ANALYZING THE UNDERLYING RELATIONSHIP BETWEEN MONETARY POLICY AND RESIDENTIAL PROPERTY PRICES IN CHINA
Bibliographic record
Abstract
Policymakers and the public express concern regarding the volatility of housing prices due to its potential to increase consumer costs and negatively impact housing affordability. Based on empirical study, it has been seen that the expansion of the real estate sector has a significant impact on the investment in fixed assets by firms. This influence is mostly attributed to the alteration of the transmission of monetary policy. Real estate investment is considered a feasible option because of the significant and rapid appreciation in property prices. The primary objective of this study is to examine the influence of monetary policy on the housing market in China. To conduct the current study, macroeconomic data from a total of 44 time periods, ranging from the fourth quarter of 2012 to the fourth quarter of 2022, was collected. The findings of our study indicate that in the context of China, an expansion in the money supply has a greater propensity to positively influence the borrowing activities of real estate suppliers and clients, as opposed to the supply of properties themselves. The housing market can be influenced by governmental actions such as adjustments to the money supply and interest rates. While scholars have extensively examined the subject matter, the housing market in China remains relatively under-researched in terms of its susceptibility to government macroeconomic policies. Moreover, the current study offers a comprehensive overview of the prevailing challenges encountered by the residential property market in China, emphasizing the significance of macroeconomic policies within this particular context.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".