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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.
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The Impact of Moving Away From The Tri-Merge StandardThoughtsIn 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.
Andrew Davidson & Co., Inc. (AD&Co) has analyzed the potential impact on the mortgage ecosystem of changing the credit score tri-merge standard to a bi-merge or a single-score standard. The analysis is based on an examination of a unique data set of VantageScore 4.0 credit scores of a very broad range of consumers constructed from data provided by the Nationwide Consumer Reporting Agencies (NCRAs): Equifax, Experian, and TransUnion.
Results of the study demonstrated that moving away from the tri-merge standard could potentially increase the risk that originators and consumers score shop during the origination process by choosing the credit score (or lender) that produces the lending outcome they desire. Even in the absence of score shopping, moving from the tri-merge could lead to less accurate pricing and mortgage qualification. Minority1 and lower-scoring borrowers would be more heavily impacted. Ultimately, if investors require higher compensation for greater uncertainty, mortgage rates could be higher for everyone.
Key Findings
Credit score uncertainty and mortgage pricing differences increase under a single or bi-merge standard compared to using the traditional tri-merge standard, which utilizes the median (middle) of three scores.
- Scores based on data from a single NCRA differed from the current tri-merge standard (median of 3 scores) often enough to impact loan pricing in meaningful ways. 35% of the 245 million scored consumers represented by the study data set had at least one score that differed from the tri-merge standard by at least 10 points, 18% had a score that differed from it by at least 20 points, and 7% had a score that differed by 40 or more points.
- For consumers in the 640–779 range, where 20-point differences guarantee a move into a higher or lower GSE pricing bucket (based on Loan-Level Pricing Adjustment categories, or LLPAs), these percentages were even higher. As an example, for a $350,000 GSE loan with a 90% loan-to-value (LTV) ratio, moving between consecutive pricing bins can raise or lower the combined cost of borrowing and mortgage insurance (MI) by $3,000 to $5,000 in present value (PV) over the life of the loan.
- The potential for pricing variances due to reduced information is greater for lower-scoring (those with credit scores of 600–639) and minority borrowers, about a quarter of whom were found in this study to have had at least one credit score that differed from the tri-merge standard by at least 20 points.
- In a non-tri-merge landscape, lending and pricing decisions that could be based on different credit scores may create an opportunity for originators to score shop during the origination process by choosing the credit score that produces the lending outcome they desire; consumers choosing between lenders would also, implicitly, be shopping for the best score outcome. Based on the study, about 9% of all consumers (and 11% of those in the 640–779 range) could increase their purported credit score by 20 or more points from what the tri-merge standard would otherwise show.
- Establishing a score cutoff such as 700 to determine whether a tri-merge is required does not eliminate the existence of meaningful score discrepancies.
Analytical Results
Table 1 describes the raw score differences that were observed between individual credit scores for a consumer, when each score is based on data from a single NCRA. Underlying data differences between the NCRAs arise for multiple reasons, including timing differences, processing differences, and acquisition of unique data elements that the others do not have. Each row in Table 1 shows the percentage of 2-score pairs that had a difference above a given threshold, in 10-point increments. We see that consumers with median scores in the lower-score bands and minorities had larger score differences in general.
Table 1. Absolute Value Raw 2-Score Differences by Various Consumer Subsets
In Chart 1, the notation “1B” is used to denote the representative credit score under a single-report standard, while “2B” denotes the representative score under the dual-report standard, wherein the two scores are averaged. These scores may be chosen randomly, or picked due to a specific attribute, such as being the highest or lowest. The chart shows the percentage of consumers in the study for which the 1B or 2B score differed from the tri-merge standard (median of 3) by at least 20 points. These results are broken down by median credit score band, and the “furthest” 1B or 2B score is the one that differed most from the median. For example, 18% of consumers in the 700–779 range had at least one score that differed from the tri-merge standard by at least 20 points.
Chart 1. Single Report and Bi-Merge 20+ Differences vs. Tri-Merge Standard
Recently, some in the industry have proposed a hybrid approach based on an initial score threshold of 700. Under this proposal, if the first-pulled score is 700 or higher, it serves as the consumer’s representative score. If the initial score falls below 700, the standard tri-merge process applies, and the median score is used. While Chart 1 above gives a sense of the ramifications for consumers in the 700–779 range (in green), Table 2 below shows how many consumers from the lower range had a maximum score that exceeded the 700 threshold, which, if pulled, would become the consumer’s representative score under this proposal. These numbers are broken down by credit score band. Note that 4% of consumers in the 640–659 range and nearly 8% of consumers in the 660–679 range had a maximum score of 700 or above.
Table 2. Consumers With Tri-Merge Score Below 700
Conclusion
A credit score predicts a consumer's credit risk, and the score may vary based on the data from the three NCRAs; therefore, using the tri-merge score captures the most complete picture of a consumer's risk. Moving to a single score or to a bi-merge approach increases the uncertainty in assessing borrower risk, with direct implications for loan pricing and underwriting outcomes; this uncertainty is greater for minority and lower-scoring borrowers. Compared to tri-merge results, single-bureau and bi-merge scores often produce large discrepancies: 18% of all consumers had a single score that differed from the tri-merge standard by at least 20 points, and 7% had a score that differed from it by 40 or more points. This can cost higher-LTV/lower-score borrowers (or investors in such mortgages) thousands of dollars in mispriced fees and risk. The call to abandon the tri-merge standard could have a meaningful negative impact and may not result in the most optimal outcome in terms of risk and price assessment for consumers or investors.
The full white paper, “The Impact of Moving Away from the Tri-Merge Standard,” can be downloaded here.
1 Consumer credit reports do not include demographic data such as gender, race, age, nationality, relationship status, education, or religion. For the purpose of this study, proprietary and anonymized third-party demographic data was used for evaluation.
© 2026 Andrew Davidson & Co., Inc. All rights reserved. You must receive permission from marketing@ad-co.com prior to copying, displaying, distributing, publishing, reproducing, or retransmitting any of the content contained in this white paper.
This publication is believed to be reliable, but its accuracy, completeness, timeliness, and suitability for any purpose are not guaranteed. All opinions are subject to change without notice. Nothing in this publication constitutes (1) investment, legal, accounting, tax, or other professional advice or (2) any recommendation or solicitation to purchase, hold, sell, or otherwise deal in any investment. This publication has been prepared for general informational purposes, without consideration of the circumstances or objectives of any particular investor. Any reliance on the contents of this publication is at the reader’s sole risk. All investment is subject to numerous risks, known and unknown. Past performance is no guarantee of future results. For investment advice, seek a qualified investment professional. Note: An affiliate of Andrew Davidson & Co., Inc. engages in trading activities in securities that may be the same or similar to those discussed in this publication.
The S-Curve Archives
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News
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Thoughts
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Thoughts
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