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.
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.
GenAI Applications for Loans and Mapping Data
RiskSpan is actively furthering the advancement of several GenAI applications aimed at transforming how mortgage loan and private credit investors work and maximizing their efficiency and performance. They include:
1. Tape-Cracking 3.0: Making RiskSpan’s Smart Mapper Even Smarter
RiskSpan’s Edge Platform currently uses machine learning techniques as part of its Smart Mapper ETL Tool. When a new portfolio is loaded in a new format, the fuzzy logic that powers the Platform’s recommended mappings gets continually refined based on user activity.
In the coming months, the Platform’s existing ML-driven ETL process will be further refined to leverage the latest GenAI technology.
GenAI lends additional context to the automated mapping process by incorporating an understanding not only of the data in an individual column, but also of surrounding data as well as learned characteristics of the asset class in question. The resulting evolution from simply trying to ensure the headers match up a more holistic understanding of what the data actually is and the meaning it seeks to convey will be a game changer for downstream analysts seeking to make reliable data-driven investment decisions.
RiskSpan made several updates in 2023 to help users automate the end-to-end workflow for loan valuation and surveillance. AI-based data loading combined with the Platform’s loan risk assumptions and flexible data model will enable users to obtain valuation and risk metrics simply by dragging and dropping a loan file into the application.
2. Modeling Private Credit Transactions
Many financial institutions and legal advisors still spend an extraordinary amount of time reading and extracting relevant information from legal documents that accompany structured private credit transactions.
RiskSpan has partnered with clients to develop a solution to extract key terms from private credit and funding transactions. Trained multimodal AI models are further extended to generate executable code valuations. This code will be fully integrated into RiskSpan’s risk and pricing platform.
The application solves a heretofore intractable problem in which the information necessary to generate accurate cash flows for private credit transactions is spread across multiple documents (a frequent occurrence when terms for individual classes can only be obtained from deal amendments).
Execution code for cash flow generation and valuation utilizes RiskSpan’s validated analytics routines, such as day count handling, payment calculations, discounting, etc.
3. “Insight Support”
Tech support is one of today’s most widely known (and widely experienced) GenAI use cases. Seemingly all-knowing chatbots immediately answer users’ questions, sparing them the inconvenience of having to wait for the next available human agent. Like every other company, RiskSpan is enhancing its traditional tech support processes with GenAI to answer questions faster and and embed user-facing AI help within the Platform itself. But RiskSpan is taking things a step further by also exploring how GenAI can upend and augment its clients’ workflows.
RiskSpan refers to this workflow augmentation as “Insight Support.”
With Insight Support, GenAI evaluates an individual user’s data, dynamically serves up key insights, and automatically completes routine analysis steps without prompting. The resulting application can understand an individual user’s data and recognize what is most important to identify and highlight as part of a loan data analysis workflow.
Insight Support, for example, can leverage insights obtained by the AI-driven “Smarter Mapping” process to identify what specific type of collateral reporting is necessary. It can produce reports that highlight outliers, recognize the typical analytical/valuation run settings a user would want to apply, and then execute the analytical run and summarize the results in management-ready reporting. All in the name of shortening the analysis time needed to evaluate new investment opportunities.
Conclusion
Considered collectively, these three applications are building toward having RiskSpan’s SaaS platform function as a “virtual junior analyst” capable of handling much of the tedious work involved in analyzing loan and structured product investments and freeing up human analysts for higher-order tasks and decision making.
GenAI is the future of data and analytics and is therefore the future of RiskSpan’s Edge Platform. By revolutionizing the way data is analyzed, AI-created and -validated models, dashboards, and sorted data are already allowing experts to redirect their attention away from time-consuming data wrangling tasks and toward more strategic critical thinking. The more complete adoption of fully optimized AI solutions throughout the industry, made possible by a rising generation of “AI-native” data scientists will only accelerate this phenomenon.
RiskSpan’s commitment to pushing the boundaries of innovation in the Loan and Structured Product Space is underscored by its strategic approach to GenAI. While acknowledging the challenges posed by GenAI, RiskSpan remains poised for the future, leveraging its expertise to navigate the evolving landscape. As the industry anticipates the promised benefits of GenAI, RiskSpan’s vision and applications stand as a testament to its role as a thought leader in shaping the future of data analytics.
Stay tuned for more updates on RiskSpan’s innovative solutions, as we continue to lead the way in harnessing the power of GenAI for the benefit of our clients and the industry at large.
Celebrating Women’s Contributions by the Numbers
Because we’re a data company after all. RiskSpan commemorates International Women’s Day by taking note of the remarkable people behind these numbers.
Case Study: How a Large Financial Institution Allayed Regulator Concerns by Digitizing its Model Performance Tracking
The Situation
One of the largest financial institutions in the world, operating in a highly competitive and regulated environment, found itself under increasing scrutiny over the fragmented state of its model performance tracking regime.
