In May 2024, new guidelines were proposed by the Consumer Financial Protection Bureau (CFPB)  that would require Buy Now, Pay Later (BNPL) providers to share consumer payment information with credit reporting agencies (CRAs). Although data reporting about BNPL activity isn’t mandated through the Fair Credit Reporting Act, issuers have begun reporting payment information to CRAs.  While this proposed guidance from the CFPB focuses on BNPL activity, it signals a broader shift in how the credit landscape is evolving—particularly for lenders relying on a holistic view of consumer financial behavior.  As of June 2025, the CFPB has stated it isn’t enforcing its guidance on BNPL activity and it isn’t issuing new guidance.The direction is clear: credit evaluations are moving toward a more complete picture of how people manage their everyday spending and credit obligations. This includes recognizing consistent patterns in recurring, nontraditional payments—such as BNPL installments, rent, and utilities—and ensuring these behaviors can be factored into credit-related decisioning models to afford consumers appropriate recognition of their financial handling, as well as giving credit granters a comprehensive view.  BNPL reform mirrors the mortgage market’s credit overhaul  BNPL activity is increasingly being evaluated using the same reporting standards as other forms of consumer credit. This shift reflects a broader transformation in how lenders assess financial behavior.  Modern credit evaluations place greater emphasis on trended data that shows patterns in how consumers have used and managed credit over time. They also incorporate alternative payment history—such as rental and utility payments—creating a more complete view of a consumer’s financial habits. These approaches have demonstrated stronger predictive performance, particularly for individuals with limited traditional credit history.  Overall, the direction of credit evaluation is moving toward broader data inclusion, both historical and real-time, for a more holistic, consumer‑centric assessment.  Although the CFPB has delayed implementation of these models, the direction is clear. Credit scoring is moving toward broader data inclusion and more accurate, consumer-centric evaluation.  Why mortgage lenders should care about BNPL rules  Modern expectations for credit evaluations are shifting toward a more complete, past-and-present view of consumers’ everyday financial behaviors. This applies across lending decisions, including those made in the mortgage ecosystem.  When consistent patterns—such as on‑time rent payments, responsible installment management, and steady cash‑flow habits—are visible, they can help create a clearer picture of an individual’s financial reliability. These signals are becoming increasingly important, especially as more future homebuyers have limited traditional credit histories.  Consider the impact of incorporating rental payment data:  More than 83% of consumers who had rental payments included in their credit files saw an improvement in their scores  15.1% of those individuals were previously unscoreable and gained a score  On average, consumers saw a 3.9% score increase after rental data was added  As more people use BNPL services and other nontraditional financial tools, it becomes increasingly important for mortgage lenders to evolve their evaluation inputs to reflect how consumers manage their financial lives today.  How mortgage lenders can prepare  With mounting regulatory and industry pressure, lenders need to move from passive observation to proactive implementation. Here’s how to begin:  1. Adopt the Experian Score Choice Bundle This solution provides both FICO 2 and VantageScore 4.0 on every mortgage transaction at no additional cost. It allows lenders to:  Compare and test new models without operational disruption  Maintain compliance with GSE guidelines  Serve more borrowers by evaluating modern credit behaviors  2. Score cash flow with decision-grade rigor Plaid captures the data; Experian turns it into decision‑ready insight. Experian’s Cashflow Attributes and Cashflow Score provide:  Decision-grade scoring built on permissioned transaction data  Clear reason codes for explainability  Stronger predictive lift backed by portfolio testing  With a growing majority of high-volume mortgage originators now implementing digital income and employment verification tools like Experian Verify, the industry is rapidly transitioning toward automated, real-time lending. As Experian positions it: “Plaid collects the signal. Experian makes it decision-ready.”  3. Align with market and regulatory trends  The FHFA’s shift to VantageScore 4.0 and FICO 10T, along with the emergence of cash flow payloads, signals that credit reporting is entering a new phase. Lenders proactively modernizing their credit strategies will be positioned to:  Expand access to credit for millions of underserved but creditworthy consumers  Reduce risk through more complete borrower insights  Stay ahead of compliance and investor expectations  With over 53% of high-volume mortgage originators already using Experian Verify, the industry is beginning to embrace this transformation. Broader adoption of inclusive scoring and permissioned data remains a critical next step.  Final thought  The CFPB’s action on BNPL, while not enforced at the moment, is not an isolated event—it is a preview of the future. The mortgage industry must prepare now for a world where rent, cash flow, and alternative financial behavior shape the foundation of credit scoring. Lenders who act early will not only meet regulatory expectations but will gain a strategic advantage in serving tomorrow’s homebuyers.  Experian is ready to support this shift with data, tools, and scoring models built for the next era of mortgage lending.  Start testing modern credit scoring strategies now—and let Experian show you the lift on your borrower population.   

