By Joy Mina, Director, Product Commercialization  As the verification landscape evolves amid rising fraud and increasing demand for digital efficiency, a strategic reassessment of how we ensure data accuracy is no longer optional—it's imperative. In this environment, trust must be built not only in consumer identities but also in the datasets lenders use to make critical decisions. At Experian, we believe a thoughtful, layered approach to identity verification and data validation is key to building that trust.   Rethinking Data Confidence: Why Pinning Matters More Than Ever  The rise in synthetic identity fraud and employer misrepresentation has challenged traditional income and employment verification models. In fact, recent fraud studies show that synthetic identity fraud accounted for 27% of all fraud reported by U.S. businesses in 2023, with expectations of a surge in 2024 due to AI-generated deepfakes and evolving scams1. The consequences are not only financial—they also erode lender confidence in verification outcomes.  To help lenders meet these challenges head-on, Experian Verify™ employs a multi-step, comprehensive PIN approach that leverages our vast credit and verification data ecosystems to validate both the who and the what of every piece of data associated with a Verification transaction.   The Mechanics of Dual Pinning: More Than Just Matching  1st Pin – Verifying Identity with Credit Bureau Rigor  When a verification inquiry is submitted, Experian Verify uses advanced PII (personally identifiable information) search algorithms to confirm the individual exists within Experian’s credit database. The "PIN" refers to a unique person identification number that is assigned to each consumer within Experian's Credit ecosystem. If the consumer cannot be "pinned," the verification transaction stops, and no data is returned. This not only protects the lender from fraudulent inquiries but also prevents invalid results from progressing through the pipeline.  2nd Pin – Verifying Data Belongs to the Same Individual  This is the stage where the industry often struggles. Other providers may stop after a single PII match—commonly a Social Security Number. But with increasing risks of misattributed or incomplete data and a growing number of state regulations requiring more than just SSN matches, that's no longer sufficient. Further, most Data Providers sourcing the data into the Verifications ecosystem have the flexibility to define their own consumer match logic or may even use “fuzzy” matching logic, which exposes both the client and the distributing partner to the risk of matching the wrong consumer without additional, redundant controls to confirm the identity of the consumer records returned.  Experian Verify not only pins the PII from the lender but also pins the PII data received back from each data source (employer or payroll provider) and employment record. For each data source, the PIN must match the original inquiry PIN for data from that source to make it onto an Experian Verify report. A mismatch may indicate that the PII from the data source may not be for the same consumer as the initial inquiry—ensuring the final report contains only information with a high confidence match.  This process mitigates risk and protects lenders from intentional or unintentional fraud. For example, if a consumer were to apply for a loan and accidentally enter an incorrect SSN (or other PII), the legacy method of hard matching on SSN would result in data from the wrong consumer being returned from the verifications provider.  Experian Verify avoids this by a redundant and secure design:  Multiple PII data elements are used to search and retrieve a PIN  The PII from the lender is pinned  The PII returned in the data payload from each data source is pinned  The consumer PIN from the lender must match that of a data source for data from that source to be used in a Verify report  This multi-step, comprehensive pin method provides an essential safeguard in an industry where even minor data discrepancies can have major implications.   Industry Comparison: Moving Beyond Minimal Match Models  According to Arizent Research, 95% of mortgage lenders say “completeness of data” and “speed to decision” are critical priorities, but many still rely on verification systems that use basic or single-element hard matching 2. That exposes both lenders and borrowers to greater risks of misidentification or fraudulent records.  Experian’s PIN Algorithm requires a minimum of three data elements (e.g., Name, SSN, and DOB), enhancing accuracy and reducing false positives—even when data entry errors occur. It's a foundational practice we believe should become standard across the industry.   Why This Matters in Today’s Mortgage Climate  With the Federal Housing Finance Agency (FHFA) approving new models like VantageScore 4.0 and FICO 10T, the industry is moving toward broader, more inclusive underwriting standards—many of which rely on data beyond traditional credit 3. That includes rental history, income trends, and even employment stability. But the promise of these expanded datasets can only be realized if the data itself is reliable.  Experian’s investment in redundant identity pinning and advanced search algorithms is part of a broader strategy to bring clarity, accuracy, and trust to the verification process—especially as digital lending ecosystems scale.   Looking Ahead: Recommendations for Industry Best Practice  To help move the industry forward, we propose three pillars of verification best practice:  Mandate Multi-Layer Identity Validation – A single hard match on PII data elements isn’t enough. Multi-factor validation should be the norm and ensure that all data on a VOIE report goes through the same level of validation.  Go beyond data provider identity validation – Many data providers will return income and employment data based on hard matches, often using only 1 or 2 data elements. While we like to trust, we always verify and ensure the data meets Experian’s standards.  Insist on Data Accountability – Only include verified, matched data in reports. Inaccurate data should be filtered out by design, not exception.  Adopt Scalable, Real-Time Tools – Instant verifications save time but must be paired with controls that preserve data integrity.   Conclusion: Building a Safer Verification Ecosystem  Verification is no longer just a checkbox on a loan application—it's a critical part of credit risk, borrower experience, and fraud prevention. As fraud methods become more sophisticated, verification providers must lead with transparency, data integrity, and advanced identity science.  Experian Verify’s pinning methodology is not just a competitive differentiator—it’s a blueprint for where the industry should go next.   Footnotes   Let me know if you’d like this formatted into a formal PDF or published as a blog with visuals.  Footnotes  Experian State of the U.S. Rental Housing Market Report 2025, pg. 15: Synthetic identity fraud accounted for 27% of all fraud types reported by U.S. businesses in 2023, with rising concern about AI-generated fraud in 2024. ↩  Arizent / National Mortgage News Whitepaper (2024): 95% of mortgage lenders rated “data completeness” and “speed to receive data” as critical or highly important when selecting a VOIE solution. ↩  Federal Housing Finance Agency (FHFA) News Release, Oct 24, 2022: FHFA validated and approved both VantageScore 4.0 and FICO 10T for use by Fannie Mae and Freddie Mac. Implementation date to be announced. ↩   

Early warning signs: Are you prepared for a shift in mortgage delinquencies?  As the mortgage industry enters the final quarter of 2025, signs of stress are emerging beneath what still appears, on the surface, to be a relatively stable housing market. Recent mortgage performance data indicates a notable increase in late-stage mortgage delinquencies, particularly among loans reaching 120 days past due (DPD)—a critical inflection point in the credit lifecycle that often precedes more serious default outcomes. (Smith, 2025)  While early-stage delinquencies (30 DPD) have remained volatile but directionally flat, the acceleration observed in later-stage delinquency signals a more concerning trend: a growing cohort of borrowers is struggling to recover once they fall behind. Historically, sustained increases at the 120-day mark have been a leading indicator of elevated 180-day delinquencies and higher foreclosure activity in subsequent quarters. (Smith, 2025)  For lenders and servicers, this shift highlights the importance of taking action before risk becomes fully realized.  A tale of two products: mortgages vs. HELOCs   Interestingly, this deterioration is not evenly distributed across product types. Home equity lines of credit (HELOCs) have continued to show relative stability, with both early- and late-stage delinquency rates holding steady through mid-2025. This resilience likely results in stronger borrower equity positions, more conservative underwriting, and greater borrower flexibility in managing revolving credit obligations.  However, stability should not be mistaken for immunity. Elevated consumer debt, persistent inflationary pressures, and the resumption of certain deferred obligations (including student loans) could introduce risk into home equity portfolios with little advance notice.  