All posts by Perry DeFelice

Summary  Borrower risk continues to evolve after loan origination. Credit scores, income, debt-to-income ratios, and escrow costs all change over time, shaping mortgage performance in ways traditional market data can’t track. This article uses Experian’s Mortgage Loan Performance dataset to uncover how borrower credit profiles evolve, why those shifts matter for lenders and investors, and how complete visibility into credit, income, and insurance trends can strengthen credit and prepayment risk modeling.  Introduction:  After a borrower opens a mortgage, their financial profile doesn’t stay static. Credit scores, debt-to-income ratios (DTI), and annual incomes evolve—sometimes positively, sometimes negatively—depending on both the individual borrower’s specific behavior and situation, as well as broader economic conditions including factors like unemployment and interest rates. When we add rising escrow costs for home insurance and property taxes into the mix, the picture becomes even more complex.   Unfortunately, traditional market data for both private label and agency MBS, as well as “servicing” datasets generally used to build analytics for whole loan strategies, contain virtually no information regarding a borrower’s current credit profile. Current pay status of the subject loan is sometimes provided.  However, credit score and DTI values (if provided at all) are as of the origination date only. No information is provided regarding the borrower’s home insurance or property tax premiums.  In other words, as a mortgage loan seasons and the borrower’s credit profile drifts as new debts are added or paid off, payments on auto loans, student loans, credit cards, even other mortgages on the subject property are made or missed, and home insurance policy costs double (or triple!) in some cases, MBS investors using traditional market data only are truly flying blind with respect to the borrowers’ current credit health. Fortunately, more complete alternatives to supplement traditional market data exist. In this article, we’ll analyze Experian’s Mortgage Loan Performance (MLP) data, a monthly-refreshed join across loan level performance, borrower credit profile and property data for all US mortgages since 2005, to explore borrowers’ credit profile drift since loan origination.  This dataset contains current credit scores, tradeline balances and performance, escrow account information, and modeled income for all borrowers.  Section 1: Credit Score Migration Since Origination — Who Improves and Who Slumps?  Using the MLP dataset, we examined current and at-origination borrower credit profiles for over 42 million mortgages originated from January 2020 through July 2025.  Segmenting the data by different mortgage products shows distinct score migration patterns since loan origination as illustrated in Figure 1:  Conventional Loans (FNMA/FHLMC):   Conventional borrowers have experienced strong positive gains in credit score since origination for the 2020–2022 vintages with average VantageScore 4.0 migration of +11 to +22 points  For the more recent 2023-2025 vintages, borrowers have experienced flat or negative drift of averaging -6 to +2 points  FHA Loans:   FHA borrowers have experienced mostly negative VantageScore 4.0 drift of -6 to -19 points, with the steepest decline to date in the 2022–2023 vintages   VA Loans:   We see a positive drift for early vintages, especially 2020 to 2022 vintages, but a slightly negative drift for more recent vintages of -1 to -4 points.  Non-Agency Loans:    Similar to conventional loans, we see a positive credit score drift for 2020–2022 vintages, turning negative for 2024–2025 with an average drift of -1.5 to -4 points  Figure 1:  Vantage 4.0 Migration Drift Since Origination[1] Key Insights: Over the past 6 years, Conventional borrowers have generally improved their credit profile post-origination, notwithstanding small dips to-date for the last couple vintages.  On the other extreme, 4 of the 6 last FHA vintages have experienced credit score deterioration to date.   Beyond the obvious increase in delinquency and default risk due to deteriorating credit scores, a borrower’s ability to refinance efficiently is also impacted by credit score deterioration. A loan’s propensity to default or voluntarily refinance is  influenced by the borrower’s current credit score, which is absent from traditional market data, though present in MLP.  In this way, current credit score is a critical field for both nonagency and agency MBS analyses. Section 2: DTI and Income   As illustrated in Figures 2 through 4, even as incomes rise, DTI often climbs faster, signaling potential borrower stress:  Example (FHLMC):  2020 Vintage: DTI +5.9 points, Income +$24K 2023 Vintage: DTI +23.5 points, Income +$28K Figure 2:  DTI and Income Drift Since Origination for all mortgages  Figure 3:  DTI and Income Drift Since Origination for Freddie Mac mortgages Figure 4:  DTI and Income Drift Since Origination for GNMA, VA mortgages  Insights: Across all loan types, on average, borrowers are earning more relative to when they opened the loan but also taking on additional obligations over time at an even faster rate, which inflates their debt-to-income ratio.  Particularly striking is the DTI drift for the 2023 GNMA VA vintage, rising over 30 points in two years!  In addition to elevated risk of delinquency and default, elevated DTI also reduces the borrower’s ability to refinance efficiently by affecting the borrower’s ability to qualify for competitive refinancing rate.  Investors relying solely on traditional market data have no vision into the borrower’s current DTI, thereby limiting their ability to model and manage both default and voluntary prepayment risk.   