Commercial Land Values in Santa Clara County, California
Bibliographic record
Abstract
Transit-oriented development has gained favor as a means of reducing traffic congestion, promoting affordable housing, and creating more efficient urban arrangements. Real estate markets reflect the degree to which concentrating development around transit facilities yields benefits. This study models the effects of proximity to light and commuter rail stations as well as freeway interchanges on commercial-retail and office properties in fast-growing Santa Clara County, California. Hedonic price models are used to control for other factors, such as regional accessibility and neighborhood quality, in isolating the effects of proximity to transit on land values. Substantial capitalization benefits were found, on the order of 23 percent for a typical commercial parcel near an LRT stop and more than 120 percent for commercial land in business district and within a quarter mile of a commuter rail station. Such evidence is of use not only to commercial developers and lenders but also to rail transit agencies embroiled in legal battles over purported negative externalities associated with being near rail. It can also help in designing creative financing, such as value capture programs. Understanding the market value of properties near rail transit stops can also inform and elevate the practice of joint public-private development. Rail Transit’s Value-Added:
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".