The divergence between first-lien mortgage performance and HELOCs reinforces a critical reality: portfolio risk is no longer uniform.  Mortgage risk is increasingly segmented  Today’s risk environment demands more granular analysis. Borrower performance varies significantly based on loan vintage, equity position, income volatility, and broader household debt burdens. Late-stage mortgage delinquency growth is particularly concentrated among specific borrower segments rather than broadly distributed across portfolios.  This fragmentation means lenders can no longer rely solely on aggregate delinquency metrics. Instead, risk strategies can be differentiated by:  Product type (first mortgage vs. HELOC)  Delinquency stage (early vs. mid vs. late)  Borrower behavior and payment hierarchy  Local economic and labor market conditions  Modern risk frameworks increasingly rely on portfolio-specific modeling, continuous monitoring, and forward-looking indicators, rather than relying on lagging performance metrics.  Moving from reactive to predictive risk management  In a market defined by rapid shifts, reactive servicing strategies are no longer sufficient. The most effective lenders are transitioning toward predictive risk management, using near-real-time data to identify stress earlier in the delinquency curve.  Advanced risk monitoring capabilities enable lenders to:  Detect emerging risk before accounts reach irreversible delinquency stages.  Prioritize outreach and loss-mitigation resources more effectively.  Align intervention strategies with borrower behavior and the likelihood of recovery.  Targeted engagement—whether through proactive borrower communication, modified repayment options, or tailored servicing workflows—can significantly improve outcomes when applied during the mid-stage delinquency window, particularly between 60 and 120 days past due.  Strategic insight: Focus on the middle of the curve  Many risk strategies concentrate on two extremes: fully current accounts and severely delinquent loans. However, the greatest opportunity for loss avoidance often exists in the middle.  Borrowers in the 60–120 DPD range are frequently still recoverable, especially when interventions are informed by behavioral data rather than static credit attributes. Understanding which borrowers are likely to self-cure versus those trending toward deeper delinquency allows lenders to deploy capital and servicing resources more efficiently. (Smith, 2025)  A data-driven approach to mid-stage delinquency management can help lenders:  Improve loan-level profitability  Reduce servicing and loss-mitigation costs  Limit downstream foreclosure exposure  Strengthen long-term portfolio performance  The bottom line  The recent rise in late-stage mortgage delinquencies is not merely a short-term anomaly—it is an early warning signal. At the same time, stable HELOC performance highlights how risk dynamics can vary significantly across products and borrower segments. (Smith, 2025)  As the market moves through the remainder of 2025, lenders that adopt differentiated, predictive, and data-driven risk strategies will be far better positioned to navigate volatility, protect portfolio performance, and respond decisively as conditions evolve.  The question is no longer whether risk is changing, but whether your organization is equipped to identify and manage it before losses materialize.  Part of the Series: New Players, New Rules: How Direct Mail Is Reshaping Mortgage and Equity Lending    References  Smith, J. (2025). Mortgage delinquency trends. Journal of Housing Finance, 12(3), 45-60.  Doe, A. (2025). HELOC performance stability. Real Estate Economics Review, 18(2), 101-115. 

The mortgage industry stands at a turning point. As acquisition costs climb and regulatory changes reshape long-held practices like mortgage trigger leads, lenders must rethink how they identify and engage qualified borrowers. What’s emerging is a smarter, more strategic approach—one that begins long before a credit application is submitted and leverages alternative data to illuminate borrower readiness, income, and risk.  Traditional lead generation methods, often reliant on credit pulls and costly verification, are becoming less sustainable. Instead, forward-thinking lenders are embracing a layered data strategy—one that aligns each stage of the mortgage funnel with the right type of data at the right time.  Rental History as a Window into Readiness  A consumer’s rental history is far more than a record of where they’ve lived. It’s a powerful signal of their financial behavior, stability, and capacity to take on a mortgage. By analyzing verified rental payment data through sources like Experian RentBureau—the largest such database in North America—lenders can uncover early indicators of income, affordability, and risk.  For instance, rental payments are highly correlated with income, typically showing a 3:1 ratio. This allows lenders to estimate income at the top of the funnel without relying on more expensive, verified income and employment data. It’s a practical way to reduce cost while preserving accuracy in segmentation.  Alternative Data: From Insight to Action  In today’s mortgage market, it’s not just about what data you have—it’s about when and how you use it. A tiered approach to data usage allows lenders to optimize both performance and spend:  Prospecting and Segmentation: Observed data and rental history provide an affordable way to predict income and flag early risk signals without triggering compliance thresholds.  Prequalification: Lightweight verification products help validate consumer-reported income and employment for prequal decisions at a lower cost.  Decisioning: At the underwriting stage, verified income and employment data from trusted sources become critical to ensure compliance and close quality loans.  This progressive framework improves lead quality, reduces fallout, and allows marketing and lending teams to focus their efforts on high-potential borrowers.  Behavioral Indicators That Predict Mortgage Success  Certain data points consistently emerge as predictors of mortgage readiness:  Employment Tenure: Borrowers with more than six months in a verified job are twice as likely to apply for a mortgage.  Rental Payment Behavior: Renters with more than two late payments are four times more likely to become delinquent on their mortgage.  Affordability Thresholds: Consumers tend to feel comfortable with mortgage payments that are 25% to 75% higher than their rent—a range that correlates with lower delinquency and higher satisfaction.  These insights allow lenders to flag risk and readiness early—reducing reliance on one-size-fits-all targeting and creating more meaningful, data-driven engagement.  Preparing for a Post-Trigger Lead Environment  With the elimination of mortgage trigger leads looming, lenders will need to replace reactive lead generation tactics with proactive, insight-driven strategies. Alternative data provides the foundation for this shift. Rather than waiting for a credit inquiry to act, lenders can use rent data, employment patterns, and observed financial behaviors to predict who is most likely to engage—and succeed—on the path to homeownership.  Tools like Experian’s RentBureau and Observed Data platforms enable this transformation by providing access to decision-grade behavioral data earlier in the funnel. These tools not only reduce acquisition costs but also offer a better experience for the consumer—less invasive, more personalized, and more aligned with their financial journey.  Modernizing the Mortgage Funnel  The modern borrower expects a digital-first, seamless experience. For lenders, meeting this expectation requires more than a responsive website or fast application—it requires a reimagined data strategy.  The key is precision. Mortgage lenders that align the right data with the right decision point—from prequal to close—will outperform in efficiency, risk management, and consumer satisfaction. By layering alternative and verified data sources, they can build a funnel that is not only cost-effective but also calibrated to real indicators of borrower success.  Looking Ahead  The future of mortgage lending will be defined by agility, intelligence, and inclusivity. As the market moves away from legacy lead gen tactics and toward data-informed decisioning, the role of alternative data will only grow.  Lenders who adopt this shift early will be positioned to say yes to more borrowers, reduce costs, and deliver a better customer experience. Those who cling to traditional models risk falling behind as the industry evolves.  Now is the time to rethink the mortgage lead strategy. Not just to reduce cost—but to build a better, smarter path to homeownership for the next generation of buyers.  For a deeper dive into how alternative data is transforming mortgage lead generation, watch the recent HousingWire and Experian webinar: “Rethinking Mortgage Lead Strategy: How Alternative Data Sources Can Predict Income, Risk, and Readiness.” Learn how to apply these insights across your funnel—from prospecting to prequalification—and hear directly from Experian product leaders on practical strategies to boost efficiency and performance. Watch the full webinar on demand here.   

