Non-Agency MBS Archives

What is the Draw of Whole Loan Investing?

Mortgage whole loans are having something of a moment as an asset class, particularly among insurance companies and other nonbank institutional investors. With insurance companies increasing their holdings of whole loans by 35 percent annually over the past three years, many people are curious what it is about these assets that makes them so appealing in the current environment.

We sat down with Peter Simon, founder and CEO of Dominium Advisors, a tech-enabled asset manager specializing in the acquisition and management of residential mortgage loans for insurance companies and other institutional investors. As an asset manager, Dominium focuses on performing the “heavy lifting” related to loan investing for clients.

Click for Free Access to Whole Loan Trading Market Color Dashboards
on RiskSpan’s Edge Platform

How has the whole loan asset class evolved since the 2008 crisis? How have the risks changed?

Peter Simon: Since 2008, laws and regulations like the Dodd-Frank act and the formation of the Consumer Financial Protection Bureau have created important risk guardrails related to the origination of mortgage products. Many loan and mortgage product attributes, such as underwriting without proper documentation of income or assets or loan structures with negative amortization, which contributed to high levels of mortgage defaults in 2008 are no longer permissible. In fact, more than half of the types of mortgages that were originated pre-crisis are no longer permitted under the current “qualified mortgage” regulations. In addition, there have been substantial changes to underwriting, appraisal and servicing practices which have reduced fraud and conflicts of interest throughout the mortgage lifecycle.

How does whole loan investing fit into the overall macro environment?

Peter Simon: Currently, the macro environment is favorable for whole loan investing. There is a substantial supply-demand imbalance – meaning there are more buyers looking for places to live then there are homes for them to live in. At the current rates of new home construction, mobility trends, and household formation, it is expected that this imbalance will persist for the next several years. Demographic trends are also widening the current supply demand imbalance as more millennial buyers are entering their early 30s – the first time-homebuyer sweet spot. And work from home trends created by the pandemic are creating a desire for additional living space.

Who is investing in whole loans currently?

Peter Simon: Banks have traditionally been the largest whole loan investors due to their historical familiarity with the asset class, their affiliated mortgage origination channels, their funding advantage and favorable capital rules for holding mortgages on balance sheet. Lately, however, banks have pulled back from investing in loans due to concerns about the stickiness of deposits, which have been used traditionally to fund a portion of mortgage purchases, and proposed bank capital regulations that would make it more costly for banks to hold whole loans. Stepping in to fill this void are other institutional investors — insurance companies, for example — which have seen their holdings of whole loans increase by 35% annually over the past 3 years. Credit and hedge funds and pension funds are also taking larger positions in the asset class.

What is the specific appeal of whole loans to insurance companies and these other firms that invest in them?

Peter Simon: Spreads and yields on whole loans produce favorable relative value (risk versus yield) when compared to other fixed income asset classes like corporate bonds. Losses since the Financial Crisis have been exceptionally low due to the product, process and regulatory improvements enacted after the Financial Crisis. Whole loans also produce risks in a portfolio that tend to increase overall portfolio diversification. Borrower prepayment risk, for example, is a risk that whole loan investors receive a spread premium for but is uncorrelated with many other fixed income risks. And for investors looking for real estate exposure, residential mortgage risk has a much different profile than commercial mortgage risk.

Why don’t they just invest in non-Agency securities?

Peter Simon: Many insurance companies do in fact buy RMBS securities backed by non-QM loans. In fact, most insurance companies who have residential exposure will have it via securities. The thesis around investing in loans is that the yields are significantly higher (200 to 300 bps) than securities because loans are less liquid, are not evaluated by the rating agencies and expose the insurer to first loss on a defaulted loan. So for insurance investors who believe the extra yield more than compensates them for these extra risks (which historically over the last 15 years it has), they will likely be interested in investing in loans.

What specific risk metrics do you evaluate when considering/optimizing a whole loan portfolio – which metrics have the highest diagnostic value?

