Bibliographic record
Abstract
This study aims to identify the effects of housing supply and loss on housing price and to predict changes in housing price due to the quantities of houses supplied and disappeared by New-Town Projects in Seoul. To do this, the quantities of houses supplied and disappeared were estimated up to 2015 through the master plans of New-Town Projects. A Stock-Flow model was employed for exploring the effects of housing supply and loss on housing price by life zones, and a Vector Auto-Regression (VAR) Impulse Decomposition model was used to predict the changes of housing price caused by New-Town Projects. The results showed that a total of five hundred thousand houses will be supplied until 2015, while a total of two hundred fifty thousand houses will be vanished. It was found that the effects of the quantities of houses supplied and appeared on housing price are varied across life zones. Especially, the loss of housing was found to increase the changing rate of housing price after the loss of housing was started. The supply of houses, however, was found to decrease the changing rate of housing price since the second quarter of the year after the supply of houses will be completed. Since the effects of New-Town Projects on housing price are varied across life zones, therefore, the location and the time of New-Town Projects should be determined with regard to the quantities of houses supplied and disappeared by life zones in Seoul.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".