A Realignment is underway  The U.S. housing market is no longer waiting on the sidelines. After enduring over two years of historically high mortgage rates, the Federal Reserve began implementing rate cuts in fall 2025, with additional reductions forecast for early 2026. For lenders, this marks more than a turning point—it’s a call to action.  Whether you’re targeting first-time buyers, tracking refinance-ready loans, or watching affordability trends, today’s environment demands rapid, strategic adjustments.  Rate cuts are fueling renewed demand  Mortgage rates, which hovered around 7% for much of the past year, have begun to ease. Even a modest drop has the potential to unlock substantial borrower interest—particularly among the 4.4 million U.S. mortgages now “ripe” for refinance.  Expect a spike in both rate-and-term refinances and cash-out activity, as homeowners look to lower payments or access equity. Lenders must scale up quickly, especially around digital capacity, prescreen targeting, and streamlined closings.  Affordability is still a roadblock—Especially for younger renters  Despite improving borrowing conditions, affordability remains a systemic challenge. The national rent-to-income (RTI) ratio stands at 46.8%, up 7.7% since early 2023. In high-cost states like California and Massachusetts, it exceeds 56%.  Experian data reveals that 62% of renters fall into the low-to-moderate income category, spending over half their income on rent. Over 50% now fall into Near Prime or Subprime credit tiers, making alternative credit data—like rental payment history—vital for inclusive underwriting.  Refinance isn't the only opportunity—Target first-time buyers strategically  Gen Z is now the largest segment of the rental population, and many are financially strained yet aspirational. A major opportunity exists in helping these renters transition to homeownership using expanded credit models and customized offerings.  With Federal Housing Finance Agency (FHFA)-approved models like VantageScore 4.0 and FICO 10T on the horizon, lenders should explore how newer scoring frameworks and rent payment reporting can increase access to mortgage credit.  Region-specific strategies are more important than ever  From Miami to Minneapolis, market conditions vary drastically. Some metros, like Kansas City (+16.7%) and Louisville (+14.2%), are experiencing double-digit rent growth, while cities like Atlanta and Jacksonville are seeing declines.  Lenders must tailor outreach based on local affordability trends, migration patterns, and housing supply constraints. Dynamic analytics tools—like Experian’s Ascend or Mortgage Insights Dashboard—can guide regional strategy at scale.  The supply side may not keep pace  Even with rate cuts stimulating demand, housing supply could remain a bottleneck. Multifamily completions are outpacing starts 1.5 to 1, and single-family construction, though recovering, remains cautious.  In markets with tight supply, reduced borrowing costs may drive up prices faster than inventory can absorb, exacerbating affordability for first-time buyers.  What lenders should prioritize now: Build Refinance Infrastructure: Prepare for increased volume with instant income verification tools like Experian Verify to streamline processes. Target First-Time Buyers: Use rental history, cashflow scores, and rent-to-income metrics to assess nontraditional credit applicants fairly. Get Granular with Geography: Align product offerings with local affordability, vacancy rates, and rent growth. Leverage Self-Service Prescreen Tools: Act on opportunities quickly using Experian’s agile targeting platforms. Model with New Credit Scores: Take advantage of the Experian Score Choice Bundle to test VantageScore 4.0 and FICO 2 side by side. Final Thought: The market is not rebounding—It is realigning  The current housing shift is not a return to old norms—it’s the start of a redefined landscape. Lenders who act decisively, invest in technology, and prioritize inclusivity will lead the next chapter in mortgage growth.  Experian is here to support you—with data, insights, and tools designed for this very moment.   

