Ginnie Mae prepayment speeds saw a substantial uptick in July, with speeds in some cohorts more than doubling. Much of this uptick was due to repurchases of delinquent loans. In this short post, we examine those buyouts for bank and non-bank servicers. We also offer a method for quantifying buyout risk going forward. For background, GNMA servicers have the right (but not the obligation) to buy delinquent loans out of a pool if they have missed three or more payments. The servicer buys these loans at par and can later re-securitize them if they start reperforming. Re-securitization rules vary based on whether the loan is naturally delinquent or in a forbearance program. But the reperforming loan will be delivered into a pool with its original coupon, which almost always results in a premium-priced pool. This delivery option provides a substantial profit for the servicer that purchased the loan at par. To purchase the loan out of the pool, the servicer must have both cash and sufficient balance sheet liquidity. Differences in access to funding can drive substantial differences buyout behavior between well-capitalized bank servicers and more thinly capitalized non-bank servicers. Below, we compare recent buyout speeds between banks and non-banks and highlight some entities whose behavior differs substantially from that of their peer group. In July, Wells Fargo’s GNMA buyouts had an outsized impact on total CPR in GNMA securities. Wells, the largest GNMA bank servicer, exhibits extraordinary buyout efficiency relative to other servicers, buying out 99 percent of eligible loans. Wells’ size and efficiency, coupled with a large 60-day delinquency in June (8.6%), caused a large increase in “involuntary prepayments” and drove total overall CPR substantially higher in July. This effect was especially apparent in some moderately seasoned multi-lender pools. For example, speeds on 2012-13 GN2 3.5 multi-lender pools accelerated from low 20s CPR in June to mid-40s in July, nearly converging to the cheapest-to-deliver 2018-19 production GN2 3.5 and wiping out any carry advantage in the sector. Figure 1: Prepayment speeds in GN2 3.5 multi-lender pools: 2012-13 vintage in blue, 2018-19 vintage in black. This CPR acceleration in 2012-13 GN2 3.5s was due entirely to buyouts, with the sector buyouts rising for 5 CBR to 29 CBR. In turn, this increase was driven almost entirely by Wells, which accounted for 25% of the servicing in some pools. Figure 2: Buyout speeds in GN2 3.5 multi-lender pools. 2012-13 vintage in blue, 2018-19 vintage in black In the next table, we summarize performance for the top ten GNMA bank servicers. The table shows loan-level roll rates from June to July for loans that started June 60-days delinquent. Loans that rolled to the DQ90+ bucket were not bought out of the pool by the servicer, despite being eligible for it. We use this 90+ delinquency bucket to calculate each servicer’s buyout efficiency, defined as the percentage of delinquent loans eligible for buyout that a servicer actually repurchases. Roll Rates for Bank Servicers, for July 2020 Reporting Date Surprisingly, many banks exhibit very low buyout efficiencies, including Flagstar, Citizens, and Fifth Third. Navy Federal and USAA (next table) show muted buyout performance due to their high VA concentration. Next, we summarize roll rates and buyout efficiency for the top ten GNMA non-bank servicers. Roll Rates for Ginnie Mae Non-bank Servicers, for July 2020 Reporting Date Not surprisingly, non-banks as a group are much less efficient at buying out eligible loans, but Carrington stands out. Looking forward, how can investors quantify the potential CBR exposure in a sector? Investors can use Edge to estimate the upcoming August buyouts within a sector by running a servicer query to separate a set of pools or cohort into its servicer-specific delinquencies. Investors can then apply that servicer’s 60DQ->90DQ roll rate plus the servicer’s buyout efficiency to estimate a CBR. This CBR will contribute to the overall CBR for a pool or set of pools. Given the significant premium at which GNMA passthroughs are trading, the profits from repurchase and re-securitization are substantial. While we expect repurchases will continue to play an outsized role in GNMA speeds, this analysis illustrates the extent to which this behavior can vary from servicer to servicer, even within the bank and non-bank sectors. Investors can mitigate this risk by quantifying the servicer-specific 60-day delinquency within their portfolio to get a clearer view of the potential impact from buyouts. If you interested in seeing variations on this theme, contact us. Using Edge, we can examine any loan characteristic and generate a S-curve, aging curve, or time series.  This post builds on our March 24 write-up on bank versus non-bank delinquencies, link here. For this analysis, we limited our analysis to loans in 3% pools and higher, issued 2010-2020. Please see RiskSpan for other data cohorts.  CBR is the Conditional Buyout Rate, the buyout analogue of CPR.  In Edge, select the “Expanded Output” to generate servicer-by-servicer delinquencies.  RiskSpan now offers loan-level delinquency transition matrices. Please email firstname.lastname@example.org for details.