Conversations about rising auto loan balances and higher monthly payments has often centered around increasing vehicle prices and elevated interest rates; and while those factors have undoubtedly played a role, another important piece of the puzzle is the type of vehicles consumers are choosing to purchase. According to Experian’s Automotive Consumer Trends Report: Q1 2026, consumers are continuing to opt for SUVs over other vehicle types, a trend that may be contributing to higher average loan amounts and monthly payments. SUVs accounted for 63.5% of all new retail vehicle registrations over the last 12 months, up from 62.8% a year ago. Additionally, more than 117 million SUVs were in operation across the United States in the first quarter of 2026, making up 42.2% of the market share. At the same time, traditional passenger cars continue to fall in share, coming in at 16.5%, a decrease from 18.4% last year. As consumers increasingly gravitate towards the larger vehicle segment, it reflects the ongoing desire for versatility, cargo capacity, and family-friendly functionality. Electrification’s growing role in consumer purchasing behavior Interestingly, electrified SUVs continue to gain traction, representing 27.7% of all new SUV registrations, these vehicles include battery-electric, hybrids, plug-in hybrids, and other alternative fuel types. Diving a bit deeper, the Tesla Model Y was the market share leader for new, retail electrified SUV registrations in the last 12 months, coming in at 15.8%. Rounding out the top five were Honda CR-V (9.6%), Toyota RAV4 (7.2%), Chevrolet Trax (7.2%), and Toyota Grand Highlander (3.4%). As model availability and familiarity with the electrification segment grows, the broader adoption of these vehicles are playing an increasingly important role in vehicle pricing and overall consumer demand. While average loan amounts and monthly payments are being driven by a combination of factors such as financing costs and consumer purchasing behavior, data in Q1 2026 demonstrates the continued interest in SUVs. This suggests that the industry’s shift toward larger vehicles is likely playing a meaningful role in today’s financing environment. To learn more about SUV insights, view the full Automotive Consumer Trends Report: Q1 2026 presentation.

In our previous post, we described the Current Second Lien Balance field, which is one of over 2,000 fields in the new Experian Mortgage Loan Performance (MLP) dataset. We showed that the Current Second Lien Balance field meets our three-pronged materiality standard for new data delivery: New: Provides information not available in existing datasets (i.e., orthogonal to currently available data). Material: Impacts a sizeable portion of the MBS universe. Significant: Differentiates collateral performance by a large enough margin to influence trading and risk management decisions. In this article, we discuss another field that satisfies the above criteria: Student Loan Balance.  We evaluate this field in the context of these criteria. First, however, we provide a summary of the MLP dataset and how it compares to standard GSE loan-level data available today. Standard GSE Data vs. Experian Mortgage Loan Performance (MLP) Data The MLP dataset contains thousands of fields describing mortgage performance from each borrower, loan, and property perspective, all refreshed monthly (including, amongst other things, new credit scores and refinance inquiry activity, loan performance, filed junior liens, and AVM values).  MLP differs from loan-level data provided byFreddie Mac, Fannie Mae, and Ginnie Mae, which the vast majority of market participants solely rely on, in a number of ways: Standard data provided by the GSEs and GNMA does not contain all the information necessary for accurate forecasting of mortgage prepayment and credit performance. Basic, critical fields like borrower’s current credit score and current junior liens on the property are missing. The new Mortgage Loan Performance (MLP) dataset from Experian contains borrower, loan, and property data fields covering the entire mortgage universe, including Agency, Non-Agency, and Esoteric mortgage products (CES, HELOC, Reverse), both securitized and non-securitized. MLP enables full three-dimensional (borrower + loan + property) tracking with persistent keys for borrower (before and after refinancing), loan (in securities/deals even after exit due to payoffs or buyouts, including before and after MSR sales), and property.  This enables end-to-end analysis of each borrower’s (and property’s) mortgage experience throughout their credit lifecycle. New, Material and Significant Field:  Student Loan Debt MLP contains a number of fields describing each mortgage borrower’s student debt load, including amounts in repayment, forbearance and collections; estimated interest rate, time remaining until forbearance expiration, and more. In the interest of simplicity, for this article we’ll focus on a single student loan-related field within MLP: Student Loans Balance, which is defined as the total balance on open non-deferred student trades reported in the last 3 months. Is Information Regarding Student Loans New to Markets? Standard loan-level data disclosed by the GSEs and GNMA contain no student-loan-specific fields. Theoretically, fields related to DTI at origination might capture some aspect of student loan debt. So, in the best case scenario for an investor relying solely on standard disclosure, a DTI value as of origination is provided -- yet is never updated as the loan seasons and the borrower’s debt and income change (see more here).  But in the case of federal student loan debt attached to mortgages originated from early 2020 to late 2023, the level of detail provided by disclosure may be even more unknown due to COVID-era repayment and reporting moratoriums. The student loan repayment moratorium was a temporary federal policy that paused required payments, set interest rates to 0%, and suspended collections on most federally-held student loans. The moratorium began in March 2020, with payments resuming in October 2023, making it approximately 3.5 years in duration—the longest consumer credit payment pause in U.S. history. (Source: NCUA ) During the moratorium, student loan-related debt loads may have been understated as federal loans were in a temporary state of $0 repayment.  As an alternative to leaving student loan debt completely out of DTI calculations, an imputed payment equal to only 0.50% of the outstanding balance was often used as a placeholder for a borrower’s DTI calculation. As a result, mortgages originated during the moratorium may have artificially low reported DTIs for borrowers with student loan debt, materially understating true post-moratorium debt .  Accordingly, prepayment risk for these loans is likely overstated in mainstream market models. Standard data only reports information related to the primary mortgage and does not include any details on the borrower’s other debts with the exception of DTI at origination, which is never updated throughout the life of the loan. In contrast, MLP provides a comprehensive view of the borrower’s full credit profile, including other obligations such as credit cards, mortgages on other properties, student loan balances, and much more. Is Student Loan debt material to the residential mortgage market? Approximately $11 trillion of residential mortgage loans were originated during the student loan payment moratorium (Source: Experian MLP Dataset), a period marked by historically low mortgage rates during the COVID era.  As discussed above, DTI data contained in standard market disclosure may be particularly inaccurate for these loans. As the Wall Street Journal recently reported, a new report from the Federal Reserve of New York shows a rise in student loan default rates by age group.  Student l Of today’s $13 trillion in outstanding mortgage debt, more than 10% of that debt ($1.5 trillion) is associated with borrowers who carry student loan debt.  For these borrowers, the average amount of student loan debt outstanding is approximately $50,000, versus a mortgage balance of approximately ~$289,000. In other words, the average student loan debt balance is almost 20% of the mortgage balance for the average borrower who carries both. For this set of borrowers, the average monthly payment is approximately $400 for student loan vs. approximately $2,200 for 1st lien mortgage—so that monthly student loan payments are a significant debt load, approximately 20% of the monthly mortgage payment.  (Source:  Experian MLP Dataset) Is the effect of student loan debt a significant driver of performance? Figure 1 illustrates prepayments by student loan balance for a sample of loans drawn from MLP. The chart illustrates that borrowers with larger student loan balances prepay much more slowly, likely because some are effectively locked out of refinancing once student loan payments resume due to elevated DTI. The debt-to-income (DTI) ratio calculated using actual student loan payments may be significantly higher than the DTI calculated during the moratorium, in some cases exceeding GSE eligibility thresholds. As illustrated in Figure 1, for in-the-money (ITM) collateral, the differential between loans with material student loan balances (greater than $200,000) and loans with no student debt can reach up to 5 CPR. Notably, even for out-of-the-money (OTM) collateral, loans with student debt prepay 1 to 3 CPR slower, likely reflecting reduced mobility due to tighter financing constraints when purchasing a new home. Pools with otherwise similar prepayment characteristics may exhibit different prepayment behavior depending on the distribution of student loan exposure within their collateral. In addition, because loans with student debt tend to prepay more slowly, this effect increases over time due to burnout: loans without student debt prepay and exit the pools more quickly, leaving a higher concentration of slower-paying loans behind.  Given that 10% of the $13 trillion outstanding mortgage market is associated with borrowers who have student loans (Source:  Experian MLP dataset)—and that student loans have a meaningful impact on prepayments—many pools issued between March 2020 and October 2023 may be subject to this student loan debt CPR throttle, and therefore mispriced by investors relying exclusively on standard market data. Fig 1. Prepayment S-Curve: Student Loans Balance Source:  Experian MLP dataset hosted on IVolatility Data-Driven Platform   _____________________________________________________ Michael Pyatski advises MBS traders, portfolio managers, quants, risk managers, loan originators, and technology professionals on making informed, data-driven business decisions that drive revenue growth, enhance risk management, and reduce trading costs. With more than 15 years of experience as an Agency RMBS trader—including serving as Head of the Proprietary Trading Desk at BNP Paribas—Michael developed and successfully implemented relative-value, data-driven profitable trading strategies to capture market opportunities embedded in data but not fully priced by the market. His trading experience, combined with a Ph.D. in econometrics, led him to found the Data-Driven Portal (https://datadrivenportal.com/), a platform that provides advanced technology for MBS trading and risk management. The platform’s No-Model Data-Driven technology leverages big data, econometric analysis, and AI to help traders identify relative-value opportunities in RMBS markets and generate above-market, risk-adjusted returns. _____________________________________________________

