In a recent post, we discussed the utility of the FHA Performance dataset in tracking borrower stress in the housing market. Here we look at other interesting market perspectives that can be obtained from this release.
First, we look at property type. This breakdown is not available in the GNM loan-level disclosures, so this is a new view. Here we have 30-day DQ rates broken down over five categories:
We recently wrote about the increase in short-dated delinquencies in FHA pools relative to loans in conforming pools. We took this as a sign of the disproportionate impact that inflation has on lower-income households. This is a useful observation, but it is limited by the lack of visibility on delinquencies of three-month duration or longer as these loans are often bought out of pools. To address this gap, we recently imported the FHA performance data. This data is not available at the loan level, but the aggregate characteristics are informative.
As pool issuance reached historical high amid record low mortgage rate with the onset of Covid-19 in 2020, Agency CMO took off correspondingly. Investors apparently looked favorably on the diversification opportunities in structured products, given the heightened uncertainty during that time. In 2021 total issuance dipped, and so far through the first eight months of 2022 volumes are well behind last year’s pace. The GSE commingle fee set force in June slowed down Fannie and Freddie’s CMO issuance significantly. However, as a share of total issuance, CMOs are holding up quite well.
The rise in mortgage rates is having a profound impact on lender strategies in the mortgage market. These can be seen by looking at trends in the use of Third-Party Originators (TPOs). Some lenders, such as Quicken, traditionally work almost exclusively with loans originated in-house, while others, such as PennyMac, primarily accumulate and package loans produced by other lenders. Most larger institutions do some of both. The advantage of acquiring loans from a mortgage broker or correspondent in addition to origination is that the lender has flexibility regarding what method they use to turn volumes up and down to fit its strategy and market views. In both cases, there are costs to increasing and cutting capacity. As the market grows, bringing on new employees carries expenses such as training, while building trusted new external relationships can also be time-consuming. As markets contract, there are direct costs to layoffs, while unwinding networks can impact relationships that can be difficult to rebuild when the cycle turns. Of course, in a sufficiently bad market, the company may have no choice but to cut back.
Recently there have been some high-profile announcements of layoffs across the mortgage lending space, but through the first half of 2022, the reported decline in employment has been modest. But employment tends to lag interest rates, so further declines cannot be ruled out.
Analyzing trends in market performance requires two things, 1) a lot of data, and, 2) a deep understanding of the structure of markets. We recently came across a good example of this with relative delinquency rates between GNM and GSE pools. In this post, we look at the dynamics of the two categories of reperforming mortgage loans.
Investors have spent many years building models of prepayment speeds for mortgage pools based on a variety of characteristics such as loan size and underwriting characteristics. However, institutional factors can come into play as well. One that comes to mind is the difference in program structure between conventional and government loans. For the conforming market, the issuer is a GSE, while for government programs, it is the servicer. In both categories, when a loan becomes seriously delinquent, it can be bought out of the pool at par, amounting to a prepayment. The difference is that for the case of conforming loans, it is the quasi-public GSEs that perform this function, while for government programs, the decision is up to private sector entities. In the first case, there are overarching policy goals that weigh on decisions about the disposition of loans in delinquency, while in the second case, these decisions are based on financial considerations.
One way to test this is to look at buyouts over the interest rate cycle. Below find a chart containing the shares of reperforming loans in new issuance for FHA, VA and the GSEs. These are loans that have been previously bought out of pools and then reissued into new pools. There can be a substantial lag between the buyout and re-issuance.
With interest rates significantly higher than those in place at the start of the year, it’s natural that potential homebuyers look for ways to reduce borrowing costs. One of these ways is through an adjustable-rate mortgage (ARM). Now that we have complete data for July, it is a timely moment to look at recent trends in ARM issuance. As we approach the topic, it’s important to note a few institutional points. First, the ARM programs available to conventional and Government borrowers are distinct, so we break the issuance down between GSE and Ginnie pools. Second, the market underwent a significant shift with the elimination of LIBOR as a benchmark.
Let’s start with the conforming market. There are several product types, but in July 2022, the largest is 7-year ARMs, followed by 5-year and 10-year ARMs:
With data in hand for the first half of 2022, it seems a good time to revisit the topic of the share of issuance between the two GSEs. This is also, at least implicitly, a hot topic in policy circles following the announcement on the part of the GSEs that they will be imposing a 50 bp fee on commingled Super and CMO pools starting on July 1. Regular readers of our blog will recall how we pointed out that the Fed purchases of Super pools created an imbalance in favor of Freddie Mac loans that may have been a contributing factor to their rise in market share in 2020 and 2021.
Interestingly, the Freddie Mac share of GSE purchase loans has fallen back. As shown in this chart, the FHL share of delivery peaked in August 2021 at 56%, while most recently it stood at 42%.