House Prices, Home Equity-Based Borrowing, and the U.S. Household Leverage Crisis
Bibliographic record
Abstract
Using individual-level data on homeowner debt and defaults from 1997 to 2008, we show that borrowing against the increase in home equity by existing homeowners is responsible for a significant fraction of both the sharp rise in U.S. household leverage from 2002 to 2006 and the increase in defaults from 2006 to 2008.Employing land topology-based housing supply elasticity as an instrument for house price growth, we estimate that the average homeowner extracts 25 to 30 cents for every dollar increase in home equity.Money extracted from increased home equity is not used to purchase new real estate or pay down high credit card balances, which suggests that borrowed funds may be used for real outlays (i.e., consumption or home improvement).Home equity-based borrowing is stronger for younger households, households with low credit scores, and households with high initial credit card utilization rates.Homeowners in high house price appreciation areas experience a relative decline in default rates from 2002 to 2006 as they borrow heavily against their home equity, but experience very high default rates from 2006 to 2008.Our estimates suggest that home equity-based borrowing is equal to 2.8% of GDP every year from 2002 to 2006, and accounts for at least 34% of new defaults from 2006 to 2008.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".