Dynamic Analysis of decisive factors in Fluctuation of the Real Estate Trust Deposit(Principal)
Bibliographic record
Abstract
The purpose of the study is to analyze the effect of economic variables on the fluctuation of real estate trust deposit. In this study, VAR model was used as an analytic model. The time range was from the 1st quarter of 2001 to the 4th quarter of 2018. The spatial range was South Korea. The dependent variable was real estate trust deposit, and the independent variables were ‘Housing Purchase Price Index’, ‘CD interest rate’, ‘The Floor Area of the Construction Permit Structure’, ‘CPI’. The real estate trust deposit showed a positive (+) response to the impact of the CPI and negative (-) response to the changes in CD interest rate. In addition the real estate trust deposit showed a positive (+) response to the Housing transaction price index of the whole country. The real estate trust deposit showed a positive (+) response to the impact of ‘The Floor Area of the Construction Permit Structure’ as well. The fluctuation of the deposit accounted for the impact of the deposit itself and the impact of housing transaction price index of the whole country and CD interest rate, which is viewed from the 2nd quarter in different time. This study will help to expect the real estate trust’s market volatility, which has a significance as the first study of the empirical analysis of the effect of the economic variables to the variation of the real estate trust deposit.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".