Peter Simon: Institutional whole loan investors are primarily focused on three risks: credit risk, prepayment risk and liquidity risk. Credit risk, or the risk that an investor will incur a loss if the borrower defaults on the mortgage is typically evaluated using many different scenarios of home price appreciation and unemployment to evaluate both expected losses and “tail event” losses. This risk is typically expressed as projected lifetime credit losses. Prepayment risk is commonly evaluated using loan cash flow computed measures like option adjusted duration and convexity under various scenarios related to the potential direction of future interest rates (interest rate shocks).

How would you characterize the importance of market color and how it figures into the overall assessment/optimization process?

Peter Simon: Newly originated whole loans like any other “new issue” fixed income product are traded in the market every day. Whole loans are generally priced at the loan level based on their specific borrower, loan and property attributes. Collecting and tabulating loan level prices every day is the most effective way to construct an investment strategy that optimizes the relative differences between loans with different yield characteristics and minimizes credit and prepayment risks in many various economic and market scenarios.

Click for Free Access to Whole Loan Trading Market Color Dashboards
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The future of analytics pricing is RiskSpan’s Usage-based delivery model

Usage-based pricing model brings big benefits to clients of RiskSpan’s Edge Platform

Analytic solutions for loans, MSRs and structured products are typically offered as software-as-a-service (SaaS) or “on-prem” products, where clients pay a monthly or annual fee to access the software and its features. The compute needed to run analytic workloads is typically purchased in advance and is fixed regardless of the need or use case.

However, this traditional pricing model is not always the best fit for the dynamic and diverse needs of analytics users. It is technologically outdated and does not meet users where they are – with varying data volumes, usage patterns, and analytical complexity requirements that fluctuate with the markets. It is simply wasteful for companies to pay for unused, fixed-fee compute capacity, year-after-year in long-term, set price contracts, when their needs don’t require it.

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.

RiskSpan Introduces Usage-based Pricing

RiskSpan has just announced the release of industry-innovating usage-based pricing that allows clients to scale up or down, based on their needs. Further, clients of the RiskSpan platform will now benefit from access to the full Edge Platform, including data, models and analytics – eliminating the need to license individual product modules. The Platform supports loans, MSRs and securities, with growing capabilities around private credit. Analyzing these assets can be compute- and data-intensive because of the need for collateral (loan-level) data and models to price, value, and calculate risk metrics.

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Usage-based pricing is an innovative alternative approach based on user-configured workloads. It enables RiskSpan to invoice its clients according to how much compute they actually need and use, rather than a fixed fee based on the modules they purchased during the last budget cycle.

Key Benefits of RiskSpan’s Usage-based Pricing

Usage-based pricing benefits RiskSpan clients in several ways, 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 Across the Enterprise: Clients can share costs across the enterprise and better manage expense based on usage by internal 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, as they can track their usage and see how it affects their bill.

- Flexibility: Clients can experiment with different features and options of the Platform, as they are not restricted by a predefined package or plan.

Usage-based pricing is not a one-size-fits-all solution, and it may not be suitable for every organization. Based on needs, large enterprise workloads will require specific, customized licensing and may benefit from locked in compute that comes with volume discounts.

Bottom Line on RiskSpan’s Usage-based Pricing Model

CONS of Traditional Fixed Fee Pricing	PROS of Usage-Based Pricing
Flat-fee pricing models force customers to pay for unused capacity.	Lower Costs — Pay only for what you use, not the wasted capacity of a dedicated cluster
Unused capacity cannot be shared across the enterprise, which translates into wasted resources and higher costs.	Cost Sharing — Costs can be shared across the enterprise to better manage expense based on usage by your internal functions and business units
Fixed pricing models make it difficult for customers to scale up or down as needed.	Transparency — Transparent pricing that fits your specific analytics workload (size, complexity, performance)
Traditional “product module-based” purchasing runs the risk of over-buying on features that will not be used.	Flexibility — Scale up and scale down your use as new and in-place features become useful to you under different market conditions

Sample Use Cases: Application to Loans, MSR, and Structured Products

With the introduction of usage-based pricing, RiskSpan is adding core value to its Edge Platform and a low-cost entry point to bring its solution to a wider base of clients. Its industry-leading capabilities solve challenges facing various users in the loans, MSR, and structured portfolio domains. For example:

1. Loan/MSR Trader seeks analytics to support bidding on pools of loans and/or MSRs. Their usage is ad-hoc and will benefit from usage-based pricing. Traders and investors can analyze prepay and credit performance trends by leveraging RiskSpan’s 20+ years of historical performance datasets.

