All posts by Michael Pyatski

  As a follow‑up to our January post on Freddie Mac’s Loan-Level Directed Collateral (LLDC) program and its use of new loan‑level data fields from Experian’s Mortgage Loan Performance (MLP) dataset, we’re highlighting another newly available field: current second lien balance.  What kind of data moves markets?  Before diving into the new second lien field, we’ll outline the criteria we use to determine whether a new data field has the potential to move MBS markets—and therefore warrants the time and effort required to prepare and deliver it to our institutional investor clients.  These criteria will apply to all new fields discussed in future posts.   Over the past decade, rapid technological innovation, combined with financial markets’ increasing focus on data and AI, has led to a steady stream of new market data and analytical products. Most of these releases don’t materially impact how MBS trade. As discussed in prior posts, two notable exceptions stand out:  The introduction of pool‑level data in the 1980s enabled the rise of specified (“spec”) pools.  The public release of agency MBS loan‑level data in 2013 ushered in a new era of advanced analytics and precision modeling.   So, what criteria must be met for new, incremental data to change how MBS trades? We believe three requirements must be met:  New: Provides information not available in existing datasets (i.e., orthogonal to currently available data).  Material: Impacts a sizeable portion of the MBS universe.  Significant: Differentiates collateral performance by a large enough margin to influence trading and risk management decisions.  With these criteria in mind, we turn to one of several new fields from Experian’s MLP that meet all three: current second lien balance.  Subsequent Second Liens: An ‘Invisible’ CPR Throttle  MLP contains several fields related to open second liens, with each loan linked to both the individual borrower and the specific property. This structure allows visibility into a borrower’s full set of open second lien loans, even across multiple properties. For the illustrative exercise below, we focus on one field: the total balance on open second‑mortgage trades reported in the past three months.  Does this field meet the first criteria—New? Yes, the current presence of junior liens is new information in agency MBS markets. In standard agency and Government National Mortgage Association (GNMA) disclosures, second‑lien information appears only at the time of first‑lien origination. Any subsequent second liens remain unreported, preventing accurate calculations of current combined LTV post-origination.  The material blind spot: Missing junior‑lien data   The absence of updated junior lien status represents a material blind spot for investors seeking to predict prepayment behavior of the associated first lien in agency MBS. Current combined LTV, inclusive of subsequently opened second liens and adjusted for home price appreciation (HPA), is one of the most important drivers of both prepayment and credit performance. Without supplementary data from MLP, information on newly originated second liens go unobserved. As a result, prepayment and credit forecasts become overly aggressive, and prepayment call protection is therefore mispriced.    In addition to information regarding the junior lien loan, Experian’s MLP dataset includes a monthly refreshed AVM value for each property, ensuring an accurate current CLTV value. Having established newness, is current junior lien data material?  Yes, particularly in the current environment of record-high home equity. Approximately 16% of active mortgages carry second liens, representing roughly $522 billion in outstanding balances—and growing (Source: Experian MLP dataset). In 2024 alone, second-lien originations exceeded $100 billion and continued to trend upward (Source: Experian MLP dataset).  Second liens added after primary‑mortgage origination, often layered onto low‑LTV agency MBS, aren’t captured in standard GSE data. Their impact is especially pronounced in periods of moderate or negative HPA. Borrowers who take on new second liens and then experience negative HPA may be unable to refinance due to re‑subordination limits, which materially affect prepayment behavior and call protection. Investors relying on standard agency disclosure have no visibility into post‑origination junior liens.  Is current junior‑lien data significant?  After having established newness and materiality, is the current junior lien data significant?     Yes—Figure 1 illustrates the impact of new second-lien balances on prepayments. This field is independent of other collateral characteristics available in standard GSE data, as the decision to take out a new second lien is made by the borrower after the primary mortgage has closed.  As shown in Figure 1, prepayments decline materially as new second-lien balances increase. On average, if approximately 20% of mortgages carry second liens and the CPR differential for in-the-money (ITM) mortgages with and without new second liens are 10 CPR, then new second liens account for roughly 2 CPR of prepayment impact on average (10 CPR × 20%).  This CPR-throttling effect is significantly more pronounced for mortgages with a current CLTV of around 80%. These loans may be effectively locked out of refinancing due to re-subordination constraints, yet they appear highly callable when evaluated using only standard GSE data, leading to materially overstated prepayment expectations.  Fig 1. Prepayments S-Curve: New Second Liens Balance Source: Experian Mortgage Loan Performance Dataset, hosted on the IVolatility MBS Data-Driven Portal  _____________________________________________________ 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.   _____________________________________________________  

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. _____________________________________________________