DO HIGHER HOUSE PRICES INDICATE HIGHER SAFETY? PRICE VOLATILITY RISK IN MAJOR CITIES IN TAIWAN
Bibliographic record
Abstract
This study investigates the housing market in Taiwan, an emerging market with relatively severe housing price inflation. Using data from the first quarter of 1991 to the second quarter of 2017 for four cities in Taiwan, this study compares the risk transmission and sources of their housing prices. The results reveal that Taipei−Taiwan’s main financial hub−has the highest house prices among the four cities but maintains the lowest risk. Thus, in terms of price volatility risk, Taipei has the safest housing market among the studied cities. Other studies have discussed the potential housing price bubbles in regions with high housing prices but have been unable to explain the continual overheating of the housing markets. The findings of this study reveal that despite having the highest housing prices and the greatest potential bubble, the Taipei housing market has the lowest fluctuation risk, making it the safest market in terms of housing investment. The results of this study imply that Taiwan’s economic development is excessively concentrated in Taipei, causing people to bear low returns and high risk when purchasing real estate in other areas, in turn increasing the continual imbalance between regional housing markets.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".