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The noisy pricing of servicer prepayment risk
admin | October 23, 2020
This document is intended for institutional investors and is not subject to all of the independence and disclosure standards applicable to debt research reports prepared for retail investors. This material does not constitute research.
Servicers can have a significant influence on MBS prepayment speeds and valuation. In extreme cases, a servicer’s loans could prepay more than 50% faster or slower than the typical loan, and the market tries to price for it. Comparing the price premium or discount on a servicer’s pools to a measure of relative prepayment speed suggests that speeds explain only about 45% of the price difference. Where there is potential inefficiency, there is opportunity.
Investors in the current market should pay a premium for a servicer’s pools that typically prepay slower than comparable pools and should pay less for pools that typically prepay faster. Comparing the price paid for a pool to the price paid for an equivalent cash window pool highlights any servicer-specific price premium or discount. For example, an investor can compare a 3.0% MLB trade to the most recent 3.0% MLB cash window trade with an adjustment to account for market moves. In the analysis here, each servicer’s premium is the average premium across all their trades over the past two months. There are other factors that contribute to pricing individual pools, but this should be reduced by using multiple trades. Amherst Pierpont’s monthly servicer ranking report shows how much faster or slower a servicer’s pools prepay than a typical pool after controlling for multiple collateral characteristics.
This comparison shows that in general faster servicers are paid less for their pools (Exhibit 1). Each data point represents a different servicer. On average a servicer that prepays 10% faster should receive roughly 4.7/32s lower price for its pools. The data is noisier in the 3.0% coupon, which could be explained by fewer trades in that coupon. On average speeds explained roughly 45% of the price difference.
Exhibit 1: Roughly 45% of servicer price differences are explained by prepayment speeds
Each dot represents a different servicer. A linear regression was performed for each coupon, weighted by the total size of each servicer’s trades.
Source: Fannie Mae, Freddie Mac, eMBS, Amherst Pierpont Securities
The significant variation suggests that investors are not always pricing in the full prepayment risk of a servicer. That is, some servicers are being overpaid for their prepayment performance, and some are not being paid enough. There are other possible explanations for the pricing noise. A small sample size could contribute. Price premiums for specified pools vary with TBA price and the specialness of the dollar roll. Some servicers’ patterns may be better known or more widely distributed. Some investors may doubt the reliability of servicers’ relative speed over the long run. However, it seems unlikely that those factors can fully explain the degree to which some servicers are priced high or low compared to the comparable cash window pool.
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