We’ve written previously that the multifamily market will be of growing interest during the course of 2021. During the Global Financial Crisis, the single-family market was ravaged by foreclosures resulting from the popping of the housing bubble. The large number of households losing their homes became renters to a large degree. This time is different. Renters are fleeing congested urban areas and are buying homes in areas with more space, serving to push up house prices while rents are under downward pressure. According to the Elliman Report, the rental vacancy rate for Manhattan in November 2020 was 6.1%, compared to 1.8% a year earlier. This figure will of course vary considerably from place to place. The potential for more vacancies remains once the various Federal and Local Covid-19 bans on evictions are allowed to expire. According to Trepp, the national 30+ day delinquency rate for multifamily loans in December 2020 was 2.75%, up modestly from 2.00% a year earlier.
The role of the Agencies in the multifamily debt market is significant, but less than the overwhelming presence seen in the single-family market. Data disclosed by the agencies provides a wealth of information about the rental market but did not receive widespread attention until recently. In this post, we discuss trends in multifamily loan maturity schedule and prepayment penalty schedule. This data is of interest because unlike the single-family market, there are fewer apartment loans, but they are generally quite large. Maturities can clump, leading to periods of time when capital demands can push borrowing costs higher. On the other hand, opportunities for lenders arise when loans mature or exit the prepayment penalty window.
Our regular readers will be aware that an ongoing theme is the collapsing bank share of mortgage deliveries to the Agencies. Our recent monthly download shows that the bank share of deliveries to the GSEs fell sharply again in December, collapsing by over 7.0%(!) from November to a record low 22.3%. A year earlier this figure stood at 41.7%. The plunge witnessed over the past year marked an acceleration in a long-term trend, as banks face a heavier regulatory burden relative to nonbanks, and as nonbanks have made inroads into the market through their development of superior technology interfaces with their clients. Covid-19 has served to accelerate this trend by pulling customers out of bank branches and putting them in front of their laptops and smart phones.
The latest drop incentivized us to dig a little deeper; we didn’t have to peer too deep to find an interesting result. Below finds a bank of the bank share of GSE deliveries, and the same chart excluding Wells Fargo and JP Morgan Chase.
In prior posts, we have pointed out the tight relationship between unemployment and mortgage delinquency. This note extends this analysis by looking at this relationship at particular durations.
Every month, the Bureau of Labor Statistics releases data on the “Duration of Unemployment”. For example, below find a table containing data for the number of unemployed people in before, during and after the shock associated with the onset of the Covid-19 crisis by how long they have been unemployed.
We received complete GSE data for November late last week and as always there is a lot to churn on. Another record high of issuance was achieved, although this was entirely due to a surge in refi deliveries (+$16 Bln from October) while purchase deliveries declined slightly (-$7 Bln). Lack of supply and softer seasonal demand appear to be weighing on purchase volumes.
A long-term trend in these comments is the trend decline in the bank share of deliveries to the GSE’s. We have commented that the Covid-19 pandemic has played to the natural technological advantage of nonbanks, while eroding the value of the bank branch networks, particularly for purchase mortgages.
Interestingly, a little bit of a reverse trade can be seen the last couple of months, at least in purchase mortgages. The chart below looks at the bank share, graphed against the gap in the weighted average coupon between nonbanks and banks (“WAC Gap”).
There is a distinct correlation between these two series, although considerable noise is also apparent. Many factors drive market share including underwriting characteristics and product types, but the basic relationship comes across. In November, the gap in the WAC between Nonbanks and Banks increased by almost 4 basis points from October, which was attained by a 3 bp drop in the nonbank WAC being exceeded by a 7 bp drop in that of banks. In a market measured in tens of billions, a single bp has significance.
The question going forward is whether the decline in the rates of banks’ offerings is supported by efficiency enhancements or simply reflects reduced profitability. The answer to this is key in determining the question of their long-term role in this market.
 See, for example: https://www.recursionco.com/blog/besieged-banks
In a recent post we noted the recent striking rise in the GSE refinance share and commented that the rate of this activity in GNM programs, while still rising, has lagged. This seems to be related to the tendency of capacity constrained lenders to provide credit to the highest quality borrowers, and to a looming 0.5% fee hike on GSE refinance deliveries scheduled for December 1.
Focusing on FHA alone, the share of refinance loans compared to those delivered to the GSE’s has plummeted in recent months:
We have posted numerous blogs about how Covid-19 has served to accelerate structural change in the mortgage market, particularly in the growing share of nonbank mortgage sales to the GSE’s. It’s natural in such an environment to look at deliveries by channel. As has been widely noted in the industry, the broker channel enjoyed a considerable increase in market share over the 2018-2019 period, as the broker community became better organized. Has this trend continued with the onset of the Covid-19 crisis?
In a recent post we compared the average credit score at origination history from NSMO with that from the agency loan-level datasets on an apples-to-apples basis.
In today’s blog, we improved the matching by using the Analysis Weights that are provided in the NSMO dataset to compute the averages. NSMO Analysis Weights are the product of the sampling weight and a non-response adjustment, which can make the NSMO dataset more representative.