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Articles Tagged with: Agency MBS

Impact of Mr. Cooper’s Cyber Security Incident on Agency Prepayment Reporting

Amid the fallout of the cyberattack against Mr. Cooper on October 31st was an inability on the large servicer’s part to report prepayment activity to investors.

According to Freddie Mac, the incident “resulted in [Mr. Cooper’s] shutting down certain systems as a precautionary measure. As a result, Freddie Mac did not receive loan activity reporting, which includes loan payoffs and payment corrections, from Mr. Cooper during the last few days of the reporting period related to October loan activity.”

Owing to Mr. Cooper’s size, were curious to measure what (if any) impact its missing days of reporting might have on overall agency speeds.

Not a whole lot, it turns out.

This came as little surprise given the very low prepayment environment in which we find ourselves, but we wanted to run the numbers to be sure. Here is what we found.

We do not know precisely how much reporting was missed and assumed “the last few days of the reporting period” to mean 3 days.

Assuming 3 days means that Mr. Cooper’s reported speeds of 4.5 CPR to Freddie and 4.6 CPR to Fannie likely should have been 5.2 CPR and 5.4 CPR, respectively. While these differences are relatively small for to Mr. Cooper’s portfolio (less than 1 CPR) the impact on overall Agency speeds is downright trivial — less than 0.05 CPR.

Fannie MBSFreddie MBS
Sch. Bal.195,221,550,383168,711,346,228
CPR (reported)4.64.5
CPR (estimated*)5.45.2
*assumes three days of unreported loan activity and constant daily prepayments for the month

Fannie Mae and Freddie Mac will distribute scheduled principal and interest when servicers do not report the loan activity. Prepayments that were not reported “will be distributed to MBS certificateholders on the first distribution date that follows our receipt and reconciliation of the required prepayment information from Mr. Cooper.”


What Do 2023 Origination Trends Mean for MSRs?

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.

FICO

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

LTV

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.

DTI

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.

WOLS

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.


Edge: Zombie Banks

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.

Chart
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”.

Chart
Source: RiskSpan’s Edge Platform

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.

1

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.

Chart
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.


Quantifying Mortgage Risk — Best Practices in the Wake of SVB

Much has been made of the Silicon Valley Bank saga, from the need for basic risk management (was there any, other than a trivial nod?) to the possibility of re-extending the Dodd-Frank rules to cover all banks. Rather than adding our voice to that noise, this post makes a pitch for best practices in MBS and whole loan risk, regardless of whether existing regulation covers your institution.

“Best practices” in mortgage risk is a broad term meaning different things to different people. For our purposes, it refers to using sophisticated risk management tools to quantify both first- and second-order risk of various factors. It also refers to using scenario analysis to capture projected P/L under combinations of risks, for example twists in the interest rate curve combined with spread changes and changes in implied volatility.

Before these risks can be offset using rate and option hedges, our first step is quantifying what the risks are.

In the simplest case, good risk management analysis should quantify projected P/L of a rate-sensitive mortgage or MBS position for shifts in the rate curve — not just local rate shifts of 25 to 50bp, but much larger shifts in rates. It’s helpful to remember that MBS and their underlying mortgages have embedded calls, which lead to significant changes in both projected durations and projected convexity as rates move. Running scenarios with large rate shifts can help highlight the sizable second-order risks in MBS, which are typically negative but turn positive under large enough shifts. In turn, this extended analysis highlights a non-trivial third-order rate effect in MBS.

In the following chart, we show P/L on a position of TBA passthroughs, securities similar to SVB’s held-to-maturity portfolio. We project price movements under parallel rate shifts as of January 3, 2022, which roughly corresponds to the start of the tightening cycle. For this analysis, we use RiskSpan’s prepayment and interest-rate models, which are available in the Edge interface or via overnight batch.

1

Chart

In this analysis, the model projected prices of FNCL 2.0 to 3.0 within 2.5% of actual observed prices on March 8, 2023, shown by the diamonds on the chart, the Wednesday before the SVB crisis began to unfold. While not exact, this analysis illustrates the power of a straightforward rate curve to help a bank’s risk management team project actual, realized prices over very large rate moves.

