The NSMO Dataset Available on Recursion!

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.