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Electric vehicle (EV) registration growth has become a common topic of discussion throughout the automotive industry for the last few years, but the bigger story may lie in what consumers are choosing when they return to market for their next vehicle. According to Experian’s Automotive Market Trends Report: Q1 2026, the bulk of EV owners (72.6%) purchased another EV, while 17.7% replaced their EV with a gas-powered vehicle and 5.6% switched to a hybrid this quarter. A similar trend was seen in hybrid owners, as 54.9% remained loyal to the fuel type through the quarter, while 32.7% replaced their hybrid with a gas-powered vehicle and 7.5% switched to an EV. Notably, 78.2% of consumers with gas-powered vehicles stayed with the same fuel type, with 5.6% swapping their gas vehicle for a hybrid and only 4.5% transitioning to an EV through Q1 2026. These purchase styles suggest that while most consumers are not making a direct leap from gasoline to fully electric vehicles, some are beginning their electrified journey through hybrid ownership. At the same time, the high rate of fuel-type loyalty across all powertrain categories highlights the importance of the ownership experience. Consumers who are satisfied with their current vehicle can often be inclined to remain with the same segment rather than exploring alternative fuel types. New vehicle registration trends reflect changing consumer preferences Looking at the new vehicle registration data from a broader level, gas-powered vehicles experienced a slight uptick, coming in at 69.5% through Q1 2026, from 67.3% last year. Meanwhile, hybrids continue to grow, going from 12.1% to 13.5% year-over-year while EVs steadily decline from 7.8% last year to 5.6% this quarter. As consumers weigh their next vehicle purchase, many seem to be sticking with the standard gas-powered choice, and others are finding a happy medium in hybrid vehicles. And while EVs receive much of the industry’s attention, buyers are exploring alternatives that allow them to adopt the electrified vehicles incrementally rather than all at once. To learn more about vehicle market trends, view the full Automotive Market Trends Report: Q1 2026 presentation on demand.

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The automotive market is entering a new phase defined not just by what consumers are buying, but by how they’re choosing to finance it. According to Experian Automotive’s State of the Automotive Finance Market Report: Q1 2026, nearly one-third (35.55%) of all new vehicle loans now stretch more than six years, up from 30.83% in Q1 2025. Similarly on the used side, 31.54% of loans extended more than six years, an increase from 28.60% last year. The shift highlights why affordability is reshaping how consumers are financing their vehicles, particularly in larger and higher-priced vehicles. Refinancing gains traction as interest rates stabilize In addition to longer-term loans, consumers are becoming increasingly deliberate with their financing decisions and managing monthly payments as refinancing activity has gained momentum. For instance, consumers who refinanced this quarter lowered their interest rate by 2.2% and saved an average of $81 on their monthly payment. Credit unions, in particular, continued to play a major role in helping consumers secure more affordable payment options. In Q1 2025, credit unions accounted for the lion’s share of automotive refinancing at 63.43%, from 62.31% a year ago. By comparison, banks went from 23.51% to 22.59% year-over-year. Furthermore, those who refinanced with a credit union saved an average of $101 this quarter, whereas those who refinanced with banks saved $60. Expanding credit access through flexible financing Another notable trend this quarter was the incessant growth in subprime financing as credit accessibility across the market continues to increase. In the first quarter of this year, subprime borrowers made up 15.75% of total vehicle financing, from 14.40% last year. For new vehicles in particular, the subprime market went from 5.61% to 6.88% year-over-year, while subprime in used vehicle financing grew to 20.60% this quarter, from 19.36% a year ago. Increased activity in the subprime segment highlights continued confidence in the automotive market and underscores the importance of expanded financing options. As consumers seek greater flexibility with financing decisions that fit their lifestyle, lenders and dealers have the opportunity to approach them with more personalized solutions. These trends are helping keep both new and used vehicle markets moving forward, while creating new opportunities for consumers to manage payments and purchase confidently. To learn more about automotive finance trends, view the full State of the Automotive Finance Market Report: Q1 2026 presentation on demand.

