A Study of Regional Real Estate Market Differences and Convergence under Panel Data Modeling
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Bibliographic record
Abstract
This paper examines the differences and convergence of regional real estate markets based on panel statistics of 28 provinces, autonomous regions and municipalities directly under the central government in China from 2010 to 2023.Relevant variables such as urban construction land area, population and economic growth are set and the data are processed.The data show that the degree of industrial convergence and circulation costs have a positive spatial correlation and an upward trend from 2015 to 2021.From the viewpoint of regional real estate market divergence, the proportion of the real estate industry in GDP has remained above 5% since 2015, and this proportion is larger in the eastern region, for example, it was 8.74% in Beijing in 2015, but it has slightly decreased in some provinces and cities.The proportion in central and western provinces and cities has been rising faster year by year.The extreme deviation and standard deviation coefficient of the eastern region are relatively large, with the extreme deviation of the eastern region being 4.35% and the standard deviation coefficient being 1.45529 in 2021, indicating that the internal development is not balanced.From the analysis of convergence, the rate of convergence in the absolute convergence test is 3.66%, and the rate of convergence in the conditional convergence test is 2.89%, with a half-life of about 23.8 years.It indicates that the regional real estate market differences are shrinking, showing a trend of convergence, but the convergence process is relatively slow, which provides an important basis for an in-depth understanding of the characteristics of the regional real estate market.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it