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Articles Tagged with: Prepayment Analytics

Webinar: An Investor’s Guide to America’s Housing Supply Crisis

Presenters

Amy Crews Cutts

President, AC Cutts and Associates and Chief Economist, NACM

Michael Neal

Equity Scholar and 
Principal Research Associate, Urban Institute

Janet Jozwik

Senior Managing Director and Head of Climate Analytics, RiskSpan

Divas Sanwal

Managing Director and Head of Modeling, RiskSpan

Wednesday, March 29th | 1:00 p.m. ET

Join us for an informative webinar on the nation’s current “out-of-whack” housing supply and what it means for mortgage investors, homeowners, prospective homebuyers, and renters alike!

Housing economists Amy Crews Cutts and Michael Neal will join RiskSpan credit and prepayment modelers Janet Jozwik and Divas Sanwal to explore the factors that contribute to the current housing supply imbalance, including the cost of building, the impact of permits and zoning, and the emergence of the “missing middle.” They will discuss how high interest rates and rental prices are incentivizing owners who relocate to hold old on to their old properties and become landlords. They will also examine the impact of ADUs, zoning issues, and the availability of renovation financing.

Mortgage loan and security investors will learn about what housing supply means for prepay speeds. The panelists will consider the role of financing in addressing housing supply issues, including the market for low-balance loans and unconventional options like contracts for deed and lease-to-own arrangements.

Join us for an engaging discussion on the evolving housing needs of the population, including the desire to age in place, the challenges posed by multigenerational living arrangements, and the viability of several proposed solutions, including the potential for converting unused commercial properties into housing.

Don’t miss out on this informative and timely webinar!


Are Recast Loans Skewing Agency Speeds?

In a previous blog, we highlighted large curtailments on loans, behavior that was driving a prepayment spike on some new-issue pools. Any large curtailment should also result in shortening the remaining term of the loan because the mortgage payment is nearly always “level-pay” for loans in a conventional pool. And we see that behavior for all mortgages experiencing large curtailments.

However, we noted that nearly half of these loans showed a subsequent extension of their remaining term back to where it would have been without the curtailment.1 This extension occurred anywhere between zero and sixteen months after the curtailment, with a median of one month after the large payment. We presume these maturity extensions are a loan “recast,” which is explained well in a recent FAQ from Rocket Mortgage. In summary, a recast allows the borrower to lower their monthly payment after making a curtailment above some threshold, typically at least $10,000 extra principal.

Some investors may not be aware that a recast loan may remain in the trust, especially since the terms of the loan are being changed without a buyout.2 Further, since the extension lowers the monthly payment, the trust will receive principal more slowly ex curtailment than under the original terms of the loan. This could possibly affect buyers of the pool after the curtailment and before the recast.

While the number of recast loans is small, we found it interesting that the loan terms are changed without removing the loans from the pool. We identified nearly 7,800 loans that were issued between 2021 Q4 and 2022 Q1 and had both a curtailment greater than $10,000 and a subsequent re-extension of loan term.

Of these loans, the typical time to term-recast is zero to two months, with 1% of the loans recasting a year or more after the curtailment.

Some of these loans reported multiple curtailments and recasts, with loan 9991188863 in FR QD1252 extending on three separate occasions after three large curtailments. It seems the door is always open to extension.

For loans that recast their maturities after a curtailment, 85% had extensions between 10 and 25 years.

Large curtailments are uncommon and term-recasts comprise roughly half of loans in our sample with large curtailments, so term recasts will typically have only a small effect on pool cash flows, extending the time of principal receipt ex curtailment and possibly changing borrower behavior.3 For large pools, any effect will be typically exceeded by prepayments due to turnover.

However, for some smaller pools the WAM extension due to recast is noticeable. We identified dozens of pools whose WAM extended after a recast of underlying loan(s). The table below shows just a few examples. All of these pools are comparatively small, which is to be expected since just one or two individual loan recasts can have an outsized effect on a small pool’s statistics.

