The Impact of Economic Policy Uncertainty on Investment in Real Estate Corporations Based on Sustainable Development: The Mediating Role of House Prices
Bibliographic record
Abstract
Since the COVID-19 outbreak, the global economy has undergone profound changes, and China’s real estate market has experienced dramatic turbulence. In order to stabilise the national economy during the epidemic, China’s macro-controls on the real estate industry have become more frequent. These regulatory policies have kept the uncertainty in China’s economic policies at a high level for almost two years. Therefore, in order to further regulate the real estate market and thus establish a sustainable macro-control mechanism, the purpose of this study is to provide the necessary practical research and policy basis for the real estate market by exploring how economic policy uncertainty and house prices affect the level of corporate investment in real estate development. Based on the theory of real options, financial friction theory and real estate characteristics theory, this paper studies the relationship between economic policy uncertainty and the investment level of real estate developers and further explores the mediating role of house prices. This paper selects the panel data of Shanghai and Shenzhen A-share real estate listed companies in the CSMR database from the first quarter of 2012 to the fourth quarter of 2022 and uses the fixed-effects regression method to identify the following conclusions. Firstly, stronger economic policy uncertainty promotes the investment level of real estate corporations; secondly, the fluctuation of house prices plays a mediating role in the positive effect of economic policy uncertainty on the investment of real estate corporations.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".