City Equilibrium with Borrowing Constraints: Structural Estimation and General Equilibrium Effects
Bibliographic record
Abstract
This paper develops a general equilibrium model of location choice with social interactions where mortgage approval rates determine household-specific choice sets that differ across neighborhoods and years in observable and unobservable dimensions.Existence and local uniqueness of city equilibria enable comparative statics estimates of the impact of changes in borrowing constraints on neighborhood-level prices and demographics.Estimation the model using micro data on property transactions, household demographics, neighborhood amenities, mortgage applications, and bank liquidity for the San Francisco Bay area, reveals that the price sensitivity of borrowing constraints explains about two-thirds of the price elasticity of neighborhood demand.General equilibrium estimates of the impact of the relaxation of lending standards on prices and neighborhood demographics bring two out-of-sample predictions for the period 2000-2006: (i) an increase in house prices accompanied by a compression of the price distribution and (ii) a reduction in the isolation of Whites in line with evidence of gentrification in the San Francisco Bay.Both predictions are supported by empirical observation.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".