Big Tech on the Block: Examining the Impact of Tech Campuses on Local Housing Markets in the San Francisco Bay Area
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid growth of tech company headquarters such as Apple, Facebook, and Google could potentially put new pressure on the housing market in adjacent residential neighborhoods, in the form of housing price appreciation and real estate speculation. This article examines the relationship between the big tech corporate campuses and Silicon Valley/San Francisco housing markets using the Zillow (ZTRAX) transaction and tax assessor data. The authors compare real estate activity adjacent to new company locations with activity in nearby areas, conducting a difference-in-differences analysis to estimate changes in housing prices and speculation. They find that housing prices increase overall by an additional 7.1% in the immediate vicinity of the tech campus 2 years after arrival, with wide variation across campuses. The authors also identify significant real estate speculation occurring prior to firms’ arrival. This suggests that cities should take a proactive role in mitigating tech firm impacts on vulnerable adjacent neighborhoods.
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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 | 0.000 |
| 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 | 0.001 | 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 it