When it comes to forecasting MSR performance and valuations, much is made of the interest rate environment, and rightly so. But other loan characteristics also play a role, particularly when it comes to predicting involuntary prepayments.
So let’s take a look at what 2023 mortgage originations might be telling us.
Average credit scores, which were markedly higher than normal during the pandemic years, have returned during the first part of 2023 to 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, 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, is trending 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.
At the market highs, banks gorged themselves on assets, lending and loading their balance sheets in an era of cheap money and robust valuations. As asset prices drop, these same companies find their balance sheets functionally impaired and in some cases insolvent. They are able to stay alive with substantial help from the central bank but require ongoing support. This support and an unhealthy balance sheet preclude them from fulfilling their role in the economy.
We are describing, of course, the situation in Japan in the late 1980s and early 1990s, when banks lent freely, and companies purchased both real estate and equity at the market highs. When the central bank tightened monetary policy and the stock market tanked, many firms became distressed and had to rely on support from the central bank to stay afloat. But with sclerotic balance sheets, they were unable to thrive, leading to the “lost decade” (or two or three) of anemic growth.
While there are substantial parallels between the U.S. today and Japan of three decades ago, there are differences as well. Firstly, the U.S. has a dynamic non-bank sector that can fill typical roles of lending and financial intermediation. And second, much of the bank impairment comes from Agency MBS, which slowly, but surely, will prepay and relieve pressure on their HTM assets.
Source: The Wall Street Journal
How fast will these passthroughs pay off? It will vary greatly from bank to bank and depends on their mix of passthroughs and their loan rates relative to current market rates, what MBS traders call “refi incentive” or “moniness.” It is helpful to remember that incentive also matters to housing turnover, which is a form of mortgage prepayment. For example, a borrower with a note rate that is 100bp below prevailing rates is much more likely to move to a new house than a borrower with a note rate that is 200bp out of the money, a trait that mortgage practitioners call “lock-in”.
As a proxy for the aggregate bank’s balance sheet, we look at the universe of conventional and GNMA passthroughs and remove the MBS held by the Federal Reserve.
The Fed’s most substantial purchases flowed from their balance sheet expansion during COVID, when mortgage rates were at all-time lows. Consequently, the Fed owns a skew of the MBS market. Two-thirds of the Fed’s position of 30yr MBS have a note rate of 3.25% or lower. In contrast, the market ex Fed has just under 50% of the same note rates.
Source: RiskSpan’s Edge Platform
From here, we can estimate prepayments on the remaining universe. Prepay estimates from dealers and analytics providers like RiskSpan vary, but generally fall in the 4 to 6 CPR range for out-of-the-money coupons. This, coupled with scheduled principal amortization of roughly 2-3% per annum means that for this level in rates, runoff in HTM MBS should occur around 8% per annum — slow, but not zero. After five years, approximately 1/3 of the MBS should pay off. Naturally, the pace of runoff can change as both mortgage rates and home sales change.
While the current crisis contains echoes of the Japanese zombie bank crisis of the 1990s, there are notable differences. U.S. banks may be hamstrung over the next few years, with reduced capacity to make new loans as MBS in their HTM balance sheets run off over the next few years. But they will run off — slowly but surely.
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 join RiskSpan credit and prepayment modelers Janet Jozwik and Divas Sanwal as they 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 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 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.
The panel discusses 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.
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.
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.
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.
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.
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.
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
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.
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.
Learn more about how one investor (a large mortgage REIT) successfully overhauled its analytics computational processing with RiskSpan. The investor migrated from a daily pricing and risk process that relied on tens of thousands of rep lines to one capable of evaluating each of the portfolio’s more than three-and-a-half million loans individually (and how they actually saved money in the process).
The Situation
One of the industry’s largest mortgage REITs sought a better way of managing its extensive investment portfolio of mortgage servicing rights (MSR) assets, residential loans and securities. The REIT runs a battery of sophisticated risk management analytics that rely on stochastic modeling. Option-adjusted spread, duration, convexity, and key rate durations are calculated based on more than 200 interest rate simulations.
The investor used rep lines for one main reason: it needed a way to manage computational loads on the server and improve calculation speeds. Secondarily, organizing the loans in this way simplified the reporting and accounting requirements to a degree (loans financed by the same facility were grouped into the same rep line).
This approach had some significant downsides. Pooling loans by finance facility was sometimes causing loans with different balances, LTVs, credit scores, etc., to get grouped into the same rep line. This resulted in prepayment and default assumptions getting applied to every loan in a rep line that differed from the assumptions that likely would have been applied if the loans were being evaluated individually.
The Challenge
The main challenge was the investor’s MSR portfolio—specifically, the volume of loans needing to be run. Having close to 4 million loans spread across nine different servicers presented two related but separate sets of challenges.
The first set of challenges stemmed from needing to consume data from different servicers whose file formats not only differed from one another but also often lacked internal consistency. Even the file formats from a single given servicer tended to change from time to time. This required RiskSpan to continuously update its data mappings and (because the servicer reporting data is not always clean) modify QC rules to keep up with evolving file formats.
The second challenge related to the sheer volume of compute power necessary to run stochastic paths of Monte Carlo rate simulations on 4 million individual loans and then discount the resulting cash flows based on option adjusted yield across multiple scenarios.
And so there were 4 million loans times multiple paths times one basic cash flow, one basic option-adjusted case, one up case, and one down case—it’s evident how quickly the workload adds up. And all this needed to happen on a daily basis.
To help minimize the computing workload, this client 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 because they could not look at the loans individually.
The Solution
The analytics computational processing RiskSpan implemented ignores the rep line concept entirely and just runs the loans. The scalability of our cloud-native infrastructure enables us to take the nearly four million loans and bucket them equally for computation purposes. We run a hundred loans on each processor and get back loan-level cash flows and then generate the output separately, which brings the processing time down considerably.
For each individual servicer, RiskSpan leveraged its Smart Mapper technology and Configurable QC feature in its Edge Platform to create a set of optimized loan files that can be read and rendered “analytics-ready” very quickly. This enables the loan-level data to be quickly consumed and immediately used for analytics without having to read all the loan tapes and convert them into a format that an analytics engine can understand. Because RiskSpan has “pre-processed” all this loan information, it is immediately available in a format that the engine can easily digest and run analytics on.
What this means for you
An investor in any mortgage asset benefits from the ability to look at and evaluate loan characteristics individually. The results may need to be rolled up and grouped for reporting purposes. But being able to run the cash flows at the loan level ultimately makes the aggregated results vastly more meaningful and reliable. A loan-level framework also affords whole-loan and securities investors the ability to be sure they are capturing the most important loan characteristics and are staying on top of how the composition of the portfolio evolves with each day’s payoffs.
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 temporarybuydown 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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