The Commodity Housing Price Analysis Model--Take Haikou (China) as an Example
Bibliographic record
Abstract
In recent decade years, the real-estate industry in China has achieved unprecedented development. Correspondingly, the rapid rise in house prices has led the government to introduce a series of macro-control policies. Based on the main regulatory mechanism of the purchase restriction policy, we take Haikou as an example to analysis the probable influence on housing price. We first select indicators from three aspects: supply, demand, and macroeconomic environment, and then establish a gray correlation model to extract the key factors of strong correlation, that is, real- estate investment, CPI, residential housing construction area, residential housing completion area. Moreover, we establish a multiple linear regression model based on GM (1, n) to obtain the multi-function relationship between commercial housing prices and these four key indicators. After that, we establish a population- purchases demand function model to predict the price of commercial housing in the coming year after introducing the purchase restriction policy. More significantly, we conclude that the purchase restriction policy can effectively regulate housing prices in the short term, but the long-term effect is limit.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".