As the U.S. rental housing market moves through 2026, renters, landlords, and property management companies face an increasingly complex operating environment. Elevated housing costs, economic uncertainty, slowing construction activity, and a rapidly evolving fraud landscape are converging to reshape both risk and opportunity across the rental ecosystem.   At the same time, advances in data, analytics, and verification technologies are equipping housing professionals with new tools to adapt — shifting decision‑making from reactive to proactive at a moment when precision matters most.  Mortgage rates continue to constrain housing mobility  One of the most significant structural forces supporting rental demand remains the cost of homeownership. In early 2025, the average 30‑year fixed mortgage rate hovered near 7%, with Freddie Mac’s weekly survey reporting a rate of 7.04% for the week of January 16, 2025. The report also showed the year beginning near 7% before ending at 6.15% (Freddie Mac, 2025a, 2025b).    This environment has created a pronounced lock‑in effect: homeowners with pandemic‑era low fixed mortgage rates are reluctant to sell, limiting for‑sale inventory and suppressing turnover (Federal Housing Finance Agency [FHFA], 2024; Bankrate, 2025). For renters, this results in longer tenures and fewer pathways to homeownership. For landlords and lenders, it reinforces expectations that rental demand will remain elevated well into 2026, even if mortgage rates ease modestly.  Rental housing supply faces structural constraints  Despite strong rental demand, rental housing supply growth remains uneven. Multifamily development has slowed as financing costs and construction expenses have risen. Industry data indicate that multifamily units under construction fell roughly 20% year over year by early 2025, while completions have outpaced new starts—approximately 1.5 apartments completed for every one that begins construction on a three‑month moving‑average basis (Nanayakkara Skillington, 2025). Forecasts from Yardi Matrix pointed to elevated completions in 2025, followed by a notable slowdown in 2026, with starts continuing to slump (Dale, 2025). Absent a meaningful acceleration in new construction, these dynamics are likely to sustain pressure on rents and intensify affordability challenges, particularly in high‑growth and high‑migration markets (Joint Center for Housing Studies, 2025).  Fraud risk is escalating in a digital-first rental market  As rental transactions increasingly move online, fraud has become a fast‑growing operational risk for property managers and owners. The Federal Trade Commission’s Consumer Sentinel data show sustained reports of identity theft and imposter scams (Federal Trade Commission [FTC], 2024), while industry surveys identify account takeover, payment fraud, and synthetic identities as some of the most frequently encountered issues (Experian, 2023). From 2024 to 2025, housing and real estate professionals reported rising exposure to AI‑enabled schemes—including deepfake voices, manipulated documents, and increasingly sophisticated application fraud (Housing Wire, 2025; Veriff, 2025; First American, 2025).  As digital leasing accelerates, robust identity verification and fraud prevention have become core components of sustainable portfolio management. FTC Consumer Sentinel data continue to highlight persistent patterns of identity theft and imposter scams (FTC, 2024), and industry research consistently shows that account takeover, payment fraud, and synthetic identities remain significant operational threats (Experian, 2023). Between 2024 and 2025, housing professionals noted a growing prevalence of AI‑enabled fraud techniques, such as deepfake audio, falsified documents, and advanced application manipulation (HousingWire, 2025; Veriff, 2025; First American, 2025).  Data and analytics are becoming the defining advantage  Access to high‑quality data and real‑time insights is increasingly decisive. Data‑driven solutions enable rental housing professionals to move beyond static screening and manual processes, supporting continuous risk assessment and smarter decision‑making. These capabilities allow housing providers to evaluate applicants and portfolios with greater accuracy, reduce operational friction, and respond more proactively to emerging risks—making data and analytics a defining advantage across the rental housing ecosystem.  Rent reporting as a credit building and risk signal building and risk signal  Rental payment history has emerged as a valuable indicator of consumer financial behavior. Surveys and evaluations show strong renter interest in having on‑time rent payments included in credit scores, and many participants experience measurable benefits. For example, Fannie Mae reports that more than 80% of renters want rent payments factored into credit scoring models (Fannie Mae, n.d.). Randomized trials also demonstrate increased credit visibility and movement into near‑prime tiers for previously unscorable consumers (Theodos, Teles, & Leiberman, 2025; Credit Builders Alliance, 2025). For property managers and owners, this creates a dual benefit: renters gain meaningful credit‑building opportunities, while housing providers gain a deeper, more reliable signal of payment behavior beyond traditional credit files.  