1. Securities Trader (Agency or Non-Agency) wants more flexibility to set their prepay or credit model assumptions to run ad-hoc scenario analysis not easily handled by their current vendor.

1. Risk Manager wants another source of valuation for periodic MSR and loan portfolios to enhance decision making and compare against the marks from their third-party valuation firm.

1. Private Credit Risk Manager needs a built-for-purpose private credit analytics system to properly run risk metrics. Users can run separate and run ad hoc analysis on these holdings.

RiskSpan’s Simple and Transparent Usage-Based Pricing Plans

For more specific information about how RiskSpan will structure pricing with various commitment levels, click below to tell us about your needs, and a representative will be in touch with you shortly.

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Temporary Buydowns are Back. What Does This Mean for Speeds?

Mortgage buydowns are having a deja-vu moment. Some folks may recall mortgages with teaser rates in the pre-crisis period. Temporary buydowns are similar in concept. Recent declines notwithstanding, mortgage rates are still higher than they have been in years. Housing remains pricey. Would-be home buyers are looking for any help they can get. While on the other hand, with an almost non-existent refi market, mortgage originators are trying to find innovative ways to keep the production machine going. Conditions are ripe for lender and/or builder concessions that will help close the deal.

Enter the humble “temporary” mortgage interest rate buydown. A HousingWire article last month addressed the growing trend. It’s hard to turn on the TV without being bombarded with ads for Rocket Mortgage’s “Inflation Buster” program. Rocket Mortgage doesn’t use the term temporary buydown in its TV spots, but that is what it is.

Buydowns, in general, refer to when a borrower pays “points” upfront to reduce the mortgage rate to a level where they can afford the monthly payment. The mortgage rate has been “bought down” from its original rate for the entire life of the mortgage by paying a lumpsum upfront. Temporary Buydowns, on the other hand, come in various shapes and sizes, but the most common ones are a “2 – 1” (a 2-percent interest rate reduction in the first year and a 1-percent reduction in year two) and a “1 – 0” (a 1-percent interest rate reduction in the first year only). In these situations, the seller, or the builder, or the lender or a combination thereof put-up money to cover the difference in interest rate payments between the original mortgage rate and the reduced mortgage rate. In the 2-1 example above, the mortgage rate is reduced by 2% for the first year and then steps up by 1% in the second year and then steps up by another 1% in the 3^rd year to reach the actual mortgage rate at origination. So, the interest portion of the monthly mortgage payments are “subsidized” for the first two years and then revert to the full monthly payment. Given the inflated rental market, these programs can make purchasing more advantageous than renting (for home seekers trying to decide between the two options). They can also make purchasing a home more affordable (temporarily, at least) for would-be buyers who can’t afford the monthly payment at the prevailing mortgage rate. It essentially buys them time to refinance into a lower rate should interest rates fall over the subsidized time frame or they may be expecting increased income (raises, business revenue) in the future which will allow them to afford the unsubsidized monthly payment.

Temporary buydowns present an interesting situation for prepayment and default modelers. Most borrowers with good credit behave similarly to refinance incentives, barring loan size and refi cost issues. While permanent buydowns tend to exhibit slower speeds when they come in the money by a small amount since the borrower needs to make a cost/benefit decision about recouping the upfront money they put down and the refi costs associated with the new loan. Their breakeven point is going to be lower by 25bps or 50bps from their existing mortgage rate. So, their response to mortgage rates dropping will be slower than borrowers with similar mortgage rates who didn’t pay points upfront. Borrowers with temporary buydowns will be very sensitive to any mortgage rate drops and will refinance at the first opportunity to lock in a lower rate before the “subsidy” expires. Hence, such mortgages are expected to prepay at higher speeds then other counterparts with similar rates. In essence, they behave like ARMs when they approach their reset dates.