Pool IDFactor DateCurrent FaceExtension (months)
FR QD76177/202220,070,7376
FR QD00061/202215,682,7755
FN CB336711/202214,839,9195
FR QD57367/202210,916,9596
FN BU05814/202210,164,0006
FR QD44926/20223,113,53216
FN BV20765/20223,165,50918
FR QD60137/20223,079,25022



RiskSpan Incorporates Flexible Loan Segmentation into Edge Platform

ARLINGTON, Va., March 3, 2023 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced the incorporation of Flexible Loan Segmentation functionality into its award-winning Edge Platform.

The new functionality makes Edge the only analytical platform offering users the option of alternating between the speed and convenience of rep-line-level analysis and the unmatched precision of loan-level analytics, depending on the purpose of their analysis.

For years, the cloud-native Edge Platform has stood alone in its ability to offer the computational scale necessary to perform loan-level analyses and fully consider each loan’s individual contribution to a mortgage or MSR portfolio’s cash flows. This level of granularity is of paramount importance when pricing new portfolios, taking property-level considerations into account, and managing tail risks from a credit/servicing cost perspective.

Not every analytical use case justifies the computational cost of a full loan-level analysis, however. For situations where speed requirements dictate the use of rep lines (such as for daily or intra-day hedging needs), the Edge Platform’s new Flexible Loan Segmentation affords users the option to perform valuation and risk analysis at the rep line level.

Analysts, traders and investors take advantage of Edge’s flexible calculation specification to run various rate and HPI scenarios, key rate durations, and other calculation-intensive metrics in an efficient and timely manner. Segment-level results run at both loan and rep line level can be easily compared to assess the impacts of each approach. Individual rep lines are easily rolled up to quickly view results on portfolio subcomponents and on the portfolio as a whole.

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

This new functionality is the latest in a series of enhancements that further the Edge Platform’s objective of providing frictionless insight to Agency MBS traders and investors, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

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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. Learn more at www.riskspan.com.


RiskSpan’s Snowflake Tutorial Series: Ep. 1

Learn how to create a new Snowflake database and upload large loan-level datasets

The first episode of RiskSpan’s Snowflake Tutorial Series has dropped!

This six-minute tutorial succinctly demonstrates how to:

  1. Set up a new Snowflake #database
  2. Use SnowSQL to load large datasets (28 million #mortgage loans in this example)
  3. Use internal staging (without a #cloud provider)

This is this first in what is expected to be a 10-part tutorial series demonstrating how RiskSpan’s Snowflake integration makes mortgage and structured finance analytics easier than ever before.

Future topics will include:

  • Executing complex queries using python functions in Snowflake’s SQL
  • External Tables (accessing data without a database)
  • OLAP vs OLTP and hybrid tables in Snowflake
  • Time Travel functionality, clone and data replication
  • Normalizing data and creating a single materialized view
  • Dynamic tables data concepts in Snowflake
  • Data share
  • Data masking
  • Snowpark: Data analysis (pandas) functionality in Snowflake

The Curious Case of Curtailments

With more than 90% of mortgages out-of-the-money from a refinancing standpoint, the MBS market has rightly focused on activities that affect discounts, including turnover and to a much lesser extent cash-out refinancings. In this analysis we examine the source of fast speeds on new issue loans and pools.

As we dig deeper on turnover, we notice a curious behavior related to curtailments that has existed for several years but gone largely ignored in recent refi-dominated environments. Curtailment activity, especially higher-than-expected curtailments on new-production mortgages, has steadily gotten stronger in more recent vintages.

For this analysis we define a curtailment as any principal payment that is larger than the contractual monthly payment but smaller than the remaining balance of the loan, which is more typically classified as payoff due to either a refinancing or house sale. In the first graph, we show curtailment speeds for new loans with note rates that were not refinanceable on a rate/term basis.1 As you can see, the 2022 vintage shows a significant uptick in curtailments in the second month. Other recent vintages show lower but still significant early-month curtailments, whereas pre-2018 vintages show very little early curtailment activity.