Smarter screening and verification  Income and employment verification remain among the most critical—and historically inefficient—steps in the rental lifecycle. Digital verification tools that leverage payroll and employment databases, along with consent‑based bank data, significantly reduce friction, deliver faster decisions, and help mitigate fraud by validating applicant information at the source (Truework, 2024; MeasureOne, n.d.; U.S. Government Accountability Office [GAO], 2025). As application volumes rise, automated verification is becoming a baseline requirement rather than a competitive differentiator. These tools enhance accuracy, streamline workflows, and strengthen fraud prevention—capabilities that are increasingly essential as application tactics grow more advanced.  What to watch as the market moves into 2026  Looking ahead, three trends are likely to shape the rental housing market over the next 12 to 18 months:    Sustained rental demand amid elevated mortgage rates and constrained for‑sale inventory, as higher borrowing costs continue to limit mobility and suppress housing turnover (Freddie Mac, 2025a; Federal Housing Finance Agency [FHFA], 2024).    Widening affordability gaps, with rent‑to‑income pressures intensifying—particularly in high‑cost and high‑growth regions (Joint Center for Housing Studies, 2025).    Data‑driven decision‑making is becoming standard across screening, pricing, fraud prevention, and portfolio monitoring, reflecting broader industry adoption of automated tools and analytics (U.S. Government Accountability Office [GAO], 2025; Snappt, 2025).   Final perspective  The U.S. rental housing market in 2026 is defined by both complexity and opportunity. Success will depend on the ability to adapt quickly, manage risk proactively, and deploy data‑driven solutions with precision. For renters, tools such as rent reporting offer pathways to greater financial stability and transparency. Ultimately, this moment is about resilience, readiness, and the systems that will shape rental housing outcomes well into the next cycle. Organizations that invest now in smarter data, stronger controls, and forward‑looking strategies will be best positioned to navigate what comes next—for themselves and for the broader rental housing ecosystem. 

Mortgage rates remain elevated by historical standards: the average 30-year fixed rate ended 2025 at 6.15% (Freddie Mac’s PMMS), after spending much of the year closer to 7% (52-week high ≈ 7.04%) (Freddie Mac, 2025; Mortgage News Daily, 2025). At the same time, the Federal Reserve’s December 2025 Summary of Economic Projections signaled a modest easing path into 2026 (median fed funds projection 3.4% at end-2026), reinforcing expectations of lower borrowing costs ahead rather than an immediate return to pre-2022 conditions (Federal Reserve, 2025). Affordability pressures persist and vary widely by metro and region: rent-to-income ratios in many Midwestern markets are below 30%, while parts of the Northeast (e.g., New York City) exceed 50% of income for a typical renter household (Moody’s Analytics, 2023; 2025). Given this fragmentation, national averages no longer provide sufficient guidance. Lenders need a data-driven playbook that translates insight into action across the lending lifecycle.  Pillar 1: Borrower insights  Today’s renter profile skews younger: Gen Z already accounts for ~30.5% of renters and, together with younger millennials (under 35), represents over half of the rental population (Experian, 2025). Zillow’s Consumer Housing Trends Report similarly shows Gen Z makes up 25% of all renters and 47% of recent movers—evidence that the next cohort of first-time buyers is emerging from today’s rental pool (Zillow Research, 2024). Traditional credit files can miss reliable payment behavior. Both Fannie Mae and Freddie Mac now consider positive rent payment history in automated underwriting—using bank or payroll-verified data to augment limited credit histories—improving access for qualified renters (Fannie Mae, 2025; Freddie Mac, 2025). Data-driven edge: Broader borrower views—incorporating verified rent payments, student loan performance, and alternative credit signals—help identify “hidden prime” consumers and responsibly expand the addressable market.  Pillar 2: Operational efficiency  Margin pressure is persistent, and manual income/employment verification remains a top pain point: manual methods can take 30 minutes to several days, raise costs, and increase drop-offs (MeridianLink, 2025). Modern VOE/VOI solutions—e.g., Mastercard Open Finance (Finicity/Argyle), Truework—deliver GSE-accepted digital verifications that reduce friction, lower per-loan costs, and provide rep/warranty relief when validations succeed ( Mastercard; Business Wire/Morningstar). Data-driven edge: Verification and documentation automation enables speed, consistency, and scalability without proportional staffing or risk increases.  