Formation of Financial Real Estate Risks and Spatial Interactions: Evidence from 35 Cities in China
Bibliographic record
Abstract
The real estate prices in urban China have been soaring sharply since the commercialization reform of the housing market in 1998, but have suffered from downward pressure recently. In addition to the peculiarities of the state-owned land system, newly built houses dominate market across the vast territories of China, and this study of China will further the understanding of the financial real estate risks. Based on theoretical analyses, a spatial Durbin model is adopted to evaluate the financial real estate risks based on various sectors’ participation in the real estate market, because it can overcome the biased results brought about by the omission of possible spatial dependence. The results show the following: (1) the four sectors’ participation in the real estate market promotes the rise of real estate prices in the both local and other cities with spatial contagion effects, while the most important factors are different across regions; (2) the real estate price fluctuations, the local government’s land revenue, the bank credit provided to the real estate industry, the demand in the local city, and the real estate developers’ investments in other cities increase the local financial real estate risks, and there are strong spatial diffusion effects among the cities. This study sheds light on the roles of the various sectors’ participation in promoting the financial real estate risk as well as their spatial interactions from both theoretical and empirical aspects. Particularly, the different roles of local governments and real estate developers in China should be highlighted. The rules on the sector and spatial levels suggest that government policy should take the different features of various sectors and regions and spatial connections into account.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".