In a recent post we looked at the agency composition of the recent surge in MBS production[1]. We observed that Ginnie Mae’s market share in the three months to July 2020 is significantly below that experienced in the same period a year ago. The bulk of the decline is due to a drop in the refi share, while the purchase market share experienced less than a 1% decline. There are several drivers of share for purchase market share, including program design, the coupon spread between the government and conforming sectors, and differences in the credit boxes between the two. This note looks at competitiveness through the lens of the latter factor, credit boxes.
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As noted in the prior post, Ginnie Mae is the securitizer for four different programs, with the two biggest being FHA and VA. VA has a fairly unique set of program requirements, so the main competition in the purchase mortgage space is between FHA and the GSEs. In the three months to July 2020 FHA lost about 2.5% of its share in the purchase mortgage space compared to the GSEs. Let’s dive into credit factors, starting with credit score:

In a recent post, we noted the surge in new mortgage deliveries to a record level[1] in July. Besides this sharp increase, there is an interesting shift in the composition of these deliveries in the form of a striking decline in the government share.

Cyclical and secular factors are coming together to boost agency mortgage production. On the cyclical front, record low mortgage rates are the key driver of surging refi activity. Purchase activity is supported as well by low rates, but there are also indications that secular changes surrounding lifestyle choices sparked by the Covid-19 pandemic are leading homeowners to change residences away from the largest urban centers[1].

Freddie Mac started to release forbearance data on its STACR CRT program in the July reporting cycle.  Previously, the GSEs disclosed only pool-level delinquency and forbearance information [1].  As the loans in the STACR program have a UPB representing over 50% of total deliveries to FHL balances with no obvious state level bias, they would serve as a representative sample in calculating GSE state level delinquency [2]. Similarly, the new STACR data is helpful in assessing the impact of the COVID-19 crisis on forbearance at loan level.
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​From the July STACR data release, we can see a clear correlation between total delinquency rates and forbearance rates at state level. States with higher delinquency rates, such as New York, New Jersey, Hawaii, and Nevada, also have higher forbearance rates.

​This is not a surprise as we noted in an earlier post that Freddie Mac servicers are not required to report loans in forbearance if loans are current [3]. In fact, for STACR data, only 0.95% of current loans are in a forbearance plan, but the forbearance rate for loans in 30 day delinquency, 60 day delinquency, and 90+day delinquency are 30.64%, 92.98%, and 92.12 % respectively.

The long-raging and complex debate about housing finance policy basically boils down to two issues: first, how much risk should there be in the system, and second, who should bear it? Previous posts have addressed the use of big data in looking at the first issue by examining the trade-off between credit standards and delinquencies[1]. With regards to the distribution of risk, the topmost issue is the breakdown between the public and private sectors. This note approaches the second question by looking at the market shares of a government agency, FHA, vs that of the GSEs, which represent a mixture of public and private risk[2].
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The competitiveness of one agency vs another is a multifaceted subject, as there are multiple aspects to their interaction. Among other approaches, they may compete via price (insurance fees) or via loan underwriting standards or product innovation. To launch this analysis, we just look at relative prices for purchase loans. As a proxy for price, we compare the weighted average coupon (WAC) between FHA and conforming loans in this category:

In a recent post[1], we pointed out an acceleration in the trend towards an increasing share of deliveries of purchase mortgages to the GSEs. This trend continued with the release of June data earlier this month, so a deeper dive is called for. First, the trend is far more evident for deliveries to Fannie Mae than Freddie Mac:

​The drop in the 30-yr mortgage rate to close to 3.0% has worked to keep purchase mortgage securitizations relatively steady while refinancing activities have soared to record highs. Refis in Q2 2020 for the GSE’s came in at more than twice the next highest quarterly figure reported over the past seven years:

Besides the sharp drop in rates, another factor in this sudden surge compared to previous years is the increasing use of Property Appraisal Waivers. With the transaction costs of refinancing going down as a result, it appears that refis accelerated compared to prior episodes of falling rates. To look at this a bit deeper, below find 1-month prepayment speeds of both PIW loans and PIW eligible purchase loans for the two agencies[1]:

​For both agencies, it appears that loans with appraisal waivers prepay faster than eligible loans without, but the impact diminishes over time in part because there are fewer such loans with a PIW left that haven’t prepaid. In fact, for non-cashout refis the impact is even more pronounced:

​In this case not only have prepayment speeds for this category of loans with appraisal waivers slowed relative to others, but in the last couple of months they have actually become slower compared to other eligible loans.
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A combination of unprecedented policy moves and market developments have served to upend conventional mortgage market analysis, underscoring the need for new thinking, and new tools.

In a recent post[1] we noted that prepayment speeds jumped in June, led by a startling 12.4% rise in 1-month FHA speeds to 36.1, likely due to a change in pool rules that prohibited loan buyouts from being re-securitized beginning on July 1. This bears looking into. If this jump is due to such a policy change then one would expect that prepayment speeds would rise more for banks than thinly capitalized nonbanks. Indeed, this is the case, and remarkably so.