The S-Curve

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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Blog - Latest
  • A More Equitable Lending System Will Not Be Created by Accident

    Andrew Davidson

    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. This gap is larger than it was in 1960, when racial bias in mortgage lending was a matter of policy in many states. Similarly, insights from the Federal Housing Finance Agency demonstrate significant differences in mortgage application approval rates between white Americans and minority applicants. In 2020, for instance, white Americans had a more than 15% higher chance of being approved for a loan based on HMDA data.

    Even as other deeply embedded social institutions have become more inclusive of people of color, equal access to housing is seemingly out of the country’s grasp. That is largely because the modern lending ecosystem is underpinned by the very technologies and processes that were originally designed to facilitate discrimination rather than prevent it. Fair lending laws alone have been unable to narrow the yawning gap in homeownership rates.

    With this in mind, how do we prevent homeownership inequities from persisting well into this century?

    For many, the answer to that question is simple: We would use artificial intelligence. After all, credit score algorithms and automated mortgage application reviewers do not see skin color. In practice, however, integrating unbiased AI-powered technologies into a biased lending system presents significant obstacles.

    The Human Obstacle

    Human lenders have both implicit and explicit biases that might affect what products they offer to prospective borrowers and how they view and evaluate loan applications. The promise of AI-driven advanced analytics is that it could remove those biases from application assessments and instead focus solely on the facts.

    First, we must recognize that AI in general is more promising than reality in many fields, and it might take time before an AI system could reliably make credit determinations. Moreover, AI development often perpetuates current processes rather than creating a new way of thinking.

    Traditional mortgage lending metrics (e.g., credit scores) are focused on identifying borrowers who are simply likely to default in the short term. With or without AI, new approaches to evaluating credit could deliver insights on underlying borrower characteristics that are more indicative of long-term creditworthiness. By evaluating a wider range of data (in addition to borrower credit history, income, property value, and other relevant information), we can begin to create a system that supports homeownership rather than discourages it. Nevertheless, that will not happen by accident.

    The Power of Purpose

    Although policy has struggled to create a fair and inclusive path toward homeownership, technology can still succeed — but only if its creators deliberately include the elimination of racial bias in mortgage lending as a key success metric. Industry innovation does not necessarily result in progress for all Americans; achieving greater equity will be difficult if we do not pursue it directly.

    As such, the data scientists and developers building AI technologies today must use decidedly different approaches from those employed in the past. Specifically, they should be trained with data that ensures less consistency with “historical” approaches in pursuit of greater equity for all.

    What does this mean? In short, we have centuries of data on loans and mortgages issued in the past, but given the inequities we are seeing, it is likely not representative of a system that enables equal access to housing. Rather than training algorithms using just historical credit data, we should envision a more just scenario and incorporate data that corresponds to a more equitable world. A reliance on flawed inputs will only exacerbate existing biases.

    Of course, the surge in companies such as Rocket Mortgage and other online lending platforms has already reduced the need for in-person applications, which in turn has helped to reduce discrimination significantly. Nevertheless, as an industry, we cannot assume that technology alone will solve a problem that is centuries old.

    The Human Solution

    Today, there is a tremendous amount of data available to power algorithms that make the mortgage lending process more streamlined and efficient. The question, now, is this: Can that data also help create equal access to housing for Americans of color? 

    Not without our help and our focus on addressing inequality.

    The AI technologies and mortgage risk analytics that anchor tomorrow’s lending processes must be designed with the express goal of eliminating racial bias in mortgage lending. Misuse of data can promote additional discrimination, and failing to keep this in mind will result in new technologies that simply do not account for the struggles of marginalized communities. To avoid perpetuating systemic inequities that have spanned generations, actual intelligence is needed.

    A magical solution does not exist — but by combining the power of human foresight with the power of emerging technology, we could certainly take many more steps forward.

Blog - Archives

The S-Curve Archives

  • Andrew Davidson

    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.

  • Eknath Belbase

    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.

  • Mickey Storms, Richard Cooperstein

    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.

  • Andrew Davidson

    Thoughts

    The growing prevalence of artificial intelligence in the mortgage industry is shining a new light on the human biases that have pervaded the industry since its inception. AI is meant to bring fairness and objectivity to mortgage decisions, but it can’t perform fairly if it was built on an unfair system.

  • Products

    The LDM v3.0.2 library adds AutoLDM to the v3.0.1 library.

    Key benefits include:

  • Events
    We at Andrew Davidson & Co., Inc. (AD&Co) are once again thrilled to celebrate Pride Month, especially the contributions of LGBTQ professionals in the field of finance including affordable housing policy and the GSEs. This year, in addition to celebrating, we are also paying increased attention to the challenges that LGBTQ individuals face, particularly around issues of housing. Our pride in our LGBTQ staff and community sits alongside our concern about discriminatory lending practices, including in mortgages. As of February 2021, for the first time, lesbian, gay, bisexual, transgender, queer, and questioning (LGBTQ) Americans will be protected from housing discrimination under the Fair Housing Act. 
  • News

    For several years, AD&Co has tracked the total rate of return (TRR) performance of the GSE CAS and STACR CRT in its U.S. Mortgage High-Yield Indices. The AD&Co Mid-Tier index constitutes a broad market measure of the TRR performance of GSE CRT. The related sub-indices segregate the CRT market into 4 index Tiers by attachment point, reflective of the credit exposure of the various classes of underlying CRT ranging from B to M1.

  • Events
    We at Andrew Davidson & Co., Inc. (AD&Co) stand in solidarity with the Asian community and speak out against the xenophobic ignorance that has led to increased racist attacks against Asians. We protest against these hate crimes. This is a time to celebrate the richness that we have gained from the diversity of the Asian culture. We pledge to support the heritage that is part of what makes us American. 
  • Events

    What does it mean to be mentally healthy? The answer is different for everyone. With all the extra anxiety that many of us have experienced since 2020, whether from uncertainty about COVID-19 or from other experiences that may be new to us, it’s important to acknowledge that it’s alright to not feel alright. Fortunately, there are numerous resources that are available locally, nationally, and in some cases through your workplace or benefits package. We might start by finding out what makes us feel better.

  • Products

    Today marks the publication of Chris Widman's Quantitative Perspective, a comprehensive article on the newest member of our LoanDynamics suite, the Auto LoanDynamics Model. Auto LDM will be integrated into vendor systems and AD&Co tools, allowing users to perform analysis on auto loan and ABS positions.