Recently, researchers at the Federal Reserve published a blog about trends in first time home buyers (FTHB)[1]. They utilize a dataset that is a 5% sample of credit files from a credit bureau. It appears from their analysis that there was a bit fewer than 5 million loans originated in 2020, so it would seem they are using a sample of around 250,000 loans. It is natural to ask how their measure of first-time home ownership compares with our calculation using Agency loan level delivery data consisting of over 4 million loans for that year.
​
To start, we can look at the trend of purchase mortgage delivery for the past seven years:

​And second, we can compare estimates of first-time homeownership:

There are several distinctions to be made between the two data sets that are worth pointing out. First, the loan level delivery data only goes back through 2014 on an annual basis. Second, the Agency delivery data does not include loans originated but held on investor balance sheets, including any non-QM loans. A third difference is that the Fed researchers consider FTHB to be the first-ever mortgage file for a household while the Agencies consider a household to be “first-time” if they have not owned a house in more than three years.
​
The number of loans delivered to the Agencies in 2020 increased by considerably more (15.1%) in Chart 1 than that from the credit bureau (10.5%) in Chart 2, which does not reflect a difference in the growth in the total number of loans originated, but rather an increase in the share of originated loans delivered to the Agencies. During Covid, investors preferred to hold mortgages with insured credit risk. This can be seen in the sharp decline seen in the “not sold” category from the recent 2020 release of HMDA data.

The share of FTHB in our data is slightly higher than in the Fed report, which may be due to a low FTHB share in segments such as Jumbos and non-QM loans not included in our datasets.
​
But a key difference is that our FTHB share declined slightly in 2020 from the prior year (52.5% from 53.1%) while it rose slightly in the Credit Bureau data (48.8% from 48.2%). A rigorous attribution of the difference is beyond the scope of this note, but a few comments based on the composition of our delivery data can be made. First, in our data, the decline in the first-time homebuyer share can be attributed to a rise in the market share of conforming loans with lower FTHB shares at the expense of GNM loans with higher FTHB shares[2]. Second, our data comes in monthly so we can look a bit at the dynamics within the year:

​Interestingly, we show the FTHB share picked up in a normal seasonal pattern in 2020 Q2 and Q3, when the pandemic hit with full force. However, in Q4 the share declined to pre-pandemic levels, and rebounded only slightly in the first half of this year. It will be very interesting to monitor these trends going forward.

In a recent post, we commented on the drop in the number of loans in forbearance in Ginnie Mae pools[1]. Because of the different capital strictures, it’s interesting to look at the breakdown between banks and nonbanks.

The economic news in March got off on a strong note with the release of payroll employment data showing a hike of 916,000, a seven-month high. This coincided with the first anniversary of the onset of the Covid-19 Pandemic. The Cares Act forbearance program was launched at the end of March 2020 and was originally designed to last for one year. More recently, the program was extended for six months, but borrowers need to recertify their status as economically impacted by Covid every three months from the 1-year anniversary data[1].
​
So naturally the end of March was a time in which many borrowers had to recertify. This was a natural time for households to reassess their financial positions, setting the stage for the possibility that they could begin repaying their mortgage obligations. In fact, they did, and we saw a sizable drop in the number of loans in Covid-related forbearance[2] in April, particularly for Ginnie Mae programs:

​The economic fundamental driving this decline is the improvement in the labor market, and a distinct correlation can be seen between declining forbearances and unemployment:

A bit more analysis is in order here. The forbearance data come from loans in agency pools, so there is always the possibility that the number of loans in forbearance decreases because some of those loans were bought out of pools by servicers. To check this, we looked at the disposition of loans in forbearance at the beginning of March that remained in pools at the beginning  of April but were not in forbearance. For FHA programs the number was 111,153 loans compared to the one-month decline in the number of loans in forbearance of 125,202. For VA the similar statistic is 27,247 compared with a 26,810 decline in the number of loans in forbearance. It seems clear that improving labor market fundamentals are the primary driver of the decline in the number of loans in forbearance in these programs.
​
To test the idea that the 12-month renewal period played an important role in this process, below we look at the loan age of those mortgages that left forbearance but stayed in pools in April. For FHA programs, the number with loan age of one month was 79,212 or 71% of the total, while for VA it was 17,863 or 66%. The next important date will be June before the program is scheduled to end at the end of September.

​As can be seen from the above table, the vast majority of the number of loans that were recorded as in forbearance in March but not in April did not exit due to buyouts. The data do not precisely add up because other outcomes are possible, including FHA – conventional refis or sales of homes, for which we have no tracking mechanism. But the close match between cures and the declines in forbearance across programs is evidence that the main impetus is improving fundamentals.

The increase in the number of cashout refis has led to some concerns about the implications for the quality of household balance sheets, similar to what occurred in the run-up to the Global Financial Crisis[1]. At that time, many homeowners were tempted to use their houses as a “piggy bank” as the national savings rate hit all-time lows. In the runup to the Global Financial Crisis, the national savings rate hit a sixty-year low of 2.2% while over the 2016-2019 period the rate averaged a much healthier 7.6%[2].