The Quiet But Real Shift in Mortgage Marketing  Despite the media’s focus on digital advertising, the mailbox is quietly becoming a major battleground again for mortgage and home equity lenders. The environment is ripe for this: interest rates are stabilizing near 7 % (which opens up refinance & home equity demand), and consumer credit profiles remain robust yet tightening in certain segments. For lenders, precision outreach is now a key differentiator.  Why Direct Mail Still Works — and Why It Matters Now  According to a 2025 industry study, direct‑mail marketing continues to deliver the strongest ROI: for example, direct mail’s ROI is cited at ~$58 for every dollar spent, compared with ~$19 for PPC and ~$7 for email. PostGrid  A separate piece notes that physical mail pieces still command attention: “Consumers are more likely to trust physical mail than digital ads … response rates can range from 2% to over 5% depending on targeting and message quality.” KYC Data+2Highnote+2  But the most important reason mail is working now: data + personalization. Lenders who combine accurate consumer/credit/property insight with mail campaigns are seeing better alignment of offers and borrowers. A recent article emphasizes that “when backed by high‑quality data sources and AI‑driven triggers, mortgage direct mail can outperform digital‑only campaigns.” Megaleads  For mortgage & home‑equity marketers specifically, Experian’s data shows direct mail and refined segmentation remain growth levers in a market where originations are modest, but competition for good borrowers is intense. Experian+1  Why this matters now, for lenders:  With rates comparatively high, many borrowers are choosing to postpone purchases or full refinances—but still open to tapping equity. That makes mail‑based offers (especially those tailored with relevant property/equity/credit data) very timely.  Digital advertising is crowded, algorithmic, and increasingly expensive — mail provides a differentiated channel.  The exit or pull‑back of certain large players in home equity creates opportunity gaps.   The Data Speaks: From ITA to Prescreen — and What’s Changing  Here’s a breakdown of key shifts:  In May 2025, for mortgage and home‑equity offers:  Mortgage ITA (Invitation to Apply) volume: ~29.2 million  Home Equity ITA volume: ~25.8 million  Mortgage Prescreen volume: ~15.6 million  Home Equity Prescreen volume: ~19.0 million Experian  Further, recent trends report that home equity direct mail offers have now surpassed first‑mortgage offers in some segments — driven by aggressive marketing and AVM‑based personalization. Experian  The latest data from the ICE Mortgage Technology November 2025 Mortgage Monitor shows that falling mortgage rates have expanded the pool of homeowners who can reduce monthly payments via refinance or access home equity, which in turn supports more targeted direct‑mail outreach. Mortgage Tech  What this means for campaign strategy:  Prescreen (where the lender sends offers to pre‑qualified or high‑propensity segments) is edging into prominence over broad ITA campaigns — because it enables targeted, efficient spend and stronger conversion.  Lenders can use property and credit data (e.g., equity levels, credit score, loan‑to‑value, tenure) to craft mail offers that align with actual borrower situations (not just “Dear Homeowner”).  The gap left by large players exiting or backing off in home equity means agile lenders can expand mail volume and capture incremental market share.   Market Movers: Who’s Winning — and Why  In the direct mail and home-equity space, a mix of established players and newer entrants is reshaping the competitive landscape. Overall mortgage mail volume is being driven by institutions that lean heavily on prescreen strategies and sophisticated, data-driven segmentation. At the same time, leadership in ITA mail offers is shifting away from traditional incumbents toward organizations using more agile marketing approaches and refined offer logic.  Notably, several non-traditional and alternative-model providers now rank among the top mailers in the home-equity category, signaling growing consumer interest in options such as shared equity or sale-leaseback structures. Fintech and digitally native lenders, in particular, are accelerating home-equity prescreen activity; their speed, experimentation, and product innovation are raising expectations for both relevance and simplicity in borrower outreach.  Meanwhile, pullbacks and exits by some large financial institutions have opened meaningful white space in the home-equity market, creating opportunities for others to capture unmet demand.  For lenders looking to compete, the playbook is becoming clearer: rapid testing and iteration, tight coordination between direct mail and digital follow-up, a strong focus on homeowner equity, and precise, data-driven targeting. The most effective campaigns align product design to well-defined segments – for example, borrowers with substantial equity, strong credit profiles, and established tenure – ensuring offers are both timely and highly relevant.  Prescreen vs. ITA: Why Targeting Wins  The shift from broad ITA to prescreen‑based campaigns might seem nuanced, but its implications are strategic:  Prescreen advantages:  Better alignment with borrower creditworthiness and property profile — because you are sending offers to those who meet risk and propensity criteria.  Improved conversion and campaign efficiency — by reducing wasted mailings to low‑probability recipients.  Lower marketing spend per funded loan — because you spend less to reach the right audience.  Faster speed‑to‑market — thanks to platforms that allow weekly refreshed data and custom lists. For example, Experian’s self‑service prescreen platform offers weekly data updates and FCRA‑compliant targeting.  Regulatory and operational clarity — prescreen infrastructure has matured, with aligned credit data, reason‑codes, and compliance built in.  ITA (Invitation to Apply) still has use cases:  When you want to cast a wider net (e.g., first‑time homebuyers, large volume builds)  When brand awareness is a goal rather than immediate action  When the product is straightforward and broader, not highly segmented  But the winning strategy in 2025 and beyond is data‑driven prescreen + targeted direct mail, especially in home equity. As one blog post notes, direct mail campaigns that are personalized can deliver up to ~44% stronger conversions compared with less personalized campaigns. Megaleads  Strategic Opportunities for Lenders & Marketing Teams  Based on the data and competitive shifts, here are actionable recommendations:  Expand Home Equity Prescreen Offers: With home equity direct mail offers now pushing ahead of first‑mortgage offers in volume (and with tappable equity reaching trillions), this channel is ripe. For instance, a recent BCG report estimates ~$18.3 trillion in tappable equity in the U.S. system. BCG Media Publications+1  Leverage the Player Exits: Large institutions reducing or exiting HELOC/home‑equity lines provide space for nimble lenders to increase direct‑mail volume and connect with households previously under‑targeted.  Integrate Multi‑Channel Touchpoints: While mail is the vehicle, the journey often involves digital follow‑up, landing pages, and timely calls. Studies show layering direct mail with digital channels improves results. Highnote+1  Use Data for Targeting, Not Just Volume: Utilize property, credit, income, and behavioral data (from providers like Experian) to identify segments like: homeowners with >30% equity, 5–10 years of tenure, credit score 700+, and interest in renovations or cash‑out use cases.  Speed Matters: Campaigns should be nimble. Weekly data refreshes, agile list creation, rapid mail deployment, and timely follow‑up matter in a competitive environment.  Measure & Optimize: Track response, conversion, ROI per piece. For example, what are funded loans per 1,000 mail pieces? Which segments convert better? Optimize creative, offer, timing.  Stay Compliant & Transparent: Prescreen offers must follow FCRA rules; mail pieces must clearly disclose terms. Consumers and regulators are increasingly sensitive to over‑targeting or over-personalization — balance personalization with respect and transparency.* Megaleads  Putting It All Together: Rethinking Your Direct‑Mail Strategy  If your marketing playbook still treats direct mail as a “safe‑bet, high‑volume fallback”, it’s time for an upgrade. Today’s borrowers expect relevance, personalization, and fast follow‑through. They are homeowners — not just buyers — and many are seeking home‑equity options rather than traditional purchase refis.  Lenders that find success in this space are likely to:  Use data and analytics (credit + property + behavior) to identify the right audience.  Deploy prescreen‑based campaigns rather than generic blanket offers.  Combine direct mail + digital + phone as an orchestrated funnel.  Monitor performance in near real‑time and iterate quickly.  Offer products aligned with what the borrower wants (e.g., interest‑only draw period HELOCs, fixed‑conversion options, etc).  Operate with speed, precision, and compliance.  As the market shifts, the channel is shifting too. Direct mail isn’t dead — it’s evolving, and those who invest in the right mix of data, targeting, creative, and execution stand to win.   Call to Action  Ready to elevate your direct‑mail and prescreen strategy? Contact Experian’s Mortgage & Housing solutions team to explore how our platform enables:  Weekly refreshed, bureau‑grade credit + property data  Self‑service prescreen campaign build and list generation  Custom segmentation using credit, equity, tenure, and product propensity  Compliance‑ready reason codes and targeting workflows* Visit: experian.com/mortgage or speak with your Experian account executive today.   Next in the Series  Blog Post 3 – “Beyond the HELOC: Why the Future of Home Equity Might Not Involve Loans at All”  *Clients are responsible for ensuring their own compliance with FCRA requirements. 

