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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/ 

For many banks, first-party fraud has become a silent drain on profitability. On paper, it often looks like classic credit risk: an account books, goes delinquent, and ultimately charges off. But a growing share of those early charge-offs is driven by something else entirely: customers who never intended to pay you back. That distinction matters. When first-party fraud is misclassified as credit risk, banks risk overstating credit loss, understating fraud exposure, and missing opportunities to intervene earlier.  In our recent Consumer Banker Association (CBA) partner webinar, “Fraud or Financial Distress? How to Differentiate Fraud and Credit Risk Early,” Experian shared new data and analytics to help fraud, risk and collections leaders see this problem more clearly. This post summarizes key themes from the webinar and points you to the full report and on-demand webinar for deeper insight. Why first-party fraud is a growing issue for banks  Banks are seeing rising early losses, especially in digital channels. But those losses do not always behave like traditional credit deterioration. Several trends are contributing:  More accounts opened and funded digitally  Increased use of synthetic or manipulated identities  Economic pressure on consumers and small businesses  More sophisticated misuse of legitimate credentials  When these patterns are lumped into credit risk, banks can experience:  Inflation of credit loss estimates and reserves  Underinvestment in fraud controls and analytics  Blurred visibility into what is truly driving performance   Treating first-party fraud as a distinct problem is the first step toward solving it.  First-payment default: a clearer view of intent  Traditional credit models are designed to answer, “Can this customer pay?” and “How likely are they to roll into delinquency over time?” They are not designed to answer, “Did this customer ever intend to pay?” To help banks get closer to that question, Experian uses first-payment default (FPD) as a key indicator. At a high level, FPD focuses on accounts that become seriously delinquent early in their lifecycle and do not meaningfully recover.  The principle is straightforward:  A legitimate borrower under stress is more likely to miss payments later, with periods of cure and relapse.  A first-party fraudster is more likely to default quickly and never get back on track.  By focusing on FPD patterns, banks can start to separate cases that look like genuine financial distress from those that are more consistent with deceptive intent.  The full report explains how FPD is defined, how it varies by product, and how it can be used to sharpen bank fraud and credit strategies. Beyond FPD: building a richer fraud signal  FPD alone is not enough to classify first-party fraud. In practice, leading banks are layering FPD with behavioral, application and identity indicators to build a more reliable picture. At a conceptual level, these indicators can include:  Early delinquency and straight-roll behavior  Utilization and credit mix that do not align with stated profile  Unusual income, employment, or application characteristics High-risk channels, devices, or locations at application Patterns of disputes or behaviors that suggest abuse  The power comes from how these signals interact, not from any one data point. The report and webinar walk through how these indicators can be combined into fraud analytics and how they perform across key banking products.  Why it matters across fraud, credit and collections Getting first-party fraud right is not just about fraud loss. It impacts multiple parts of the bank. Fraud strategy Well-defined quantification of first-party fraud helps fraud leaders make the case for investments in identity verification, device intelligence, and other early lifecycle controls, especially in digital account opening and digital lending. Credit risk and capital planning When fraud and credit losses are blended, credit models and reserves can be distorted. Separating first-party fraud provides risk teams a cleaner view of true credit performance and supports better capital planning.  Collections and customer treatment Customers in genuine financial distress need different treatment paths than those who never intended to pay. Better segmentation supports more appropriate outreach, hardship programs, and collections strategies, while reserving firmer actions for abuse.  Executive and board reporting Leadership teams increasingly want to understand what portion of loss is being driven by fraud versus credit. Credible data improves discussions around risk appetite and return on capital.  What leading banks are doing differently  In our work with financial institutions, several common practices have emerged among banks that are getting ahead of first-party fraud: 1. Defining first-party fraud explicitly They establish clear definitions and tracking for first-party fraud across key products instead of leaving it buried in credit loss categories.  2. Embedding FPD segmentation into analytics They use FPD-based views in their monitoring and reporting, particularly in the first 6–12 months on book, to better understand early loss behavior.  3. Unifying fraud and credit decisioning Rather than separate strategies that may conflict, they adopt a more unified decisioning framework that considers both fraud and credit risk when approving accounts, setting limits and managing exposure.  4. Leveraging identity and device data They bring in noncredit data — identity risk, device intelligence, application behavior — to complement traditional credit information and strengthen models.  5. Benchmarking performance against peers They use external benchmarks for first-party fraud loss rates and incident sizes to calibrate their risk posture and investment decisions.  The post is meant as a high-level overview. The real value for your teams will be in the detailed benchmarks, charts and examples in the full report and the discussion in the webinar.  If your teams are asking whether rising early losses are driven by fraud or financial distress, this is the moment to look deeper at first-party fraud.  Download the report: “First-party fraud: The most common culprit”  Explore detailed benchmarks for first-party fraud across banking products, see how first-payment default and other indicators are defined and applied, and review examples you can bring into your own internal discussions.  Download the report Watch the on-demand CBA webinar: “Fraud or Financial Distress? How to Differentiate Fraud and Credit Risk Early”  Hear Experian experts walk through real bank scenarios, FPD analytics and practical steps for integrating first-party fraud intelligence into your fraud, credit, and collections strategies.  Watch the webinar First-party fraud is likely already embedded in your early credit losses. With the right analytics and definitions, banks can uncover the true drivers, reduce hidden fraud exposure, and better support customers facing genuine financial hardship.

Financial security has become one of the most pressing concerns in today’s workforce. Rising living costs, higher debt, market volatility and an ever-growing threat of identity theft are putting pressure on employees across every income level. For employers, this shift presents both a challenge and an opportunity: how to meaningfully support employees’ financial well-being in a way that drives engagement, loyalty and long-term success. The reality is clear. Traditional benefits alone are no longer enough. While a steady paycheck, insurance coverage, and retirement plans remain essential, employees increasingly expect their employers to play a more active role in helping them manage day-to-day finances, build credit confidence and protect their identities. When financial stress goes unaddressed, it impacts productivity, mental health and retention, and employers feel the effects just as strongly as their people do. Why fragmented benefits fall short Many organizations offer pieces of financial protection, but those offerings are often disconnected. Identity protection may stop at basic monitoring and alerts. Credit education is frequently limited to static resources that don’t reflect real-life financial behavior. Financial wellness tools, when available, are often treated as optional perks rather than foundational benefits. The problem isn’t a lack of tools; it’s a lack of connection. Employees don’t need more standalone solutions; they need integrated support that meets them where they are and evolves with their financial lives. The rise of all-in-one financial protection Forward-thinking employers are redefining their benefits strategies by adopting a holistic approach to financial well-being. The new standard combines three essential pillars into a single, cohesive experience: Credit education helps employees understand their credit profiles, build healthier habits and make informed financial decisions that unlock better opportunities. Financial wellness tools provide personalized guidance for budgeting, saving, managing debt and planning for the future, reducing stress and improving confidence along the way. Identity protection safeguards employees against fraud and cyber threats with proactive monitoring, alerts and hands-on recovery support when it matters most. When these elements work together, employees are better equipped to protect their paychecks, secure their identities and plan for long-term stability. The result is a workforce that feels supported, empowered and engaged. Turning financial well-being into a strategic advantage At Experian®, we believe financial well-being should be a strategic advantage, not an afterthought. Our all-in-one employee benefits solutions are designed to deliver measurable impact, from improved credit outcomes and reduced financial stress to stronger engagement and retention. By partnering with us, employers can offer a seamless, scalable and trusted experience that supports employees through every stage of their financial journey, while reinforcing their commitment to employee well-being. Download the full report Want to learn more about how credit education, financial wellness and identity protection come together to create a stronger benefits strategy? Download our report to explore the data, insights and strategies shaping the future of employee financial benefits and how your organization can lead the way. Download now

Financial services leaders are dealing with numerous pressures at the same time. These growing challenges for financial services organizations include sophisticated fraud, rapid Artificial Intelligence (AI) adoption without clear regulatory direction, rising customer expectations and the need for compliant, sustainable growth. Businesses are rethinking how they manage risk, growth and customer trust. These financial industry challenges are no longer confined to internal risk teams. They directly impact long-term customer loyalty. How organizations navigate these challenges will determine how effectively they deliver value to their customers. We’ve outlined the six challenges for financial services oranizations that consistently rank highest among industry leaders today. Challenge 1: Fraud is becoming harder to detect and eroding customer trust 72% of business leaders expect AI-generated fraud and deepfakes to be major challenges by 20261 As fraud tactics evolve quickly, driven in part by AI, customers are being targeted through identity-based attacks from account takeovers to synthetic identities and misuse of personal information. When these threats go undetected, or when legitimate activity is incorrectly flagged, the result isn’t just financial loss. It’s a breakdown of trust. Organizations that want to stay ahead must move beyond isolated fraud controls. By embedding identity management and monitoring into the customer experience, organizations can move from reactive fraud response to proactive identity protection. Identity theft protection and monitoring help organizations turn fraud prevention into a visible, trust-building experience for customers — offering early alerts, guidance, and peace of mind when identity risks arise. Challenge 2: AI decisions must be trusted by customers, not just regulators 76% of businesses say implementing responsible AI is one of their biggest challenges2 As AI becomes more embedded in financial services, it shapes the experiences customers see every day. From credit decisions to eligibility outcomes and personalized offers. While AI can drive faster and more inclusive decisions, it also introduces a new expectation: customers want to understand why a decision was made. Responsible AI is no longer just about regulatory compliance. It’s about delivering outcomes that feel fair, consistent and easy to understand. When decisions appear unclear, confidence erodes. When organizations can clearly explain outcomes, not just internally, they build confidence across regulators, partners and customers. This allows AI to scale responsibly while reinforcing trust in every interaction. Financial wellness tools such as credit scores, reports and education help make AI-driven decisions more transparent, giving customers clarity into outcomes and confidence in how their financial health is assessed. Challenge 3: Digital experiences are failing to deliver clarity and confidence 57% of U.S. consumers remain concerned about conducting activities online3 Customer confidence is affected by day-to-day interactions such as onboarding, payments and issue resolution. Inconsistent decisions, unclear outcomes and friction in digital journeys can quickly erode confidence and increase confusion, disengagement and abandonment. Financial services leaders will need to rebuild and strengthen confidence. Improving key decision points with better data and analytics helps ensure customers receive timely insights, understandable outcomes and meaningful guidance, turning everyday interactions into opportunities to build stronger relationships. By delivering ongoing financial wellness insights and education, organizations can replace confusion with clarity — helping consumers better understand their financial standing and stay engaged over time. Challenge 4: Gen Z continues to raise the bar It's no secret that Gen Z stands out for its strong preference for digital financial services and digital interactions, but Gen Z is also pushing the envelope on financial wellness. 48% of Gen Z report that they do not feel financially secure, indicating strong demand for financial support and tools4 Their expectations for instant decisions, seamless digital experiences, transparency and tools that help them manage their financial lives are quickly becoming the baseline. To meet and exceed these expectations, financial institutions will need to support real-time, data-driven decisioning that adapt to individual needs. Delivering modern, app-like financial experiences, without compromising risk management. Increasingly, organizations are meeting Gen Z expectations by offering financial wellness and protection tools through employee benefits, supporting everyday financial confidence beyond traditional compensation. Challenge 5: Limited data limits meaningful consumer engagement 62 million U.S. consumers are thin-file or credit invisible under traditional credit scoring.5 Growth will always be a priority, but it must be responsible and inclusive. Traditional credit data alone often provides an incomplete picture of consumer financial behavior, limiting visibility and making it harder to confidently expand access. By incorporating alternative and expanded data, organizations can gain a more holistic view of consumers. This broader perspective supports smarter decisions, personalized insights and more inclusive engagement, which enables growth while maintaining compliance and managing risk responsibly. Expanded data supports more personalized financial wellness experiences, enabling organizations to provide relevant insights, responsible access and guidance tailored to individual consumer needs. Challenge 6: Disconnected decisions create inconsistent customer experiences Increasingly, fintech leaders are moving toward unified risk and decisioning strategies to deliver more personalized experiences6 While customers interact with a single institution, decisions are often made across disconnected data sources, systems and teams. These silos create inconsistent experiences, slow responses and operational complexities that customers feel directly through conflicting messages and uneven outcomes. Experian helps organizations break down these silos by unifying data, analytics and decisioning across the enterprise. When data incidents occur, integrated experiences enable faster data breach resolution, helping consumers understand what happened, take action, and recover with confidence. Looking ahead These challenges for financial services organizations are not emerging; they’re already here and reshaping how financial institutions engage with consumers. Leaders who proactively address financial industry challenges by connecting data, analytics, and responsible AI are better positioned to deliver trusted, transparent and meaningful experiences. Learn More References:1. https://www.experian.com/blogs/insights/2025-identity-fraud-report2. https://www.techradar.com/pro/businesses-are-struggling-to-implement-responsible-ai-but-it-could-make-all-the-difference3. https://www.experian.com/blogs/insights/2025-identity-fraud-report4. https://www.deloitte.com/global/en/issues/work/genz-millennial-survey.html5. https://www.experian.com/thought-leadership/business/the-roi-of-alternative-data6. https://us-go.experian.com/2025-state-of-fintech-report?cmpid=IM-2025-state-of-fintech-report-livesocial-share

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.   

Since 1996, The Internal Revenue Service (IRS) has issued more than 27 million individual taxpayer identification numbers (ITINs) –⁠ a 9-digit number used by individuals who are required to file or report taxes in the United States but are not eligible to obtain a Social Security number (SSN). Across the country, ITIN holders are actively contributing to their communities and the U.S. financial system. They pay bills, build businesses, contribute billions in taxes and manage their finances responsibly. Yet despite their clear engagement, many remain underrepresented within traditional lending models.  Lenders have a meaningful opportunity to bridge the gap between intention and impact. By rethinking how ITIN consumers are evaluated and supported, financial institutions can: Reduce barriers that have historically held capable borrowers back Build products that reflect real borrower needs Foster trust and strengthen community relationships Drive sustainable, responsible growth Our latest white paper takes a more holistic look at ITIN consumers, highlighting their credit behaviors, performance patterns and long-term growth potential. The findings reveal a population that is not only financially engaged, but also demonstrating signs of ongoing stability and mobility. A few takeaways include: ITIN holders maintain a lower debt-to-income ratio than SSN consumers. ITIN holders exhibit fewer derogatory accounts (180–⁠400 days past due). After 12 months, 76.9% of ITIN holders remained current on trades, a rate 15% higher than SSN consumers. With deeper insight into this segment, lenders can make more informed, inclusive decisions. Read the full white paper to uncover the trends and opportunities shaping the future of ITIN lending. Download white paper

Growth, risk and the rise of "hidden" business accounts As inflation remains elevated and early signs of labor market cooling emerge, the credit card landscape is entering its next phase. Over the past few weeks, policy actions and discussions around potential interest-rate caps have driven increased uncertainty across the credit card industry and broader global markets. Lenders face a careful balancing act: capturing growth opportunities while maintaining disciplined risk oversight. Our second annual State of Credit Cards Report explores the macroeconomic forces influencing the market, key shifts in originations and delinquency trends, and lender mix. New this year, the report also digs into an often‑overlooked segment: business accounts hidden inside consumer credit card portfolios. Additionally, the report offers actionable strategies to help lenders segment risk and drive disciplined growth more effectively. Key insights include: 30+ DPD delinquency rates remained above pre-pandemic levels in 2025, underscoring the need for disciplined asset‑quality monitoring. Fintechs continue to gain ground, posting a 71% YOY increase in account originations.  Business accounts masked in the consumer credit card universe represent roughly 14% of balances and are more than 50% larger than the business card universe — a material segment with distinct risk and profitability dynamics that many lenders are not explicitly managing today. The report also outlines practical strategies to: Identify and segment business behavior within consumer portfolios. Align underwriting and account management with actual usage patterns. Capture targeted growth while protecting long‑term portfolio performance. Ready to dive deeper? Download the full 2026 State of Credit Cards Report to uncover insights that can help your organization manage risk more precisely and grow with confidence. Download report

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. 

In our latest Experian fireside chat, Unlocking Alternative Data for Smarter Fintech Decisions, two powerhouse voices in the industry, Ashley Knight, SVP of Product Management at Experian, and Haiyan Huang, Chief Credit Officer at Prosper Marketplace, came together for an exclusive discussion on how alternative data is transforming risk, marketing and growth strategies across the fintech space. Now available to watch on demand, the conversation reveals the data-driven innovations that are empowering fintechs to reach new markets, improve decision-making, and build more inclusive financial experiences. What you'll learn During the session, Ashley and Haiyan explored how fintech leaders are utilizing alternative data to address real-world challenges with smarter, more scalable solutions. Topics include: Identity matching redefined: Discover how Individual Taxpayer Identification Numbers (ITINs), Clarity insights, and device intelligence empower fintechs to gain a competitive edge in verifying and validating identities for thin-file or underserved applicants. Precision credit marketing: Learn how email and phone intelligence help fintechs more accurately connect with qualified consumers, driving better engagement and higher conversion rates. Enhanced risk management with real-time data: Discover how Buy Now, Pay Later (BNPL) data and open banking insights are providing fintechs with a more comprehensive view of consumer financial behavior, beyond what traditional credit scores can reveal. To understand how fintech professionals are approaching alternative data, we asked attendees to weigh in throughout the webinar. Here's what we learned: What the audience had to say Which alternative asset is most important for the underwriting of the insurance? 50% chose open banking. 38% selected behavioral/device intelligence. 12% pointed to asset ownership. Takeaway: Open banking is leading the way, but fintechs are clearly embracing a multi-dimensional data approach. 2. Are you currently using ITINs or planning to in the future? 53% said yes. 47% said no. Takeaway: The adoption of ITINs is gaining momentum, supporting efforts to expand access to underrepresented segments. 3. What’s the most compelling reason to use open banking data? 70% said to better assess risk. 10% said to say yes to more consumers. 10% said to price more effectively.  10% said to improve marketing and personalization. Takeaway: Risk assessment remains the top use case, but marketers and pricing teams are starting to take notice. Why it matters Alternative data isn’t just a trend; it’s a response to the urgent need for smarter, more inclusive lending models. As fintechs continue to grow, the ability to reach new audiences, personalize offers, and manage risk with greater accuracy is no longer a competitive advantage; it’s a requirement. Whether you're already integrating cash flow, open banking, and behavioral insights, or just beginning to explore the possibilities, this webinar offers valuable frameworks and firsthand examples from industry leaders who are putting alternative data into action. Don’t miss this opportunity to catch up on the conversation that's helping define the future of fintech innovation. Watch on-demand webinar