It is useful to scale the magnitude of assets being cashed out and Freddie Mac releases a very useful file documenting this quarterly for their book of business back to 1994[3]. In the fourth quarter of 2020, the amount cashed out reached $48.4 billion, about 58% of the $84.0 billion peak attained in the second quarter of 2006. As a share of household net worth, the most recent data point is 3.7%, well below the peak of 12.7% reached in Q2 2006.

​It’s useful along these lines to ask about the credit profile of cashouts compared to other refinancings. Freddie Mac didn’t report cashout refi separately until 2008 Q3, but the following useful picture can be obtained.

In general, lenders tend to “lean against the wind” by loosening credit conditions when demand for credit declines, and vice versa. Interestingly, it appears that the share of noncashout loans follows a pattern in which the share rises when credit in general is tightened. At the present time, the average FICO score in March for noncashouts was a tight 764, vs 753 for cashouts. It is difficult to pin the rise in equity cashouts in the current cycle to loosening credit conditions.
​
Of course, the proof is in performance, and now that we have loan-level DQs for the GSE’s beginning last month, we can look at this broken down by loan purpose for the Freddie Mac book:

​The performance of cashouts is mildly worse than that for noncashouts, but more in line with purchase mortgages. These statistics will bear watching in future months, particularly as forbearance programs begin to expire towards the end of the year.

On March 30, FHA released its Quarterly Report to Congress on FHA Single-Family Mutual Mortgage Insurance Fund Programs for Q4 2020[1]. The report shows that the MMI fund grew to $82.3 billion from $79.9 billion the prior quarter. However, the year-to-date actual net loss rate on claim activity of 35.2% is higher than the projection of 30.1% percent, as the portfolio-level serious delinquency rate increased in the quarter to 11.9%, from 11.6% percent last quarter. Consequently, Secretary Fudge in a statement indicated stated that “Given the current FHA delinquency crisis and our duty to manage risks and the overall health of the fund, we have no near-term plans to change FHA’s mortgage insurance premium pricing.”[2]

As we have noted previously, the Covid-19 crisis is very distinct from the Global Financial Crisis (GFC) insofar as while both periods experienced high delinquency rates, house prices now are soaring as opposed to collapsing in the earlier crisis.

In a recent post, we discussed the relative performance of loans with property inspection waivers vs those with traditional appraisals that qualified for a waiver[1]. We commented that the observed out-performance of loans with waivers as measured by lower total delinquency rates (DQs) was likely influenced by relatively tighter lending standards (eg higher credit scores, lower DTI) for these loans compared to eligible loans that received a traditional waiver.
​
A fully rigorous examination of this issue would be an extensive undertaking outside the scope of these brief posts. But let’s do a quick example as a demonstration of what our tools can produce along these lines. To make for an apple-to-apple comparison, below find two grids containing the difference between the total delinquency rates for purchase loans with PIWs compared to those that are eligible but obtain a traditional appraisal. The first is for loans originated in 2019 and the second is for those originated in 2020:

​We find that PIW’s are more extensively used in 2020 than in 2019. In addition, in 2019 the range of PIW takeup across cells was 8%-13%, while for 2020 it was 14%-28%. In both cases, takeup tends to rise with credit score. Lenders appear to be more willing to allow a waiver for borrowers with better credit.

For 2019, there are a number of outliers, but there is no clear pattern across the grid. Many lenders were just beginning to implement their waiver programs that year. By 2020, PIWs became a standard part of the toolkit. For most of the center of the grid, loans with waivers very slightly outperform those eligible loans using appraisals. Bigger outperformance can be seen, however, along the edges, i.e. loans with credit scores less than 720, and DTIs greater than 47. It appears it is not the waiver itself that leads to outperformance, but likely that underwriters are more careful and pay more attention in general to these riskier classes of loans.
​
Further work would look at performance across the largest servicers, and by state.

In a recent post, we discussed our comment letter to FHFA regarding policies and procedures related to property inspection wavers (PIWs)[1]. In that note we commented that one of the best ways to assess the impact of the program is to look at the performance of loans with appraisal waivers vs those eligible to obtain waivers but did not. At the time the note was posted (late February 2021) the loan-level data needed to perform such a calculation was not available, so we used a sample obtained from the reference loans in the pools used by the Fannie Mae Connecticut Avenue Security (CAS) Credit Risk Transfer (CRT) program.

Earlier this month we obtained the loan level DQ data for the books of the GSEs as of the end of February 2021 so a more comprehensive analysis is now possible. As stated in the comment letter, the eligibility rules to obtain a PIW vary by product type and agency, so to obtain an apples-to-apples comparison we need to look at the performance of loans with waivers against those that are eligible to use them but did not, as opposed to all loans. Since waivers are generally a recent development, we look at performance for loans originated in 2019 and 2020.

​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: