhaps years). Turnover, the rate at which people move, is the most cited of these factors. In this blog post, we’ll consider two other potential drivers: curtailments, or partial prepayments, and mortgage payoffs that don’t involve taking out a new loan.
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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.