Section 3: Escrow Pressure—Taxes and Insurance Surge  As illustrated in Figure 5, MLP data reveals that from 2021 to 2024:   Taxes haves increased by an average of 28.8%  Home Insurance rates have increased by an average of 54.4%, becoming the fastest-growing home ownership expense within this period  Higher escrow payments squeeze borrower budgets, driving increased delinquency risk and decreased affordability. Traditional market data contains no information regarding borrowers’ tax or insurance premium burdens. Figure 5: Average escrow payment increases from 2021 to 2024 Conclusion  Score migration, evolving income and DTI, and escalating escrow & tax costs create a dynamic risk environment for borrowers. Borrowers’ constantly changing credit health drives both credit (likelihood of default) and voluntary prepayment (credit score and DTI influence both ability and incentive to refinance) risks. In this context, monitoring borrower credit and income post-origination is critical.  Traditional market data for both private label and agency MBS contains no information related to a borrower’s current credit score, DTI, income or tax & escrow burden. Experian’s Mortgage Loan Performance dataset contains all this information, at the loan level, for ~100% of the US mortgage market, enabling better segmentation, predictive modeling, and risk management for both credit and prepayment risk.  Read our previous blog about Residential Mortgage Prepayments   [1] All statistics are derived from Experian's Mortgage Loan Performance (MLP) Dataset

Since mortgage rates have remained high even after recent Federal Reserve rate-cutting activity, there is limited rate incentive to refinance for the vast majority of borrowers. In the absence of significant rate incentive, borrower mobility and behavioral tendencies have become outsized drivers of both prepayment speeds and origination volumes. Unfortunately, traditional MBS market data does not contain adequate information for investors to analyze either borrower mobility or behavioral tendencies like sources of payoff funds (i.e., cash payoff, refinance of existing loan, opening of a new lien on a 2nd home, etc.).  By using Experian's Mortgage Loan Performance (MLP) Dataset, a monthly-updated time series featuring combined loan, borrower, and property-level details covering nearly the entire US mortgage market since 2005, it's possible to examine patterns in behavior for borrowers who have prepaid their loans early, such as: The proportion of paid-off borrowers who retain the subject property (“stayers”) versus those who move (“movers”); and for both of these subsets, the percentage of people who re-enter the mortgage market with a new loan ("returners") compared to those who leave the mortgage market after paying off their loan ("leavers"). Classification as returner or leaver in the charts below is based on whether the paid-off borrower opened a new mortgage loan as of the end-of-August observation date. Sources of mortgage payoff funds — what proportion of pay-off was via refinance of the subject property vs. opening a new lien on a 2nd home or investment property?  What proportion pays off in cash resultant from a sale of the subject property or cash out-of-pocket while retaining the subject property? For the set of returners, what is the typical time lapse between payoff and opening of a new mortgage, i.e., are most payoffs simultaneous or are a significant number of borrowers utilizing bridge financing, or paying off a current loan while they shop for a new home and new loan? For the set of leavers, what are the credit, income and demographic characteristics of these borrowers?  Are they leaving the mortgage market because they are unable to get a new loan due to weak credit or insufficient income? Mobility and source of payoff cash dynamics are summarized below for a sample of ~ 63,000 mortgage payoff events, drawn from MLP, which occurred from February to July 2025.   Amongst other trends, we see that approximately 70% of borrowers who paid off their loan exited the mortgage market (~40% retained property after a cash payoff + ~4% sold property and bought a new property in cash + ~24% sold property and didn’t purchase another property).  This high proportion is probably driven in part by the relative lack of rate/term refinance and purchase activity given the current rate environment. When we look at all payoffs in MLP over the same time period — 2.3 million payoff events — the ~70% proportion of leavers holds. Within this larger sample, we also break down time to re-entry for the returners.  Unsurprisingly, of the 30% returners, the vast majority open a new loan just prior to or within a month of prepayment: Since MLP contains monthly-refreshed, joined credit profile data for every mortgaged borrower, we can also examine the credit and income characteristics of leavers to determine if poor credit or limited income prohibited re-entry. This analysis reveals that leavers are generally not credit or income limited; the pool of leavers is characterized by the following average metrics: 746 current Vantage 3.0 credit score 49 years of age $99,759 current modeled income 34.8 back-end DTI The following table stratifies the leaver population by generation: Further segmentation by loan servicer, originator and borrower credit profile (e.g., dollar amount of student loan debt outstanding) and past behavior (e.g., how many mortgages has this borrower refinanced in the past?) across all tradelines are potential next steps. As the rates environment evolves, we will monitor mobility trends, the ratio of borrowers paying off loans while moving versus those staying, and how borrowers decide to finance their prepayments. In addition to rates, changes in HPI, unemployment and underwriting guidelines will influence these behaviors. By leveraging new datasets like MLP which capture not only loan performance, but also regularly refreshed credit profile, behavioral trends and property details over the entire credit lifespan of a consumer and all their tradelines, investors can incorporate a 360-degree view of loan, borrower and property into their predictive analyses.