Trigger leads have long been the preferred solution for identifying high-intent mortgage borrowers. But with the implementation of the Homebuyers Privacy Protection Act (HPPA), which introduces new limitations and consumer protections around trigger leads, that playbook will need to shift. Now, lenders are quickly facing a pivotal shift in how they discover, engage, and convert prospective borrowers into customers. The industry now stands at a crossroads. Lenders who adapt early—leaning into predictive tools, consent-based engagement, and smarter prescreening—will redefine borrower acquisition in a more privacy-centric era.  HPPA: A structural change to mortgage marketing  The HPPA amends the Fair Credit Reporting Act by significantly restricting the use of mortgage inquiries for prescreen purposes. As of March 5, 2026, credit bureaus may only provide or utilize mortgage inquiries to:  End users with explicit borrower consent  The originator of the consumer’s current mortgage  The servicer of the consumer’s current mortgage  An insured depository institution or credit union where the consumer has an existing account  While these exemptions may provide continuity for banks and credit unions, many mortgage brokers and nonbank lenders will need to overhaul their prescreen practices—or risk being cut off entirely from a previously high-performing acquisition channel.  Why this isn’t just a compliance shift—It’s a strategic recalibration  Mortgage triggers in prescreen allow lenders to react instantly to consumer intent. Lenders rely on a prompt and convincing narrative to entice applicants to switch lenders. Mortgage inquiry triggers are effective and were, therefore, a prospecting strategy for many lenders. Recent legislative changes significantly restrict the availability of these inquiry triggers, and impacted lenders are focusing on a more intentional prospecting strategy to compete.   Without these mortgage triggers in prescreen, lenders need to ask:  Who are we trying to reach?  What early signals can we act on?  How do we earn permission and attention before a mortgage inquiry ever happens?  Transforming the funnel: From reaction to anticipation  The shift in mortgage inquiry-based prescreen isn’t the end of high-intent lead targeting. It’s the beginning of a more strategic and intentional approach—one that leverages earlier indicators of mortgage readiness and focuses on building relationships, not just closing transactions.  Here’s where the momentum is evolving, creating a new and smarter funnel:  Prescreen marketing: Using credit and behavioral attributes to help identify consumers who meet specific lending criteria before they signal active intent.  Predictive modeling: Leveraging propensity scores or custom models to prioritize outreach based on conversion likelihood.  Consent-based engagement: Implementing compliant mechanisms to capture and manage borrower opt-ins at scale.  The power of predictive modeling  According to recent industry interviews, propensity modeling is emerging as one of the most effective replacements for trigger-based prescreen. These models analyze hundreds of credit attributes—such as utilization, account mix, account age, and depth—to help identify consumers statistically more likely to seek a mortgage.  For lenders just beginning to use predictive modeling, off-the-shelf models can be a quick way to identify potential borrowers. For example, when layering propensity scores on top of credit eligibility, which can improve borrower targeting, many lenders see an increase in open mortgage loan rates.  Meanwhile, custom-built models, which analyze a lender’s own campaign performance over time, offer the highest level of precise targeting. These models isolate the attributes most predictive of conversions within a specific product mix—optimizing not just volume, but fit.  Speed without traditional triggers? It’s possible  One of the biggest concerns among lenders is maintaining the speed historically enabled by trigger leads. But that concern may be overblown.  Self-service prescreen platforms now allow marketers to generate qualified lead lists in as little as 24 hours, enabling rapid response during rate drops, competitive shifts, or seasonal demand spikes.   For those new to prescreening, batch campaigns still offer value, especially with analyst support.   Don’t overlook retention  In an era of intense acquisition competition, retention becomes a key differentiator.  Lenders who monitor property status, cash flow, and consumer credit behavior can proactively identify when an existing borrower is likely to list, refinance, or exit. Armed with that intelligence, lenders can re-engage with the borrower at the right moment—sometimes before a competitor is considered or contacted.  This level of behavioral intelligence may soon separate proactive lenders from reactive ones.  Actions instead of reactions  The evolution of trigger-based prescreen doesn’t just require new tools; it demands new thinking. Lenders should begin by auditing their current pipelines and determining:  What percentage of our acquisition is dependent on triggers?  What share of our book falls under the HPPA exemptions?  