Pillar 3: Geographic precision  Affordability is deeply local. The national rent-to-income ratio has recently eased back toward ~27–30%, but disparities persist: several Midwest markets track below 30%, while New York City reaches ~67% and Miami exceeds 40% (Moody’s Analytics, 2023; 2025). Recent rent reports also show metros like Miami ranking as least affordable and others (e.g., Austin) more affordable for typical renter incomes, underscoring the need for metro-level targeting (Realtor.com, 2025). Data-driven edge: Market-level data—local affordability, migration, inventory, and labor trends—helps focus growth where demand is most likely to convert and perform over time.  Pillar 4: Refinance readiness  Refinance activity is muted but not gone. With rates dipping from 2025 highs, millions are positioned to benefit: as of Nov. 2025, about 4.1 million mortgage holders were “in the money” (≥ 75 bps savings), including 1.7 million highly qualified candidates; the cohort could grow toward ~5 million with small additional rate declines (ICE Mortgage Technology, 2025). Homeowners also held $11.2 trillion in tappable equity entering Q4 2025, supporting additional refinance and home-equity lending opportunities (ICE Mortgage Technology, 2025). Data-driven edge: Segment portfolios by rate sensitivity, pre-model operational capacity, and streamline digital processes to capture volume quickly while preserving experience.  Bringing it together  These four pillars—borrower insights, operational efficiency, geographic precision, and refinance readiness—form a unified framework for outperforming peers in today’s housing market. Lenders that operationalize this approach will be better positioned to: • Serve more borrowers responsibly by leveraging verified rent and payroll data to expand access (Fannie Mae; Freddie Mac). • Manage risk with greater precision through automated verifications and underwriting validations (Mastercard). • Build sustainable regional strength by deploying resources in metros where affordability and demand align (Moody’s; Realtor.com). • Capture refinance demand at scale as candidates and tappable equity expand when rates ease (ICE Mortgage Technology). The housing market is shifting—not back to what it was, but toward something more fragmented and data-dependent. Lenders who build strategy on insight rather than instinct will define the next generation of market leaders.  References  Experian. (2025, January 10). The shifting demographics of today’s renters. https://www.experian.com/blogs/insights/the-shifting-demographics-of-todays-renters/  Federal Reserve Board. (2025, December 10). Summary of Economic Projections (Table PDF). https://www.federalreserve.gov/monetarypolicy/files/fomcprojtabl20251210.pdf  Freddie Mac. (2025, December 31). Primary Mortgage Market Survey® (PMMS®) weekly data. FRED series MORTGAGE30US. https://fred.stlouisfed.org/series/MORTGAGE30US/  Fannie Mae. (2025, January). FAQs: Positive rent payment history in Desktop Underwriter. https://singlefamily.fanniemae.com/originating-underwriting/faqs-positive-rent-payment-history-desktop-underwriter  ICE Mortgage Technology. (2025, November 10). November 2025 Mortgage Monitor (press release & report). https://mortgagetech.ice.com/resources/data-reports/november-2025-mortgage-monitor  Mastercard. (2024, June 25; updated September 23, 2024). How data-enabled income and employment verifications deliver smarter, seamless financial experiences. https://www.mastercard.com/us/en/news-and-trends/Insights/2024/data-enabled-income-and-employment-verifications-deliver-smarter,-seamless-financial-experiences.html  MeridianLink. (2025, April 8). Instant verification: Rethinking income and employment tools. https://www.meridianlink.com/blog/its-time-to-take-a-new-look-at-income-and-employment-verification-tools/  Moody’s Analytics. (2023, November 27). 30% of income on rent remains the norm in U.S. metros (Data story). https://www.moodys.com/web/en/us/insights/data-stories/q3-2023-us-rental-housing-affordability.html  Moody’s CRE. (2025, March 11). Q4 2024 housing affordability update. https://www.moodyscre.com/insights/cre-trends/q4-2024-housing-affordability-update/  Mortgage News Daily. (2026, January 2). Freddie Mac mortgage rates—weekly survey (historic table). https://www.mortgagenewsdaily.com/mortgage-rates/freddie-mac  Realtor.com Economics. (2025, October 14). September 2025 rental report: Rental affordability improved compared to a year ago. https://www.realtor.com/research/september-2025-rent/  Zillow Research. (2024, October 14). Renters: Results from the Zillow Consumer Housing Trends Report 2024. https://www.zillow.com/research/renters-housing-trends-report-2024-34387/ 

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.     

