Does gentrification constrain housing markets for low-income households? Evidence from household residential mobility in the New York and San Francisco metropolitan areas
Bibliographic record
Abstract
This research investigates whether gentrification restricts housing markets for low-income households by focussing on the New York and San Francisco metropolitan areas from 2013 to 2019. We investigate whether gentrification correlates with increased out-migration and decreased in-migration of low-income residents in affected neighbourhoods, and how it shapes where out-movers relocate. We leverage a unique longitudinal dataset to compare two extreme regional contexts characterised by significant affordability challenges and intense housing regulations. By doing so, this study aims to provide a more refined understanding of gentrification and residential mobility dynamics, avoiding broad generalisations or a narrow focus on single metropolitan contexts. The findings indicate that in both regions, low-income households are indeed more likely to leave gentrifying neighbourhoods compared to non-gentrifying ones and less likely to enter them compared to higher-income households. The study also finds mixed results regarding the subsequent residential situations of these low-income movers. Based on these findings, we provide implications for research and policies oriented towards improving housing and neighbourhood access for low-income households in rapidly changing urban areas.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".