National Survey of Mortgage Originations (NSMO)
Bibliographic record
Abstract
The National Survey of Mortgage Originations (NSMO) is a component of the National Mortgage Database (NMDB®) program. It is a quarterly mail survey jointly funded and managed by the Federal Housing Finance Agency (FHFA) and the Consumer Financial Protection Bureau (CFPB). NSMO 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. This voluntary survey is administered by Westat, a survey and data collection corporation, to the borrowers associated with the sample mortgages. The respondents can either return the English questionnaire by mail or complete the survey online in English or Spanish. NSMO draws its sample from newly originated mortgages that are part of the NMDB, which is a 1-in-20 sample of closed-end first-lien residential mortgages newly reported to one of the three national credit bureaus. 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 NMDB. The NSMO survey has been conducted quarterly since the first quarter of 2014. The current survey package sent to the respondents can be viewed here.The NSMO public use file was updated on July 1, 2024 to append additional survey records and additional quarters of mortgage performance information. It replaced the public use file released on March 3, 2023. The updated file contains 50,542 sample mortgages originated from 2013 through 2021 based on the first 34 quarterly waves of the NSMO survey. For these mortgages, the updated file contains mortgage performance information through the third quarter of 2023.The original NSMO public use file was published on November 8, 2018, containing mortgages originated from 2013 through 2016. It was first updated on February 20, 2020, containing mortgages originated through 2017. Subsequent updates were published on July 29, 2021 (containing mortgages originated through 2019) and on December 13, 2022 and March 3, 2023 (both containing mortgages originated through 2020).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".