Failing to meet both internal standards and external regulatory expectations, the the institution’s model performance tracking relied on a loan-level analytical framework that overloaded its legacy systems and hindered its ability to react to changing market dynamics. These inadequacies led to significant challenges beyond regulatory scrutiny, including inefficiencies in risk management processes and higher overhead costs. The outlook for rectifying these shortcomings was murky.
The Challenge
The institution’s challenges were twofold.
First, regulatory pressure was mounting, with potential repercussions including fines and restrictions on business activities. Regulators demanded transparent, accurate, and timely reporting of model performance, which the institution’s existing system could not provide.
Second, the operational issues stemming from lackluster model performance tracking were beginning to affect the institution’s ability to capitalize on opportunities. These impacts included inaccurate risk assessments, suboptimal asset allocation, and impaired decision-making capabilities, all of which eroded the institution’s competitive edge.
The Solution
The institution sought RiskSpan’s expertise to deliver a sustainable and effective MPT framework. The trust the institution placed in RiskSpan was grounded in RiskSpan’s history of helping other financial institutions navigate similar MPT shortcomings.
RiskSpan conducted an in-depth gap analysis, developed a customized solution, and provided training and support. Designed to enhance the accuracy, efficiency, and transparency of model performance tracking, the solution incorporated advanced analytics, a holistic governance approach, and robust data management practices. Key components included:
Model Inventory Management: Creating a centralized repository for all models, including inputs, assumptions, and ownership to streamline tracking and compliance.
Model Performance Dashboard: Implementing a real-time monitoring dashboard that provides insights into each model’s performance, deviations from expected outcomes, and potential areas of concern.
Regulatory Compliance: Automating the generation of reports to ensure compliance with regulatory standards, reducing manual errors, and freeing up resources for other critical functions.
Training and Support: Providing comprehensive training to the institution’s staff to ensure they can effectively utilize the new system and offering ongoing support to address any issues promptly.
The partnership led to transformative outcomes, including improved risk management, reduced manual errors, and operational costs.
What this means for you (and your bank)
Precise model performance tracking can enhance risk management, regulatory compliance, and operational efficiency. Our expertise ensures that our clients are equipped with robust, cutting-edge solutions tailored to their specific needs. If you are encountering challenges, we encourage you to reach out to us for a consultation.
RiskSpan to Launch Usage-based Pricing for its Edge Platform at SFVegas 2024
New innovative pricing model offers lower costs, transparency, and flexibility for analytics users
RiskSpan, a top provider of cloud-based analytics solutions for loans, MSRs, structured products and private credit, announced today the launch of a usage-based pricing model for its Edge Platform. The new pricing model enables clients flexibility to pay only for the compute they use. It also gives clients access to the full platform, including data, models, and analytics, without having to license individual product modules.
Usage-based pricing is a trend that reflects the evolving nature of analytics and the increasing demand for more flexible, transparent, and value-driven pricing models. It is especially suited for the dynamic and diverse needs of analytics users, whose data volumes, usage patterns, and analytical complexity requirements often fluctuate with the markets.
RiskSpan was an early adopter of the Amazon Web Services (AWS) cloud in 2010. Its new usage-based pricing, powered by the AWS cloud, enables RiskSpan to invoice its clients based on user-configured workloads, which can scale up or down as needed.
“Usage-based pricing is a game-changer for our clients and the industry,” said Bernadette Kogler, CEO of RiskSpan. “It aligns our pricing with the value we deliver and the outcomes we enable for our clients. It also eliminates the waste and inefficiency of paying for unused, fixed-fee compute capacity, year after year in long-term, set price contracts. Now our clients can optimize their spending while experimenting with all the features our platform has to offer.”
“We are excited RiskSpan chose AWS to launch its new pricing model. Our values are aligned in earning trust through transparent variable pricing that allows our customers to innovate and remain agile.” said Ben Schreiner, Head of Business Innovation, at Amazon Web Services. “By leveraging the latest in AWS technology, including our generative AI services, RiskSpan is accelerating the value they deliver to their customers, and ultimately, the entire financial services industry.”
Usage-based pricing offers several benefits for RiskSpan clients, including:
- Lower Costs: Clients pay only for what they need, rather than being locked into an expensive contract that may not suit their current or future situation.
- Cost Sharing: Clients can share costs across the enterprise and better manage expense based on usage by individual functions and business units.
- Transparency: Clients can monitor their usage and directly link their analytics configuration and usage to their results and goals. They can also better control their spending by tracking their usage and seeing how it affects their bill.
- Flexibility: Clients can experiment with different features and options of RiskSpan’s Edge Platform, as they are not restricted by a predefined package or plan.
For a free demo, visit https://riskspan.com/ubp/.
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About RiskSpan, Inc.
RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With an unparalleled team of data science experts and technologists, RiskSpan is the leader in data as a service and end-to-end solutions for loan-level data management and analytics.
Its mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments. Learn more at www.riskspan.com.
