Endogenous Gentrification and Housing Price Dynamics
Bibliographic record
Abstract
In this paper, we begin by documenting substantial variation in house price growth across neighborhoods within a city during city wide housing price booms. We then present a model which links house price movements across neighborhoods within a city and the gentrification of those neighborhoods in response to a city wide housing demand shock. A key ingredient in our model is a positive neighborhood externality: individuals like to live next to richer neighbors. This generates an equilibrium where households segregate based upon their income. In response to a city wide demand shock, higher income residents will choose to expand their housing by migrating into the poorer neighborhoods that directly abut the initial richer neighborhoods. The in-migration of the richer residents into these border neighborhoods will bid up prices in those neighborhoods causing the original poorer residents to migrate out. We refer to this process as "endogenous gentrification". Using a variety of data sets and using Bartik variation across cities to identify city level housing demand shocks, we find strong empirical support for the model's predictions.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".