How will we scale compliant opt-in collection?  Are our current prescreen or modeling capabilities future-ready?  Those who answer these questions today—and act on them—won’t just be in compliance with the new laws, they’ll lead in a transformed market. Lenders should also be asking:   Do we have the infrastructure to collect and act on borrower consent?  Are our acquisition teams equipped to run prescreen campaigns — both batch and self-service?  What predictive models are we using (or could we use) to prioritize leads?  Are we proactively monitoring our portfolio to catch retention risks early?  How are we preparing our sales teams for longer, more consultative buying journeys?  Conclusion  The HPPA signals a shift away from relying on passive, inquiry-based prescreen acquisition and the beginning of smarter, more strategic engagement with potential borrowers. Lenders who embrace this transition early will find themselves not just compliant, but competitive—with deeper borrower insights, better conversion rates, and stronger long-term customer relationships.  The market is moving. The only question is: will you lead the change or chase it?  Citation  Experian. (2025, November). Interview: How the Homebuyers Privacy Protection Act is reshaping mortgage marketing—and what lenders should do now [transcript]. Experian Mortgage Insights. Insights based on lender feedback, campaign performance data, and analysis of prescreen marketing strategies and predictive modeling outcomes were gathered from Experian client engagements and internal mortgage analytics between May and October 2025. Homebuyers Privacy Protection Act timeline and legal context referenced from legislation signed September 5, 2025, with implementation beginning March 5, 2026.   

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Customers rarely announce their departure. Instead, they quietly reduce engagement, move deposits and explore competing offers. By the time attrition shows up in reporting, competitors may have already captured meaningful wallet share. For lenders, the risk isn’t just lost accounts, it’s silent revenue erosion within relationships that still appear intact. The hidden risk in your portfolio Today’s consumers often hold less than half of their deposits or loans with a single provider. At the same time: Competition for prime borrowers continues to intensify. Cross-sell remains one of the most effective and efficient growth strategies available. Even small improvements in retention can drive outsized profitability gains. The opportunity is real, but only if you can see momentum early and act before competitors do. From static reviews to strategic signals Traditional monthly and quarterly reviews confirm what has already happened, but they rarely surface early indicators like emerging behavioral shifts or improving credit capacity. Modern portfolio management requires continuous visibility into behavioral signals, trended credit data and event-based triggers that highlight change as it happens. When you can see momentum forming, you can act with precision, intervene before balances leave, engage customers as capacity strengthens, and activate compliant prescreen cross-sell campaigns at the right moment. Our new interactive strategic snapshot outlines the modern approach to portfolio management, one that connects ongoing account review with timely, event-based signals, helping you protect, retain and grow high-value customers. Download it now to see how to turn early signals into stronger customer lifetime value. Read the snapshot

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Lending hasn’t slowed down—but many decisioning processes have. Applications are coming in faster. Fraud is becoming more sophisticated. Borrowers expect near-instant responses. And yet, inside many organizations, decisions are still being made across fragmented systems, manual reviews, and rigid strategies that weren’t designed and aren’t optimized for today’s environment. That broadening gap isn’t just an operational issue but often stems from a lack of innovation as well. And it’s quietly costing lenders growth, efficiency, and competitive position. When decisioning falls behind, some symptoms are easy to recognize, like applications taking days to process, teams overloaded with manual reviews, and credit and fraud decisions happening in separate platforms. Others are not as obvious, but arguably more impactful, slipping bottom lines and fraud and therefore losses lurking in lenders’ portfolios. The root issue is a fragmented infrastructure. Experian has reported that while 79% of financial institutions surveyed globally want fewer vendors or more unified approaches, they typically use eight or more tools across credit, fraud and compliance. As most decisioning environments cannot integrate data, adapt strategies, and execute decisions in real time, lenders often have to make tradeoffs. Speed vs. accuracy; growth vs. risk; and automation vs. control are just some. Meanwhile, the market has moved on. Leading lenders are no longer optimizing individual steps. They’re rethinking decisioning as a connected, intelligent