Vertical phasing as a corporate real estate strategy and development option
Bibliographic record
Abstract
Purpose The purpose of this paper is to demonstrate the potential value of significant vertical phasing – that is, the addition of five or more stories to an existing building – as a valuable real option in real estate development, in particular for corporate real estate strategy. Design/methodology/approach The demonstration is done through in‐depth case studies of four major projects in North America: the 24 story, 880,000 square feet expansion of the Health Care Service Corporation building in Chicago; the Court Square Citicorp Campus in New York City; the Bentall Five project in Vancouver; and the Tufts University School of Dental Medicine building in Boston. Findings Vertical expansion appears to have significant organizational and logistical advantages for corporate developers, such as the ability to keep staff in one building, and the elimination of the need to relocate with its resulting inconvenience and potential to lose employees. Further, the financial analysis indicates that the option to expand vertically is a reasonable way for corporate developers to access convenient expansion space, while limiting their downside risk. Commercial developers on the other hand may find that the ability to scale back designs in the case of market downturns is particularly valuable. The case studies also confirm by example that the vertical expansion of buildings is technically possible. Although the process of erecting a major new building on top of a fully occupied building is clearly complex, it is not extraordinary difficult so long as the possibility of vertical expansion is built into the original design. Originality/value Vertical expansion of buildings has not been appreciated as an attractive feasible option for flexible development of real estate in a risky environment. These case studies and analysis bring this possibility to the attention of the real estate industry and corporate real estate managers.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".