Latest / Market Pulse / TriMerge vs. Single Credit Reports: What’s at Stake?

Transcript

TriMerge vs. Single Credit Reports: What’s at Stake? · Market Pulse

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  .
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  Welcome to the Market Pulse podcast from Equifax,
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  where we break down the latest economic and credit insights to help you navigate
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  today's business landscape.
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  Hello everybody,
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  and welcome to the special edition of the Equifax Market Pulse podcast.
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  I'm Wendy Hannah Olson, senior Vice President,
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  Digital Alliances and Strategy Execution,
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  and I'm delighted to be hosting today's conversation.
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  We have a very bright mind joining us. Joni Baker,
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  a mathematician and senior researcher on the behavioral modeling team of
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  Andrew Davidson and Company. Welcome Joni.
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  Hi. Thank you for having me.
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  Absolutely. Thanks for being here.
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  Today we're going to be diving into a topic that sits the very heart of the
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  American Dream, the mortgage process, and the role of credit reporting.
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  Since the late 1990s, the Trier credit report,
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  which pulls data from all three nationwide consumer credit reporting agencies,
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  including Equifax,
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  briefly known as b Crass or Credit Bureaus,
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  has been the gold standard for determining who gets what loan at what price.
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  But the ground is shifting.
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  There has been much heated debate in the industry.
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  Should we move away from the Trier standard in favor of a single or bar me
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  credit report? A new white paper from Andrew Davidson Company called The
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  Impact of Moving Away from the Tri-merge Standard suggests that this move might
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  come with hidden multi-billion dollar cost.
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  So let's dig in. So Johnny, I'm going to start with the foundation.
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  What was the study that you performed?
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  Okay. Well
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  basically our study examined the credit score differences across
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  the three NCRAs bureaus, Equifax, TransUnion,
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  and Experian. And again, I'm really excited to be here talking about it.
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  I think this was the first time that a study like this was
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  able to be performed on an unbiased uncensored data dataset.
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  And so I'm excited to share the analysis that we did.
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  So first we examined the raw underlying score differences between
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  any two individual NCRA credit scores for the same consumer.
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  So in other words,
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  what does the collection of those differences look like across all possible
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  credit score pairs and all consumers?
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  So we're just looking at those raw differences.
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  And then we considered various metrics that result from choosing or combining
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  consumers scores in different ways representing either a dual or a single
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  credit reporting standard.
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  And we compared that against the current tri-merge standard for GSE mortgages,
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  which is to take the median or the middle value of all three scores.
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  And we did this across different assumptions such as the scores are chosen
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  randomly, or the outlier scores chosen or the maximum scores chosen,
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  and we also subdivided the consumers in different ways to see if these metrics
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  varied for different populations.
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  So I,
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  I kind of like to put this study into context and explain why studying the
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  differences between the bureaus is important. Absolutely.
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  And describe a little more how credit scores are used for mortgage qualification
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  and pricing. I.
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  Welcome that. Thanks.
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  Okay, great. I just think it might help people sort of,
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  especially with some of the things that I suspect, you know,
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  I might want to talk about later on. Yes.
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  but so since 2008 the gse,
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  Fannie and Freddie have used a loan level pricing adjustment table or LLPA
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  table to determine the risk-based fees when a mortgage is
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  originated.
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  And the fees vary for credit scores from six 20,
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  which was the traditional cutoff up through 7 79 in 20
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  point increments.
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  And the fees are also based on the loan to value for the mortgage.
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  And if that loan to value is greater than 80,
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  then the required mortgage insurance or PMI also varies.
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  The cost of that insurance also varies based on credit score and LTV.
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  And meanwhile,
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  six 20 has been the traditional hard cutoff for qualifying for A GSE
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  mortgage.
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  And while they have very recently moved away from a hard cutoff for consumers
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  in that general credit score range, the credit score of course,
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  will be a major deciding factor as to whether they qualify for the loan.
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  So the question is, what credit score is used? And as we've said,
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  it's been the tri-merge standard requirement,
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  pull all three credit reports from all three bureaus,
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  and then for the consumer's representative credit score use the median of those
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  three scores.
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  So the F-H-A-F-H-F-A has previously considered relaxing this
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  requirement to a buy merge.
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  And under that the lender may poll just two reports and use the
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  average of the two scores.
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  More recently it's been suggested that the GSEs could even move to a single
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  report standard,
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  in which case a consumer's scores based on just one of three
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  sets of NCRA data.
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  So basically to understand the ramifications of such a move,
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  it's important to study the score differences between the bureaus and that's
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  what we did.
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  Absolutely.
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  And I know right at the beginning you mentioned a little bit about which data
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  was used, but I'd like to, for the audience reiterate that.
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  So can you explain exactly what data was used in your study?
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  Yeah, sure.
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  We had an anonymized sample of consumers for a
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  snapshot from October,
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  2023 that included for each consumer,
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  each of the three individual credit scores based on a, each,
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  each of those scores based on a single set of bureau data.
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  And I should say that for that,
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  for the credit score model we used VantageScore four.
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  Because this model is the same across all three bureaus,
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  there aren't any inconsistencies between the scoring formulas.
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  So what that means is that differences between the CRA's scores
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  generally reflect differences in the underlying data.
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  We also had a flag indicating whether a consumer was
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  low to moderate income,
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  meaning that the household income was below 80% of the median family
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  income in that metro area. And when available,
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  we had another field indicating race or ethnicity information.
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  And I should note that this information is not in a consumer's credit file,
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  but was attached anonymously from a third party.
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  And it's also important again to note like these weren't just
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  consumers already holding GSE backed mortgages or even having a
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  mortgage at all. In fact,
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  we had all consumers below 700 that had credit scores at all three bureaus.
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  And we had a random 10% sample for consumers above 700.
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  So each sampled consumer above 700 represented 10
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  consumers. And accounting for that rescaling.
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  Of those above 700,
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  there were almost 250 million consumers represented in this study
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  dataset study.
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  That is a lot of data, two 50 million. Thank you for that.
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  So can you help us understand how much difference can scores be between the
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  three nras since you had VantageScore four,
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  which we know leverages the full breadth and depth of the three
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  CRAs plus the trended data?
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  So it's additional alternative information within the credit reports.
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  How, how different can the scores actually be between the three NCRAs?
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  Yeah, so that was the very first thing that we looked at.
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  What are the raw score differences between any two given scores for a
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  consumer when those scores are based on data from a single bureau.
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  So just thinking like if you were to choose two reports randomly, you know,
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  how different are those two scores?
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  And what we found was that the two scores were actually identical about
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  42% of the time. So that maybe sounds pretty good.
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  However, 27% of the time, the two scores by at least 10 points,
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  14% of the time,
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  they differed by at least 20 points and 9% of the time they differed by
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  at least 30 points.
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  So that's almost one out of 10 pairs where you're seeing at least a 30 point
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  difference and then moving up from there 4% of the time,
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  the two scores differed by at least 50 points. That's a lot .
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  Yeah. So in mathematical terms,
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  we observed that the distribution of the two score differences was "heavy
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  tailed" meaning that large differences like 50
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  or 60 or more occurred more often than you would expect
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  based on just the average or the standard deviation of differences.
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  So yeah, they were often near zero,
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  but they also had more extreme highs and lows than you would see in your typical
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  bell curve.
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  That's, yeah, that's very insightful. Thank you.
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  So what do you believe causes the difference between the scores, knowing,
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  as you mentioned before, VantageScore four, the,
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  and I'm going to say this, you can correct me if I'm wrong.
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  The algorithm used is the same across all three in crass.
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  So what could cause the difference?
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  Well, I mean, credit scores are based on the data in a consumer's credit report,
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  including things like number of accounts, amount of credit available,
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  utilization, delinquency, history, and so on.
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  And the bureaus or nccra have different relationships with different
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  data providers such as mortgage servicers, for example.
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  And what this means is they might learn about new accounts or charges or
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  delinquencies at different times. And so that can lead to timing differences.
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  But even aside from timing differences the CRAs can have
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  permanent data differences, for example, due to a competitive market.
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  They each try to acquire new or unique data elements that the other ones don't
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  have.
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  And there can be differences based on just the processing that it takes to get
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  from the trade line data to the inputs that are required for the credit
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  score algorithms.
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  And some models even have slightly different formulas across the bureaus
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  which isn't the case with VantageScore four.
