Bibliographic record
Abstract
The essay utilizes a unique dataset of 30 Chinese provinces and municipal cities residential selling prices from 1998 to 2013, and examines the herding behavior. Using a least squares method and quantile regression method, we study the herding effect of China housing market at both national and cities levels. Results show that herding formation is stronger in increasing markets than that in decreasing markets. But when the markets are turning turbulent, in the high quantile regression, there is herding activity in decreasing markets. Our results also support the asymmetry of herding behavior in increasing and decreasing markets. By examining the financial crisis on the level of herding behavior, investors in China residential housing markets tend to herd before the crisis, and there is no herding behavior during and after financial crisis by quantitle regression. This study is important for three main reasons. Firstly, although the existing theoretical and empirical studies have investigated abnormal increasing price of China housing market, to the best of author knowledge, we are the first to apply this method to investigate herding phenomena in China housing market, which this essay focuses. Secondly, due to China residential housing market unique characteristics, this study makes a first attempt to examine the herding behavior by using quantile regression. Thirdly, our studies of this paper extend our knowledge of China real estate market to practitioners, academia and policymakers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".