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RiskSpan Introduces Enhanced Non-QM Prepayment Model Leveraging Loan-Level Data

Arlington, VA – February 18, 2025 – RiskSpan, a leading provider of innovative trading, risk management and data analytics for loans, securities and private credit, has announced the release of its latest Non-QM Prepayment Model (Version 3.11), incorporating CoreLogic’s loan-level non-QM performance data. This update significantly enhances prepayment forecasting accuracy for non-QM loans and mortgage-backed securities by leveraging a robust, segmented modeling approach.

RiskSpan’s new non-QM prepayment model introduces a two-component framework that improves the precision of prepayment predictions:

  • The first component is a Unified Turnover Model, designed to capture base prepayment trends.
  • The second component, a Refinance Model Categorized by Documentation Type, is capable of distinguishing among and modeling behavioral characteristics specific to bank statement, debt service coverage ratio/investor, full documentation, and other documentation types

The model is built on loan performance data spanning October 2019 to March 2024 and intelligently incorporates long-term prepayment behavior with conventional loans, addressing the challenge of limited non-QM data history. Key enhancements include:

  • Sensitivity to SATO (Spread at Origination) and Burnout Effects, refining prepayment behavior projections.
  • DSCR-Specific Adjustments, incorporating prepayment penalty terms and amounts to refine refinance calculations.

By integrating granular loan-level insights from CoreLogic, this release enhances market participants’ ability to accurately assess non-QM prepayment risk, optimize portfolio strategies, and improve secondary market pricing.

“Our latest model delivers a more precise view of non-QM borrower behavior, equipping market participants with the insights needed to manage risk effectively,” said Divas Sanwal, Senior Managing Director and RiskSpan’s Head of Modeling. “By leveraging CoreLogic’s expansive dataset and an expansive GSE dataset, we’re enabling investors to better anticipate prepayment trends and make more informed decisions.” The new model is now available for integration into RiskSpan’s Platform.

The new model is now available for integration into RiskSpan’s Platform.

About RiskSpan

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions.   Learn more at www.riskspan.com.

Case Study: How a leading loan and MSR investor reduced costs with a loan-level approach

Learn more about how one whole loan and MSR investor (a large mortgage REIT) successfully overhauled its analytics computational processing with RiskSpan. The investor migrated from a daily pricing and risk process that relied on tens of thousands of rep lines to one capable of evaluating each of the portfolio’s more than three-and-a-half million loans individually (and how they actually saved money in the process). 

The Situation 

One of the industry’s largest mortgage REITs sought a more forward-thinking way of managing its extensive investment portfolio of mortgage servicing rights (MSR) assets, residential loans and securities. The REIT runs a battery of sophisticated risk management analytics that rely on stochastic modeling. Option-adjusted spread, duration, convexity, and key rate durations are calculated based on more than 200 interest rate simulations.

The investor used rep lines for one main reason: it needed a way to manage computational loads on the server and improve calculation speeds. Secondarily, organizing the loans in this way simplified the reporting and accounting requirements to a degree (loans financed by the same facility were grouped into the same rep line).  

This approach had some significant downsides. Pooling loans by finance facility was sometimes causing loans with different balances, LTVs, credit scores, etc., to get grouped into the same rep line. This resulted in prepayment and default assumptions getting applied to every loan in a rep line that differed from the assumptions that likely would have been applied if the loans were being evaluated individually. 

The Challenge 

The main challenge was the investor’s MSR portfolio—specifically, the volume of loans needing to be run. Having close to 4 million loans spread across nine different servicers presented two related but separate sets of challenges. 

The first set of challenges stemmed from needing to consume data from different servicers whose file formats not only differed from one another but also often lacked internal consistency. Even the file formats from a single given servicer tended to change from time to time. This required RiskSpan to continuously update its data mappings and (because the servicer reporting data is not always clean) modify QC rules to keep up with evolving file formats.  

The second challenge related to the sheer volume of compute power necessary to run stochastic paths of Monte Carlo rate simulations on 4 million individual loans and then discount the resulting cash flows based on option adjusted yield across multiple scenarios. 

And so there were 4 million loans times multiple paths times one basic cash flow, one basic option-adjusted case, one up case, and one down case—it’s evident how quickly the workload adds up. And all this needed to happen on a daily basis. 

