Verification has become an increasingly important area of focus in mortgage lending, but success is about more than just coverage. In the latest episode of the Chrisman Commentary Podcast, Experian's Jamie Norris, Senior Manager of Strategic Alliances, shares why distribution and integration are increasingly the keys to driving adoption, automation, and better borrower experiences.  Why Distribution Matters in Verification  As lenders continue to pursue faster, more efficient mortgage processes, verification solutions must fit seamlessly into the systems they already use. Norris explains how Experian's strategy is focused on helping lenders access trusted income and employment data while minimizing workflow disruption by making Experian Verify accessible across loan origination systems (LOS), point-of-sale platforms, underwriting technologies, and reseller networks.  Building a Smarter Verification Strategy  The conversation explores why lenders benefit from having access to multiple verification providers, how they can optimize verification strategies to maximize automation while minimizing costs and borrower friction, and why an "instant-first" approach is gaining momentum across the industry.  Looking Ahead: AI, Automation, and the Future of Mortgage Lending  Norris also discusses how AI-driven underwriting and decisioning are reshaping mortgage technology. As lending platforms become increasingly automated, real-time verification data is expected to support faster decisioning and more streamlined borrower experiences.  She shares Experian's vision for expanding its verification ecosystem and delivering a broader suite of solutions that meet lenders wherever they work.  Listen to the full episode above to hear Jamie's insights on verification strategy, partner integrations, AI-enabled lending, and what's next for mortgage automation. 

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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 by Freddie 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. 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 loan delinquencies have been broadly trending higher across all age groups.  Also, the average age of a borrower in default has risen to 40, and borrowers aged 50 and older are now at a higher risk of default than younger groups. This 40 to 50-year-old age group represents prime home ownership years.  Defaulted borrowers are also struggling to make other debt payments, too.   The same report stated that almost 40% of past due student loan borrowers with auto loans are past due, 56% have at least one credit card past due, and 20% have a past due mortgage.  In addition to increased delinquency risk on their mortgage, borrowers with student loan debt also have fewer mortgage refinance options, as their elevated DTI may prevent qualification for a refinance, or increase the offered rate of a refinance and thereby reduce their incentive.  These dampening effects of student loan debt on mortgage CPR are clearly evident in the data, as described further below.  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. _____________________________________________________

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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