The study of the relationship between hedging attitude and the factors of purchasing real estate, and the precitions of investor satisfaction and purchase intentions(c)
Bibliographic record
Abstract
2021 after the epidemic,According to Xinyi Global Assets statistics, the amount of land purchased by listed counter builders in the first three quarters of Liudu in 2020 was as high as 83.5 billion yuan, which may exceed 100 billion yuan in the whole year. The most eye-catching is that the transaction amount of Taichung City exceeds that of Taipei City. In the first quarter of 2021, Taipei City’s accumulated land transactions for listed counter builders amounted to 22.8 billion yuan, while New Taipei City’s total land transactions were 13.6 billion yuan. Due to the difficulty of obtaining land, Shuangbei’s land transaction amount was lower than Taichung’s 28.7 billion yuan. However, Taipei's construction companies, including Farglory Construction, Guande Construction, and Changhong Construction, are also actively deploying Taichung. Therefore, the high value of real estate investment in the hedging method is affirmed! Obviously, the return of Taiwanese businessmen has led to a general increase in real estate prices across the country. Although the government has repeatedly announced the policy of real estate speculation, the interest rate is low because of the amount of money. Idle funds lack investment targets, whether they are builders, insurance companies, general enterprises, or high-asset investors, all vying to see real estate as a safe haven for funds. Therefore, in the future, while taking into account the large amount of funds from Taiwanese businessmen returning to expand factories and taking into account employment opportunities How to re-draw real estate-related policies is also a major issue worthy of attention!Therefore, this research can provide the latest information on real estate. The investor satisfaction and willingness to buy are both high and detailed research. It is worth providing Taiwanese real estate investors. This set of good data and good predictions for value preservation! Strengthen the promotion of economic miracles Be rich with the country!
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".