To help minimize the computing workload, the innovative REIT had devised a way of running all these daily analytics at a rep-line level—stratifying and condensing everything down to between 70,000 and 75,000 rep lines. This alleviated the computing burden but at the cost of decreased accuracy because they could not look at the loans individually.

The Solution 

The analytics computational processing RiskSpan implemented ignores the rep line concept entirely and just runs the loans. The scalability of our cloud-native infrastructure enables us to take the nearly four million loans and bucket them equally for computation purposes. We run a hundred loans on each processor and get back loan-level cash flows and then generate the output separately, which brings the processing time down considerably. 

For each individual servicer, RiskSpan leveraged its Smart Mapper technology and Configurable QC feature in its Edge Platform to create a set of optimized loan files that can be read and rendered “analytics-ready” very quickly. This enables the loan-level data to be quickly consumed and immediately used for analytics without having to read all the loan tapes and convert them into a format that an analytics engine can understand. Because RiskSpan has “pre-processed” all this loan information, it is immediately available in a format that the engine can easily digest and run analytics on. 

What this means for you

An investor in any mortgage asset benefits from the ability to look at and evaluate loan characteristics individually. The results may need to be rolled up and grouped for reporting purposes. But being able to run the cash flows at the loan level ultimately makes the aggregated results vastly more meaningful and reliable. A loan-level framework also affords whole-loan and securities investors the ability to be sure they are capturing the most important loan characteristics and are staying on top of how the composition of the portfolio evolves with each day’s payoffs. 

CLICK HERE to talk to us and learn how a loan-level approach can help you

Preparing For Impact: How Will Non-QM Prepay Speeds React to Lower Rates?

In a recent post, we addressed some of the less obvious ways in which a lower interest rate environment is likely to impact an agency universe with such a large volume of loans that are still out-of-the-money to refinance. In this post, we turn our attention to non-QM loans, whose unique characteristics mean they will likely feel the coming rate cuts differently.

Understanding the Distinctive Prepayment Dynamics of Non-QM Loans

Non-QM loans cater to borrowers who do not meet the stringent criteria of traditional agency loans, often due to factors like non-standard income documentation, credit issues, or investment property financing. Non-QM loans generally carry higher interest rates, and, unlike their agency counterparts, many have prepayment penalties designed to protect lenders from early payoff risk. Non-QM loans are also more likely than agency loans to involve investment properties – and thus, the underlying mortgages are not subject to the same “ability to repay” constraints that apply to agency/QM loans.

All these factors play a role in forecasting prepay speeds.

As rates decline, the incentive for some non-QM borrowers to refinance should increase, but several unique factors will shape the extent to which borrowers respond to this incentive:

  1. Prepayment Penalties: Many non-QM loans, especially those structured as Debt Service Coverage Ratio (DSCR) loans for investment properties, include prepayment penalties that can deter refinancing despite a favorable rate environment. These penalties vary widely, from a fixed percentage over a set period to declining penalties over time. The economic calculus for borrowers will hinge on whether the potential savings from refinancing outweigh these penalties
  2. Diverse Loan Structures: The non-QM market includes a variety of loan products, such as 40-year terms, hybrid ARMs and loans with interest-only periods, reminiscent of the pre-2008 lending landscape. This diversity means that not all non-QM loans will see the same incentive to refinance and the slope of the mortgage curve will matter. For example, loans with higher rates are likely to exhibit a stronger refinance response, particularly as the shape of the mortgage rate curve plays a significant role, with hybrid ARMs resetting off short-term rates and 30-year fixed-rate mortgages being influenced by movements in the 10-year Treasury yield
  3. Interest Rate Spread Compression: Historically, the spread between non-QM and agency mortgage rates has varied significantly, ranging from 100 to 300 basis points. A narrowing of this spread, driven by falling rates, could heighten the refinance incentive for non-QM borrowers, leading to faster prepayment speeds. However, the extent of this spread compression is uncertain and will depend on broader market dynamics. Souring economic conditions, for example, would likely contribute to a widening of spreads.

Key Factors Influencing Non-QM Prepayment Speeds

Loan Characteristics and Documentation Types

Non-QM loans can vary significantly by documentation type, such as full documentation, bank statements, or DSCR. Historically, as illustrated in the following chart, full documentation loans have shown faster prepayment speeds, because these borrowers are closer to qualifying for agency refinancing options as rates drop.