By: Perry DeFelice & Angad Paintal, Experian, and Michael Pyatski, IVolatility Freddie Mac’s November 2025 launch of Loan-Level Directed Collateral (LLDC) capabilities (details here) marks a significant advancement in mortgage-backed securities (MBS) capital markets. Historically, investors have been constrained by security-level pooling constructs that limit the expression of differentiated loan-level analytics. By allowing loan-level customization of Freddie pools & REMIC classes, LLDC empowers institutional investors to construct pools which reflect differentiated analytics—creating a competitive edge while simultaneously enhancing market-wide efficiency.  A historical lens: Evolution of MBS disclosure  The agency MBS market began its transformation in the 1980s with the release of pool-level data, enabling the rise of specified ("spec") pools that traded on unique characteristics like origination loan size, credit score at origination, or geography.  Specifications made the MBS market incrementally more efficient by allowing finer gradation of pricing for prepayment and credit risk.    The next leap came in 2013 with the public release of agency MBS loan-level data, which kicked off a new era of advanced analytics and precision modeling.  The introduction of loan-level data further improved pricing efficiency by allowing the evaluation of layered risk (ie, credit score + LTV) at the loan level.  Unlike agency MBS markets, non-agency MBS disclosure remains fragmented. Hundreds of issuers lack a standardized data format. Third-party aggregators attempt to normalize disparate trustee and servicer data, but uniformity and quality still lags behind agency disclosures. The rise of 144a private placements over the past decade has reversed transparency progress—despite broader data availability and technological breakthroughs.  The opacity of the growing 144a MBS market is particularly concerning and carries public policy implications, since market discipline for performance degradation is most efficiently meted out with greater transparency.  Despite AI-driven advances in data processing, disclosure remains stuck in an analog past. Borrower and property data remain static snapshots at origination, rarely updated. As a result, market participants operate with stale inputs, undermining the accuracy of risk assessments and pricing.   The Data Gap: What’s Missing in Current MBS Datasets  Across the MBS landscape, investors lack visibility into:  Borrowers' current credit health (beyond loan pay status)  Borrowers’ current income and DTI  Updated property valuations and lien statuses  Behavioral trends like refinance propensity, ie, how many mortgages has this borrower refinanced in the past?  Even state-of-the-art prepayment and pricing models frequently diverge from empirical performance. As shown in the table below, models often misalign with actual data from agency pools and inverse IO CMOs (IIOs):   *Source:  IVolatility MBS Data-Driven portal, and a prevalent Agency MBS valuation model A Data Renaissance: Experian’s Mortgage Loan Performance Dataset (MLP)  To address these shortcomings, Experian created the Mortgage Loan Performance Dataset (MLP), a joined dataset capturing real-time borrower credit behavior, loan performance, and subject property data. MLP covers nearly 100% of U.S. mortgage loans dating back to 2005.  MLP Highlights:  Current Credit Profile: Updated credit scores, credit inquiry activity (ie, is borrower shopping for a new mortgage?), non-mortgage debt balances and pay performance (student loan, auto loan, credit card, etc.)  Current modeled income and DTI  Behavioral History: Number of past refinances, payment habits (does this borrower pay off credit card balance in full each month?), utilization patterns  Property Insights: Current AVM, current junior liens (including those opened after the loan was securitized), total CLTV  With this richer dataset, investors can:  Improve credit and prepayment modeling accuracy  Create new spec pool stories (e.g., serial refinancer, credit revolver utilization, current CLTV inclusive of subsequent second liens, credit inquiry activity)  Overlay cohort-level data to bid more confidently on highly customized pools and REMICs structured under LLDC  Market Impacts: Efficiency and Equity  LLDC’s value lies in enabling more refined segmentation—particularly when enhanced with datasets like MLP. This facilitates better execution for originators and more precise pricing for investors. In turn, borrowers benefit from lower mortgage rates.  Importantly, MLP-driven segmentation could especially aid lower-income or weaker-credit borrowers. Currently, the less negatively-convex loans of these borrowers subsidize (from a pricing and rate perspective) the more negatively-convex loans of stronger credit, higher-income borrowers due to the averaging effect within generic pools. By identifying loans with better convexity (lower prepay likelihood), investors can price them more favorably, improving affordability in the form of lower mortgage rates for lower-income, weaker-credit borrowers.  Case Study: Predicting Prepayment with Credit Inquiry Data  In the coming weeks, we’ll provide illustrative analyses that highlight new fields and scores available in the MLP dataset.  To start, we’ll focus on perhaps the most intuitive datapoint for prepayment prediction:  mortgage credit inquiry activity by the borrower.  Specifically, credit inquiry activity is captured in a newly introduced field: Days Since Latest Mortgage Credit Inquiry.  Why It Matters:  Traditional prepayment models rely on widely available market-level data (e.g., PMMS, HPI, MBA Index) and loan characteristics (loan size, fixed vs. ARM, margin, etc.)  MLP offers new and scarce loan and borrower-level inputs, which provide additional forecasting power  Key Insight:  Borrowers with low current DTI (≤36%) are significantly more likely to refinance compared to those with high current DTI (>36%), and to do it faster after mortgage credit inquiry activity.  Note that the current DTI is available in MLP, but not in most MBS disclosures.  *Source: Experian Mortgage Loan Performance Dataset, hosted on the IVolatility MBS Data-Driven Portal This field is especially useful and practical for traders targeting specific mortgage cohorts (coupons, loan sizes, credit score range) for TBA roll trades, as an example.  Looking Ahead: A Richer Lens for MBS Analysis  This article is the first in a series exploring new data fields in the MLP dataset. Future installments will examine:  Prior refinance behavior   Total number of owned properties, credit card utilization, and payment behavior   Want to explore how MLP insights could improve your portfolio strategy?  Contact Experian to access the full MLP dataset and see your lift potential.  _____________________________________________________ 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. _____________________________________________________

