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 Covid-19 pandemic has resulted in a great economic shock that has been met with a tremendous policy reaction in the form of interest rate cuts and MBS purchases by the central bank. Prepayments have picked up substantially during the year. The question arises as to whether the magnitude of the response is unusual compared to previous episodes of rate declines, and whether they can rise further should borrowing costs fall further.
The magnitude of the relationship between rates and refis is complex and depends crucially on a number of factors. First, the relationship is path-dependent. That is, it doesn’t just matter if rates fall 1.0%, but whether this decline takes rates to new lows so that the biggest possible set of borrowers can profitably refinance.
Great gobs of sophisticated statistics and modeling go into forecasting prepays on the part of lenders and investors. But a look at prepays over time shows three main waves of refis over the past 20 years. The first (A) is 2000-2003, (B) 2008-2013, and (C) 2019-2020. In all three instances, rates reached new lows. The dates correspond roughly to the times when the mortgage rate broke to a new low until a new trough was formed (or the present in the case of “C”)
Here are the corresponding periods for prepays:
Here are the corresponding periods for originations:
Here is a summary of the three periods:
Notably, the biggest jump in prepays occurred in the early 2000s, reaching a record high of 60 CPR. Rates had fallen substantially based on aggressive Fed ease in the wake of the bursting of the tech stock bubble. This passed right through to refis. There was also a substantial decline in rates with the Global Financial Crisis(GFC), but the refi response was more muted due to declining house price, but perhaps also more stretched out in time (off and on through 2013). The current episode with Covid-19 has resulted in record-low mortgage rates, and a substantial spike in refis, but still well below the experience of 2002-04.
What might explain the differences? A clear place to look is at credit conditions. If rates drop the same amounts in time periods X and Y, but credit conditions are tighter in Y than X, we can reasonably expect a bigger refi impact in X than Y. Below shows the Urban Institute’s Housing GSE Credit Availability Index, which is used to evaluate lender’s risk tolerance:
Note: Urban Institute’s Housing Credit Availability Index for GSE Chanel. Adapted from Urban Institute Housing Finance at a Glance (August 2020). (https://www.urban.org/research/publication/housing-finance-glance-monthly-chartbook-august-2020)
As can be seen, credit was extremely loose before the GFC, very tight thereafter, followed by a period of modest loosening until 2019. This correlates well with the magnitude of the response of prepays to interest charges in the three regimes. Too hot, too cold and just right? Maybe.
All of this is important not just for investors but for the central bank as the Fed attempts to steer the economy through this uniquely uncertain period. Recently, credit conditions appear to have started tightening. It’s unclear whether this is a “normal” market reaction as volumes rise and capacity is constrained (good credits are easier to process) or whether lenders are becoming more cautious based on a more pessimistic view of the economic outlook. There is a big difference between the two in terms of choosing a successful investment strategy or an optimal monetary policy. The answer is unlikely to come from attempts to model borrower and lender behavior in a nuanced way and more likely to be discerned by careful observation of emerging trends in big data sets.
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.
In our third look at 2019 HMDA characteristics we look at mortgage originations by income bracket. Lending to low- and moderate-income households is an important regulatory requirement of banks. 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. According to Zillow data, 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%.
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.
 The first two 2019 HMDA blogs are available at
 See for example, https://www.fdic.gov/regulations/resources/director/virtual/cra.pdf
 Data from 1984 – 2018 can be found https://www2.census.gov/programs-surveys/cps/tables/time-series/historical-income-households/h08.xls
 Taken from June 2018 data at https://www.zillow.com/ok/home-values/ and https://www.zillow.com/ca/home-values/
In a recent post we looked at the differences in bank underwriting characteristics between those conforming loans held on book compared to those delivered to the GSEs using data pulled from Recursion HMDA Analyzer. We now extend this into another dimension via the addition of LTV.
Below find the difference in share of such deliveries between sold loans and those held on book:
In a recent post we established a correlation between the 30 day dq rate of the loans in the reference pools for the Freddie Mac High LTV STACR CRT program and the share of these loans with high indebtedness as measured by DTI>45 for the month of May. Recently Fannie Mae released the corresponding data for its CAS program and the results are striking. First, the pattern of results we saw for STACR is confirmed. This can be clearly seen if the results of the two programs are overlaid one over the other.
*The Chart 1 and Chart 2 can be duplicated using the following two queries
Appraisals play an important role in managing the risks associated with residential mortgages. Since 2017, both Fannie Mae and Freddie Mac (GSEs) have published multiple rules (see Appendix below) for lenders to qualify mortgage applications for property inspection waivers (PIW). PIW can reduce the cost of mortgage transactions. However, PIW raised concerns of improper usage among investors, mortgage insurers, regulators and other players in the mortgage market. In particular, research has shown that loans with PIWs prepay much faster than loans without.
In March 2020, both Fannie Mae and Freddie Mac released loan level information regarding “Property Valuation Method” which included the Appraisal Waiver information. The new data regarding PIW’s offers the opportunity to study how this program affects the market.