In a recent post, we discussed the impact of the COVID-19 pandemic on the mortgage market[1]. We noted that there has been a trend toward higher median credit scores in Agency mortgage deliveries since 2020 due to policy stimulus and asset price appreciation. In addition, there was a jump in median bank credit scores in early 2022, accompanied by a drop in the bank share relative to nonbanks of about 5%. This is not negligible, and the share decline has persisted over the past year. We attribute this to the shock of the collapse of four banks, notably Silicon Valley Bank (SVB), during this time. A more thorough assessment of the impact the bank shock had on the mortgage market requires access to the financial statements of a range of banks. This data is available via the Bank Call Reports. Data are released quarterly by the FDIC and made available through the FFIEC[2] with trends discussed in the FDIC Quarterly[3]. This note is meant to be a technical description of the data infrastructure we have built to support this sort of analysis by tying together mortgage production data with bank financial information from the Call Reports. The Recursion Call Report Analyzer The Call Report data is readily accessible, and we like many others have for some time pulled data down for individual institutions for use in various studies. If you want to look at the whole system, however, we are looking at well over 3,800 datapoints covering some 4,600 institutions. This is not big data like the Agency loan level disclosures or HMDA data, however, normalization of the dataset takes considerable subject expertise, as banks report different forms with different datapoints. Since we are interested in applications to the mortgage market, we start by linking institution names to the consistent naming database we have built in the Agency mortgage space. That allows us to make quick inferences into the mortgage world from bank shocks. In next step, we designed a front-end system (the Analyzer) that allows users to pull down data for designated characteristics for subsets of banks using a graphic interface without programming. To demonstrate the application of this tool, we look at some aggregate figures with a breakdown for the Big 4 Banks: JP Morgan, Wells Fargo, Citi and Bank of America. These banks were not the source of the turmoil last Spring but are of general interest. The Overall Bank Picture First, we break down the banks into three buckets: large (assets over $1 trillion), medium ($100 billion to $1 trillion), and small (under $100 billion) using the total assets of them obtained from Bank Call Reports. Then we analyze them through the origination data from two traditional Recursion tools, HMDA Analyzer and Cohort Analyzer (for agency loan level disclosure). Below find charts of the distributions from each source for conforming mortgages by loan count back to 2019: Note that the HMDA data ends in 2022 as 2023 HMDA has not been released yet. In both cases, we see a trend decline in the share of the Big 4 banks. There are a couple of observations that are notable here. First, the share of mortgage activity of the Big 4 institutions with regard to mortgage activity is in decline. (Originations by 9% from 2019 to 2022, deliveries by 22% from 2019 to 2023). It seems that the bulk of declines in the bank share of deliveries in 2023, cited in the earlier note, was concentrated in bigger banks.
Below find a table of the share of deliveries of the Big 4 Banks along with the median credit score quarterly since 2021: On March 8, 2023, Silicon Valley Bank (SVB) announced a loss of $1.8 billion in a sale of assets and collapsed two days later. The ensuing market turmoil resulted in a string of bank failures and a temporary surge in Federal Reserve lending to the banking sector. What impact has this event had on the mortgage market?
To answer this question, we need to be able to distinguish the impact of the SVB collapse from other factors that can impact bank lending. The economy in general and labor markets in particular have posted robust performance statistics over the past year. Mortgage rates, which were about 6.75% in early March last year, surged to a 23-year peak of 7.75% in November and currently stand nearly unchanged from a year ago at about 6.75%. Some months ago, we set off to assess the impact of high interest rates on the usage of appraisal waivers. It soon became clear that we first needed to look in some detail at recent developments in the structure of the new market for appraisal modernization[1]. In this piece, we return to the original question.
To begin we look at the landscape of loan deliveries for Freddie Mac and Fannie Mae across the suite of available approaches towards appraising property values. Below find such charts for purchase mortgages. The recent release of pool-level buydown data led us to write a note contrasting the use of this product across Agencies, including Ginnie Mae[1]. In the process of writing this, we discovered that FHA, unlike the GSEs, also provides loan-level data for buydowns. This allowed us to run an analysis showing loan performance as measured by EPDs was better for FHA loans with buydowns than without, reflecting increased lender caution in using buydowns, which, on average, had higher credit scores than those without. The availability of loan-level data for FHA on this topic provides us with an opportunity to conduct an additional informative analysis specific to this program, which we document here. To start, let’s look at the share of the market with buydowns: ...... ...... ...... To read the full article, please send an email to inquiry@recursionco.com We get a lot of requests at Recursion, the bulk of which never make it to these posts, but one that struck home recently was from a regulator who asked what we know about Lahaina. Given the scope of this tragedy, we thought it worth the effort to talk about what we do and don’t have. Specific to that location, the answer is relatively limited. The Agency disclosure data is provided at the state level. The population of Lahaina was 12,700 as of the 2020 Census, out of a population of the Island (County) of Maui of 164,000 and 1.4 million for the state of Hawaii overall. So, this level of detail seems unlikely to be sufficiently granular to provide a basis for analysis. However, it seems we can take a bottom-up approach that may yield something of value. This would be based on the HMDA data.