When rates stay static or increase, temporary buydowns will behave like their counterparts except when they get close to the reset dates and will see faster speeds. Two factors would contribute to this phenomenon. The most obvious reason is that temporary buydown borrowers will want to refinance into the lowest rate available at the time of reset (perhaps an ARM). The other possibility is that some of these borrowers may not be able refi because of DTI issues and may default. Such borrowers may also be deemed “weaker credits” because of the subsidy that they received. This increase in defaults would elevate their speeds (increased CBRs) relative to their counterparts.

So, for the reasons mentioned above, temporary buydown mortgages are expected to be the faster one among the same mortgage rate group. In the table below we separate borrowers with the same mortgage rate into 3 groups: 1) those that got a normal mortgage at the prevailing rate and paid no points, 2) those that paid points upfront to get a permanent lower rate and 3) those who got temporary lower rates subsidized by the seller/builder/lender. Obviously, the buydowns occurred in higher rate environments but we are considering 3 borrower groups with the same mortgage rate regardless of how they got that rate. We are assuming that all 3 groups of borrowers currently have a 6% mortgage. We present the expected prepay behavior of all 3 groups in different mortgage rate environments:

*Turnover++ means faster due to defaults or at reset

Rate	Rate Shift	6% (no pts)	Buydown to 6%(borrower-paid)	Buydown to 6% (lender-paid)
7.00%	+100	Turnover	Turnover	Turnover++*
6.00%	Flat	Turnover	Turnover	Faster (at reset)
5.75%	-25	Refi	Turnover	Refi
5.00%	-100	Refi (Faster)	Refi (Fast)	Refi (Fastest)

Overall, temporary buydowns are likely to exhibit the most rate sensitivity. As their mortgage rates reset higher, they will behave like ARMs and refi into any other lower rate option (5/1 ARM) or possibly default. In the money, they will be the quickest to refi.

Incorporating Covid-Era Mortgage Data Without Skewing Your Models

What we observed during Covid represents a radical departure from what we observed pre-Covid. To what extent do these observations impact long-term trends observed for mortgage performance? Should these data fundamentally impact the way in which we think about the effects borrower, loan and macroeconomic characteristics have on mortgage performance? Or do we need to simply account for them as a short-term blip?

The process of modeling mortgage defaults and prepayments typically begins with identifying long-term trends and reference values. These aid in creating the baseline forecasts that undergird the model in its most simplistic form. Modelers then begin looking for deviations from this baseline created by specific loan, borrower, and property characteristics, as well as by key macroeconomic variables.

Identifying these relationships enables modelers to begin quantifying the extent to which micro factors like income, credit score, and loan-to-value ratios interact with macro indicators like the unemployment rate to cause prepayments and defaults to depart from their baseline. Data observations aggregated over extended periods give a comprehensive picture possible of these relationships.

In practice, the human behavior underlying these and virtually all economic models tends to change over time. Modelers account for this by making short-term corrections based on observations from the most recent time periods. This approach of tweaking long-term trends based on recent performance works reasonably well under most circumstances. One could reasonably argue, however, that tweaking existing models using performance data collected during the Covid-19 era presents a unique set of challenges.

What was observed during Covid represents a radical departure from what was observed pre-Covid. To what extent do these observations impact long-term trends and reference values. Should these data fundamentally impact the way in which we think about the effects borrower, loan and macroeconomic characteristics have on mortgage performance? Or do we need to simply account for them as a short-term blip?

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How Covid-era mortgage data differs

When it comes to modeling mortgage performance, we generally think of three sets of factors: 1) macroeconomic conditions, 2) loan and borrower characteristics, and 3) property characteristics. In determining how to account for Covid-era data in our modeling, we first must attempt to evaluate its impact on these factors. Three macroeconomic factors have played an especially significant role recently. First, as reflected in the chart below, we experienced a significant home-price decline during the 2008 financial crisis but a steady increase since then.

Second, mortgage rates continued to decline for the most part during the crisis and beyond. There were brief periods when they increased, but they remained low by and large.

