Explore how the Experian Verify™ preview report helps mortgage lenders reduce unnecessary verification costs, avoid overbuying records, and make smarter VOIE decisions with greater visibility before they purchase.

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Explore how Experian Verify™ and Mezzo’s Centro orchestration engine are helping mortgage lenders modernize income and employment verification, reduce workflow complexity, and make faster, more confident lending decisions at scale.

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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The mortgage industry is adapting to a structural shift. Experian’s 2026 State of the U.S. Housing Market Report shows a market in transition. Conventional loans account for 72% of originations, FHA 17.5% and VA 10.8% with VA showing the strongest growth from 2023 to 2025. But origination mix only tells part of the story. Beneath it lies an arguably more consequential shift: borrower expectations, affordability pressures and regulatory changes are converging. On the regulatory front, the Homebuyers Privacy Protection Act (HPPA) may reduce mortgage trigger leads and limit broad competitive outreach. As competitive visibility narrows, the lender relationship becomes more central and important beyond the closing transaction. In this environment, lenders must provide value to win, and that increasingly means financial wellness. A growing trust gap Only 34% of first mortgage hard inquiries of first mortgage hard inquiries convert into funded originations, according to Experian. That means two-thirds of borrowers who initiate the process never close. External data confirms the trend as Mortgage Bankers Association reported retail mortgage pull-through rates declined to roughly 69% in early 2025 – the lowest in over a decade – and as low as 55% among depository lenders. While pull-through can be impacted by a number of factors not influenced by the lender, when borrowers abandon applications, it can be a biproduct of uncertainty – something that the lender can influence. This is where financial wellness becomes strategic and lenders can close the trust gap by providing proactive credit visibility and guidance before underwriting friction occurs. Read more in our white paper, “The New Unlock for Mortgage.” Affordability stress While rates have eased from their 2023 highs, they remain above 6%, sustaining the lock-in effect and limiting housing supply, according to Experian’s 2026 State of the U.S. Housing Market Report. Approximately 70% of homeowners are locked into sub-6% mortgages, according to Freddie Mac. Beyond mortgage rates, increases in property taxes and non-tax escrow amounts (i.e. insurance) increase affordability pressures for consumers. Financial wellness solutions that incorporate credit monitoring, budgeting insights and cashflow visibility help borrowers understand whether they are prepared. Opportunity among millennials and Gen Z Nearly 47% of U.S. renters expect to purchase a home within four years, rising to 67% within eight years, according to Experian. This signals the time to invest in financial wellness as a differentiator, and both a growth and retention driver, is now. Financial wellness as the new unlock for mortgage Financial wellness is not an ancillary service but the foundation upon which borrower confidence, long-term engagement, conversion and risk management connect. Lenders who embed solutions like credit education, score visibility, alerts, and identity protection directly into the consumer experience can differentiate themselves from the competition above and beyond rates alone. Read more in our white paper, “The New Unlock for Mortgage.” Learn more about Experian Mortgage

What do BNPL rules mean for mortgage credit scoring? As regulators push for greater transparency and alternative data reporting, lenders must adopt modern credit models that include rent, cash flow, and trended data. Learn how evolving BNPL guidance signals the future of mortgage underwriting and why forward-thinking lenders are modernizing scoring strategies now.

U.S. rental housing market outlook 2026: Analyze elevated mortgage rates, rental supply constraints, affordability pressure, and rising fraud risk. Discover how data analytics, rent reporting, and digital income and employment verification help property managers reduce risk, improve screening accuracy, and make smarter, faster leasing decisions.

  Experian Verify is redefining how lenders streamline income and employment verification; a value clearly reflected in Marcus Bontrager’s experience at Freedom Mortgage. With access to the second-largest instant payroll network in the U.S., Experian Verify connects lenders to millions of unique employer records, including those sourced through Experian Employer Services clients, delivering instant results at scale. This reach enables lenders to reduce manual processes, accelerate loan decisions, and enhance the borrower experience from the very first touchpoint. Unlike traditional verification providers, Experian Verify offers transparent, value-driven pricing: it charges only when a consumer is successfully verified, not simply when an employer record is found. As lenders navigate increasing compliance requirements and secondary market expectations, they can also rely on Experian Verify’s FCRA-compliant framework, fully supporting both Fannie Mae and Freddie Mac. Combined with Experian’s industry-leading data governance and quality standards, lenders gain a verification partner they can trust for accuracy, security, and long-term operational efficiency. Perhaps most importantly, Experian Verify delivers 100% U.S. workforce coverage through its flexible, automated waterfall: instant verification, consumer-permissioned verification, and research verification. This multilayered approach ensures lenders meet every borrower where they are, whether they’re connected to a large payroll provider, a smaller employer, or require additional document-based validation. As Marcus highlights in the video, this comprehensive and configurable design empowers lenders to build verification workflows that truly fit their business needs while enhancing speed, completeness, and borrower satisfaction. Explore Experian Verify

Lenders who want to outperform peers in today’s housing market should embrace a data-driven playbook. These four strategic pillars—borrower insights, efficiency, geography, and refinance readiness—define the path forward. 