S-Curves by Doc Type (Full vs. Alt. vs. Bank Statement vs. DSCR)

Unlike agency mortgages, which include a substantial volume of loans originated at much lower rates, the non-QM market predominantly consists of loans originated in the past few years when rates were already elevated. As a result, a larger portion of non-QM loans is closer to being “in the money” for refinancing. This distinction suggests that the non-QM sector may see a more pronounced increase in prepayment activity compared to agency loans, where the lock-in effect remains stronger.

S-Curve (line) vs UPB (bars) by Refi Incentive

Economic Sensitivity to Rate Moves

For many non-QM borrowers, the primary barrier to agency loan qualification—whether credit score, income documentation, or property type—remains static despite lower rates. Thus, while a rate cut could improve the appeal of refinancing into another non-QM product, it might not significantly shift these borrowers towards agency loans. However, as noted, those closer to the threshold of agency eligibility could still be enticed to refinance if the rate spread and penalty structures align favorably.

Conclusion

The coming interest rate cuts are poised to influence the non-QM market in unique ways, with prepayment speeds likely to increase as borrowers seek to capitalize on lower rates. However, the interplay of rate spreads, prepayment penalties, and diverse loan structures will create a complex landscape where not all non-QM loans will behave uniformly. For lenders and investors, understanding these nuances is crucial to accurately forecasting prepayment risk and managing portfolios in a changing rate environment.

As the market evolves, ongoing analysis and model updates will be essential to capturing the shifting dynamics within the non-QM space, ensuring that investors and traders are well-prepared for the impacts of the anticipated rate cuts. Contact us to learn how RiskSpan’s Edge Platform is helping a growing number of non-QM investors get loan-level insights like never before.

Is Your Prepay Analysis Ready for the Rate Cut?

The forthcoming Federal Reserve interest rate cuts loom large in minds of mortgage traders and originators. The only remaining question is by how much rates will be cut. As the economy cools and unemployment rises, recent remarks by the Fed Chair have made the expectation of rate cuts essentially universal, with the market quickly repricing to a 50bp ease in September. This anticipated move by the Fed is already influencing mortgage rates, which have already experienced a notable decline.

Understanding the Lock-in Effect

One of the key factors influencing prepayments in the current environment is the lock-in effect, where borrowers are deterred from selling their current home due to the large difference between their current mortgage rate and prevailing market rates (which they would incur when purchasing their next home). As rates decrease, the gap narrows, reducing the lock-in effect and freeing more borrowers to sell and move.

As Chart 1 illustrates, a significant share of borrowers continues to hold mortgages between 2 and 3 percent. These borrowers clearly still have no incentive to refinance. But historical data suggests that the sizeable lock-in effect, which is currently depressing turnover, diminishes as the magnitude of their out-of-the-moneyness comes down. In other words, even a 100-basis point reduction can significantly increase housing turnover, as borrowers who were previously 300 basis points out of the money move to 200 basis points, making selling their old home and buying a new one, despite the higher interest rate, more palatable.

CHART 1: Distribution of Note Rates for 30-Year Conventional Mortgages: July 2024

Current Market Dynamics

Recent data from Mortgage News Daily indicates that mortgage rates have dropped over the past four weeks from around 6.8% to nearly 6.4%. This decrease is expected to continue, potentially bringing rates below 6% by the end of the year. This will likely have a profound impact on mortgage prepayments, particularly in the Agency MBS market.

Most outstanding mortgages, particularly those in Fannie and Freddie securities, currently have low prepayment speeds, with many loans sitting at 2% to 3% coupons. While a drop in mortgage rates to 6% (or lower) will still leave most of these mortgages out of the money for traditional rate-and-term refinances, it may bring a growing number of them into play for cash-out refinances, given significant home price appreciation and equity buildup over last 4 years. It will also loosen the grip of the lock-in effect for a growing number of homeowners currently paying below-market interest rates.

Implications for Prepayment Speeds

Factoring in the potential increase in turnover and cash-out refis, the impact of rate cuts on prepayment speeds could be substantial. For instance, with a 100-bp drop in rates, loans that are deeply out of the money could see their prepayment speeds increase by 1 to 2 CPR based on the turnover effect alone. Loans that are just at the money or slightly out of the money will see a more pronounced effect, with prepayment speeds potentially doubling. Chart 2, below, illustrates both the huge volume of loans deep out of the money to refinance as well as the small (but significant) uptick in CPR that a 100-bp shift in interest rates can have on CPR even for loans as much as 300 bps out of the money.

CHART 2: CPR by Refinance Incentive (dotted line reflects UPB of each bucket)

Historical data suggests that if mortgage rates move to 6.4%, the volume of loans moving into the money to refinance could increase up to eightfold — from $39 billion to $247 billion (see chart 3, below.) This surge in refinance activity will significantly influence prepays — impacting both turnover and refi volumes.

CHART 3: Volume and CPR by Coupon (dotted line reflects UPB of each bucket)

The Broader Housing Market

Beyond prepayments, the broader housing market may also feel the effects of rate cuts, but perhaps in a nuanced way. A reduction in rates generally improves affordability, potentially sustaining or even increasing home prices despite the increased supply from unlocked homes. However, this dynamic is complex. While lower rates make homes more affordable, the release of previously locked-in homes could counterintuitively depress home prices due to increased supply. With housing affordability at multi-decade lows, an uptick in housing supply could swamp any effect of somewhat lower rates.

While a modest rate cut may primarily boost turnover, a more significant cut could trigger a wave of refinancing. Additionally, cash-out refinances may become more attractive, offering a cheaper alternative to HELOCs and other more expensive options.

Conclusion

The forthcoming Fed interest rate cuts are poised to have a significant impact on mortgage prepayments. As rates decline, the lock-in effect will ease, encouraging more refinancing and increasing prepayment speeds. The broader housing market will also feel the effects, with potential implications for home prices and overall market dynamics. Monitoring these trends closely will be crucial for market participants, particularly those in the agency MBS market, as they navigate the changing landscape.

Contact us to staying informed and prepared and learn more about how RiskSpan can help you make strategic decisions that align with evolving market conditions.

Leveraging Pool-Specific Performance and Recapture Analysis: A Game Changer for MSR Investors

Successfully forecasting MSR cash flows demands a level of precision and granularity in data analysis that few other asset classes require. This is especially true for investors seeking to estimate how much prepayment runoff they can reasonably expect to recapture, which is key to the performance of the asset. And often investors need to measure that performance by the specific pools of MSRs they purchase — as each pool may have its own unique recapture arrangements.

RiskSpan’s Edge Platform has incorporated a robust framework for managing MSR investment performance by enabling investors to track pool-specific performance and recapture analyses, thus obtaining a more nuanced understanding of their portfolios. In this post, we delve into some of the specific challenges MSRs pose, the benefits of transaction-specific segmentation, and the unique capabilities of RiskSpan’s Edge Platform.

Understanding Pool-Specific Performance

Owning MSRs requires investors to track the performance of various loan pools over time. For example, an investor may purchase an MSR pool and rely on a sub-servicer to service the loans as well as make efforts to recapture borrowers that are looking to refinance. It is important for the investor to understand and track the returns on that pool which may be largely driven by recapture efficiency.  

While performance needs to be monitored on a pool-level, the modeling of the underlying loans is dependent on the distinct characteristics of the loans within a pool and will be more accurate if the models are run at the loan-level (or at granular rep lines determined by smart rep line logic).  The ability to capture and analyze these pool-specific cash flows based on granular loan-level modeling is crucial for several reasons:

  1. Valuation Accuracy: Each loan can be valued more accurately by considering its unique attributes, such as the original loan terms, interest rates, and borrower profiles (e.g., FICO, LTV); at the same time, pools can be valued based on pool-specific assumptions such as recapture rates and prepayment penalties.
  2. Risk Management: Understanding the performance of individual pools helps in identifying which pools are more prone to prepayments or defaults, enabling more focused efforts on recapture and other risk management activities.
  3. Performance Tracking: Investors can track historical returns, CPRs, CDRs, Recapture and other historical performance metrics for each pool, facilitating more informed decision-making.

Supporting this functionality is RiskSpan’s ability to share and integrate data on Snowflake’s data cloud. RiskSpan’s Snowflake integration enhances the data management and analytics capabilities available to clients. Investors can easily share transaction-specific data through Snowflake, which is then seamlessly integrated into the Edge platform. The platform can then handle the large datasets (tens of millions of loans in some instances), providing real-time analytics and insights.

Recapture Analysis: Enhancing Portfolio Performance

Recapture analysis is a critical component for MSR portfolio risk management. When borrowers refinance or otherwise pay off their loans, the servicer’s cash flows usually vanish entirely. However, if, in the case of refinance, the investor retains the rights to service the loan replacing the refinanced loan, then the new loan can be considered as a recapture. RiskSpan’s Edge platform excels in tracking these recaptures, offering several advantages:

  1. Detailed Tracking: The platform allows for the separation and detailed tracking of original loans and their recaptures, maintaining the distinction between the two. Recaptures should have better performance (i.e., lower CPRs) than original loans.
  2. Performance Comparison: By comparing the performance of original loans and recaptures, investors can gauge the effectiveness of their recapture strategies.
  3. Granular Assumptions: Edge supports highly granular recapture rate assumptions used for projecting cash flows, which can be tailored to specific pools or deals, enhancing the precision of valuation.

A Case Study: Supporting a Large Mortage REIT’s MSR Portfolio Management Regime

A practical example of these capabilities involves a mortgage REIT, which relies on RiskSpan’s platform to manage a large MSR portfolio. Specifically, the Edge platform has enabled the REIT investor to accomplish the following:

  • Capture Transaction-Specific Data: the investor can track and analyze data at the transaction level, maintaining detailed records of each pool’s performance and its recaptures. This allows, for example, investors to review performance with sub-servicers and evaluate whether certain changes can be made to enhance performance either on the existing pool or on future pools.
  • Custom Assumption Setting: The platform allows for custom segmentation and assumption setting for valuation purposes, such as different recapture rates based on prepayment projections or loan age. This provides an ability to more accurately measure future projected cash flows and factor that into valuation of owned MSRs as well as potential purchases.

RiskSpan’s Edge platform offers MSR investors a robust toolset for managing their portfolios with precision not available anywhere else. By enabling pool-specific performance and detailed recapture analysis, Edge helps investors optimize their strategies and enhance portfolio performance. The ability to capture and analyze nuanced data points sets RiskSpan apart, making it a valuable ally in the complex landscape of MSR investments.

MSR investors, contact us to discover how tailored analytics and granular data management can transform your investment strategy and give you a competitive edge.

RiskSpan Launches MBS Loan Level Historical Data on Snowflake Marketplace

ARLINGTON, Va., June 18, 2024 – RiskSpan, a leading provider of data analytics and risk management solutions for the mortgage industry, announced today that it has launched MBS Loan Level Historical Data on Snowflake Marketplace. RiskSpan’s MBS Loan Level Historical Data on Snowflake Marketplace enables joint customers to access RiskSpan’s normalized and enriched loan-level data for Fannie Mae, Freddie Mac, and Ginnie Mae mortgage-backed securities.

“We are thrilled to join the Snowflake Marketplace and offer our loan-level MBS data to a wider audience of Snowflake users,” said Janet Jozwik, Senior Managing Director at RiskSpan. “This is a first step in what we believe will ultimately become a cloud-based analytical hub for MBS investors everywhere.”

RiskSpan and Snowflake, the AI Data Cloud company, are working together to help joint customers inform business decisions and drive innovations by enabling them to query the data using SQL, join it with other data sources, and scale up or down as needed. RiskSpan also provides sample code and calculations to help users get started with common metrics such as CPR, aging curves, and S-curves.

“RiskSpan’s launch of a unique blend of enriched data onto Snowflake Marketplace represents a major opportunity for Snowflake customers to unlock new value through data on their business journey,” said Kieran Kennedy, Head of Marketplace at Snowflake. “We welcome RiskSpan to the ecosystem and look forward to exploring how we can support our customers as they look to leverage the breadth of the Snowflake platform more effectively.”

Joint customers can now leverage Loan-Level MBS Data on Snowflake Marketplace, allowing them to access RiskSpan data enhancements, including servicer normalization, refinements, mark-to-market LTV calculations, current coupon. These and other enhancements make it easier and faster for users to perform analysis and modeling.

Snowflake Marketplace is powered by Snowflake’s ground-breaking cross-cloud technology, Snowgrid, allowing companies direct access to raw data products and the ability to leverage data, data services, and applications quickly, securely, and cost-effectively. Snowflake Marketplace simplifies discovery, access, and the commercialization of data products, enabling companies to unlock entirely new revenue streams and extended insights across the AI Data Cloud. To learn more about Snowflake Marketplace and how to find, try and buy the data, data services, and applications needed for innovative business solutions, click here.

About RiskSpan, Inc. 

RiskSpan delivers a single analytics solution for structured finance and private credit investors of any size to confidently make faster, more precise trading and portfolio risk decisions and meet reporting requirements with fewer resources, and less time spent managing multiple vendors and internal solutions. Learn more at www.riskspan.com.