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  So lastly in some cases a consumer or
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  a third party may intentionally try to manipulate or hide certain data
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  like a bankruptcy or a foreclosure by falsely disputing it
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  or claiming identity theft, for example. And that's called credit washing.
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  And so while that dispute is being resolved,
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  the consumer can qualify for a loan or get a better rate if the loan is based on
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  data from just that bureau where they filed the dispute.
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  So currently this doesn't work for a mortgage because it's hard to like wash all
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  three bureaus at the same time,
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  but it can be causes because for differences between the bureaus.
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  Wow. Thanks for that insight. I would,
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  I would imagine lenders would be very in tune to credit washing
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  knowing that, you know, later down the road it could reappear.
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  So what do you, based off of your study and the actual data that you've,
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  you guys have been analyzed,
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  what are the implications of moving away from Tri-merge and how could this
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  affect pricing and loan qualification?
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  Okay, so this is a, it's a big question. Yes.
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  to analyze it for each consumer,
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  we considered the representative score differences that would result when moving
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  away from the Tri-merge standard.
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  Our paper describes the statistics for both a single score framework and a
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  Bi-merge framework.
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  But I'll give you a few examples for the single score framework since it's the
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  one that's been discussed most recently.
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  And I don't want to throw out too many numbers, . So,
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  so note that when choosing a single score randomly at least a third of the time,
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  you'll get the median itself. So you'll actually get the Tri-mergestandard.
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  But still that random choice in our study,
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  the random choice differed from the median by 10 or more points about 14% of the
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  time. And that difference could be either higher or lower than the median.
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  On the other hand, if you consider, well,
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  what's the furthest single score for a given consumer?
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  What if you choose the outlier score away from the median,
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  then fully 35% of consumers had at least one score that differed
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  from the median by at least 10 points.
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  Looking at that range like say from six 40 to 7 79,
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  where a consumer could potentially move between those pricing bins that I was
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  mentioning earlier this actually increased to 40%.
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  And a difference of like 10 or more points might not sound like a lot,
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  but we found that in that crucial range from six 40 to
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  7 79,
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  a score of 10 or more resulted in a move between the 20 point pricing
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  bins either up or down about 83% of the time. Wow. And then, yeah,
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  . Yeah. Yeah.
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  So because it doesn't require a 20 point score difference to move bins,
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  it just kind of depends on where you are.
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  Where you are within the bin. Yeah.
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  Got it.
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  Where you are within the bin and what that difference is and whether it's higher
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  or lower. Yeah. so on the other hand,
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  a 20 plus point difference will always result in a move either
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  up or down to a different pricing point for consumers in that range.
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  And we found that more than one fifth of these consumers or 21%
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  had at least one score that differed from the median by at least 20 points.
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  So basically guaranteeing that that's going to happen if it's used. Oh wow.
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  Yeah.
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  So you are asking how this could affect pricing and loan qualifications.
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  So kind of moving on to that I want to give a sense of what moving
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  between bins means and sort of practical dollar amounts.
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  So let's consider a mortgage for 350,000 that has an initial
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  loan to value of 90%.
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  To qualify for A GST mortgage alone with an LTV of 90 will
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  usually require PMI private mortgage insurance.
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  And that cost will also depend on the consumer's representative credit score.
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  So looking at the difference in present value for those PMI payments
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  you know, depending on where you are and,
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  and combining it with the difference in the GSCs
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  LLPA risk fee moving between 20 point pricing bins
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  can cost between like 3000 and $5,000 in present value
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  depending on the underlying credit score. So if you're on the lower end,
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  maybe you're more, you're closer to the 5,000. Got it.
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  For some changes you're closer to 3000 so that,
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  you know, the 3000 to $5,000 just for moving between consecutive bins.
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  Yes. and yeah, . Yes. So ,
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  so when it comes to loan qualification
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  those on the margins could be denied a mortgage if only the lowest score is
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  used. Right. And also those on the margins,
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  some of them maybe really can't afford a mortgage,
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  but if the highest score is used, they,
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  they might end up qualifying and then ultimately having to foreclose
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  and that's worse than if they just hadn't gotten a loan in the first place.
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  Wow. That's again, very insightful.
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  So talking about consumers, I mean we're,
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  we're down to an example of a anonymous individual consumer.
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  What populations had the largest differences? Because you had mentioned,
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  you know, you had third party out different data, so mm-hmm .
