In a recent post we compared the average credit score at origination history from NSMO with that from the agency loan-level datasets on an apples-to-apples basis.
In today’s blog, we improved the matching by using the Analysis Weights that are provided in the NSMO dataset to compute the averages. NSMO Analysis Weights are the product of the sampling weight and a non-response adjustment, which can make the NSMO dataset more representative.
The MBS market is the second-most liquid market in the US after the Treasury market. The collateral for these securities is completely domestic but a significant share of the ownership is held by offshore entities. As such, investors need to be cognizant of the attitudes of overseas investors towards this market, particularly in the current highly charged geopolitical climate.
The following chart shows total holdings of Agency and GSE-backed securities over time broken down by a broad category of investors. The most recent figure is as of Q1 2020; Q2 data will be released later in September.
The National Survey Mortgage Originations (NSMO) is a quarterly mail survey jointly funded and managed by FHFA and CFPB. It provides unique and rich information for a nationally representative sample of newly originated closed-end first-lien residential mortgages in the United States, particularly about borrowers’ experiences getting a mortgage, their perceptions of the mortgage market and their future expectations. Beginning with mortgages originated in 2013, a simple random sample of about 6,000 mortgages per quarter is drawn for NSMO from loans newly added to the database.
A recent Fed paper referencing the NSMO dataset can be found here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3422904
The paper analyzes which borrower types appear to overpay due to a lack of shopping and knowledge about mortgages based on the NSMO dataset.
The NSMO Database comes in with a long lag: the last update was on February 20, 2020. It contains 29,962 sample loans originated from 2013 to 2017. Below are several highlighted observations:
The NSMO dataset contains 2 types of data points: first, 309 borrower characteristic variables such as borrower age, sex and shopping mortgage behaviors, and second, 118 underwriting characteristic variables, such as loan size category, LTV, DTI, loan type, loan purpose, etc. Especially interesting is that credit score and delinquency status history can be traced from origination date to Sep 2019 for each loan.
The purpose of the line charts below is to show an assessment of data quality on the NSMO dataset. We compare the average credit score at origination history from NSMO with that from the agency loan-level datasets on an apples-to-apples basis. The results show credit score histories from the two data sets match quite well.
From the below table, iIt does appear that the sampling in the NSMO data selects loans with modestly higher credit scores than that in the agency loan-level data. It is an approximately 6-10 basis points across each loan size category.This may reflect a tendency of lenders to keep higher-quality loans on their books than they deliver to the agencies. This is not the case for Government programs as a greater share of these loans are delivered.
The following two tables demonstrate loan type and GSEs distribution in the NSMO dataset.
As is the case for residential mortgages, every month Ginnie Mae publishes data on loan-level delinquencies for its commercial real estate programs. The structure is a bit different than for single family, with different categories (the dominant one being FHA multifamily, but also hospitals and nursing homes). In this short post we look at recent performance for FHA multifamily and nursing homes.
Traditionally, multifamily DQs for FHA are low because these loans are concentrated in affordable housing, where there is a persistent condition of excess demand. The costs of eviction are low and new tenants are ready to move in. But this is not necessarily the case in the COVID-19 era as the economic impact falls most heavily on the lower income working class, so there are fewer people who can afford affordable housing without support from government income programs such as jobless benefits.
In a recent post we looked at the agency composition of the recent surge in MBS production. We observed that Ginnie Mae’s market share in the three months to July 2020 is significantly below that experienced in the same period a year ago. The bulk of the decline is due to a drop in the refi share, while the purchase market share experienced less than a 1% decline. There are several drivers of share for purchase market share, including program design, the coupon spread between the government and conforming sectors, and differences in the credit boxes between the two. This note looks at competitiveness through the lens of the latter factor, credit boxes.
As noted in the prior post, Ginnie Mae is the securitizer for four different programs, with the two biggest being FHA and VA. VA has a fairly unique set of program requirements, so the main competition in the purchase mortgage space is between FHA and the GSEs. In the three months to July 2020 FHA lost about 2.5% of its share in the purchase mortgage space compared to the GSEs. Let’s dive into credit factors, starting with credit score:
One of our major rules at Recursion is that we are a fintech data and analytics company and that we don’t give investment advice. So spoiler alert: the answer to the question is that anything is possible.
But we noticed in the most recent weekly Freddie Mac survey that the 30-year mortgage rate edged up to 3.01% from a record-low 2.98% the prior week, the first sub-3.0% level ever recorded. Market lore says that at a certain level, rates give lenders sticker shock and mark a point below which they are reluctant to venture. In an early blog post we noted that mortgage rates were at record lows, but that Treasury yields were deeper into record-low territory, so mortgage spreads were actually quite wide.
Mortgage rates are set in the market reflecting offsetting pressures including: downward pressure from Federal Reserve purchases, upward pressure from record demand, and the costs of forbearance borne by servicers that they seek to recoup with higher margins on new business. Below is an update to the March chart with a new variable added: the inelegantly named OPUC:
The long-raging and complex debate about housing finance policy basically boils down to two issues: first, how much risk should there be in the system, and second, who should bear it? Previous posts have addressed the use of big data in looking at the first issue by examining the trade-off between credit standards and delinquencies. With regards to the distribution of risk, the topmost issue is the breakdown between the public and private sectors. This note approaches the second question by looking at the market shares of a government agency, FHA, vs that of the GSEs, which represent a mixture of public and private risk.
The competitiveness of one agency vs another is a multifaceted subject, as there are multiple aspects to their interaction. Among other approaches, they may compete via price (insurance fees) or via loan underwriting standards or product innovation. To launch this analysis, we just look at relative prices for purchase loans. As a proxy for price, we compare the weighted average coupon (WAC) between FHA and conforming loans in this category: