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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] 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.
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:  With affordable housing for Low-Moderate Income (LMI) households at the top of the policy agenda, we take a look at loan data for manufactured housing (MH). In a recent report, the CFPB provided a comprehensive survey of this market based on enhancements to the HMDA data first made available in 2018[1]. These include data on Secured property type:
In their survey, the CFPB looked deeply into the data for 2019. In this note, we update some of their work with 2020 HMDA data. This is important because of the onset of Covid-19 that year. The site-built market performed strongly, but this cannot necessarily be presumed to carry over to MH as Covid is a supply shock, impacting labor markets and supply chains. Another innovation in this note is that rather than looking at this market by state the way the CFPB does, as a policy guide we look at it bifurcated between rural and nonrural MSAs. Below finds a chart of the progression of single-family manufactured housing origination volumes for personal loans (securitized by chattel) and mortgages (securitized by real property) from 2018-2020, along with the share of all single-family manufactured housing loans (personal loans plus mortgages) of the total single-family mortgages including those for site-built homes.  Financial support for rural communities has been a feature of US economic policy since the Farm Credit System was established as the first GSE in 1916[1], sixteen years before the Federal Home Loan Banks were established in 1932[2]. This support continues to this day and has expanded to encompass additional programs. Ginnie Mae Program While the best-known collateral for Ginnie Mae securities is loans underwritten through the FHA and VA programs, another form is loans underwritten by the US Department of Agriculture Rural Development (RD) Program, launched in 1990 as part of the Farm Bill passed that year[3]. The Single-Family Direct Home Loan Program[4] in particular is designed to provide payment assistance to low- and very-low-income households in rural communities. Of course, FHA and VA provide loans in rural areas under the terms of their programs as well. GSE Rural Lending Single-family lending at Fannie Mae and Freddie Mac was handed a mandate to provide liquidity to rural communities through the adoption of the Duty-to-Serve provision of the HERA Act enacted in 2008[5]. This program requires the GSE’s to engage in activities to facilitate liquidity in three underserved markets:
A key feature of this regulation is that FHFA has provided new datasets and tools to enhance the analysis of these markets[6]. In particular, Rural Areas FHFA's Duty to Serve regulation defines "rural area" as: (1) a census tract outside of a metropolitan statistical area, as designated by the Office of Management and Budget; or (2) a census tract in a metropolitan statistical area, as designated by the Office of Management and Budget, that is outside of the metropolitan statistical area's Urbanized Areas as designated by the U.S. Department of Agriculture's Rural-Urban Commuting Area Code #1, and outside of tracts with a housing density of over 64 housing units per square mile for USDA's RUCA Code #2. Below is a link to the specific geographies which meet the Rural Areas definition. Using this segmentation, we are now in a position to load their definition into our databases and look at trends in this market segment. Using HMDA data as a base, we produce the following chart[7]:  In a recent post[1] we looked at the evolution of the FHA purchase mortgage market share broken down between areas with a high percentage of Low-Moderate Income (LMI) households and those without. While the overall FHA share has generally declined since the onset of the pandemic, its share has held up in areas with a preponderance of LMI households. There are many factors behind these trends, but a natural consideration is underwriting standards. To examine this factor, we use the Recursion Matched Dataset[2], where we create a large sample of loans with characteristics from both HMDA and the Agency disclosure data. A very high share of mortgages can be matched using our proprietary algorithm over the years 2018-2020. The coverage ratio from the Matched Dataset is provided in a previous post[3]. We proceed by looking at three major underwriting characteristics for LMI and non-LMI areas for FHA and the GSE’s: Credit Score (CS), Loan-to-Value (LTV) and Debt-to-Income (DTI), Most interesting is Credit Scores:  In a recent post[1], we looked at the declining FHA share of the purchase mortgage market relative to the GSE’s across a variety of price points. Another way to look at this question is by a geographic breakdown focusing on those census tracts with a Low-Moderate Income (no greater than 80% of area median income) population greater than 51% (we will call these LMI areas)[2]. To address this issue, we utilize the HMDA dataset, and then apply the LMI information to compute shares of originated purchase loans delivered to FHA vs GSE. This is done on both a loan count and loan balance basis.  We received the first loan-level performance data for the GSE’s a few months ago, so it’s about time to see what tentative observations can be drawn from this new data set. As a popular theme for this blog is the bank/nonbank share this seems a good place to start. In general, we have noticed that nonbank DQ’s tend to be higher than those for banks, and that this distinction is correlated with the relatively more generous credit terms available in the nonbank sector. Below find a table that demonstrates this for 2018 and 2019 vintage mortgages:  This can be summarized:  While market commentary is focused on developments such as inflation and house price increases, the key housing policy issues in the post-Covid world are financial inclusion and climate change. Our agency loan-level data provide us with many insights into market trends, but these do not contain demographic or geological details that are necessary to perform in-depth analysis in these areas. On the topic of financial inclusion, the key supplemental data set is the Home Mortgage Disclosure Act (HMDA) dataset, an annual disclosure made by lenders in support of fair lending. HMDA data contains relevant data points such as income, gender, and race. Any assessment of fair lending practices requires an analysis of how these factors influence the availability of credit. To accomplish this, Recursion has applied a proprietary matching algorithm to create a robust dataset consisting of loans with both underwriting and demographic characteristics. Over the period 2008 – 2020 the data set consists of about 20 million loans. Below finds a chart of average credit scores by race (as measured by race of the first borrower) over the 2008 – 2020 period from this matched data set:  |
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