Digging deeper, we separate the loans by purpose: purchase vs. refi. Curtailment speeds are significantly higher among purchase loans than among refis in the first six months, with a noticeable spike at months two and three.

Focusing on purchase loans, we notice that the behavior is most noticeable for non-first-time homebuyers (non-FTHB) and relatively absent with FTHBs. The 2022-vintage non-FTHB paid nearly 6 CPR in their second month of borrowing.

What drives this behavior? While it’s impossible to say for certain, we believe that homeowners purchasing new homes are using proceeds from the sale of the previous home to partially pay off their new loan, with the sale of the previous loan coming a month or so after the close of the first loan.

How pervasive is this behavior? We looked at purchase loans originated in 2022 where the borrower was not a first-time home buyer and noted that 0.5% of the loans account for nearly 75% of the total curtailment activity on a dollar basis. That means these comparatively high, early speeds (6 CPR and higher on some pools) are driven by a small number of loans, with that vast majority of loans showing no significant curtailments in the early months.

High-curtailment loans show large payments relative to their original balances, ranging from 5% to 85% of the unpaid balance with a median value of 25%. We found no pattern with regard to either geography or seller/servicer. Looking at mortgage note rates, 80% of these high-curtailment loans were at 3.5% or lower and only 10% of these borrowers had a positive refinancing incentive at all. Only 1.5% had incentives above 25bp, with a maximum incentive of just 47bp. These curtailments are clearly not explained by rate incentive.

The relatively rarity of these curtailments means that, while in aggregate non-FTHBs are paying nearly 6 CPR in the early months, actual results within pools may vary greatly. In the chart below, we show pool speeds for 2022-vintage majors/multi-lenders, plotted against the percentage of the pool’s balance associated with non-FTHB purchases. We controlled for refi incentive by looking at pools that were out of the money by 0bp to 125bp. As the percentage of non-FTHBs in a pool increases, so does early prepayment speed, albeit with noise around the trend.

We observe that a very small percentage of non-FTHB borrowers are making large curtailment payments in the first few months after closing and that these large payments translate into a short-term pop in speeds on new production at- or out-of-the-money pools. Investors looking to take advantage of this behavior on discount MBS should focus on pools with high non-FTHB borrowers.


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 3rd 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.

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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?

SPEAK TO AN EXPERT

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:

  1. 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.
  2. 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.
  3. Determine whether the new data in fact reflects a regime shift. How long will the new regime last?
  4. Avoid creating a model that will break down during future unusual periods.
  1. Prepare for other extremes. Incorporate what was learned into future stress testing
  1. 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


How Rithm Capital leverages RiskSpan’s expertise and Edge Platform to enhance data management and achieve economies of scale

 

BACKGROUND

 

One of the nation’s largest mortgage loan and MSR investors was hampered by a complex data ingestion process as well as slow and cumbersome on-prem software for pricing and market risk.

A complicated data wrangling process was taking up significant time and led to delays in data processing. Further, month-end risk and financial reporting processes were manual and time-pressured. The data and risk teams were consumed with maintaining the day-to-day with little time available to address longer-term data strategies and enhance risk and modeling processes.

 

OBJECTIVES

  1. Modernize Rithm’s mortgage loan and MSR data intake from servicers — improve overall quality of data through automated processes and development of a data QC framework that would bring more confidence in the data and associated use cases, such as for calculating historical performance.

  2. Streamline portfolio valuation and risk analytics while enhancing granularity and flexibility through loan-level valuation/risk.

