Bibliographic record
Abstract
1. CONTENTS (1) RESEARCH OBJECTIVES In this paper, we analyze factors that influences the bid price in the real estate auction market from a macroscopic and a microscopic perspective. (2) RESEARCH METHOD We implement the cross-correlation analysis and the VECM from the year of 2002 to that of 2012. Based on those data and models, we try to find influential factors on the bid price. In addition, employing the data from the first half of the year of 2012 and doing a microscopic analysis, we conduct the Hedonic Price Model. (3) RESEARCH FINDINGS Macroeconomic variables such as GDP, price appraisals, and monetary aggregates make an influence on the bid price. Some demographic variables such as districts, special rights, number of rooms, number of successful bids, number of floors, land shares, number of buildings, duration of years, duration time of auction, and number of households make an effect on the bid price. 2. RESULTS As a result, the cross-correlation relationship shows that the bid price are accompanied by the changes in GDP, appraised value, and monetary aggregates. The selling rate antecedes the second quarter, while exchange rates and housing lease prices antecede the third quarter, and interest rates antecede the fourth quarter. According to the VECM, the above factors were accountable in the following order: exchange rates, interest rates, lease prices, monetary aggregates, and price appraisals. The Hedonic Price Model show that the number of factors that determine the bid prices can be listed in the following order according to their level of influence: 3 Gangnam districts, 3 Gangbuk districts, special rights, number of rooms, number of successful bids, number of floors, land shares, number of buildings, duration of years, duration time of auction, and number of households.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".