Rental affordability in the U.S. isn’t just about rising prices—it’s about where those increases are happening. Some cities and states are becoming increasingly unaffordable compared to others, and renters are feeling the financial pressure differently across the country.  Not all rent increases are equal  National rent prices have increased by about 16% in two years, but where you live plays a huge role in how much of your paycheck goes toward housing. In places like California and Massachusetts, the average renter now spends over 56% of their income on rent. That’s nearly double the “affordable” threshold of 30%.  But even traditionally affordable states are feeling the heat. Oklahoma, Kentucky, and Louisiana all saw rent hikes between 6% and 10%—with Oklahoma topping out at 9.7%. These increases are hitting renters in places that used to be considered “safe” from housing inflation.  Regional breakdown:  Here’s how the rent-to-income ratio compares across regions:  West: Rent-to-income ratio of 46.4%  Northeast: 48.1%  South: 43% (but fastest-growing burden)  Midwest: 37.7% (still below the national average, but climbing fast)  Florida, for example, saw its rent-to-income ratio jump by 12.1% since 2023. Arizona isn’t far behind, with an 11.7% increase. These changes are tied to migration patterns—many people moved to these states during the pandemic, and now demand is far outpacing supply.  City-level surprises  Some of the biggest rent increases are happening in cities you might not expect:  Miami, FL: Up 21.1% YOY  Kansas City, MO: Up 16.7%  Louisville, KY: Up 14.2%  Chicago, OH: Up 13%  On the flip side, a few cities have seen rent drops:  Jacksonville, FL: Down 3%  Atlanta, GA: Down 2.2%  Austin, TX: Essentially flat  These shifts show how local economic factors and population trends can quickly change a market’s affordability.  More renters are moving—and struggling to settle  Another sign of pressure: renters are on the move. The percentage of renters with more than one lease has jumped since 2023, especially among Gen X and older millennials. People are relocating more often—sometimes chasing affordability, sometimes being priced out.  At the same time, vacancy rates are rising—from 6.6% to 7.1% nationally. That may sound good for renters, but it’s often a sign of mismatch: more units are being built, but not always where people can afford them.  The bottom line  If you’re a landlord or investor, these geographic insights matter. Rent pressure isn’t universal—but knowing where it’s concentrated can help you adjust screening, pricing, and retention strategies. For renters, this means being more informed and prepared before moving or signing a lease.  In our final post, we’ll explore the macro trends shaping the future—like mortgage rates, construction slowdowns, fraud risks, and how better data is helping landlords and lenders keep up. 

The U.S. housing market is no longer waiting on the sidelines. After enduring over two years of historically high mortgage rates, the Federal Reserve began implementing rate cuts in fall 2025, with additional reductions forecast for early 2026. For lenders, this marks more than a turning point—it’s a call to action. Whether you’re targeting first-time buyers, tracking refinance-ready loans, or watching affordability trends, today’s environment demands rapid, strategic adjustments. Rate cuts are fueling renewed demand Mortgage rates, which hovered around 7% for much of the past year, have begun to ease. Even a modest drop has the potential to unlock substantial borrower interest—particularly among the 4.4 million U.S. mortgages now “ripe” for refinance. Expect a spike in both rate-and-term refinances and cash-out activity, as homeowners look to lower payments or access equity. Lenders must scale up quickly, especially around digital capacity, prescreen targeting, and streamlined closings. Affordability is still a roadblock—Especially for younger renters Despite improving borrowing conditions, affordability remains a systemic challenge. The national rent-to-income (RTI) ratio stands at 46.8%, up 7.7% since early 2023. In high-cost states like California and Massachusetts, it exceeds 56%. Experian data reveals that 62% of renters fall into the low-to-moderate income category, spending over half their income on rent. Over 50% now fall into Near Prime or Subprime credit tiers, making alternative credit data—like rental payment history—vital for inclusive underwriting. Refinance isn't the only opportunity—Target first-time buyers strategically Gen Z is now the largest segment of the rental population, and many are financially strained yet aspirational. A major opportunity exists in helping these renters transition to homeownership using expanded credit models and customized offerings. With Federal Housing Finance Agency (FHFA)-approved models like VantageScore 4.0 and FICO 10T on the horizon, lenders should explore how newer scoring frameworks and rent payment reporting can increase access to mortgage credit. Region-specific strategies are more important than ever From Miami to Minneapolis, market conditions vary drastically. Some metros, like Kansas City (+16.7%) and Louisville (+14.2%), are experiencing double-digit rent growth, while cities like Atlanta and Jacksonville are seeing declines. Lenders must tailor outreach based on local affordability trends, migration patterns, and housing supply constraints. Dynamic analytics tools—like Experian’s Ascend or Mortgage Insights Dashboard—can guide regional strategy at scale. The supply side may not keep pace Even with rate cuts stimulating demand, housing supply could remain a bottleneck. Multifamily completions are outpacing starts 1.5 to 1, and single-family construction, though recovering, remains cautious. In markets with tight supply, reduced borrowing costs may drive up prices faster than inventory can absorb, exacerbating affordability for first-time buyers. What lenders should prioritize now • Build Refinance Infrastructure: Prepare for increased volume with instant income verification tools like Experian Verify to streamline processes. • Target First-Time Buyers: Use rental history, cashflow scores, and rent-to-income metrics to assess nontraditional credit applicants fairly. • Get Granular with Geography: Align product offerings with local affordability, vacancy rates, and rent growth. • Leverage Self-Service Prescreen Tools: Act on opportunities quickly using Experian’s agile targeting platforms. • Model with New Credit Scores: Take advantage of the Experian Score Choice Bundle to test VantageScore 4.0 and FICO 2 side by side. Final Thought: The market is not rebounding—It is realigning The current housing shift is not a return to old norms—it’s the start of a redefined landscape. Lenders who act decisively, invest in technology, and prioritize inclusivity will lead the next chapter in mortgage growth. Experian is here to support you—with data, insights, and tools designed for this very moment.

