Fourth Quarter 2013: Flight to Quality: Big Trumps Small
Bibliographic record
Abstract
A new hotel investment performance metric is introduced. Starting with this issue, we will apply our new economic value added (EVA) indicator as a barometer of hotel investment performance. Complete details of how to use this benchmark and why it is superior to evaluating cap rates relative to 10-year Treasury rates can be found in the recent publication from the Center for Hospitality Research and Center for Real Estate and Finance entitled “Using Economic Value Added (EVA) as a Barometer of Hotel Investment Performance,” by Matthew J. Clayton and Crocker H. Liu. Essentially, the hotel EVA spread tells us whether the current hotel yield (cap rate) exceeds the total borrowing cost (weighted average cost of capital; also includes the cost of equity financing) for doing a typical deal. Intuitively, if an investor finances a hotel project using 7-percent financing, the current yield on the project should exceed the 7-percent borrowing cost. Exhibit 1 (next page) shows that the EVA spread for hotels was positive until the first quarter of 2008. Subsequent to this period, the EVA spread has been either negative or near zero except for the second quarter of 2012 when it was positive. A negative EVA spread indicates that any return for hotel investors must come at the back end of the project. The expectation is that they will make their money when they sell the hotel due to price appreciation rather than making their money immediately.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".