Bibliographic record
Abstract
The purpose of this study is to estimate the time-periodic constant-quality capitalization rates in the office building market. This study collects 396 cases of office building transaction in Seoul from the fourth quarter of 1999 to the fourth quarter of 2011, and then estimates the constant-quality capitalization rates using the hedonic price index model by quarter, half-year, and year. And it compares the constant-quality capitalization rates with the simple-mean capitalization rates. This study finds that the sum of estimation errors of the constant-quality capitalization rates estimated quarterly or semiannually is smaller than that of simple mean approach. This result shows that the quarter or semiannual simple-mean capitalization rates are less accurate than the quarter or semiannual constant-quality capitalization rates because of the sample-selection errors. However, in the annual model there is no distinct difference of the sum of the estimation errors between the hedonic price index model and the simple mean approach. In addition, this study finds that the sum of the estimation errors of the annual capitalization rates by the stratified mean approach, which divides Seoul area into two regions(Gangnam/ Yeouido region and CBD/ other region) and aggregates the simple-mean capitalization rates of the two regions, is smaller than that of the annual simple-mean capitalization rates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".