Asset Quality Misrepresentation by Financial Intermediaries: Evidence from RMBS Market
Bibliographic record
Abstract
We contend that buyers received false information about the true quality of assets in contractual disclosures by intermediaries during the sale of mortgages in the $2 trillion non-agency market.We construct two measures of misrepresentation of asset quality -misreported occupancy status of borrower and misreported second liens -by comparing the characteristics of mortgages disclosed to the investors at the time of sale with actual characteristics of these loans at that time that are available in a dataset matched by a credit bureau.About one out of every ten loans has one of these misrepresentations.These misrepresentations are not likely to be an artifact of matching error between datasets that contain actual characteristics and those that are reported to investors.At least part of this misrepresentation likely occurs within the boundaries of the financial industry (i.e., not by borrowers).The propensity of intermediaries to sell misrepresented loans increased as the housing market boomed.These misrepresentations are costly for investors, as ex post delinquencies of such loans are more than 60% higher when compared with otherwise similar loans.Lenders seem to be partly aware of this risk, charging a higher interest rate on misrepresented loans relative to otherwise similar loans, but the interest rate markup on misrepresented loans does not fully reflect their higher default risk.Using measures of pricing used in the literature, we find no evidence that these misrepresentations were priced in the securities at their issuance.A significant degree of misrepresentation exists across all reputable intermediaries involved in sale of mortgages.The propensity to misrepresent seems to be largely unrelated to measures of incentives for top management, to quality of risk management inside these firms or to regulatory environment in a region.Misrepresentations on just two relatively easy-toquantify dimensions of asset quality could result in forced repurchases of mortgages by intermediaries up to $160 billion.
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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 | 0.011 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".