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:
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 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).
In a recent post 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, 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.
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:
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.
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.
Since the onset of the Covid-19 pandemic in early 2020, the share of FHA purchase mortgages of the total 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.
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.
 In this case we view the total as FHA + GSE
Credit provision is one of the great areas of concern addressed by the New Housing Policy. In a previous post, we mentioned that we have integrated HUD LMI Neighborhood information with our tools. We can view aggregate credit creation through Cohort Analyzer, and its composition through HMDA Analyzer.
2020 marked an unprecedented year for mortgage production as the pandemic sparked aggressive moves by the Federal Reserve driving mortgage rates to record lows, coupled with a flight of households away from density towards more sparsely populated areas. Trends in the major programs by loan count can be seen here:
*This chart can be duplicated using the above two queries