As the U.S. housing market enters a new phase, the 2026 State of the U.S. Housing Market Report from Experian provides a data-driven overview for lenders, servicers, and property managers. This article synthesizes findings related to mortgage originations, affordability pressures, home equity utilization, credit risk, and generational sentiment, with implications for lender strategy in 2026 (Experian, 2026).  Mortgage market in flux: Opportunity amid transition  The mortgage market presents mixed signals. Rate moderation in late 2025 contributed to renewed demand, while the product mix continued to evolve. Conventional loans remained dominant at approximately 72% of originations, yet Veterans Affairs (VA) loans experienced the highest growth between 2023 and 2025, reaching 10.8% market share (Experian, 2026).  At the same time, second mortgages and home equity lines of credit (HELOCs) gained momentum as homeowners sought liquidity without refinancing out of historically low interest rates. This trend reflects growing demand for equity-based solutions that preserve favorable first-mortgage terms (Experian, 2026).   Pull-through challenges: Only 34% of inquiries become loans  Conversion efficiency remains a key challenge. Only 34% of first-mortgage hard credit inquiries resulted in a completed mortgage origination, highlighting friction between borrower interest and loan fulfillment (Experian, 2026).  Consumer research reinforces this gap. In an Experian survey, 50% of respondents reported that understanding what they could qualify for would be the most helpful step in their homeownership journey, suggesting that improved prequalification tools could materially increase pull-through rates (Experian, 2026).   Affordability pressure goes beyond the mortgage  Between 2021 and 2025, property taxes increased by 15.2%, while non-tax escrow costs—primarily homeowners' insurance—rose by 67.4% nationwide (Experian, 2026).  State-level variation further complicates affordability assessments. Florida recorded the highest average non-tax escrow expenses at $430 per month largely due to sharp increase in home insurance costs. California, by contrast, exhibited the highest average property tax burden at $626, largely driven by elevated home values despite lower statutory tax rates (Experian, 2026). These dynamics underscore the importance of holistic cost modeling, particularly for first-time buyers.   Home equity: A lender’s growth frontier  Home equity remains a significant growth opportunity. An estimated 96.2 million consumers reside in owner-occupied homes, with substantial portions owning their homes outright or holding more than 20% equity (Experian, 2026). HELOC usage is increasing, particularly among younger borrowers, 50% of whom utilize more than 60% of their available HELOC credit, compared with 36% of older borrowers (Experian, 2026).  Market share shifts are also notable. Fintech lenders experienced a 140.2% increase in HELOC originations from 2023 to 2025, significantly outpacing banks and credit unions. These gains suggest that digital-first experiences and streamlined workflows are increasingly decisive factors for borrowers (Experian, 2026).   Risk and resilience: What credit and property data reveal  Overall delinquency rates eased slightly; however, near-prime and prime borrowers demonstrated early signs of stress, particularly within first-mortgage portfolios (Experian, 2026).  Property-level risk is also intensifying. Flood exposure increased by 3.7% nationally, with 26.4% of Florida homes identified as at risk. Rising exposure has contributed to escalating insurance costs, further affecting affordability and credit performance (Experian, 2026).  From a credit hierarchy perspective, secured debt—especially mortgages and auto loans—continued to show the lowest delinquency rates. In contrast, student loans and credit cards exhibited higher delinquency risk, particularly among financially constrained renters and homeowners (Experian, 2026).   Generational optimism versus macroeconomic constraints  Despite affordability headwinds, consumer optimism persists. Approximately 47% of renters believe they will be ready to purchase a home within four years, increasing to 67% within eight years (Experian, 2026).  Structural constraints remain significant. Roughly 70% of homeowners hold mortgage rates below 6%, contributing to limited housing inventory as current owners remain rate-locked. With 30-year mortgage rates still above that level and a softening labor market, even modest increases in unemployment could further pressure affordability (Experian, 2026).   Implications for lenders  Experian’s analysis highlights several strategic priorities for housing industry stakeholders:  Expand access to credit. Incorporate alternative data sources, such as cash-flow analytics and rental payment history, to responsibly extend credit to underserved but qualified borrowers (Experian, 2026).  Capitalize on equity demand. Develop HELOC offerings that are fast, flexible, and digitally enabled to meet the needs of equity-rich, rate-locked homeowners (Experian, 2026).  Enhance risk precision. Integrate credit, property, and behavioral data to identify emerging risk early, particularly among near-prime segments, and to support more accurate pricing and portfolio management (Experian, 2026).   Conclusion  The 2026 housing market reflects a complex interplay of macroeconomic pressure, shifting borrower behavior, and growing reliance on home equity solutions. Agility and data-driven decision-making will be essential for lenders navigating this environment. The 2026 State of the U.S. Housing Market Report offers critical insight to support growth while managing risk in an evolving landscape (Experian, 2026).  📘 Access the full report here: Experian 2026 State of the U.S. Housing Market Report  References  Experian. (2026). 2026 state of the U.S. housing market report. Experian.     

Who is renting in 2025 and why it matters. Explore renter demographics, affordability pressures, credit trends and how Experian data helps predict housing risk and demand.