Household Debt and House Prices-at-risk: A Tale of Two Countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To identify and quantify downside risks to housing markets, we apply the house price-at-risk methodology to a sample of 37 cities across the United States and Canada using quarterly data from 1983 to 2018. This paper finds that downside risks to housing markets in the United States have seemingly fallen over the past decade, while having increased in Canada. Supply-side drivers, valuation, household debt, and financial conditions jointly play a key role in forecasting house price risks. In addition, capital flows are found to be significantly associated with future downside risks to major housing markets, but the net effect depends on the type of flows and varies across cities and forecast horizons. Using micro-level data, we identify households vulnerable to potential housing shocks and assess the riskiness of household debt.
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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 it