The value of a floor: valuing floor level in high‐rise condominiums in San Diego
Bibliographic record
Abstract
Purpose This paper aims to analyze the effect of floor level on condominium prices in San Diego, California. The authors determine whether “higher‐floor premiums” exist in the condominium market for a large California city. Further, they investigate how the floor premium varies throughout a building, particularly whether it is quadratic and whether there is a “penthouse premium” for top‐floor units. Design/methodology/approach The paper utilizes a data set of 2,395 condominium sales occurring in San Diego between 2006 and the second quarter of 2011. Using hedonic pricing analysis, the authors model the housing price as a function of condominium, building and neighborhood characteristics. Findings The results suggest that there is a higher‐floor premium for condominiums in San Diego. Specifically, an increase in the floor level is associated with about a 2.2 percent increase in sale price. The higher‐floor premium appears to be quadratic in price, suggesting that price increases at a decreasing rate above the mean floor level. The authors also find evidence for a penthouse premium, though this effect disappears once “floor” is controlled for in the model. Originality/value There has been little direct research on the floor effect in condominium prices. The studies that have used floor level as an explanatory variable have been predominately in Southeast Asia. The results suggest that the floor effect is more complex than previously modeled.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".