In the next chart, we show a P/L chart that is duration-neutral at outset. This chart shows the losses from negative convexity,

2

driven by the homeowner’s option to refinance moving from at-the-money to significantly out-of-the-money. As rates continue to rise (moving right on the chart), underperformance from convexity continues to increase, but only to a point. This is where the homeowner’s call option is offset by the natural, positive convexity of discounting. Beyond that point, MBS become mildly positively convex as the call options become less relevant.

Chart

What does this change in convexity look like? In the final chart, we show convexity at various rate shifts for a par-priced passthrough.

3

This highlights convexity changes over large moves (and a non-trivial third derivative with respect to changes in rates) and underscores the importance of a quantitative approach to risk management for MBS.

Chart

From these straightforward scenarios, banks and other institutions can overlay combinations of other risk shocks, for example curve flatteners and steepeners, OAS changes, and changes in implied volatility. These mixed scenarios can quantify risk from cross-partial derivatives and inform potential hedges under multiple changes in inputs. All these simple and more complex user-defined scenarios are available in RiskSpan’s Edge platform, giving small and mid-sized banks the ability to quantify risk on high-quality MBS, which is the first fundamental in a rigorous risk management framework. Recent events have highlighted the tradeoff between cost savings generated by taking a light approach to rate risk management and the existential risk of insolvency. Yes, small and mid-sized banks can save costs while remaining within the current regulatory framework. But, as SVB has taught us, to do so can be tantamount to unwittingly betting the entire enterprise. Laying out a few basis points to ensure you’ve quantified the interest rate risk properly has never looked like a more worthwhile investment.


Duration Risk: Daily Interest Rate Risk Management and Hedging Now Indispensable

The rapid decline of Silicon Valley Bank and Signature Bank affirms the strong need for daily interest rate risk measurement and hedging. All financial institutions should have well documented management and board limits on these exposures.

Measuring risk on complex mortgage-backed securities and loan portfolios that have embedded prepayment and credit risk is challenging. RiskSpan has a one-stop risk measurement solution for all mortgage-backed securities, structured product, loan and other related assets including data management, proprietary models and risk reporting.

Our bank clients enjoy the benefit of daily risk measurement to ensure they are well-hedged in this volatile market environment.

For a limited time, under full non-disclosure, RiskSpan will offer a one-time analysis on your securities portfolio.

Please reach out if we can help your institution more fully understand the market risk in your portfolios.

AssetLiabilityRpt

There are many lessons to learn through the SVB failure. While technology (the internet) enabled the fastest run on a bank in US history, technology can also be the solution. As we just saw US Government securities are risk-free for credit but not interest rate movements. When rates rose, security prices on the balance sheet of SVB declined in lock-step. All financial institutions (of all sizes) need to act now and deploy modern tech to manage modern risks – this means managing duration risk on a daily basis. It’s no longer acceptable for banks to review this risk monthly or weekly. Solutions exists that are practical, reliable and affordable.


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.

Chart

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.

Chart

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 ID Factor Date Current Face Extension (months)
FR QD7617 7/2022 20,070,737 6
FR QD0006 1/2022 15,682,775 5
FN CB3367 11/2022 14,839,919 5
FR QD5736 7/2022 10,916,959 6
FN BU0581 4/2022 10,164,000 6
FR QD4492 6/2022 3,113,532 16
FN BV2076 5/2022 3,165,509 18
FR QD6013 7/2022 3,079,250 22




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.

Curtailment CPR by WALA

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.

Age vs Curtailment CPR

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.

Age vs Curtaiment CPR

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.

Chart

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.

Graph

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.


Video: Mortgage Market Evolution

As any mortgage market veteran will attest, the distribution and structure of the mortgage market is constantly in flux. When rates fall, at-the-money coupons become premiums, staffing at originators rises, the volume of refis increase, and the distribution and seasoning of coupons change.

And then the cycle turns. Rates rise. Premiums become discounts. Originators cut staff and prepay speeds plummet. But this too changes, and longtime participants will recognize echoes of 1994 or 1999-2000 in today’s washout.

The brief video animation below tracks the evolution of the mortgage market since 2006, with an eye on distribution and seasoning of borrowers.

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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 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. Covid Era

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. Covid Era

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

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.

Covid Era

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.

Covid Era

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.

Covid Era

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.

Covid Era

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.

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