Loans & MSRs: Managing model assumptions and tuners the easy way

One of the things that makes modeling loan and MSR cash flows hard is appropriately applying assumptions to individual loans. Creating appropriate assumptions for each loan or MSR segment is crucial to estimating realistic performance scenarios, stress testing, hedging, and valuation. However, manually creating and maintaining such assumptions can be time-consuming, error-prone, and inconsistent across different segments and portfolios.

Fortunately, hidden among some of the Edge Platform’s better-known features is a powerful and flexible way of running loan-level analytics on a portfolio using the Platform’s segment builder and loan model assumptions features.

These sometimes-overlooked features allow users to create and apply granular and customized modeling assumptions to a particular loan portfolio, based on its various, unique loan characteristics. Assumptions can be saved and reused for future analysis on different loans tapes.  This feature allows clients to effectively build and manage a complex system of models adjustment and tuners for granular sub-segments.

Applying the segment builder and loan model assumptions features, loan investors can:

    • Decouple how they run and aggregate results from how they assign modeling assumptions, and seamlessly assign different assumptions to various segments of the portfolio, based on user-defined criteria and preferences. For example, investors can assign different prepayment, default, and severity assumptions to loans based on their state, LTV, UPB, occupancy, purpose, delinquency status, loan type, collateral features, or virtually any other loan characteristic.

 

    • Choose from a variety of models and inputs, including RiskSpan models and vector inputs for things like CPR and CDR. Investors can define their own vector inputs as an aging curves by loan age or based on the forecast month, and apply them to different segments of the portfolio. For example, they can define their own CDR and CPR curves for consumer or C&I loans, based on the age of the loans.

    • Set up and save modeling assumptions one time, and then reference them over and over again whenever new loan tapes are uploaded. This saves time and effort and ensures consistency and accuracy in the analysis.

This hidden feature enables investors to customize their analysis and projections for different asset classes and scenarios, and to leverage the Edge Platform’s embedded cash flow, prepayment and credit models without compromising the granularity and accuracy of the results. Users can create and save multiple sets of loan model assumptions that include either static inputs, aging curves, or RiskSpan models, and apply them to any loan tape they upload and run in the forecasting UI.

Contact us and request a free demo or trial to learn more about how to use these and other exciting hidden (and non-hidden) features and how they can enhance your loan analytics.

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How an MSR Analytical Solution Can Boost Your Mortgage Banking Business

And why it’s probably less expensive than you think

Mortgage servicing rights (MSRs) are complex and volatile assets that require careful management and analysis. Inherent in MSR risk management is the need to monitor portfolio performance, assess risks and opportunities, evaluate and implement risk-reducing strategies such as recapture and interest rate hedging, and effectively communicate all this to investors and regulators. Handling all this has traditionally required an enormous budget for data, software, and consultants. Many mortgage banks are left with either using outdated and inflexible internal systems or outsourcing their analytics to third parties that lack full transparency and bill clients for every request. 

Not anymore.

The answer is a cloud-native MSR analytical solution that includes slice-and-dice-able Agency loan performance data as well as the models necessary to produce valuations, risk analytics and cash flows across both MSRs and associated derivative hedges, where applicable.

By integrating data, models, and reports, this combined solution enables mortgage banks to:

  • Generate internal metrics to compare with those received from third party brokers and consultants
  • Measure the fair value and cash flows of their MSRs under different scenarios and assumptions including a variety of recapture assumptions
  • Analyze the sensitivity of their MSRs (and associated hedges) to changes in interest rates, prepayment speeds, defaults, home prices and other factors
  • Compare their portfolio’s performance and characteristics with the market and industry peers
  • Generate customized reports and dashboards to share with investors, auditors, and regulators

More specifically, RiskSpan’s comprehensive data and analytics solution enables you to do the following:

1. Check assumptions used by outside analysts to run credit and prepayment analytics

Even in cases where the analytics are provided by a third party, mortgage banks frequently benefit from having their own analytical solution. Few things are more frustrating than analytics generated by a black box with no/limited visibility into assumptions or methodology. RiskSpan’s MSR tool provides mortgage banks with an affordable means of checking the assumptions and methodologies used by outside analysts to run credit and prepayment analytics on their portfolio.

