State Intervention in Land Supply and Its Impact on Real Estate Investment in China: Evidence from Prefecture-Level Cities
Why this work is in the frame
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Bibliographic record
Abstract
State intervention in land supply can be a powerful tool in shaping real estate investment. Yet, few studies have examined the effect of central state intervention on land supply at the municipal level and the impact of land supply on real estate investment with respect to different tiers of prefecture-level cities in China. Varying central–local dynamics of land supply in different tiers of cities, and the often taken-for-granted relationship between land supply and real estate investment, warrant further investigation. This study aims to fill these gaps. It is found that the multi-purposed central land policy and the varying land leasing strategies adopted by different tiers of cities contribute to the varying land supply trajectories, calling for more nuanced and better-tailored central land policies that focus on the socioeconomic conditions of cities. The general significant and positive correlation between land supply and real estate investment, revealed by a panel regression analysis incorporating 280 prefecture-level Chinese cities, suggests that land supply control can function as a critical tool in governing real estate investment in China, which also sheds light on the governance and promotion of sustainable real estate markets in other parts of the world. This study also reveals a higher possibility of land speculation in first- and second-tier cities than that of low-tier cities. The nuanced correlations between land supply and real estate investment and the varying land development strategies employed in different tiers of Chinese cities imply that the effectiveness of land supply intervention in shaping healthy real estate investment may depend on local contingencies, calling for meticulous and tailored governance on land supply and real estate investment behaviors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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