The Characteristics of Uninsured Mortgages and their Securitization Potential
Bibliographic record
Abstract
Following changes to housing finance policies that target insured mortgages, uninsured mortgage credit has been growing. This robust growth creates a larger pool of mortgages that may be suitable for private-label residential mortgage-backed securities (RMBS). The development and viability of the Canadian private-label RMBS market would depend on the characteristics of the underlying collateral. We address this data gap by documenting the key features of uninsured mortgages originated since 2014, comparing them across two groups of federally regulated financial institutions, i.e., domestic systemically important banks (DSIBs) and non-DSIBs. We find that on average, non-DSIB mortgages exhibit riskier characteristics, including lower credit scores, and higher debt-service and loan-to-income ratios. When compared with the prime quality collateral backing domestically issued RMBS to date, we estimate that the non-DSIBs’ securitization potential since 2014 has been about $17 billion. Growing that issuance further would require approaches to broaden the pool of acceptable mortgages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".