Bibliographic record
Abstract
There were some changes in the Seoul office rental market after the global financial crisis. The typical changes included introduction of rent-free terms and a rise in the vacancy rate. The purpose of this studywas to identify the characteristics and causes of rent-free that exists after the global financial crisis. Depending on market conditions, rent-free terms deflate the real rental price, not the asking price, and some gaps between the two types of rental prices have been observed. However, most of the literature on the asking rental price for rents attempts to estimate relationships among variables in the rental market. This study assumed that vacancy rates and related variables (building size, rental price, building age) influenced the rent-free period and the effects of its determinants using the ordinal logit model. The results show that the vacancy rate, building size, and rental price are positively correlated with the rent-free period, but that building age is negatively correlated. This study analyzed the rent-free data on office buildings in Seoul containing at least 3,000 square feet of office space in the third quarter of 2013. The influencing factors for the rent-free terms were analyzed with multiple logistic regression models included in the Appendix.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".