  3. Ensure data availability for accounting, finance and other downstream processes.

  4. Bring scalability and internal consistency to all of the processes above.

THE SOLUTION



THE EDGE WE PROVIDED

By adopting RiskSpan’s cloud-native data management, managed risk, and SaaS solutions, Rithm Capital saved time and money by streamlining its processes

Adopting Edge has enabled Rithm to access enhanced and timely data for better performance tracking and risk management by:

  • Managing data on 5.5 million loans, including source information and monthly updates from loan servicers (with ability in the future to move to daily updates)
  • Ingesting, validating and normalizing all data for consistency across servicers and assets
  • Implementing automated data QC processes
  • Performing granular, loan-level analysis​

 


With more than 5 million mortgage loans spread across nine servicers, Rithm needed a way to consume data from different sources whose file formats varied from one another and also often lacked internal consistency. Data mapping and QC rules constantly had to be modified to keep up with evolving file formats. 

Once the data was onboarded Rithm required an extraordinary amount of compute power to run stochastic paths of Monte Carlo rate simulations on all 4 million of those loans individually and then discount the resulting cash flows based on option adjusted yield across multiple scenarios.

To help minimize the computing workload, Rithm had been 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 and limited reporting flexibility because results were not at the loan-level.

Enter RiskSpan’s Edge Platform.

Combining the strength of RiskSpan’s subject matter experts, quantitative analysts, and technologists together with the power of the Edge platform, RiskSpan has helped Rithm achieve its objectives across the following areas: 

Data management and performance reporting

  • Data intake and quality control for 9 servicers across loan and MSR portfolios
  • Servicer data enrichment
  • Automated data loads leading to reduced processing time for rolling tapes
  • Ongoing data management support and resolution
  • Historical performance review and analysis (portfolio and universe)

Valuation and risk

  • Daily reporting of MSR, mortgage loan and security valuation and risk analytics based on customized Tableau reports
  • MSR and whole loan valuation/risk calculated based at the loan-level leveraging the scalability of the cloud-native infrastructure
  • Additional scenario analysis and other requirements needed for official accounting and valuation purposes

Interactive tools for portfolio management

  • Fast and accurate tape cracking for purchase/sale decision support
  • Ad-hoc scenario analyses based on customized dials and user-settings

The implementation of these enhanced data and analytics processes and increased ability to scale these processes has allowed Rithm to spend less time on day-to-day data wrangling and focus more on higher-level data analysis and portfolio management. The quality of data has also improved, which has led to more confidence in the data that is used across many parts of the organization.


LET US BUILD YOUR SOLUTION

Models + Data management = End-to-end Managed Process

The economies of scale we have achieved by being able to consolidate all of our portfolio risk, interactive analytics, and data warehousing onto a single platform are substantial. RiskSpan’s experience with servicer data and MSR analytics have been particularly valuable to us.

          — Head of Analytics


RiskSpan Introduces Proprietary Measure for Plotting Burnout Effect on Prepays, Adds RPL/NPL Forecasting

ARLINGTON, Va., June 22, 2022 —

RiskSpan, a leading provider of residential mortgage and structured product data and analytics, has announced a series of new enhancements in the latest release of its award-winning Edge Platform.  

Comprehensive details of these new capabilities are available by requesting a no-obligation demo at riskspan.com.

  • Burnout Metrics MBS traders and investors can now look up a proprietary, cumulative burnout metric that quantifies the extent to which a defined pool of mortgages has continued to pay coupons above refinance rates over time. The metric goes beyond simple comparisons of note rates to historic prevailing rates by also tracking the number of times borrowers have ignored the “media effect” of repeatedly seeing rates reach record lows. Edge users can plot empirical prepay speeds as a function of burnout to help project performance of pools with various degrees of burnout. A virtual walk-through of this functionality is available here.
  • Reperforming Loans Investors in nonperforming and reperforming loans – particularly RPLs that have recently emerged from covid forbearance – can now project performance and cash flows of loans with deferred balances. Edge reads in the total debt owed (TDO) recovery method and has added key output fields like prepaid principal percent reduction and total debt owed to its cash flow report.
  • Hedge Ratios – The Edge Platform now enables traders and portfolio managers to easily compute, in one single step, the quantity of 2yr, 5yr, 10yr, or 30yr treasuries (or any combination of these or other hedges) that must be sold to offset the effective duration of assets in a given portfolio. Swaps, swaptions and other hedges are also supported. Clearly efficient and useful for any portfolio of interest-rate-sensitive assets, the functionality is proving particularly valuable to commercial banks with MSR holdings and others who require daily transparency to hedging ratios.  

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About RiskSpan, Inc. 

RiskSpan offers end-to-end solutions for data management, historical performance, predictive analytics and portfolio risk management on a secure, fast, and scalable platform that has earned the trust of the industry’s largest firms. Combining the strength of subject matter experts, quantitative analysts, and technologists, RiskSpan’s Edge platform integrates a range of datasets – structured and unstructured – and off-the-shelf analytical tools to provide you with powerful insights and a competitive advantage. Learn more at www.riskspan.com.  

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Value Opportunities in Private-Label Investor Loan Deals

The supply of investor loan collateral in private securitizations has surged in 2021 and projects to remain high (more on this below). To gain an informational edge while selecting bonds among this new issuance, traders and investors have asked RiskSpan for data and tools to dissect the performance of investor loans. Below, we first show the performance of investor loans compared to owner-occupied loans, and then offer a glimpse into a few relative value opportunities using our data and analytics platform, Edge.

As background, the increase of investor loan collateral in PLS was spurred by a new FHFA policy, recently suspended, that capped GSE acquisitions of investor and second home loans at 7% of seller volume. This cap forced originators to explore private-label securitization which, while operationally more burdensome than GSE execution, has been more profitable because it bypasses the GSEs’ high loan-level pricing adjustments. Now that this difficult but rewarding PLS path has been more widely traveled, we expect it to become more efficient and to remain popular, even with the GSE channel reopening.

Subsector Performance Comparison: Investor Vs. Owner-Occupied Loans

Investor Loans Promise Longer Collection of Above-Market Rates

Compared to owner-occupants, investors have historically paid above-market mortgage rates for longer periods before refinancing. Figure 1 shows the prepayment rates of investors vs. owner-occupants as a function of refinance incentive (the borrower’s note rate minus the prevailing mortgage rate). As their flatter “s-curve” shows, the rise in investor prepayments as refinance incentive increases is much more subdued than for owner-occupants.

Crucially, this relationship is not fully explained by higher risk-based pricing premiums on investor loans. Figure 2 shows the same comparison as Figure 1 but only for loans with spreads at origination (SATO) between 50 and 75 bps. The categorical difference between owner-occupied and investor prepay speeds is partially reduced but clearly remains. We also tried controlling for property type, but the difference persists. The relative slowness of investors may result from investors spreading their attention across many elements of their P&L besides interest expense, from higher underwriting obstacles for a rental income-driven loan, and/or from lenders limiting allocation of credit to the investor type.

While we plot these graphs over a five-year lookback period to balance desires for recency and sample size, this relationship holds over shorter and longer performance periods as well.


Figure 1: The Investor Loans S-Curve is Significantly Flatter Than the Owner-Occupied Curve
Investor s-curve vs. owner-occupied s-curve. Includes prime credit, no prepayment penalty, original loan size $200K-$400K, ages 6-48 months for the past 5yr period performance.

Source: CoreLogic’s Private-Label RMBS Collateral Dataset, RiskSpan. Note: because the increase in private-label investor loan volume is coming from Agency cutbacks, the historical performance of investor loans within both Agency and private-label datasets are relevant to private-label investor loan future performance. In this analysis we show private-label data because it straightforwardly parses voluntary prepays vs. defaults, which of course is a critical distinction for PL RMBS investors. Nonetheless, where applicable, we have run the analyses in both datasets, each of which corroborates the performance patterns we show.


Figure 2: Even Controlling for SATO, The Investor vs. Owner-Occupied S-Curve Difference Persists

Same as Figure 1 but includes only loans with SATO between 50-75 bps Source: CoreLogic, RiskSpan


Investor Loans Pose Comparable Baseline Risk, Greater Downside Risk to Credit Investors

Credit performance of investor loans has been worse than owner-occupied loans during crises, which justifies a pricing premium. During benign periods, investor loans have defaulted at similar or lower rates than owner-occupied loans – presumably due to more conservative LTVs, FICOs and DTIs among the investor loan type – and have therefore been profitable for credit investors during these periods. See Figure 3.