The U.S. rental housing market has offered a clear window into broader economic pressures shaping household finances. Renting is no longer a short-term phase for many Americans; it has become a longer-term reality across generations. Rising housing costs, elevated interest rates, and persistent debt burdens are reshaping who rents, how long they rent, and the financial resilience of the rental population. For landlords, lenders, and housing stakeholders, understanding these shifts is essential not only for near-term risk management but also for anticipating how today’s renters will influence tomorrow’s housing demand.  A rental market increasingly defined by younger households  Younger Americans continue to dominate the rental landscape. More than half of U.S. renters are now under the age of 35, with Generation Z accounting for approximately one-third of the total rental population, according to Experian’s 2025 State of the Rental Housing Market report. While renting has always served as a starting point for younger households, the length of time spent renting is increasing. High home prices, ongoing student loan repayment obligations and mortgage rates that remain well above pre-2022 norms have delayed the transition to homeownership for many first-time buyers. As a result, renting has shifted from a temporary necessity to a longer-term financial strategy, often by default rather than by choice.  At the same time, renting is no longer confined to younger demographics. Business Insider reports that since 2023, the population of renters aged 44 and older has grown, reflecting a combination of affordability constraints and evolving lifestyle preferences.  Rent growth continues to outpace income  Affordability remains the defining challenge of the rental market. Experian data shows that average monthly rent has increased steadily over the past two years, rising from approximately $1,520 in early 2023 to roughly $1,760 by early 2025. Over the same period, the average renter’s income has stagnated or declined slightly, hovering near $51,600 annually. The combined effect of rising rents and softening income is a widening affordability gap. The national rent-to-income (RTI) ratio now averages 46.8%, far exceeding the 30% threshold commonly used to define housing affordability, according to Experian’s 2025 analysis.  Credit profiles reflect mounting financial pressure  As housing costs rise, renters’ financial health is increasingly under strain. Experian’s rental market insights indicate that more than half of renters now fall within Near Prime or Subprime credit tiers, representing a meaningful increase since 2023. This shift does not necessarily signal declining financial responsibility. Instead, it reflects younger and thinner credit files, higher overall debt balances, and sustained cost-of-living pressures. Experian also reports that renters paying more than $1,000 per month have experienced a double-digit increase in negative payment activity over the past two years.  Understanding payment priorities when budgets tighten  When households face financial strain, trade-offs become unavoidable. According to payment hierarchy data published by the Consumer Financial Protection Bureau (CFPB), renters tend to prioritize auto loans first, followed by personal loans and student loans. Rent payments and unsecured revolving credit, such as credit cards, often fall lower on the priority list. This dynamic helps explain why rent delinquencies can rise even among renters who remain current on other financial obligations.  Why Experian?  As the rental market evolves, having a clear, data-driven view of renter behavior is more critical than ever. Experian helps landlords, lenders, and housing stakeholders understand shifting demographics, affordability pressures, and payment priorities through trusted data and advanced analytics. With deeper insight into today’s renters, businesses can better manage risk, uncover opportunity, and prepare for what’s next in housing demand.   