Different analysts use different assumptions and models to run credit and prepayment analytics, often leading to inconsistent results that are difficult to explain. Some analysts use historical data while others rely on forward-looking projections. Some analysts simple models while others turn to complex one. Some analysts are content with industry averages while others dig into portfolio-specific data.

Having access to a fully transparent MSR analytical solution of their own allows mortgage banks to check the assumptions and models used by outside analysts for reasonableness and consistency. In addition to helping with results validation and identification of discrepancies or errors, it also facilitates communication of the rationale and logic behind assumptions and models to investors and regulators.  Lastly, the ability for a mortgage bank to internally generate MSR valuations and cash flows allows for a greater understanding of the economic value (vs. accounting value) of the asset they hold.

2. Understand how your portfolio’s prepayment performance stacks up against the market

Prepayment risk is one of the main drivers of MSR value and volatility. Mortgage banks need to know how their portfolio’s prepayment performance compares with the market and their peers. Knowing this helps mortgage banks field questions from investors, who may be concerned about the impact of prepayments on profitability and liquidity. It also helps identify areas of improvement and opportunity for the portfolio.

RiskSpan’s MSR analytical solution helps track and benchmark portfolio prepayment performance using various metrics, including CPR and SMM. It also helps analysts understand the drivers and trends of prepayments, such as interest rates, loan age, loan type, credit score, and geographic distribution. RiskSpan’s MSR analytical solution combined with its historical performance data provides a deeper understanding of how a portfolio’s prepayment performance stacks up against the market and what factors affect it.

And it’s less expensive than you might think

You may think that deploying an MSR analytical solution is too costly and complex, as it requires a lot of data, software, and expertise. However, this is not necessarily true.

Bundling RiskSpan’s MSR analytical solution with RiskSpan’s Agency historical performance tool actually winds up saving clients money by helping them optimize their portfolios and avoid costly mistakes. The solution:

  • Reduces the need for external data, software, and consultants because all the information and tools needed are in one platform
  • Maximizes portfolio performance and profitability by helping to identify and capture opportunities and mitigate risks, including through recapture analysis and active hedging
  • Enhances reputation and credibility by improving transparency to investors and regulators

RiskSpan’s solution is affordable and easy to use, with flexible pricing and deployment options, as well as user-friendly features and support, including intuitive interfaces, interactive dashboards, and comprehensive training and guidance. Its cloud-native, usage-based pricing structure means users pay only for the compute they need (in addition to a nominal licensing fee).

Contact us to learn more about how RiskSpan’s Edge Platform can help you understand how your MSR portfolio’s performance stacks up against the market, check assumptions used by outside analysts to run credit and prepayment analytics, and, most important, save money and time.

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What Do 2024 Origination Trends Mean for MSRs?

While mortgage rates remain stubbornly high by recent historical standards, accurately forecasting MSR performance and valuations requires a thoughtful evaluation of loan characteristics that go beyond the standard “refi incentive” measure.

As we pointed out in 2023, these characteristics are particularly important when it comes to predicting involuntary prepayments.

This post updates our mortgage origination trends for the first quarter of 2024 and takes a look at what they could be telling us.

Average credit scores, which were markedly higher than normal during the pandemic years, have returned and stayed near the averages observed during the latter half of the 2010s.

The most credible explanation for this most recent reversion to the mean is the fact that the Covid years were accompanied by an historically strong refinance market. Refis traditionally have higher FICO scores than purchase mortgages, and this is apparent in the recent trend.

Purchase markets are also associated with higher average LTV ratios than are refi markets, which accounts for their sharp rise during the same period.

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Consequently, in 2023 and 2024, with high home prices persisting despite extremely high interest rates, new first-time homebuyers with good credit continue to be approved for loans, but with higher LTV and DTI ratios.

Between rates and home prices, ​​borrowers simply need to borrow more now than they would have just a few years ago to buy a comparable house. This is reflected not just in the average DTI and LTV, but also the average loan size (below) which, unsurprisingly, continues to trend higher as well.

Recent large increases to the conforming loan limit are clearly also contributing to the higher average loan size.

What, then, do these origination trends mean for the MSR market?

The very high rates associated with newer originations clearly translate to higher risk of prepayments. We have seen significant spikes in actual speeds when rates have taken a leg down — even though the loans are still very new. FICO/LTV/DTI trends also potentially portend higher delinquencies down the line, which would negatively impact MSR valuations.