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  Can you explain to us the populations that you saw the largest differences in
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  your study?
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  Sure. So first I'll talk about differences based on the credit score.
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  In general, lower
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  scoring consumers had higher variations between scores.
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  So I've already mentioned that for consumers in the 640 to 779 range
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  score differences maybe I didn't mention this,
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  but they were a bit larger than for consumers in general.
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  Once you start reaching the higher end where the median is between 780 and 850,
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  there are fewer differences between scores,
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  partly because there is that cap at 850, so you just can't get any higher.
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  Right. so for example, I said earlier,
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  21% of consumers between 640 and
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  779 had at least one score that differed from the median,
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  either higher or lower by at least 20 points. For those above 780,
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  it was much lower, just 6%.
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  And for those consumers it also matters less because they're above that top
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  LLPA bucket and as a group they have the lowest delinquency rate,
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  so they are lower risk. On the other hand,
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  when you go down in credit score for consumers with scores,
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  say between 600 and 639,
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  now we're talking about whether or not you qualify for a GSC loan in the first
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  place. And more than a quarter of these consumers,
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  26% had at least one score that differed from the median by
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  20 or more points. In fact,
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  more than 10% had a score that differed by at least 40 points.
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  And and that's compared with 7% of consumers in general.
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  Which is two bands. That would be two, two, yeah.
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  You're guaranteeing two bands at that point, or you know,
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  or you're falling way below or above qualification where maybe you shouldn't.
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  So so that's showing some of the credit score variation.
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  In addition to that, though, there are differences.
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  We saw variation between different minority groups and
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  variation based on household income. And for example,
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  if focusing again on those 20 plus point differences,
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  18% of all consumers had at least one such score.
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  But for a specific minority,
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  a group that increased from 18% up to 23% whereas
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  for white consumers it was only 15%.
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  So you're looking at a pretty big difference in like how many consumers have
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  some difference of at least 20 points from the tri-merge median.
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  So moving away from the tri-merge standard would affect different groups in
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  different ways.
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  Makes sense. Thank you for that.
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  So as we're talking about the single impossible dual bi-merge
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  debate in the industry,
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  can you explain to us what score shopping is and how you analyze that?
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  Okay,
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  so score shopping is when the lender pulls credit reports
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  from multiple bureaus and picks the consumer's highest credit score to
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  get the lowest fees. And the best PMI rate for the consumer
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  currently under the tri-merge standard,
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  it's not possible to do this for a GSE mortgage because all three reports must
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  be pulled and the immediate score must be moved, must be used.
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  But there's a lot of competition among lenders to get,
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  get a consumer's business. So if the tri-merge standard is relaxed,
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  lenders may be incentivized to do this.
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  And even with rules in place to try to prevent lenders from score shopping,
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  consumers who shop around between lenders for the best rate and the lowest fees
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  would implicitly be score shopping themselves.
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  So they would tend to choose a lender that uses their best possible score.
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  To analyze this,
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  we considered metrics around what would happen if the consumer's highest single
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  or bi-merge scores used.
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  How often could the consumer increase their credit score by a given amount over
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  the tri-merge median,
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  and how often would they move up by at least one pricing bin?
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  So we found under a single score framework,
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  consumers in our study, again,
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  in that kind of critical range between 6 47 79 mm-hmm
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  they could increase their score by at least 10 points 22% of the time,
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  and by at least 20 points 11% of the time.
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  So that's more than one out of 10 of these consumers that could achieve a 20
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  point increase, which guarantees, again, moving up in the pricing grid.
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  However, as we kind of mentioned alluded to before,
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  increases of less than 20 can also result in a move up in bins just depending on
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  where you lie within that bin. So in fact,
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  more than a quarter of consumers in this range or 26% could get a better
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  price by score shopping or comparison shopping.
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  Meanwhile for consumers on the margins of approval,
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  these numbers are even higher .
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  So you potentially have consumers appearing to qualify for loans that they,
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  they cannot afford.
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  Which we might find out later. Right. If,
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  if they get the loan in the future. Right. that would be unfortunate.
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  Right, right.
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  not, you know, I'm sure the consumers would be happy,
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  but it would be unfortunate for possibly the market.
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  How about in your study, you know,
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  you guys have tried to do some real life.
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  Do you see a real life dollar consequence with score shopping?
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  Were you able to articulate a sample on that?
• 23:07
  Going back to the,
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  the previous example that I gave that sample loan,
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  $350,000 at 90 loan to value,
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  we mentioned that moving between adjacent 20 point pricing bins results in
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  a cost difference between 3000 and
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  $5,000 in present value.
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  So now we're talking about that being a savings for the consumer.
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  And of course jumping more than one bin would roughly double that amount.
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  Right.
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  so I should have maybe mentioned that like 4% of the consumers between
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  six 40 and 7 79 could increase their score by 40 or more points,
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  which basically guarantees that move up by two bins. And so,
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  you know, on the face of it, to the consumer,
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  this represents a pretty big savings in cost. Yeah.
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  To the mortgage insurer and the credit investor,
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  this represents potentially underpriced risk.
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  And if more consumers started to engage in things like credit washing
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  to hide derogatory events, then it could be even worse.
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  So these stakeholders could end up resing loan pricing across the
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  board to offset that risk that you know, that,
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  that they're not,
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  they need to be compensated for the credit risk that they're taking. Yes.
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  and so,
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  so basically rates could go up across the board and there could be sort of a
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  feedback thing going on where this even increases the frequency of score
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  shopping even more and sort of blocks in less precise credit pricing.
• 24:45
  Yes.
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  Or I can imagine some startup company out there,
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  helping with the situation, but ,
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  let's hope not. So
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  along the debate in the industry,
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  we've also seen a number thrown out a few times about, you know,
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  what about doing a cutoff for GSE loans at the 700
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  score range.
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  So how do you feel about that cutoff based off
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  of your study? Would it eliminate score shopping or.
• 25:24
  I don't think so.
• 25:24
  You don't think so? Okay.
• 25:26
  No, I don't think so. ,
• 25:27
  so basically this hybrid approach allows
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  the lender to choose a single report framework if the first
• 25:36
  score polled is above 700.
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  But if the first score is below 700, then all three reports would be polled.
• 25:46
  And the, and you know, all three reports would be used.
• 25:51
  You'd had, you'd have the median,
• 25:53
  so you'd be back to the tri-merge framework for that consumer. Right.
• 25:57
  so aside from consumers between I guess 700 now
• 26:02
  and 7 79,
• 26:04
  still being able to score shop around for the lender that uses the highest score
• 26:10
  there are consumers with median below 700.
• 26:14
  So their tri-merge standard would be below 700,
• 26:17
  but the maximum score is above 700.
• 26:20
  And if that maximum score is the first one polled,
• 26:24
  it would become the representative score for the loan.
• 26:27
  So for consumers like these, there would still be an incentive to score shop.
• 26:32
  So we looked at how often this could happen.
• 26:35
  And in our study dataset overall there were about a hundred million
• 26:41
  scored consumers with a tri-merge median below 700.
• 26:45
  And of those 100 million consumers,
• 26:48
  about 6% had a maximum score above 700.
• 26:53
  And of course these percentages varied depending on the consumer's
• 26:57
  underlying credit score.
• 26:58
  So it was more common for consumers up closer to 700.
• 27:04
  So looking at the 660 to 679 range,
• 27:09
  about 8% by using the maximum score could get up above 700.
• 27:14
  And if you move up to the 680 to 699 range, now we're looking at about 90,
• 27:20
  or sorry, not 90,
• 27:21
  29% of those consumers had a maximum score above 700.
• 27:26
  And so they would still be able to increase their score through score shopping
• 27:30
  under this hybrid approach.
• 27:33
  So we've talked about a lot and Joni,
• 27:36
  you've shown us that the data between the bureaus isn't just slightly different.
• 27:40
  It can actually lead to significant different outcomes for both the
• 27:45
  consumer and lenders.
• 27:48
  If there is one fundamental truth that lenders and investors should keep in mind
• 27:53
  about credit transparency as the industry evolves
• 27:58
  based off of your study, what would it be?
