The Nonprime Mortgage Crisis and Positive Feedback Lending
Bibliographic record
Abstract
The “great recession” of 2007–2009 was sparked by a bubble in U.S. housing prices, driven in turn by a bubble in nonprime mortgage lending. We collect evidence that the risk of a nonprime housing bubble (not the certainty, but a meaningful risk) should have been obvious to the main participants in the markets for nonprime lending and related mortgage-backed securities (nonprime MBS), including originators, securitizers, rating agencies, money managers, and institutional investors. Those who did not see the risk were, in many cases, willfully blind. We also discuss the strong positive feedback nature of typical nonprime mortgages. This positive feedback made it highly likely that, if nonprime housing prices flattened, let alone fell, they would soon crash and take many nonprime MBS with them. We discuss regulatory responses that might limit positive feedback lending, cause the next bubble to be smaller and less likely, and make the post-bubble aftermath less painful.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".