Nevertheless, today’s MSR trading market remains healthy, and demand is starting to catch up with the high supply as more money is being raised and put to work by investors in this space. Supply remains high due to the need for mortgage originators to monetize the value of MSR to balance out the impact from declining originations.

However, the nature of the MSR trade has evolved from the investor’s perspective. When rates were at historic lows for an extended period, the MSR trade was relatively straightforward as there was a broader secular rate play in motion. Now, however, bidders are scrutinizing available deals more closely — evaluating how speeds may differ from historical trends or from what the models would typically forecast.

These more granular reviews are necessarily beginning to focus on how much lower today’s already very low turnover speeds can actually go and the extent of lock-in effects for out-of-the-money loans at differing levels of negative refi incentive. Investors’ differing views on prepays across various pools in the market will often be the determining factor on who wins the bid.

Investor preference may also be driven by the diversity of an investor’s other holdings. Some investors are looking for steady yield on low-WAC MSRs that have very small prepayment risk while other investors are seeking the higher negative convexity risk of higher-WAC MSRs — for example, if their broader portfolio has very limited negative convexity risk.

In sum, investors have remained patient and selective — seeking opportunities that best fit their needs and preferences.

So what else do MSR holders need to focus on that may may impact MSR valuations going forward? 

The impact from changes in HPI is one key area of focus.

While year-over-year HPI remains positive nationally, servicers and other investors really need to look at housing values region by region. The real risk comes in the tails of local home price moves that are often divorced from national trends. 

For example, HPIs in Phoenix, Austin, and Boise (to name three particularly volatile MSAs) behaved quite differently from the nation as a whole as HPIs in these three areas in particular first got a boost from mass in-migration during the pandemic and have since come down to earth.

Geographic concentrations within MSR books will be a key driver of credit events. To that end, we are seeing clients beginning to examine their portfolio concentration as granularly as zipcode level. 

Declining home values will impact most MSR valuation models in two offsetting ways: slower refi speeds will result in higher MSR values, while the increase in defaults will push MSRs back downward. Of these two factors, the slower speeds typically take precedence. In today’s environment of slow speeds driven primarily by turnover, however, lower home prices are going to blunt the impact of speeds, leaving MSR values more exposed to the impact of higher defaults.

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Enriching Pre-Issue Intex CDI Files with [Actual, Good] Loan-Level Data

The way RMBS dealers communicate loan-level details to prospective investors today leaves a lot to be desired.

Any investor who has ever had to work with pre-issue Intex CDI files can attest to the problematic nature of the loan data they contain. Some are better than others, but virtually all of them lack information about any number of important loan features.

Investors can typically glean enough basic information about balances and average note rates from preliminary CDI files to run simple, static CPR/CDR scenarios. But information needed to run complex models — FICO scores, property characteristics and geography, and LTV ratios to name a few — is typically lacking. MBS investors who want to run to run more sophisticated prepayment and credit models – models that rely on more comprehensive loan-level datasets to run deeper analytics and scenarios – can be left holding the bag when these details are missing from the CDI file.

The loan-level detail exists – it’s just not in the CDI file. Loan-level detail often accompanies the CDI file in a separate spreadsheet (still quaintly referred to in the 21st Century as a “loan tape”). Having this data separate from the CDI file requires investors to run the loan tape through their various credit and prepayment models and then manually feed those results back into the Intex CDI file to fully visualize the deal structure and expected cash flows.

This convoluted, multi-step workaround adds both time and the potential for error to the pre-trade analytics process.

A Better Way

Investors using RiskSpan’s Edge Platform can streamline the process of evaluating a deal’s structure alongside the expected performance of its underlying mortgage loans into a single step.

EDGEPLATFORM

Here is how it works.

As illustrated above, when investors set up their analytical runs on Edge, RiskSpan’s proprietary credit and prepayment models automatically extract all the required loan-level data from the tape and then connect the modeling results to the appropriate corresponding deal tranche in the CDI file. This seamlessness reduces all the elements of the pre-trade analytics process down to a matter of just a few clicks.

Making all this possible is the Edge Platform’s Smart Mapper ETL solution, which allows it to read and process loan tapes in virtually any format. Using AI, the Platform recognizes every data element it needs to run the underlying analytics regardless of the order in which the data elements are arranged and irrespective of how (or even whether) column headers are used.

Contact us to learn more about how RMBS investors are reaping the benefits of consolidating all of their data analytics on a single cloud-native platform.

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