Size and Characteristic of Housing Bubbles in China's Major Cities: 1999–2010
Bibliographic record
Abstract
Abstract There is some disagreement in the published literature regarding the definition and the measurement of housing bubbles in China. Extending the analytical framework of Black et al. (2006), the present paper measures the housing bubbles of China's 35 major cities from the second quarter of 1999 to the second quarter of 2010. The results indicate that the housing bubbles in China's 35 major cities were relatively small in the sample interval, but the bubbles in eastern metropolises, such as Beijing, Shanghai, Shenzhen, Hangzhou and Ningbo, have been relatively big since 2005. The changing tendency of housing bubbles in most cities highly corresponds with the changes in real estate policies. This paper decomposes the housing bubbles of the 35 cities, and finds a great proportion of irrational bubbles rather than rational intrinsic bubbles generated by price speculation. Based on empirical analysis, this paper proposes policy recommendations for preventing the generation and expansion of housing bubbles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".