Figure 3: Investor Loans Have Defaulted at Greater Rates During Crises and Similar Rates in Other Periods vs. Owner-Occupied Loans
Default rates over time, investor loans vs. owner-occupied. Includes prime credit, ages 12-360 months.

Source: CoreLogic, RiskSpan

Relative Value Opportunities Within Investor Loans

California Quicker to Refinance

California has the largest share of U.S. investor mortgages, as it does with all residential mortgages. California borrowers, both investors and owner-occupieds, have exhibited a steeper response to refinance incentives than have borrowers in other states. Figure 4 shows the comparison focusing on investors. While historical home price appreciation has enabled refinances in California, it has done the same in many states. Therefore, the speed differences point to a more active refinance market in California. All else equal, then, RMBS investors will prefer less California collateral.


Figure 4: California Prepays Significantly Faster In the Money
Investor s-curves bucketed by geography (California vs. Other). Includes prime credit, no prepayment penalty, original loan size $200k-$400k, ages 6-48 months for the past 3yr performance period.

Source: CoreLogic, RiskSpan


For AAA Investors, Limited-Doc Investor Loans May Offer a Two-Sided Benefit: They Buoy Premium Bonds, and a Small Sample Suggests They Lift Discount Bonds, Too

Limited-doc investor loans offer senior tranche holders the chance to earn above-market rates for longer than full-doc investor loans, a relative edge for premium bonds (Figure 5). This is intuitive; we would expect limited-doc borrowers to face greater obstacles to refinancing. This difference holds even controlling for spread at origination. Based on a smaller sample, limited-doc investor loans have also turned over more (see greater prepay rates in the negative refinance incentive bucket). This may result from a correlation between limited documentation and more rapid flipping into the rising HPI environment we have had nationally throughout the past seven years. If so, this would mean that limited-doc investor loans also help discount bonds, relative to full-doc investor loans, accelerate repayments at par.

Because limited-doc investor loans are rare in the RMBS 2.0 era, we widened the performance period to the past seven years to get some sample in each of the refinance incentive buckets. Nonetheless, with all the filters we have put on to isolate the effect of documentation type, there are only a few hundred limited-doc investor loans in the negative refinance incentive buckets.


Figure 5: Limited-Doc Investor Loans Have Prepaid Slower In-The-Money and Faster Out-of-the-Money
Investor s-curves bucketed by doc type. Includes prime credit, no prepayment penalty, original loan size $400K-$800K, ages 6-48 months, SATO 25-125bps for the past 7yr performance period.

Source: CoreLogic, RiskSpan


Size Affects Refi Behavior – But Not How You Think

An assumption carried over from Agency performance is that rate-driven prepays get likelier as loan size increases. This pattern holds across conforming loan sizes, but then reverses and refinance response gets flatter again as balances cross $800K. This is true for investor and owner-occupied loans in both Agency and private-label loan data, though of course the number of loans above $800K in the Agency data is small. Figure 6 shows this pattern for private-label investor loans. As shown, in-the-money prepayments are slowest among loans below $200K, as we would expect. But despite their much higher motivation to refinance, loans above $800K have similar S-curves to loans of just $200K-$400K.

The SATO is generally a few basis points higher for these largest loans, but this does not explain away the speed differences. Figure 7 shows the same comparison as Figure 6 except only for loans with SATO between 50-75 bps. Except for a slightly choppier graph because of the reduced sample size, the same rank-ordering is evident. Nor does controlling for property type or geography remove the speed differences. The largest loans, we conclude, have fewer credit alternatives and/or face more stringent underwriting hurdles than smaller loans, hampering their refi rates.