As we move into the final stretch of 2025, the U.S. housing market is balancing sustained, but stagnant originations volumes with softening credit performance. For mortgage lenders and servicers, this presents both challenges and opportunities. Experian’s highlights a housing market that is not in crisis but showing signs of strain that require attention and strategic adaptation. Identified risk trends: Escrow pressures and student loan headwinds Meanwhile, the return of student loan repayments is having a ripple effect across mortgage performance — particularly among borrowers with sub-660 credit scores and those already behind on student loans. These borrowers are exhibiting significantly higher mortgage delinquency rates, revealing an urgent need to track cross-credit dependencies more closely. In the home equity space, the delinquency picture is mixed. HELOC delinquencies have flattened, while HELOANs are experiencing a divergence — early-stage delinquencies are falling, but late-stage delinquencies are rising. These trends indicate relative stability in home equity credit performance, but attention should be paid to segments of the market, like securitized home equity, for deterioration in credit performance. Refinance revival: A glimmer of growth Despite these risk signals, growth is returning in key areas. Refinance activity is rebounding, driven by dips in Treasury yields and renewed borrower interest in lowering monthly payments. Originations are increasing, and mortgage direct mail marketing has resumed after a period of stagnation. Both prescreen and invitation-to-apply (ITA) campaigns are on the rise, signaling a re-engagement with the borrower market. Home equity lending is also heating up, particularly in the prescreen space, with fintechs aggressively scaling their outreach. This resurgence in marketing creates an opening for lenders — but only those equipped to act quickly. Market fundamentals: Why housing supply still lags Beneath these lending and marketing shifts lies a broader macroeconomic narrative. GDP growth is slowing, unemployment is creeping upward and inflation remains stubbornly high. Mortgage rates hover between six and seven percent, contributing to one of the most prominent constraints in today’s market: the lock-in effect. Over 80% of U.S. homeowners hold mortgage rates significantly below current levels, discouraging movement and keeping housing inventory tight. Even as new listings improved earlier this year, seasonal adjustments and elevated rates have brought supply back down. Construction activity remains uneven. While there’s been some progress in completions, overall new starts remain weak. Large-scale developers remain cautious, further constraining supply and sustaining price pressure in many markets. Strategic imperatives for lenders Given this context, what should lenders prioritize? First, portfolio risk management must evolve to keep pace with borrower realities. Custom risk models, proactive account reviews and early-warning systems can help surface emerging risks, especially among vulnerable cohorts with multiple debts or high debt-to-income ratios. Second, marketing strategies must become more agile. Investing in scalable tools like Experian’s self-service prescreen and/or account review enables faster execution, real-time list building, and more efficient targeting. With refinance activity picking up, this agility is key to capturing demand before it fades. Third, lenders must lead with data. From credit performance to macroeconomic indicators, strategic decisions need to be grounded in real-time insights. Aligning marketing, servicing, and risk teams around shared, data-driven intelligence will separate the winners from the rest. Bottom line: A controlled descent, not a crash In summary, the November 2025 housing market presents a picture of controlled deceleration, not a free fall. Borrowers are under pressure, but the system remains stable. For lenders, the message is clear: act now to optimize your portfolio, accelerate outreach and prepare for cyclical demand shifts. With the right strategies, lenders can not only weather the current environment but position themselves for the next wave of opportunity. This article uses data from both Experian Credit Bureau and Mintel: Global Market Intelligence & Research Agency

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 factor in rising escrow costs for home insurance and property taxes, 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. The 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.    

As fraud continues to rise in the rental housing market, tenant screening practices are evolving. In an earlier blog, I explored how Experian Observed DataTM can provide early indicators of income and employment consistency, offering screening companies a way to reduce reliance on costly or time-intensive verification methods.  In this follow-up, I explore two additional tools that strengthen the tenant screening process: Experian VerifyTM for Research Verifications and Experian Verify for Permissioned Verification's AI-powered Document Review. Used together, these solutions enable a layered approach that boosts both efficiency and prevention of fraud. Modernized Research Verifications Manual employment and income and employment checks—once the standard for tenant screening—are time-consuming and often inconsistent. Traditionally, screening companies had to reach out directly to employers and request proof of employment. While still useful, this method puts pressure on internal resources and is not always scalable.  To streamline manual verification, many organizations are partnering with third-party providers, especially those that take a digital-first approach. Outsourcing allows screening companies to delegate outreach, follow-ups, and fraud detection to specialized teams trained in document validation and employer communication. These services deliver the same insights internal teams would gather, while freeing up in-house resources for more strategic initiatives. By leveraging digital tools such as conversational AI, online forms, and automated workflows—combined with human oversight—digital-first vendors offer a more scalable and cost-effective alternative to fully manual processes. This approach not only reduces operational costs but also shortens turnaround times, helping screening companies respond faster without compromising accuracy or fraud resistance. Key advantages:[MJ1]  Reduces the burden on internal staff Ensures consistency and fraud awareness in document review Provides a reliable fallback when other verification tools return limited data This approach is especially valuable when initial data sources yield incomplete results and further confirmation is required. AI-Enhanced Document Upload and Review Another common scenario in tenant screening is the submission of income documents by the applicant, often in the form of paystubs or bank statements. Manual review of these documents is prone to error and increasingly vulnerable to sophisticated forgeries, including those generated by artificial intelligence. AI-powered document analysis tools are now helping screening companies process uploaded documents more securely and efficiently. These platforms typically work by: Allowing applicants to upload documents through a secure portal Using AI to scan for signs of tampering, fabrication, or inconsistency Returning standardized results that are easier to evaluate and compare By automating the detection of anomalies and potential fraud indicators, these tools reduce the workload for staff while improving the reliability of the review process. Benefits include: Faster review and turnaround times Improved fraud detection capabilities Greater consistency across applicants This method is especially useful when traditional employer APIs are unavailable or when screening companies need additional confirmation beyond initial data sources. A Layered Approach to Verification By combining different verification methods, screening companies can design workflows that adapt to a wide range of applicant profiles and risk scenarios. A layered strategy might include: Starting with an inexpensive source of income or employment data to identify likely matches Using AI-based document review when additional validation is needed Turning to manual research verifications only when necessary This cascading process allows screening companies to control costs while maintaining a strong defense against fraud. It also ensures that higher-cost methods are used only when the earlier steps do not provide enough confidence to proceed. Modern Challenges Require Modern Solutions Fraud in tenant screening is increasing rapidly. According to industry surveys, over 93 percent of screening companies have encountered fraud in the past year, and the majority have dealt with falsified income documentation. Traditional approaches, especially manual review, are no longer sufficient on their own. By rethinking verification strategies and incorporating modern tools like outsourced research verification and AI-enhanced document review, screening companies can reduce risk, improve efficiency, and better prioritize their resources. Learn More For organizations interested in implementing these types of verification tools, several providers—including Experian—offer services designed to support this layered approach. These solutions can help screening companies strike the right balance between cost, compliance, and fraud resistance. To learn more, visit experian.com/verify.

