The Impact of the COVID-19 Pandemic on the Demand for Density: Evidence from the U.S. Housing Market
Bibliographic record
Abstract
Cities are shaped by the strength of agglomeration and dispersion forces.We show that the COVID-19 pandemic has re-introduced disease transmission as a dispersion force in modern cities.We use detailed housing data to study the impact of the COVID-19 pandemic on the location demand for housing.We find that the pandemic has led to a greater decline in the demand for housing in neighborhoods with high population density.We further show that the reduced demand for density is partially driven by the diminished need of living close to jobs that are telework-compatible and the declining value of access to consumption amenities.Neighborhoods with high pre-COVID-19 home prices also see a greater drop in housing demand.While the national housing market partially recovered in June, we show that the negative effect of the pandemic on the demand for density persists, indicating that the change in the demand for density may last beyond an aggregate recovery of housing demand.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".