The digital acceleration of the mortgage and rental industries has transformed how we verify income and employment—but it has also elevated the risks. As fraud grows in sophistication, lenders and verification providers alike must re-examine how they source, validate, and secure consumer data.  In this new landscape, real-time trust requires real-time data. That’s why Experian Verify (EV) has embraced a transactional, on-demand approach—often referred to as the “Go Fetch” model—which we believe is fundamental to building a safer, more resilient verification infrastructure.   Why Legacy Models Leave Gaps  Many verification providers still rely on a “data-at-rest” model, where employment or income data is stored indefinitely in static databases. This approach creates a prime target for attackers and increases the risk of data becoming outdated, incomplete, or even manipulated by bad actors. Reducing the number of places where employment data is stored significantly strengthens security.  Traditional models that maintain large databases can also introduce confirmation bias. They often send both the individual’s personally identifiable information (PII) and employer name to data partners, which can open the door to synthetic identities or fraudulent employer match backs. In fact, synthetic ID fraud accounted for 27% of business fraud cases in 2023, and by 2024, more than 70% of U.S. businesses identified deepfakes and AI-generated fraud as top threats. (https://www.experianplc.com/newsroom/press-releases/2024/new-experian-report-reveals-generative-ai--deepfakes-and-cybercr)  Some legacy verification providers still transmit both PII and employer details when requesting information. At Experian, we take a different approach: we search based on the consumer rather than the employer, and we pin—that is, cross‑check—the submitted consumer data against Experian credit file information to verify authenticity from the start.  Experian Employer Services maintains a secure copy of payroll data provided by our clients and updates it regularly. We have live, ongoing connections with employers and refresh data every two weeks directly from the source when payroll information is received. We never use stale data; every search pulls fresh, verified information.   The “Go Fetch” Model: Built for a Modern Threat Environment  In contrast, Experian Verify uses a real-time “Go Fetch” model, requesting data directly from the sources of truth at the time of the inquiry. No stale databases. No guessing games. This method reduces the window for fraud and ensures accuracy by design.   For each Experian Verify transaction, the following ‘Go Fetch’ approach and controls are applied:  Employment and income data are sourced in real-time with APIs from employers via Experian Employer Services (EES) and vetted payroll partners.  The PII data from the inquiry and the PII data returned from each data provider each undergo a pinning process, which cross-references the multiple PII data elements with Experian credit data to validate the identity of the individual and confirm the correct individual's data is being returned by each data provider, for each employment record returned.  Any income/employment data for which the second pin (based on data from the data provider) does not match the original first pin (from the inquiry) is disregarded to mitigate any risk of fat fingers/human error resulting in an incorrect consumer’s data on a VOIE report.  This multi-stage pinning process is more robust than a hard match on SSN and results in fewer errors. This not only minimizes the risk of bad data—it blocks it before it enters the pipeline.  More Than Technology: Trust Through Governance  Trustworthy data isn’t just about speed—it’s about the quality and integrity of the source. Experian Verify only partners with enterprise payroll providers and employers who pass rigorous onboarding and credentialing requirements to connect to Experian systems. This ensures we’re sourcing data from legitimate entities, helping prevent “fake employer” vectors used in synthetic employment schemes.  On top of this, data reasonability checks are run on every response, flagging anomalies like:  End dates before start dates  Net income exceeding gross income  Illogical or invalid birthdates  Any inconsistencies prompt an internal investigation, and where necessary, Experian Verify works directly with the data provider to resolve discrepancies—further reducing the propagation of fraudulent data.  Further, minimum field checks are performed on every response, which ensures the minimum data necessary is returned before delivering to the client. This helps provide an additional safeguard on the data received from Data Providers, providing reasonable assurance that the data delivered to clients can be used in their decisioning flow.    Industry Recommendation: A Call for Real-Time Integrity  As more lending moves online and fraudsters grow more creative, the verification industry must evolve. Experian advocates for a new standard, built on these principles:  Fetch data in real-time from sources of truth—don’t store it at rest.  Avoid employer name matching, which can inadvertently validate fake entities.  Validate PII match using multiple data elements instead of any hard match logic.  Automated reasonability & minimum field checks, monitored and investigated by human oversight for flagged issues.   Final Thought: Secure Growth Requires Secure Data  In an era where risk moves fast, stale data is a liability. Real-time models like Experian Verify’s “Go Fetch” approach do more than deliver speed—they help lenders make decisions with greater confidence, mitigate exposure to fraud, and ultimately, protect both borrowers and the institutions that serve them.  If trust is the foundation of lending, then real-time integrity must be the framework we build it on.   

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