By the Numbers
Agency Servicer Prepayment Rankings
This material is a Marketing Communication and does not constitute Independent Investment Research.
A frequent concern in agency prepayments is the impact of the servicer of a pool on prepayments. If that servicer is also an originator, they have an incentive to solicit the borrower to refinance to prevent the borrower from prepaying with another lender and thereby losing the mortgage servicing rights (MSR) associated with that loan. Some servicers have proven very “efficient” at this solicitation.
In this article we describe the methodology we use to differentiate servicer prepayments and provide a simple example to illustrate the process. Then we will discuss some of the benefits and limitations of the approach.
Methodology
The core difficulty for comparing speeds from one servicer to the next is that each servicer’s loan characteristics are potentially different. For example, it is pretty obvious you wouldn’t compare one servicer’s 7/1s with another servicer’s 30-year mortgages. Similarly, one has to account for different coupon and vintage distributions. Furthermore, in order to truly get to the pure impact of the servicer, you would want to account for all other factors that drive prepayments—loan size, LTV, FICO, TPO % (third-party originator %), and so on.
One way to do this is to run a prepayment model for each servicer’s pools, and see if speeds were faster or slower than the model predicts for every servicer. However, this assumes that the model covers all important attributes and, more importantly, that the model is perfectly accurate—overall, and across all the included factors. But no model is perfect.
Another approach, and the one we took, doesn’t require a model. Instead, we compare each servicer’s pools to the universe of comparable pools. By definition, this benchmark is “accurate”, since it is constructed from actual prepayments. On the other hand it isn’t possible to incorporate nearly as many prepayment factors as a model. On balance, though, we feel this the proper trade-off.
For fixed rate pools, we chose to compare cohorts comprised of the product type (30-year, 15-year), coupon, vintage, and a few specified pool categories (principally loan balance and MHA pools). For hybrid pools we only look at product type (5/1, 7/1, etc.), coupon, and vintage. Also, although we will discuss this as if each pool is single-servicer, our rankings incorporate pools with multiple servicers.
Therefore, each pool is mapped to the speed of the cohort of pools with the same product type, coupon, vintage (and specified pool type). To get the servicer’s overall prepayment speed, we aggregate the individual pool speeds, weighted by the outstanding balance of each pool. To get the overall reference speed, we aggregate the speed that was mapped to each pool, weighted by the same weights as the actual pool speeds. This means that each servicer is given a customized reference speed to compare to, based on the specific portfolio of pools they service.
Example
An example should make this clearer. To keep things simple we’ll use a universe of three servicers with a handful of cohorts, with the speeds shown in the exhibit below:
Figure 1:
Source: Santander US Capital Markets
The numbers in the “Universe” column, which represent the overall speed of each cohort, are the speeds we will use to benchmark each servicer. At first glance, it appears that Servicer 2 might be the best (slowest) servicer, with its overall speed of 8.2 CPR much slower than the overall speed of 9.3 CPR. But we’ll see that this is not the case.
First, let’s step through the calculations for Servicer 1:
Figure 2:
Source: Santander US Capital Markets
In each row we print the servicer’s outstanding balance and actual CPR for that cohort as well as the “reference CPR”, which is a direct lookup of the universe CPR in the first table for that cohort. The last column shows the % difference between the two speeds (negative numbers ⇒ the servicer is slower; note that calculations are done on SMM, not CPR). In the bottom row we show the aggregate speeds, and in the bottom-right corner show the aggregate % difference; this number, −7.2% in this case, is what we will use to rank the servicers.
The next tables show that Servicer 2, although the slowest servicer in terms of absolute speed at 8.2 CPR, actually scores at −4.1%, which is faster than Servicer 1’s −7.2%. Servicer 3 ends up ranking fastest at +4.5%.
Figure 3: Servicer 2 Detail
Source: Santander US Capital Markets
Figure 4: Servicer 3 Detail
Source: Santander US Capital Markets
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