The matched dataset continues to pay dividends (sorry no buy-backs). This time we take a look at appraisal waivers. The very straightforward question based on the new data is to ask if there are differences in the rate of PIW take-up among eligible loans between areas with a higher share of low to moderate income people and those with a lower share. Our breakpoint is areas with LMI>=51(Low-or Moderate-Income Areas) and LMI<51 (Not Low-or Moderate-Income Areas), and we look here at just purchase loans.
Before we begin, as this query is focused entirely on GSE loans, we felt it necessary to put the bots into overdrive to improve the match rate between HMDA and the GSE loan tapes and for those keeping track the updated match rate is:
Our proprietary matching algorithm continues to chug along and our match rates between the Agency loan tapes and HMDA continue to improve. Here is an up-to-date summary table:
The new FHFA Acting Director Sandra Thompson has lost no time in implementing new policies designed to support homeownership with the aim of creating greater wealth equality. This is the basis of the New Housing Policy we described in a recent post. At first, this involved extending foreclosure moratoriums for distressed families until the end of the year. Then recently, the GSE regulator announced a change in its modification policy to broaden the eligibility for rate mods to any qualifying household that were previously only available to those with a mortgage greater than or equal to 80% of the current home valuation (Current LTV>=80). This program is designed to allow as many credit-worthy borrowers to stay in their homes as possible.
The LTV limit is significant because the surge in house prices we have witnessed over the past year has meant that a relatively small share of loans should have Current LTVs greater than or equal to 80. Our loan-level data set allows us to examine this question by looking at over 25 million GSE loans. Below finds a snapshot of the total combined June books of the GSEs broken down in this manner:
In a previous post, we mentioned the Recursion Matched data set, which uses a proprietary algorithm to match the loans provided in the monthly Agency loan tapes, with HMDA data. This allows for a broad analysis of loan performance (delinquency and prepayment rates) in terms of both underwriting standards (credit score, DTI, LTV) with demographic and household economic characteristics (income, race, gender, etc). We are always working to improve our algorithm, below find the match rates for Ginnie Mae loans over the 2013-2020 period. HMDA has released more characteristics in recent years, allowing for a greater matching rate.
We received the monthly GSE data download for the June book of business over the weekend and prepayment speeds dropped for the second consecutive month, with the 1-month CPR printed 22.4, the low posted since 17.1% was reached in February 2020 just before the onset of the pandemic.
Mortgage rates are of course the key driver here, but other issues matter as well, notably lending capacity. With the onset of the pandemic and the associated loosening of monetary policy and spike in demand for housing away from dense locations, the mortgage industry became overwhelmed. Originators were busy hiring and increased their capacity over the past 18 month to deal with the long period of refinancing activity. However, as prepayment speeds slow down, it appears that the capacity building may be overshooting. In response, originators have started to lower their underwriting standards to create enough volume to fully utilize the capacity.
Traditionally, the industry fine-tunes its production through tweaking its credit standards to keep its pipeline as full as possible. This is occurring now notably for refinance mortgages:
What we can see is that purchase demand remains strong, with the swing product being refinance mortgages. It is evident that lenders are trying to smooth out refinance production with countercyclical credit tightening and loosening. As credit scores are higher than was the case in the pre-pandemic period there is room to ease further, but the ultimate extent is highly uncertain.
Recently, the Federal Reserve released its May 2021 Financial Stability Report, with a particular emphasis on asset valuations. Valuations are raised as a concern as “Prices of risky assets have generally increased since November with improving fundamentals, and, in some markets, prices are high compared with expected cash flows”. While not cited as a matter of high alarm the report commented that “House price growth continued to increase, and valuations appear high relative to history.”
On May 25, FHFA released the purchase-only house price index for March, showing a record-high growth rate of 13.9%, far above the bubble-era peak of 10.7% attained in 2005. Housing fundamentals are of course supportive with mortgage rates below 3% and economic activity rebounding as vaccine optimism spreads. The unique factor now in housing is the impact of the pandemic on preferences for housing away from density and towards suburban and smaller-urban centers. This new fundamental can easily be seen via booming housing demand during the pandemic as measured here by purchase mortgage deliveries to Freddie Mac.
In a recent post we discussed trends in the conforming purchase market by occupancy type. In this note we look at performance metrics.
To begin, we look at prepayment speeds. It’s important to note that certain fees (which Fannie Mae calls LLPA’s and Freddie Mac calls Credit Fees) vary by occupancy type, particularly for those with high LTV’s.