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.
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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).

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.
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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:

• Manufactured housing
• Affordable housing preservation 
• Rural housing

 
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]:

In a recent post[1] we looked at the evolution of the FHA purchase mortgage market share broken down between areas with a high percentage of Low-Moderate Income (LMI) households and those without. While the overall FHA share has generally declined since the onset of the pandemic, its share has held up in areas with a preponderance of LMI households. There are many factors behind these trends, but a natural consideration is underwriting standards.

To examine this factor, we use the Recursion Matched Dataset[2], where we create a large sample of loans with characteristics from both HMDA and the Agency disclosure data. A very high share of mortgages can be matched using our proprietary algorithm over the years 2018-2020. The coverage ratio from the Matched Dataset is provided in a previous post[3].

We proceed by looking at three major underwriting characteristics for LMI and non-LMI areas for FHA and the GSE’s: Credit Score (CS), Loan-to-Value (LTV) and Debt-to-Income (DTI),
Most interesting is Credit Scores:

In a recent post[1], we looked at the declining FHA share of the purchase mortgage market relative to the GSE’s across a variety of price points. Another way to look at this question is by a geographic breakdown focusing on those census tracts with a Low-Moderate Income (no greater than 80% of area median income) population greater than 51% (we will call these LMI areas)[2].
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To address this issue, we utilize the HMDA dataset, and then apply the LMI information to compute shares of originated purchase loans delivered to FHA vs GSE. This is done on both a loan count and loan balance basis.

One of the many recurring themes of these posts is that the shock of the Covid-19 Pandemic and subsequent policy response has resulted in structural changes in behavior that cause loan performance metrics to shift compared to the pre-crisis world. An interesting example of this can be found in the performance of modified loans in Ginnie Mae programs.

Modified loans in these programs are those that have been purchased out of pools by servicers that are past due that subsequently have features such as rate and term adjusted in order to bring households back to a current status. These are then often resecuritized into a new GNM pool.
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• Securitization of modified loan volumes more than doubled after the pandemic

The Covid shock of course has resulted in a surge in delinquencies and triggered large volumes of buyouts and modifications. Below find a plot of the share of modified loans of total new issuance for FHA and VA programs since January 2020:

Food for Thought is a speaker series that discuss the Covid-19 crisis and social justice reform at the Columbia University School of International and Public Affairs (SIPA). Our Chief Research Office Richard Koss will give a talk at this forum on Wednesday, September 22 at 12PM EST about housing policy changes during the Covid-19 Pandemic.
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“The onset of the Covid-19 Pandemic represents a massive shock impacting all sectors of the global economy. It has been particularly felt in the real estate sector as households reconsider their work/living arrangements and adjust their lives accordingly. It has also greatly exacerbated the trend towards wealth inequality. The new Biden Administration and other government agencies, notably the Federal Reserve, are engaging in innovative policy making to improve the functioning of the economy, expand access to credit and provide affordable rental housing to low-income households. What are the barriers to success facing these programs and what more needs to be done?”  
​Read More

​The recent unprecedented surge in home prices to a record 18% jump on a year-year basis as measured by the FHFA purchase-only index brings affordability front and center to the current housing policy debates. In May 2021, indexed home prices stood 15.5% above indexed aggregate earned income, a bit less than half of the peak house price overvaluation of 29.0% reached in December 2005, just before the onset of the Global Financial Crisis.

The topic of affordability is very broad, and will be the subject of much further commentary, but in this post we look briefly at this topic through the distribution of the purchase mortgage market across securitization agencies, notably FHA and the GSE’s.

Looking at the distribution between the GSEs and FHA is informative in this issue because the FHA program is aimed at low-income borrowers. According to 2020 HMDA data, the weighted average household income for FHA borrowers of purchase mortgages was $85K while for those in conforming mortgages the figure was $228K.
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Since the onset of the Covid-19 pandemic in early 2020, the share of FHA purchase mortgages of the total[1] delivered to agency pools as been in general decline, on both a loan count and outstanding balance basis:

​With a base consisting of relatively lower-income borrowers, it makes sense that the borrowers in this program are struggling to qualify for loans in a skyrocketing market. To check this out, we calculate the change in the distribution of loans between FHA and the GSE programs by original loan sizes:

Intuitively, larger loans comprise a greater share of the distribution of purchase loans in both programs between January 2020 and July 2021.
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Over this period, FHA lost a bit over 5% in market share to the GSE’s in this category. The change in share by loan size bucket and the contribution of each of these to the total loss in share is given below:

​In fact, it turns out that about three quarters of the loss in FHA’s purchase market share comes from losses in loan sizes less than $250,000. Further analysis is needed to look at the fundamental and structural factors that are behind this result.