The third piece is the unemployment rate. Unemployment spiked to around 10 percent during the financial crisis and then slowly declined.

When home prices declined in the past, we typically saw the government attempt to respond to it by reducing interest rates. This created something of a correlation between home prices and mortgage rates. Looking at this from a purely statistical viewpoint, the only thing the historical data shows is that falling home prices bring about a decline in mortgage rates. (And rising home prices bring about higher interest rates, though to a far lesser degree.) We see something similar with unemployment. Falling unemployment is correlated with rising home prices.

But then Covid arrives and with it some things we had not observed previously. All the “known” correlations among these macroeconomic variables broke down. For example, the unemployment rate spikes to 15 percent within just a couple of months and yet has no negative impact at all on home prices. Home prices, in fact, continue to rise, supported by the very generous unemployment benefits provided during Covid pandemic.

This greatly complicates the modeling. Here we had these variable relationships that appeared steady over a period of decades, and all of our modeling was being done (knowingly or unknowingly) relying on these correlations, and suddenly all these correlations are breaking down.

What does this mean for forecasting prepayments? The following chart shows prepayments over time by vintage. We see extremely high prepayment rates between early 2020 (the start of the pandemic) and early 2022 (when rates started rising). This makes sense.

Look at what happens to our forecasts, however, when rates begin to increase. The following chart reflects the models predicting a much steeper drop-off in prepayments than what was actually observed for a July 2021 issuance Fannie Mae major of coupon 2.0. These mortgage loans with no refinance incentive are prepaying faster than what would be expected based on the historical data.

What is causing this departure?

The most plausible explanation relates to an observed increase in cash-out refinances caused by the recent run-up in home prices and resulting in many homeowners suddenly finding themselves with a lot of home equity to tap into. Pre-Covid , cash-outs accounted for between a third and a quarter of refinances. Now, with virtually no one in the money for a rate-and-term refinance, cash-outs are accounting for over 80 percent of them.

We learn from this that we need to incorporate the amount of home equity gained by borrowers into our prepayment modeling.

Modeling Credit Performance

Of course, Covid’s impacts were felt even more acutely in delinquency rates than in prepays. As the following chart shows, a borrower that was 1-month delinquent during Covid had a 75 percent probability of being 2-months delinquent the following month.

This is clearly way outside the norm of what was observed historically and compels us to ask some hard questions when attempting to fit a model to this data.

The long-term average of “two to worse” transitions (the percentage of 60-day delinquencies that become 90-day delinquencies (or worse) the following month) is around 40 percent. But we’re now observing something closer to 50 percent. Do we expect this to continue in the future, or do we expect it to revert back to the longer-term average. We observe a similar issue in other transitions, as illustrated below. The rates appear to be stabilizing at higher levels now relative to where they were pre-Covid. This is especially true of more serious delinquencies.

How do we respond to this? What is the best way to go about combining this pre-Covid and post-Covid data?

Principles for handling Covid-era mortgage data

One approach would be to think about Covid data as outliers that should be ignored. At the other extreme, we could simply accept the observed data and incorporate it without any special considerations. A split-the-difference third approach would have us incorporate the new data with some sort of weighting factor for use in future stress scenarios without completely casting aside the long-term reference values that had stood the test of time prior to the pandemic.

This third approach requires us to apply the following guiding principles:

Assess assumed correlations between driving macro variables: For example, don’t allow the model to assume that increasing unemployment will lead to higher home prices just because it happened once during a pandemic.
Choose short-term calibrations carefully. Do not allow models to be unduly influenced by blindly giving too much weight to what has happened in the past two years.
Determine whether the new data in fact reflects a regime shift. How long will the new regime last?
Avoid creating a model that will break down during future unusual periods.

Prepare for other extremes. Incorporate what was learned into future stress testing

Build models that allow sensitivity analyses and are easy to change/tune. Models need to be sufficiently flexible that they can be tuned in response to macroeconomic events in a matter of weeks, rather than taking months or years to design and build an entirely new model.