HMDA data has the advantage of granularity down to the census tract level. Out of over 84,000 Census Tracts, we can identify 6 for Lahaina. We can then pull-down originations from HMDA: We’ve written many, many times about the inexorable rise in the role of nonbanks in the mortgage market[1]. A variety of factors have contributed to these gains, including superior technology, a relatively less oppressive regulatory environment, and Covid-19 chasing people out of bank branches online.
This picture can be a little blurry, however, depending on the way you look at the market. There is, for example, the distinction between servicing book shares and origination shares. Our agency disclosure data doesn’t provide information on originators, but we can use “seller” as a proxy. The table below looks at the trends in outstanding portfolio and issuance for the conforming and Government markets over the period January 2022 – July 2023: Back in April 2022, we asked the question: The Fed’s Holdings of MBS Holdings Will Decline, Who Will Buy?[1] The release of the Q1 2023 Z.1 data[2] gives us an opportunity to begin to formulate an answer. In fact, the Fed’s share of holdings of MBS peaked at just under a quarter of the market in Q1 2022 (24.4%) and stood at just over one-fifth of the market in 2023Q1 (20.3%). Below find a table breaking this down by major purchaser. There are two sectors that declined: the Fed and Banks, and two that rose: Money markets and households. There are a few comments below: International Sector The set of international investors in MBS is a complex web of public and private sector participants in developed and emerging markets. They are motivated to invest by a broad range of considerations ranging from short-term returns to currency stabilization. A lot of ink is spilled over these issues, but there is an interesting point to be made that their positions, in aggregate, are motivated to a reasonable first degree by the returns to be found in the market. Local stories matter, of course. China remains on a long-term path of decelerating growth, bordering on deflation, weighed down by debt and a slump in the real estate sector. The Yuan is near a 15-year low, and further easing measures could lead to additional currency weakness and associated trade tensions. In Japan, the Bank of Japan recently took another step in easing its Yield Curve Control policy that would allow the 10-yr JGB rate to rise. Market concerns are mounting that an acceleration in the yield could lead to widespread adverse market consequences globally, including MBS. For all this, the share of the international sector rose by just over 1% over the last year. Banks In the previous post, we noted that the shares of bank and Fed holdings are positively correlated. This is because as the Fed sells securities, the funds used by the investors that purchase them often come out of bank deposits. That remains true, but per the table above, the decline in the bank share over the last year exceeded that of the central bank by about 2.6%: The other factor responsible for the decline in the bank share is higher interest rates which act to draw funds out of depositories into private investment vehicles. Over the past year, the formula “Change in bank share = change in Fed share – change in money market & pension share” is accurate within 1%. Private Investors Most analysts, us included, have been looking for the private sector to pick up its MBS holdings as the Fed steps back. That has in fact occurred, but with an unexpected twist. Below find the shares of holdings over time for mutual funds and households[3]: The share of MBS held in mutual funds has declined steadily since 2014, with no evidence yet that higher yields are attracting more funds into this sector. The striking result is a surge in ownership by the household sector, now accounting for over 12% of the total, the highest level attained since the GFC was raging, and the integrity of the banking sector was widely questioned. In June 2022, CPI inflation was reported at 8.9% yr/yr, the highest rate since 1981. Along with ChatGPT and the metaverse, our younger readers can learn a new term: coupon clip. It was all the rage in the early ‘80s, along with “Indiana Jones”. Wait long enough and everything comes back. [1] https://www.recursionco.com/blog/the-feds-holdings-of-mbs-will-decline-who-will-buy
[2] https://www.federalreserve.gov/releases/z1/ [3] “The households and nonprofit organizations sector is the residual holder of agency- and GSE-backed securities.”https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=LM153061705&t=L.101&suf=Q The value of research depends on the consumer. Traders look for actionable ideas to shore up their P/(L), policymakers look for insights into the impact of various regulatory changes, and risk managers look for potholes in the road ahead. The best research informs all of these constituencies by impacting the “big picture” thinking of all of these constituencies.
We just got a fine example of the latter from a new paper by Camelia Minoiu of the Atlanta Federal Reserve, and Andres Schneider and Min Wei of the Federal Reserve Board, “Why Does the Yield Curve Predict GDP Growth? The Role of Banks.”[1] An old puzzle in economics is why Treasury Curve yield flattening is an excellent predictor of recessions. The authors conduct a comprehensive investigation into the role of banks in the relationship. A lower term premium, they argue, reduces profitability and the availability of credit. What does this have to do with the mortgage market? Mortgage credit is provided by both banks and nonbanks. Nonbanks, it may be argued, are monoline credit providers whose credit provision is less impacted by this factor. This leads us to the following chart: |
Archives
February 2024
Tags
All
|