Ripple Effect of Housing Prices Fluctuations among Nine Cities of China
Bibliographic record
Abstract
This paper applies cointegration test, error correction model, vector error correction model, impulse response analysis and variance decomposition to examine the ripple effect of housing prices fluctuations among Chinese cities during the period of first quarter of 1999 to third quarter of 2008. Empirical analysis indicates that housing prices fluctuations among nine Chinese cities do have ripple effect. We divide the cities into three layers: Beijing, Shanghai as the first layer; Shenyang, Tianjin and Xi’an as the second; and Qingdao, Chongqing, Guangzhou and Dalian as the third one. Empirical results show that: (1) housing prices of municipalities directly under the central government such as Beijing and Shanghai representing the first layer have strong influence and still be the main regulatory objects; (2) cities in the second layer can transmit the fluctuations of housing prices and should be concerned; (3) intense fluctuations of housing prices of cities in the third layer should be avoided. So, the government should make targeted regulatory policies to cities in different layers, which is a more efficient way to control the whole system of housing prices, maintain housing prices in a reasonable range, and eventually achieve the goal of building a harmonious society.Key Words: housing prices; ripple effect; cointegration test; error correction model; vector error correction model
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".