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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Introducing MARS+ as the Next Generation of Mortgage Analysis and Reporting SystemProductsAndrew Davidson & Co., Inc (AD&Co) is pleased to announce the beta release of MARS+, the next generation of Mortgage Analysis & Reporting System (MARS), which has been in use since 2008 for performance reporting of AD&Co models. MARS+ aims to provide enhanced and advanced capabilities and features for mortgage analysis and reporting.
The new enhancements of MARS+ include:
- Simplified access to AD&Co models and data from a single page
- Enhanced filtering and stratification of back-testing data
- Distribution of data for key mortgage data analysis parameters
- Model performance visualization and tabular display for selected data of interest
- Customizable dashboards for effective reporting of actual and model results along with data characteristics
In what follows, we summarize how to use the various capabilities and features of MARS+.
Selecting, Filtering, and Stratifying Data of Interest
The initial screen of MARS+ labeled as ‘Model & Data’ provides user interface components that are grouped into three categories: (1) Model & Data Inputs, (2) Filters, and (3) Stratification.
For selected filter and stratification data parameters, the ‘Model & Data’ panel also provides a distribution icon to the right of each filter and the parameter selected for stratification. Upon clicking on a distribution icon, the distribution of the selected data parameter is displayed as a pie chart or a bar chart to the right of filters or stratification bucket definitions.
Any distribution chart shown in the ‘Filters’ and ‘Stratification’ panels can also be added to a dashboard to save it for future use, like re-using and continuing the analysis later or for reporting purposes.
Once the data of interest has been defined in ‘Model & Data Inputs,’ ‘Filters,’ and ‘Stratification,’ clicking the ‘Run Analysis’ button will start the retrieval of actual and pre-calculated model data, and display both actual, that is, historical back-testing data and model results for comparison.
Performance Results
The ‘Performance’ panel displays both actual and model results in an aggregated and stratified form in terms of an interactive chart and a data table as shown in the screenshot below.
While the default chart view typically displays a few parameters, like CPR and Balance, the view parameters can be selected or de-selected from the chart legend.
Any chart or data table viewed in the ‘Performance’ panel can also be added to an existing or a new dashboard to save it for later use.
Dashboard Views
MARS+ provides its users with the facilities to create several dashboards as needed and save them in their local browser’s storage. A dashboard can have up to four components, including user-selected charts and tables generated during the user’s MARS+ session. The following is a sample dashboard with four components that are generated from an analysis run based on selections, filtering, and stratification done in each of the sample screenshots above.
Each dashboard component can also be edited, deleted, or moved within the dashboard.
What is next?
MARS+ will evolve during the beta period in terms of its capabilities, features, and data coverage based on the feedback we receive from our clients and users as ideas, suggestions, questions, issues, and requests. There are also a few planned improvements that we would like to implement during the beta period, including but not limited to:
- Extending the data distributions covering all mortgage data used by our models
- Providing data distribution charts for user-selected dates
- Developing a persistent and secure server-side storage infrastructure enabling users to re-use saved filters, stratifications, and dashboards with original as well as updated data
- Providing additional chart types to display residuals as well as model forecasts beyond the historical back-testing data
Please contact us at support@ad-co.com with any feedback you may have, including your ideas, suggestions, questions, issues, and requests.
The S-Curve Archives
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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.
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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.