In a recent post, we discussed the attributes of manufactured housing data that came with the final 2021 HMDA release in July, which were not available in the preliminary release. Another important data point from this final release is the conforming flag, identifying which loans satisfy the requirements for delivery to Fannie Mae and Freddie Mac. Obviously, any loan sold to a GSE is conforming, so its main use is to enable analysts to examine these loans which are held on bank balance sheets.
The lag between the preliminary and final releases of HMDA data can be four months or longer, so it would be useful to be able to identify these loans right after the preliminary release. One way to approach this is to flag non-government loans with balances above the conforming limits as “jumbo”. How does Recursion’s “Jumbo” flag compared with HMDA’s Conforming flag? If the information is perfect, “Jumbo” loans should be all the loans that are not “Conforming”. However, the exact original balance of a mortgage is not provided by HMDA to protect privacy. For those loans close to the conforming boundary, our program can misjudge which category to assign. Given all that, going back to 2018, there is still a very strong negative connection between the two measures:
While the Fed has clearly been the dominant player in the MBS market for the last 13 ½ years, the consistent biggest holders of MBS have been the banks. When the Federal Reserve launched its QE program in late 2008, banks held about 16% of the outstanding balance at the time, and that share has more than doubled as of Q1 2022 to stand at about one-third of the total. In a recent blog, we dug into the details behind international investor behavior with an ancillary dataset, for the banks, we look for guidance from the Call Reports.
The Call Reports provide details on portfolio holdings of individual banks across financial asset categories (e.g., equities and bonds) for both loans and securities. For the purposes of this note, we just look at securities. What makes bank behavior so challenging to assess is that various types of policy actions have profound impacts on their investment decisions. To begin, we look at the share of residential agency MBS out of total bank assets, including both pass-through securities and CMOs:
In a recent post, we posed the question: as the Fed’s portfolio shrinks, who will buy? In this report, we dig a bit deeper and look at the differential market impacts based on whether the loans on the balance sheet roll off (as in, for example, a refinance transaction) or are sold off.
In the first case, we assume that a mortgage in a pool the Fed holds is extinguished, and a new mortgage is created through a refinance transaction, or via a home sale followed by the purchase of a new home financed by a new purchase mortgage, or through a buyout that generates a new modified loan. This case describes the situation described in the earlier post: a mortgage on the Fed’s balance sheet disappears, and some other investor has to pick up the new loan. The question here is how much higher the yield on the new pools has to be to attract sufficient demand from private investors. These new loans tend to have characteristics that investors find attractive, including a coupon which is near the most liquid part of the market (the current coupon).
The second case is quite different. In this instance, the question is how much higher the yield has to be (lower price) for the existing mortgages being sold to find buyers, not for a new mortgage with the most desirable characteristics. In this case, it’s not just a matter of who has the capacity to increase their holdings, but how much additional yield investors will be required for characteristics that are less than pristine. These are challenging issues that we can’t offer precise solutions to, but one way to approach the issue is to see how different the Fed’s portfolio looks from those held in the private sector.
To conduct this exercise, we look at two characteristics. The first is the coupon distribution of what the Fed holds vs. the portfolios held by private investors. This rather technical exercise can be conducted by supplementing the data provided in the Agency disclosures contained in our Pool-level Analyzer with the central bank’s holdings provided by the New York Fed. In fact, the distinction is quite notable for 30-year fixed-rate mortgage pools:
In a recent post, we discussed findings obtained with the recent release of 2021 HMDA data. Among other things, we looked at the share of mortgage originations by income group and product type. In this note, we look at the difference in lending patterns between the banks and nonbanks.
The incentive behind this approach is policy driven. There is a long history of measures taken to encourage lenders and builders to foster economic development in low-income areas via the housing market. For example, the Community Reinvestment Act (CRA) stipulates that a bank’s performance with regards to compliance of their regulatory requirements depends in part on:
“the geographic distribution of loans—that is, the proportion of the bank's total loans made within its assessment area; how these loans are distributed among low-, moderate-, middle-, and upper income locations”
To assess this issue, we assign a flag to each of the census tracts designated by HUD as having a greater than 51% share of households with incomes in the Low-to-Moderate (LMI) range in the larger MSA the tract is part of, which are called LMI area by HUD, or “low income” tracts by FHFA. Below find a chart of the 10-year trend in the share of loans originated in this category by institution type for conventional and FHA loans:
Agency-based Metrics for Assessing the Resolution of Mortgage Forbearance and Delinquencies (Part II – The GSE’s)
In a recent post, we discussed the utility of secondary market indicators to track the progression of loans that are coming out of forbearance in Government programs. This short note looks at this progression in the conforming loan market.
For the Ginnie Mae programs, issuers may buy loans out of pools after they are delinquent more than 90 days and begin a workout process that culminates in one of the options, including loan modification. The situation is quite a bit different for Fannie Mae and Freddie Mac. The main distinction is that on January 1, 2021, the GSEs extended their timeline for buying loans out of pools to 24 consecutive months of missed payments. As the Covid-19 pandemic began in March 2020, we expect to see buyouts being extended as much as to April and May this year.
However, we can obtain a view on future loan modifications through the trial mod flag in the borrower assistance plan field in the monthly disclosures the GSEs started to release in March, 2021. In order to obtain a permanent modification, borrowers must first successfully complete a three-month trial modification plan.
Below find the progression in the number of loans in such plans since March 2021:
On December 30, 2021, FHFA announced that the baseline conventional loan limit for 2022 would rise by $98, 950 to $647,200. The new ceiling for high-cost areas is set at 150% of the baseline limit or $970,800. Since loan limits apply when they are delivered to the agencies, not when they are originated, it would be of interest to look at the pace at which loans reaching to the new limit are delivered at the start of the year.
The chart below shows the distribution of mortgages delivered to the GSE’s in Q1 2021 by loan size($K).
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
Big Data and the New Housing Policy
As we accumulate more data at a fine local level, the opportunities to evaluate policies derived from insights into lender, borrower and supervisor behavior grow massively. Our recent post looking at the potential impact of FHFA’s new rate mod policy is our most recent example of the application of digital tools in the policy space, but as noted previously, the new policy framework is designed to focus on wealth creation and housing sustainability at the local level.
To look at this issue, we need to have data at hand that tells us which local areas have a preponderance of low-income borrowers on which we can overlay the HMDA data set, which reports census tract level indicators related to the policy issue at hand.
It turns out that the income data can be found in the American Community Survey (ACS). A key facet of this survey is information regarding the share of every census tract where low and moderate income (LMI) people comprise less than or equal to 51% of the total population. This data is available through the HUD Exchange.
With that, we now have a robust tool for analysis. An immediate challenge in this regard is to come up with specific queries out of the myriad of possibilities that demonstrate their power. Below finds a chart that provides a big-picture view of lender behavior in LMI neighborhoods broken down between banks and nonbanks. Specifically, we look at the trend in purchases from third-party originators of both FHA and conventional loans: