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It’s always interesting to look at the underlying dynamics within the mortgage market to get a deeper handle on the forces behind recent trends and to gain insights into the market impact of policy changes. This time we will look at a breakdown of the market between the cash window and swaps. Simply, in a swaps transaction the lender sends loans to one of the Enterprises, Fannie Mae or Freddie Mac, and in return obtains a security which it can keep as an investment (mostly in the case of banks) or else sell into the market (both banks and nonbanks). The alternative is to sell the loans directly to the Agencies for cash. This is important to nonbanks in particular as this cash is used as a funding source for running their businesses. As it turns out, neither GSE reports the path by which a loan is obtained in their loan-level disclosures. However, in the case of Freddie Mac, cash loans are placed in their own pools with distinct prefixes. As a result we can unpack these pools and perform a matching exercise with the loan tape and assign these accordingly. This allows us to perform queries on this characteristic across our loan-level querying tool Cohort Analyzer. Below find the share of deliveries made to Freddie Mac from the Cash Window by loan purpose.  Recently, the GSE’s Fannie Mae and Freddie Mac released loan-level data associated with their “Special Eligibility Programs” that look to extend credit to low-income borrowers. As housing policy is increasingly focused on providing this market segment access to this market segment, this data will prove useful to housing analysts looking to assess the effectiveness of these programs as well as to traders looking to understand the impact on the performance of MBS containing these loans. Briefly, each agency has three programs. There are many differences in details between the programs.
As the refi programs are relatively new and volumes are small, in this post we focus on the first two. For convenience, we refer to the first as the “Low-Income Programs” and the second the “HFA Programs”. Below find the market share of Home Ready and Home Possible out of total volumes for their respective Agencies by loan count:  Our Chief Research Office Richard Koss gave a speech at the forum Food for Thought on Wednesday, September 22 at Columbia University about housing policy changes during the Covid-19 Pandemic. Food for Thought is a speaker series that focuses on the Covid-19 crisis and social justice reform. Richard will discuss his paper about the policy response to the unexpected arrival of the global pandemic. His speech paper The New Housing Policy is available to download.
As we mentioned in our previous blogs, Recursion’s proprietary tools Cohort Analyzer, and Pool Level Analyzer can analyze FED and CMO portfolios recursively down to the “simple pool” level. There are a wide range of applications of these powerful tools. We previously demonstrated how to calculate FED portfolio and CMO lockup rates at the macro level. Another important application is to study the collateral of mortgage bonds directly at the loan level in order to support investor’s trading decisions. Before we delve into this particular CMO bond, we want to discuss the loan leverage coverage ratio for all agency pools. As we know, Fannie Mae discloses loan level information for all pools issued in and after 2013, and Freddie Mac does this for pools issued in and after 2006, while Ginnie Mae disclosures include loan level information for all pools that are not paid off. When Recursion was founded in 2015, due to its short disclosure history, Fannie Mae pools’ loan level coverage was fairly low. However, as of today, Fannie Mae’s loan level coverage has improved to close to 90%. As time goes on and pools issued before 2013 gradually pay off, the loan level coverage of outstanding agencies pools will reach 100% as will the CMO loan level coverage.  With affordable housing for Low-Moderate Income (LMI) households at the top of the policy agenda, we take a look at loan data for manufactured housing (MH). In a recent report, the CFPB provided a comprehensive survey of this market based on enhancements to the HMDA data first made available in 2018[1]. These include data on Secured property type:
In their survey, the CFPB looked deeply into the data for 2019. In this note, we update some of their work with 2020 HMDA data. This is important because of the onset of Covid-19 that year. The site-built market performed strongly, but this cannot necessarily be presumed to carry over to MH as Covid is a supply shock, impacting labor markets and supply chains. Another innovation in this note is that rather than looking at this market by state the way the CFPB does, as a policy guide we look at it bifurcated between rural and nonrural MSAs. Below finds a chart of the progression of single-family manufactured housing origination volumes for personal loans (securitized by chattel) and mortgages (securitized by real property) from 2018-2020, along with the share of all single-family manufactured housing loans (personal loans plus mortgages) of the total single-family mortgages including those for site-built homes.  In a recent post[1], we discussed using Recursion’s proprietary tools to unravel the Federal Reserve’s MBS holdings of Fannie Mae and Freddie Mac loans. The Fed’s holdings, however, are part of a bigger picture issue regarding the notion of “float” in the MBS market, that is, the amount of securities outstanding that are available to trade. The holdings of the central bank serve to reduce the float as the Fed is a buy-and-hold investor. These loans are said to be “locked up”. Besides the Fed, loans can be locked up in structured products, notably Collateralized Mortgage Obligations (CMOs). The first CMOs were launched by Freddie Mac as Real Estate Mortgage Investment Conduits (REMICs) in 1988 and allow cash flows to be tranched to meet the needs of different investors[2]. Pools in CMOs’ collateral groups are also locked up. In a recent paper, researchers at the Philadelphia Fed (Liu, Song and Vickery, May 2021) discussed the history of the differential between the pricing and the trading volume differential between Fannie Mae and Freddie Mac securities[3]. Historically, Fannie Mae securities had traded ten times as often as those of Freddie Mac, with the consequence that trading costs for Freddie Mac could be twice those of Fannie Mae. In this paper they comment that Freddie Mac compensated for this by raising its g-fee, and by locking up its securities in CMOs. Using the same recursive algorithms as in our prior blog, we can back out the CMO lockup, FED lockup and Float by agency:  With talk of taper at the top of the monetary policy discussion, it is worthwhile to dig a bit into the role of the Federal Reserve in the functioning of the MBS market. As is well known, the onset of the Covid-19 pandemic resulted in a resurgence of central bank purchases of Agency mortgage-backed securities (MBS). Chart 1  Financial support for rural communities has been a feature of US economic policy since the Farm Credit System was established as the first GSE in 1916[1], sixteen years before the Federal Home Loan Banks were established in 1932[2]. This support continues to this day and has expanded to encompass additional programs. Ginnie Mae Program While the best-known collateral for Ginnie Mae securities is loans underwritten through the FHA and VA programs, another form is loans underwritten by the US Department of Agriculture Rural Development (RD) Program, launched in 1990 as part of the Farm Bill passed that year[3]. The Single-Family Direct Home Loan Program[4] in particular is designed to provide payment assistance to low- and very-low-income households in rural communities. Of course, FHA and VA provide loans in rural areas under the terms of their programs as well. GSE Rural Lending Single-family lending at Fannie Mae and Freddie Mac was handed a mandate to provide liquidity to rural communities through the adoption of the Duty-to-Serve provision of the HERA Act enacted in 2008[5]. This program requires the GSE’s to engage in activities to facilitate liquidity in three underserved markets:
A key feature of this regulation is that FHFA has provided new datasets and tools to enhance the analysis of these markets[6]. In particular, Rural Areas FHFA's Duty to Serve regulation defines "rural area" as: (1) a census tract outside of a metropolitan statistical area, as designated by the Office of Management and Budget; or (2) a census tract in a metropolitan statistical area, as designated by the Office of Management and Budget, that is outside of the metropolitan statistical area's Urbanized Areas as designated by the U.S. Department of Agriculture's Rural-Urban Commuting Area Code #1, and outside of tracts with a housing density of over 64 housing units per square mile for USDA's RUCA Code #2. Below is a link to the specific geographies which meet the Rural Areas definition. Using this segmentation, we are now in a position to load their definition into our databases and look at trends in this market segment. Using HMDA data as a base, we produce the following chart[7]:  |
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