Covid-era mortgage data presents modelers with a unique challenge. How to appropriately consider it without overweighting it. These general guidelines are a good place to start. For ideas specific to your portfolio, contact a RiskSpan representative.

SPEAK TO AN EXPERT

New Refinance Lag Functionality Affords RiskSpan Users Flexibility in Higher Rate Environments

ARLINGTON, Va., September 29, 2022 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced that users of its award-winning Edge Platform can now fine-tune the assumed time lag between a rate-incentivized borrower’s decision to refinance and ultimate payoff. Getting this time lag right unveils a more accurate understanding of the rate incentive that borrowers responded to and thus better predictions of coming prepayments.

The recent run-up in interest rates has caused the number of rate-incentivized mortgage refinancings to fall precipitously. Newfound operational capacity at many lenders, created by this drop in volume, means that new mortgages can now be closed in fewer days than were necessary at the height of the refi boom. This “lag time” between when a mortgage borrower becomes in-the-money to refinance and when the loan actually closes is an important consideration for MBS traders and analysts seeking to model and predict prepayment performance.

Rather than confining MBS traders to a single, pre-set lag time assumption of 42 days, users of the Edge Platform’s Historical Performance module can now adjust the lag assumption when building their S-curves to better reflect their view of current market conditions. Using the module’s new Input section for Agency datasets, traders and analysts can further refine their approach to computing refi incentive by selecting the prevailing mortgage rate measure for any given sector (e.g., FH 30Y PMMS, MBA FH 30Y, FH 15Y PMMS and FH 5/1 PMMS) and adjusting the lag time to anywhere from zero to 99 days.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo below or at riskspan.com.

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This new functionality is the latest in a series of enhancements that is making the Edge Platform increasingly indispensable for Agency MBS traders and investors.

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About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Media contact: Timothy Willis

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Boost trading profits and efficiency with Edge. Be ready to act when opportunities arise by pre-analyzing RMBS performance data and ranking cohorts. Confirm relative credit value against benchmarks; value bonds with loan-level forecasts and Intex-integrated cash flows and analytics; and infer current prices with filtered market color. Analyze new issue deals with precision by linking loan and CDI files. Lift portfolio returns over time by using Edge’s risk signals to informing reweighting and leverage.

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View a bond’s credit support and collateral composition and performance alongside user-selected benchmarks.

Compare bond credit support and thickness
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Spot risk pockets with collateral composition visuals, like geographic concentration maps and FICO-LTV scatterplots with color scaling to flag loans with additional risky features
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Query aging curves for custom cohorts from Edge data
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Compared to manual data manipulation, save time, add precision, and avoid errors

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Load a loan tape and Intex CDI file to:

See the loan credit quality vs. benchmarks
Forecast bond cash flows based on loan-level data

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See recent transaction prices of bonds that meet user-defined criteria
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How has the whole loan asset class evolved since the 2008 crisis? How have the risks changed?

How does whole loan investing fit into the overall macro environment?

Who is investing in whole loans currently?

What is the specific appeal of whole loans to insurance companies and these other firms that invest in them?

Why don’t they just invest in non-Agency securities?

What specific risk metrics do you evaluate when considering/optimizing a whole loan portfolio – which metrics have the highest diagnostic value?

How would you characterize the importance of market color and how it figures into the overall assessment/optimization process?

Usage-based pricing model brings big benefits to clients of RiskSpan’s Edge Platform

RiskSpan Introduces Usage-based Pricing

A Single PlatformIntegrated Data | Trade Analytics | Risk Management

Key Benefits of RiskSpan’s Usage-based Pricing

Bottom Line on RiskSpan’s Usage-based Pricing Model

Sample Use Cases: Application to Loans, MSR, and Structured Products

RiskSpan’s Simple and Transparent Usage-Based Pricing Plans

How Covid-era mortgage data differs

Modeling Credit Performance

Principles for handling Covid-era mortgage data

by Pre-Analyzing History

by Benchmarking Bond, Collateral

with Loan-Level, Intex-Integrated Forecasts

with Filtered Market Color

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Integrated Data | Trade Analytics | Risk Management