The Price of Work from Home: Commercial Real Estate in the City and the Suburbs
Bibliographic record
Abstract
We examine how the shift to working from home (WFH) differentially affected urban and suburban commercial real estate (CRE) prices in U.S. cities. Using CRE transaction data from 2017 to 2023, we document significant suburban price appreciation relative to urban prices beginning in 2021. This relative appreciation results in the overall CRE suburban price discount shrinking from 9% to zero by 2023. Heterogeneity analysis reveals that relative suburban price appreciation is concentrated in cities with a high proportion of jobs amenable to WFH. Office real estate displays the most significant shift in the suburban price discount—previously stable at 20%, the discount shrinks to zero by 2021 and trends toward a suburban price premium by 2023. These findings support the hypothesis that the transition to WFH reorganized the demand for commercial real estate assets with respect to urban and suburban locations within cities and that this effect is likely not temporary.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".