system. Gaps forming from status quo in 8 key decision areas Across the lending lifecycle, there are eight critical moments where decisioning can either accelerate growth or create friction. Pre-qualification: Pre-qualification should expand your funnel with confidence. But limited data access and static criteria often result in overly conservative targeting or missed opportunities. Additionally, the delay in acting on a pre-qualification funnel highlights a key area for opportunity among many lenders. Instant credit decisions: Customers expect real-time outcomes. When decisions rely on manual intervention or fragmented inputs, speed and conversions suffer. Prescreen and targeting: Disconnected data and rigid segmentation can lead to poorly aligned offers, reducing response rates and wasting acquisition spend. Credit line management: Without dynamic strategies, credit lines may be too restrictive (limiting growth) or too aggressive (increasing risk). Early delinquency management: Missed early signals and delayed interventions make it harder to prevent accounts from deteriorating. Mid- and late-stage delinquency: Strategies that don’t adapt to evolving borrower behavior reduce recovery effectiveness and increase losses. Collections and recovery: Manual, one-size-fits-all approaches limit recovery rates and increase operational cost. Ongoing strategy optimization: Perhaps the most overlooked gap: many lenders lack the ability to continuously test, learn, and refine decision strategies as conditions change. What these gaps are really costing you Individually, each of these breakdowns may seem manageable. Together, they can create systemic drag on performance. That shows up in four critical ways: Missed growth opportunities: Good borrowers are declined, abandoned, or never targeted in the first place. Credit offers fail to align with actual borrower potential. Higher operational costs: Manual reviews and disconnected workflows consume time and resources that could be spent on higher-value work. Increased fraud exposure and friction: Fraud is proliferating and becoming more expensive to manage. The Federal Trade Commission reported $12.5B were lost to fraud in the U.S. in 2024, a 25% increase over the prior year. For many financial institutions, the first reaction is often to add more steps to the decisioning process, which can impact good borrowers. Increased competitive pressure: Fintechs and modern lenders are focused on delivering faster, more personalized experiences, capturing share while traditional processes lag behind. 80% of banks and credit unions plan to increase their technology spending in 2026, yet many continue to fall short on planned system deployments, according to Cornerstone Advisors’ annual “What’s Going On in Banking” research report. What innovative decisioning leaders are doing differently Leading lenders are changing how decisions are made, creating a competitive advantage. Instead of stitching together point solutions, they’re adopting a more integrated approach that brings together: Comprehensive data – including both credit and fraud insights Optimized decision strategies – designed to balance growth and risk Real-time execution – enabling faster, more consistent outcomes Continuous optimization – adapting to changing market conditions Strategic partnerships – leveraging third-party industry expertise to augment their own This shift eliminates the need for tradeoffs and instead allows lenders to increase approvals while maintaining control, reducing manual effort while improving consistency, and responding faster without sacrificing confidence. The stakes are high and the competition for consumers is even higher, particularly against a backdrop of ever-evolving fraud risks, continuously increasing consumer expectations for seamless, digital-first experiences and often limited resources. Nearly half of banks and 59% of credit unions have already deployed generative AI, with more investing now, according to the Cornerstone Advisors’ report. Closing the innovation gap requires a more fundamental shift toward decisioning systems that are connected, scalable, and built for continuous change. A new foundation for decisioning This is where platforms like Experian Decisioning are changing the landscape. By bringing together credit and fraud insights, decision strategies, and a flexible technology architecture, lenders can move beyond fragmented processes and build a more unified, intelligent decisioning approach. One that fits within existing systems but also evolves with your needs. Where to start Impactful change doesn’t need to be an overhaul of everything at once for most organizations. The first step is understanding where your biggest gaps exist, and which decision areas are creating the most friction or missed opportunity. Once you can see where decisioning is not optimized, you can begin to redesign it in a way that’s faster and more adept for what lending has become. By making better decisions, faster, and with greater confidence, lenders can process applications more efficiently and also break away from the pack by leveraging decisioning as a strategic advantage. Learn more