Rate refinances are fastest among the mid-sized loans between $400K-$600K and $600K-$800K. That these last two groups have similar S-curves – despite the greater dollar motivation to refinance for the $600K-$800Kgroup – suggests that the countervailing effect of lower ability to find refinancing outlets is already kicking in for the $600K-$800K size range.

All of this means that high-balance collateral should be more attractive to investors than some traditional prepayment models will appreciate.


Figure 6: The Largest Investor Loans Refinance Slower Than Medium-Sized
Investor s-curves bucketed by loan size. Includes prime credit, no prepayment penalty, ages 6-48 months for the past 5yr performance period.

Source: CoreLogic, RiskSpan


Figure 7: Controlling For SATO, Largest Investor Loans Still Refinance Slower Than Medium-Sized
Same as Figure 4 but includes only loans with SATO between 50-75 bps

Source: CoreLogic, RiskSpan


Preliminarily, Chimera Has Lowest Stressed Delinquencies of Top Investor Shelves

For junior-tranche, credit-exposed investors in the COVID era, 60-day-plus delinquencies have been significantly rarer on Chimera’s shelf than on other top investor shelves. The observable credit mixes of the three shelves appear similar. We ran this analysis with only full-doc loans and from only one state (California), and the rank-ordering of delinquency rates by shelves remains the same. Further to this point, note that the spread at origination of Chimera’s shelf is nearly as high as Flagstar’s. All of this suggests there is something not directly observable about Chimera’s shelf that has generated better credit performance during this stressed period. We caution that differences in servicer reporting of COVID forbearances can distort delinquency data, so we will continue to monitor this performance as our data updates each month.


Figure 8: Chimera Posts Lowest COVID Delinquencies, with Nearly Highest SATO of Top Investor Shelves
Investor DQ60+ rates over time, bucketed by shelf. Includes prime credit, ages 12-60 months.


Source: CoreLogic, RiskSpan


The Greater Default Risk of Low-Doc Investor Loans Lasts About 10 Years

Low-doc investors default more frequently than full-doc investors, but only during the first roughly 120 months of loan age. Around this age, the default rates converge. For loans seasoned beyond this age, full-doc loans begin to default slightly more frequently than low-doc loans, likely due to a survivorship bias. This suggests that credit investors are wise to require a price discount for new issuance with low-doc collateral. For deals with heavily seasoned collateral, junior-tranche investors may counterintuitively prefer low-doc collateral — certainly if they can earn an extra risk premium for it, as it would seem they are not actually bearing any extra credit risk.


Figure 9: Low-Doc Investor Loans Default More Frequently Than Full-Doc Until Loan Age = 120
Investor default rates over time, bucketed by doc type. Includes prime credit, RMBS 2.0 era, for the past 7yr performance period.

Source: CoreLogic, RiskSpan


Summary

  • Investor loans face higher barriers to refinance than owner-occupied, offering RMBS investors the opportunity to earn higher coupons for longer periods.
  • For junior tranche investors, the credit performance of investor loans has been similar to owner-occupied loans during benign economic periods and worse during stressed times.
  • California borrowers respond more quickly to refinance incentives than borrowers from other states; investors will prefer less California collateral.
  • Limited-doc investor loans offer AAA investors a double benefit: slower refinances in the money, extending premium bonds; and faster turnover out of the money, limiting extension risk.
  • Low loan balances are attractive for their slow refinance response – as are non-conforming (high) loan balances above $800K. Traditional prepay models may miss this latter dynamic.
  • For credit investors, Chimera’s delinquency rates have been significantly better during the pandemic than other investor shelves. We will continue to monitor this as different ways of reporting COVID forbearances may confound such comparisons.
  • For credit investors, limited-doc investor loans default at higher rates than full-doc loans for about the first ten years of loan age; after this point the two perform very similarly, with limited-doc loans defaulting at slightly lower rates among these seasoned loans, likely due to survivor biases.


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Contact us if you are interested in seeing variations on this theme. Using Edge, we can examine any loan characteristic and generate an S-curve, aging curve, or time series.


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