On May 25, 2022, Ginnie Mae announced that starting on June 8 it would enhance its pool and loan-level multifamily disclosures through the addition of an Affordable Status Field[1]. This field marks every FHA loan in pools with a Ginnie Mae guarantee as:

• AFF (Affordable) 
• BAF (Broadly Affordable) 
• NAF (Not Affordable) 
• blank (Unknown)[2]

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We received this data on the 6th business evening, and below find some summary descriptions:

Back in 2021, we wrote a comment on the properties of the GSE Special Eligibility Programs designed to provide lower income households with access to mortgages (HomeReady for Fannie Mae and Home Possible for Freddie Mac)[1]. Given the increasing policy focus on the provision of credit to these households[2], it is appropriate for investors to look at the investment opportunities in this area. In this note, we look at the performance of HomeReady/Home Possible Program loans (referred as Low-Income Program in the following) vs. non-special-eligibility program loans, as measured by one month CPR, controlling coupon and vintage. We focus on just two such cohorts, 1.5% and 2.0% coupon pools of 2021 vintage. By loan count, the share of Low-Income Program loans in these pools by agency for May 2022 is:

As we have noted many times, one of the best features of loan-level analysis is the ability to segment the mortgage market into components that allow for a deepening of understanding of the behavior of the various market players. In this note we look at two groups: borrowers who get an appraisal and those who are eligible to get one but do not.

In previous posts we pointed out that analysis of the performance characteristics of mortgages with and without appraisal waivers cannot be accomplished by looking at loans with waives vs those without as many loans without waives are ineligible to obtain them. A robust analysis can only be conducted by looking at loans with waivers against loans that are qualified to get one. The qualification characteristics can be complex, but the main factor is LTV, which differs by loan purpose.[1]
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The question that naturally arises is why do some eligible borrowers not obtain a waiver when doing so would save money on the transaction? To address this issue, we look at the distribution of loan sizes for purchase loans with waivers vs those without them that are eligible. Here is the pattern of loans delivered to the GSEs YTD October 2021 by Agency:

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.

1. Low down-payment programs. Designed for low-income households with limited savings for a down payment, with flexibilities for down payment sources.
   1. FannieMae: Home Ready[1]
   2. Freddie Mac: Home Possible[2]
2. Loans underwritten by Housing Finance Agencies (HFAs)[3]
   1. FannieMae: HFA Preferred[4]
   2. Freddie Mac: HFA Advantage[5]
3. Refi programs for low-income borrowers
   1. FannieMae: Refi Now[6]
   2. Freddie Mac: Refi Possible[7]

​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:

​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.
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Since the onset of the Covid-19 pandemic in early 2020, the share of FHA purchase mortgages of the total[1] 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.
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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.

The millennial generation has reached peak home buying age, covering a range of about 25-40 years old. Just this year this cohort at 72.1 million passed the baby boom generation in sheer size[1] although this is a bit less than the 78.8 million peak reached by boomers in the late 90’s[2]. Millennial presence or lack of it in the housing market has been the subject of voluminous commentary, ranging from extreme optimism derived from the magnitude of the population bulge to caution related to affordability and impediments to building savings stemming from student loan debt[3] and high rental and child care costs[4]. What can big data tell us about this?

A 2018 study by the Federal Reserve showed that Millennials had lower incomes and assets and higher debt than previous cohorts at a similar age[5]. Our data sets can provide some useful, but far from conclusive, insights into these trends since the onset of the Covid-19 pandemic. Covid-19 is both a cyclical and structural shock. On the cyclical side mortgage rates have fallen to record lows as the Federal Reserve responded quickly and decisively to the health crisis. On the structural side, residents of urban areas have picked up and fled to less dense locations, resulting in a sharp increase in house prices nationally. According to Redfin these price gains have been led by those in rural and suburban areas, rendering these areas less affordable, while urban areas remain out of reach for most.

​The Global Financial Crisis (GFC) of a dozen years ago was at its core a housing crisis. More specifically, it was a single-family mortgage crisis as imprudent lending led to a huge surge in home sales in a bubble that simply could not be sustained. The resulting collapse led to the biggest economic shock since the Great Depression. Until, perhaps, now. The other segment of residential housing, the rental market, skated through almost unscathed. While almost nine million people lost their jobs, including renters, millions of families lost homes and had to turn to rent, keeping the multifamily market afloat.

In our third look at 2019 HMDA[1] characteristics we look at mortgage originations by income bracket. Lending to low- and moderate-income households is an important regulatory requirement of banks[2]. The definition of “low” and “moderate” depends on the local area in which the bank operates. HMDA data is well-suited to regulators looking to track the performance of the institutions they oversee and allows banks to benchmark their performance against their competition. If banks need to add low- to moderate-income loans to their portfolio to meet requirements, HMDA can provide direction regarding which institutions might be a source of product that meets needed characteristics.

Below we present a quick high-level example. HMDA data operates down to the census tract level, but for our purposes here let’s look at two distinct states: California and Oklahoma. In 2018, median income in the two states was $70,500 and $54,400, respectively[3]. According to Zillow data[4], the median house prices in California and Oklahoma that year were $550,000 and $122,000 respectively. Clearly housing is relatively unaffordable for households at or below median income in California compared to Oklahoma. So it is not surprising that the homeownership rate in Q2 2018 for California, at 54.3%, is substantially below that of Oklahoma, at 69.1%[5].
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Confirming this, the following table from 2018 and 2019 HMDA show that there is a substantially greater share of lower- and moderate- income loans available in Oklahoma than in California. Interestingly this share declined in 2019 relative to 2018, particularly for Oklahoma. It is not clear whether this is due to fundamental factors or technical issues related to an increase in the share of “N/A” responses between the two years.

​Finally, to be consistent with prior posts we look at the share of conforming loans originated by banks that are sold to the GSEs, broken down by income brackets:

​A few interesting observations pop up. First, in California the loans that banks keep on their book are almost entirely made to the highest-income households. For Oklahoma, it’s a mixture of highest income and lowest income. This suggests that policy requirements regarding serving poorer communities plays a relatively greater role in Oklahoma than California.