• 28:05
  I guess like something for everyone involved in the mortgage
• 28:10
  industry,
• 28:11
  to keep in mind is that when changes are made in the
• 28:15
  mortgage industry,
• 28:17
  they tend to remain in effect for many years as the new standard
• 28:22
  mm-hmm . So it's important to get things right you know,
• 28:27
  to,
• 28:27
  to be cautious and look at all the data and perform a whole bunch of analysis
• 28:32
  and think through the potential ramifications and the unintended
• 28:36
  consequences of reducing the data set that's used to qualify
• 28:42
  and price mortgages. And,
• 28:45
  and especially if there's any information asymmetry that's introduced to
• 28:50
  the system
• 28:52
  I think we all have the same goal of ensuring a functioning and fair mortgage
• 28:57
  market while increasing affordability for consumers and getting consumers into
• 29:02
  homes and mortgages that they actually can afford. Mm-Hmm .
• 29:06
  However,
• 29:06
  those holding the credit risk have an important role to play and they may
• 29:10
  require higher compensation for risk if new standards result in pricing
• 29:14
  uncertainty or adverse selection.
• 29:18
  And that potentially means higher mortgage rates for everyone.
• 29:21
  So I guess in short any changes need to be carefully and thoughtfully
• 29:25
  considered.
• 29:26
  Yeah.
• 29:26
  Well luckily you guys were able to conduct a study that
• 29:31
  I don't believe has ever been done before. So we greatly appreciate
• 29:37
  your ability to do the study.
• 29:40
  And let's get an update from our economists at Moody's Analytics.
• 29:45
  There's a lot to get to, but let's start in the Middle East.
• 29:48
  US and Israeli start strikes on Iran have sent oil markets into a tailspin.
• 29:53
  Crude oil prices have surged since the start of the conflict.
• 29:56
  The Strait of Hormuz, which carries about a fifth of global oil supply,
• 30:00
  has essentially closed. Our base case is that the disruption is real,
• 30:04
  but will be relatively short-lived. We expect prices to moderate,
• 30:08
  but stay above $70 into the latter half of the year before gradually returning
• 30:13
  to prior levels.
• 30:14
  But there's about a 2010 to 20% chance this gets worse.
• 30:18
  And in that scenario, oil hits and stays above a hundred dollars per barrel.
• 30:23
  So for American consumers,
• 30:25
  every $10 increase in the price of oil translates to a roughly 25 cent increase
• 30:29
  in the price of gas.
• 30:31
  And every sustained $10 increase leads to an addition of roughly 15 basis points
• 30:36
  to inflation in the following year.
• 30:39
  So this is a stagflationary pressure layered on top of what tariffs and tight
• 30:43
  immigration policy were already doing.
• 30:46
  So on jobs February was rough, payrolls fell by 92,000,
• 30:52
  the unemployment rate ticked up to 4.4%,
• 30:55
  and labor force participation hit its lowest level since late
• 30:58
  2021 3-month average job growth is now just 6,000,
• 31:04
  and the labor market has essentially gone nowhere since the Liberation day
• 31:07
  announcements last year. Now that said,
• 31:09
  we expect job growth to pick up but remain anemic in the year ahead
• 31:14
  with weakness on both the demand and the supply side. So on tariffs,
• 31:19
  despite the Supreme Court's recent IEPA ruling,
• 31:22
  we believe that the administration has enough policy tools to keep the effective
• 31:25
  tariff rate more or less. Where it has been,
• 31:28
  the composition may shift in terms of which trading partners bear the burden,
• 31:32
  but the macro impact is largely unchanged.
• 31:36
  So the bottom line is the US economy is still advancing,
• 31:39
  but the risks are rising swiftly. The longer the Iran conflict persists,
• 31:44
  the steeper the damage and that clock is ticking.
• 31:47
  Thank you immensely for being our guest on today's Market Pulse podcast.
• 31:52
  I really enjoyed my time with you.
• 31:55
  And I hope that we run into each other again in the future, .
• 32:00
  Yeah, me too. I enjoyed it too. Thank you, Wendy. It's stay here, .
• 32:05
  Yes. Have a great day. Thank you.
• 32:07
  The information and opinions provided in this podcast are intended as general
• 32:11
  guidance only, and are subject to change without notice.
• 32:14
  The views presented during the podcast are those of the presenter as of the
• 32:18
  date.
• 32:18
  This podcast was recorded and do not necessarily reflect official positions of
• 32:23
  Equifax.
• 32:25
  Investor analysts should direct inquiries using the Contact us box on the
• 32:29
  investor relations section@equifax.com.