Why Do We Still Use the Tri-Merge Standard in Mortgage Lending?

Many still consider the tri-merge standard as the best way to understand a consumer’s creditworthiness, but why is that the case? A tri-merge credit report uses scores from the three top credit reporting agencies in the U.S. to determine the middle score instead of the highest or lowest score from the data set. This method is preferred because all of the reporting agencies do not use the exact same data sets to produce a credit score. Some information can be excluded from certain reports but included in others, resulting in each reporting agency producing a different score. However, with three data sources, a more complete determination can be made about a person’s financial standing compared to relying on just a singular source. 

Credit scores are absolutely not fixed numbers, and may differ more than people realize. Several people who compared their scores from the various reporting agencies noticed that at least one of the scores differed by about 10 points. Others had a score that differed by at least 20 points while certain cases found a score that differed by 40 points or more. This means many consumers’ credit scores could look different depending on which reporting agency’s report is used, either making their credit scores look weaker or stronger than they actually are. With the tri-merge standard, this discrepancy is reduced by using more than one data source to determine a reliable credit score that accurately portrays a borrower’s financial profile. 

It may seem like a 20-point difference in credit scores is not too important. But when considering a mortgage loan, the smallest difference in credit scores can mean putting someone within a different credit range or even tier. These changes can then affect one’s interest rates, loan pricing, mortgage insurance costs, and overall borrowing costs. And these changes are not small. A move to a different credit range can increase costs by thousands of dollars over the life of the loan. So depending on just a single data source may result in a borrower being priced incorrectly. 

Furthermore, each of the reporting agencies not holding the exact same information means some tradelines are absent from several reports, payment histories will have inconsistencies, and risk indicators might go unnoticed. Using just one reporting agency makes it highly likely that some important financial information might be missing from the final credit score. But with a complete data set, blind spots can be reduced and a more accurate evaluation of borrower risk is produced. 

A non-tri-merge standard approach creates a situation where people may gravitate towards “score shopping”, which refers to the phenomena where borrowers or originators pick the credit score that produces the best lending outcome. It is estimated that about 9% of consumers could potentially inflate their apparent credit score by 20 or more points if they use the highest available score instead of the tr-merge standard method. Unfortunately, if more people are inclined to “score shop,” this could dilute the overall risk performance and make risk assessment more inaccurate and difficult to do. 

If credit scores are no longer reliable, this could lead investors to respond unfavorably as these investors rely on accurate credit data to estimate risk, expected returns, and financial performance of borrowers. If credit scores introduce increased unpredictability, investors may view mortgages as riskier and demand higher returns to compensate for the risk. As a result, borrowers will suffer from higher interest rates. 

Some think that using fewer data sets to create a credit score could reduce costs. Although obtaining fewer reports can lower initial expenses at first, this will be short-lived. A large financial burden will be put on lenders in the form of “unrecouped fallout,” which refers to applications that enter the mortgage credit lending process but never end up closing. In these cases, lenders will have wasted time and resources evaluating loans that will lead to lost costs when the transactions do not finalize. Instead of reducing data sources, lenders can gain better savings if they use techniques such as using soft credit pulls early in the process, providing reliable cost estimates, and improving the overall borrower qualification screening. 

It is interesting to note that lower-scoring borrowers experience more of a score discrepancy between the top reporting agencies than higher-scoring borrowers. So reducing available data could significantly affect those whose credit profiles are already harder to evaluate accurately. A fuller data set can make risk evaluations more accurate for these borrowers and provide lenders with more confidence in making the right lending decision. 

When it comes to mortgage credit lending, accuracy and consistency are crucial for comprehending a borrower’s actual financial behavior. With the tri-merge standard, risk assessment can be properly perceived, loan pricing can be accurately calculated, investor confidence is boosted, and consistent lending choices are made while limiting the risk of score manipulation. Although using one data set sounds easier and lower costs in the short term, the long-term uncertainty could ultimately result in greater costs and risks later down the mortgage lifecycle.

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