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.
-
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
-
ThoughtsRecently, aggregators have crossed market borders by issuing residential mortgage-backed securities (RMBS) backed by owner-occupied (OO), GSE-eligible conforming loans. Additionally, conforming mortgage loans have drawn investment interest from insurance companies fronted by aggregators and evaluated by third-party firms. These developments constitute historically rare disintermediations of the nearly monopsonist purchases of conforming loans by the GSEs.
-
PodcastJoin Rob Landauer in a conversation with Abe Martin as they discuss his recent Pipeline article, "Modeling the Balance Behavior of HELOC Borrowers." In this episode, they highlight key points from the article as he shares insights into the draw component of HELOCs and provide an update on the beta rele
-
News
We’re excited to announce a major addition to the Andrew Davidson & Co., Inc. (AD&Co) team. Industry leaders Kelli Sayres and Gene Park, known for building and scaling leading fixed-income analytics platforms, have joined AD&Co’s Business Development team.
-
ThoughtsBuilding on our earlier research on expanded consumer attributes, AD&Co continues to explore how credit data contributes to modeling delinquency and prepayment risk, which are key drivers of mortgage servicing rights cash flows and valuation.
-
EventsAndrew Davidson recently joined NFM Lending’s Greg Sher on the One On One podcast to discuss our recent white paper, “The Impact of Moving Away From the Tri-Merge Standard.”
-
EventsAD&Co recently sponsored and attended SFVegas 2026 and Optimal Blue Summit 2026. This post shares the AD&Co team's unique perspectives and key takeaways from attending both conferences.
-
NewsAD&Co US Mortgage High Yield Indices
The Federal Reserve Economic Data (FRED) portal, housed by the Federal Reserve Bank of St. Louis, has been publishing AD&Co’s CRT indices since 2019. These series posted under the overall name of “US Mortgage High-Yield” include total return rates and credit and option-adjusted spreads (crOAS) – a projected return’s spread over Treasury (in the past, Libor). These series are available going back to 2014-end and tiered by CRT initial supports.
-
ThoughtsIn July 2025, the US Federal Housing Finance Agency (FHFA) announced that the government-sponsored entities (the Enterprises or GSEs), Fannie Mae and Freddie Mac, would permit lenders to choose between Classic FICO and VantageScore 4.0 credit score models for loans sold to the GSEs. FHFA also stated in a social media post that the tri-merge standard would be maintained for mortgage underwriting. Nevertheless, some mortgage industry stakeholders recommend moving away from the tri-merge standard for GSE mortgages in favor of a single or bi-merge report standard.
-
News
As housing faces more climate threats that result in more losses, the insurance program that it sits on is teetering on the brink of collapse. Yet, the home insurance market has three distinct stakeholders that have competing priorities, and today, there is no motivation for a collaborative solution.
Understanding how to strengthen and protect the current structure requires looking at the cost burdens along with the risk for each of those parties.
-
ThoughtsThere has been a flurry of activity in the mortgage markets since the 2018 passage of the Economic Growth, Regulatory Relief, and Consumer Protection Act. This act requires the Federal Housing Finance Agency (FHFA, now known as US Federal Housing) to validate and modernize the credit score models used in the housing finance system. It should be noted that so far, the discourse has been around mortgages sold to the Enterprises (Fannie Mae and Freddie Mac). Ginnie Mae has not provided any guidance on their plans to start using new credit score models.