​The release of loan-level dq data by the GSEs opens the door for much new analysis. In today’s blog we will look at servicer type. Below find a table of average DQ’s for each available type, along with average levels of underwriting characteristics:

It’s interesting to note that banks tend to service loans with a modestly higher total DQ rate than the “Nonbank Other” category. The table also shows that banks have a tighter credit box with respect to credit score (higher) and DTI (lower) than nonbanks but have a more generous appetite for higher LTV loans.
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The data also presents financial analysts and strategists with a great deal of information about the performance of individual institutions. As an example, we look at the 100 largest servicers from the bank and “nonbank other” category (known as “nonbanks” from now on). There are 43 banks and 57 nonbanks in this group. The charts below plot total DQ’s vs credit score and DTI for each servicer type. Comparing different points or a single point vs trend lines can provide useful insights regarding the competitive landscape.

​Of course, these charts just scratch the surface of what is possible here.

​Recursion Co recently provided commentary in response to a Request for Information (RFI) regarding appraisal policies, practices, and processes. We comment on how big data technology can be applied to monitor the performance of loans where appraisals have been waived compared to a benchmark of eligible loans where traditional appraisals have been utilized. In addition, we provide a framework for analyzing how these tools can address issues such as the impact of new processes on fairness and the safety and soundness of the system of mortgage finance from such topics as environmental vulnerability.

​With the release of GSE DQ data for the first time late last week, we can start to slice and dice the information in a myriad of ways. Let’s look first at total DQ’s for the GSEs by state:

New View Advisors and Recursion are introducing a set of expanded HECM reverse mortgage prepayment indices, which can be found here: New View Advisors Recursion Cohort Speeds 01_2021. The indices are derived from underlying HECM data in HMBS made public by Ginnie Mae, as well as private sources. This new expanded set of prepayment data is calculated using dollar principal balance, not unit count. Data presented are for HECMs outstanding as of January 31, 2021, representing 311,364 loans totaling $56.5 billion. Future updates of the indices will be available after the 6th business day of each month.

​On Tuesday February 23, FHFA released its monthly purchase-only HPI for December, showing a 1.1% rise from the prior month, and a striking 11.1% increase from December 2019, the record-high annual growth rate reported since this data was first released in the early 1990s.

​Assigning letters to economic recoveries (“V”, “L”, “U” etc.) has become a standard part of the economist’s toolkit for expressing a view on the nature of a particular forecast. The Covid-19 crisis has added a new letter to the lexicon, “K”. In a “K-shaped” recovery, some segment of the population experiences relatively strong growth, while others are left behind. Since housing tenure is an essential determinant of the distribution of household wealth, it is not surprising that we can clearly see this shape in the relative trends in house prices versus rents:

Quoting Recursion Data, Debtwire reported that Non-Bank EBOs fell back in February, even though the level is still multiple times greater than the average recorded across most months in 2020.
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The analysis was performed using Recursion’s flagship product: Cohort Analyzer.