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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".