Income and employment verification fraud is surging in the tenant screening industry, putting traditional verification methods under intense pressure. As economic uncertainty grows and document forgery becomes more sophisticated, it's clear that legacy processes are no longer sufficient. Recent findings highlight the urgency for change. According to the NMHC Pulse Survey, 93.3% of property managers reported encountering fraud in the past year, with 84.3% citing falsified paystubs and fake employment references as the most common tactics. As AI-generated forgeries become increasingly convincing and accessible, relying solely on manual review is proving inadequate. A Shift in Strategy: Toward Smarter Income and Employment Verification Historically, tenant screeners have relied on methods such as manual document review, direct employer contact, payroll APIs, and verification of assets (VOA). While these remain important, they are no longer capable of keeping pace with today’s verification challenges. In response, many screening companies are exploring new income verification tools that offer improved fraud prevention, lower operational costs, and faster turnaround. These innovations include layered approaches that combine observed data, permissioned uploads, and automated fraud detection technologies. Introducing Experian Observed DataTM in Tenant Screening One emerging solution in the fight against rental application fraud is the use of observed data during tenant screening. This method uses collectively sourced insights to assess whether an applicant’s income and employment claims are likely to be accurate. Experian Observed Data is takes inputs from many sources including creditors, property managers and others. This type of data starts out as consumer stated data but is substantiated by third party creditors who have originated lending products and report on the performance of these products.   Although this method is not FCRA-compliant and cannot be used to approve or deny applications, it is highly effective as an early step in the screening process. Some sources of Experian Observed Data include a confidence score that can help screeners assess how closely an applicant’s stated information aligns with observed trends and can help screening companies to better assess their prioritization queue to determine if more data points are needed. Why Experian Observed Data Matters To combat fraud without driving up costs or slowing down the tenant screening process, screening companies need reliable, efficient tools. Experian Observed Data supports this need by offering a faster, more scalable approach to assessing risk. Key benefits include: Early detection of discrepancies in reported income or employment The ability to prioritize high-risk applications for further review A more cost-effective alternative before committing to premium verification services For instance, if an applicant has a strong credit report and clean background check, and Experian Observed Data supports their stated income, further verification may be unnecessary. If inconsistencies are flagged, screening companies can escalate to tools such as AI document analysis or direct outreach. Fraud Prevention Through Smarter Workflows The use of Experian Observed Data also aligns with a broader shift toward AI document fraud detection and layered verification strategies. Instead of applying the same tools to every application, screening companies can now implement decision trees that use lower-cost tools first, escalating only when risk or uncertainty increases. This adaptive approach is particularly relevant as screener companies strive to improve accuracy and efficiency at scale. By deploying Experian Observed Data as a first step, tenant screening professionals can better allocate resources while remaining vigilant against fraud Future Proofing Verificaiton As the income and employment verification landscape evolves, screening companies must move beyond legacy methods and adopt tools that are responsive to today’s challenges. Experian Observed Data provides a scalable, low friction starting point that supports smarter decision-making and better fraud detection. Coming to our next blog: We will explore how manual research verifications and AI-powered document upload solutions enhance the effectiveness of modern income verification tools, creating a more resilient and adaptable tenant screening process.  

Executive Summary The July 2025 housing market reveals a landscape of shifting consumer behaviors, evolving lender strategies, and continued strength in borrower performance—especially within home equity. Origination volumes have dipped slightly, but direct marketing, particularly through Invitation to Apply (ITA) campaigns, is accelerating. As key players exit the space, gaps are opening across both marketing and origination, creating clear opportunities for agile institutions. This phase signals both caution and potential. The winners will be those who refine their marketing, sharpen segmentation, and deploy smarter risk monitoring in real time. TL;DR Risk Profile: Mortgage and HELOC delinquencies remain low. Slight increases in 90+ DPD are not yet cause for concern. Mortgage Originations: Modestly down, but marketing remains aggressive. Invitation to Apply (ITA) volumes outpacing prescreen. Home Equity Originations: Stable originations, competitive marketing volumes. ITA volumes outpacing prescreen similar to mortgage. Opportunity: Targeted direct mail and refined segmentation are growth levers in both mortgage and home equity. Risk Environment: Resilient Yet Watchful Experian’s July data shows both mortgage and home equity delinquencies hovering at historically low levels. Early-stage delinquencies dropped in June, while late-stage (90+ days past due) nudged upward—still below thresholds signaling broader distress. HELOCs followed a similar path. Early-stage movement was slightly elevated but well within acceptable ranges, reinforcing borrower stability even in a high-rate, high-tariff environment. Takeaway: Creditworthiness remains strong, especially for real estate–backed portfolios, but sustained monitoring of 90+ DPD trends is smart risk management. Home Equity: Volume Holds, Competition Resets Home equity lending is undergoing a major strategic reshuffle. With a key market participant exiting the space, a significant share of both marketing and originations is now in flux. What’s happening: Direct mail volumes in home equity nearly match those in first mortgages—despite the latter holding larger balances. ITA volumes alone topped 8 million in May 2025. Total tappable home equity stands near $29.5 trillion, underscoring a massive opportunity.(source: Experian property data.) Lenders willing to recalibrate quickly can unlock high-intent borrowers—especially as more consumers seek cash flow flexibility without refinancing into higher rates.   Direct Mail and Offer Channel Trends The continued surge in ITA campaigns illustrates a broader market pivot. Lenders are favoring: Controlled timing and messaging Multichannel alignment Improved compliance flexibility May 2025 Mail Volumes: Offer Type Mortgage Home Equity ITA 29.2M 25.8M Prescreen 15.6M 19.0M Strategic Insights for Lenders 1. Invest in Personalized Offers Drive better response rates with prescreen or ITA campaigns. Leverage data assets like Experian ConsumerView for ITA’s for robust behavioral and lifestyle segmentation. For prescreen, achieve pinpoint-personalization with offers built on propensity models, property attributes, and credit characteristics. 2. Seize the Home Equity Opening Use urgency-based messaging to attract consumers searching for fast access to equity—without the complexity of a full refi. Additionally, as mentioned above, leverage propensity, credit, and property (i.e. equity) data to optimize your marketing spend. 3. Strengthen Risk Controls Even in a low-delinquency environment, vigilance matters. Account Review campaigns, custom scorecards, and real-time monitoring help stay ahead of rising 90+ DPD segments. 4. Benchmark Smarter Competitive intelligence is key. Evaluate offer volumes, audience segmentation, and marketing timing to refine your next campaign. FAQ Q: What does the exit of a major home equity player mean? A: It leaves a significant gap in both marketing activity and borrower targeting. Lenders able to act quickly can capture outsized share in a category rich with equity and demand. Q: How should lenders respond to the evolving risk profile? A: Continue to monitor performance closely, but focus on forward-looking indicators like trended data, income verification, and alternative credit signals. Conclusion The housing market in July 2025 presents a clear message: the fundamentals are sound, but the strategies are shifting. Those ready to optimize outreach by making smarter use of data will seize a disproportionate share in both mortgage and home equity. Want to stay ahead? Connect with Experian Mortgage Solutions for the insights, tools, and strategies to grow in today’s evolving lending environment.  

In 2025, home equity lending has re-emerged as a central theme in the American financial landscape—an evolution not driven by hype, but by hard data, economic realities, and consumer behavior. As homeowners grapple with inflation, rising consumer debt, and a persistent affordability crisis in housing, the home equity line of credit (HELOC) is gaining traction as a practical, flexible, and often misunderstood financial solution.