Empirical Research on the Determinants of House Price Behaviour in China, From January Quarter 2000 to December Quarter 2008
Bibliographic record
Abstract
Abstract \n \nThe house prices in China have increased dramatically after the housing finance market reform since 1998. The housing market boom has contributed to domestic consumption, investment and rapid economic growth. But at the same time, many Chinese people have experienced increasing difficulties to purchase a house as the run-up house prices become too expensive relative to household income, rents and it seems will never fall. This problem became more severe in metropolitan areas, such as Beijing, Shanghai, Hangzhou and Shenzhen. As the housing problem is not only highly related to family life, but also to the economic growth and financial stability, it turns out to be a concern nationwide. Some academics argue the high house price is contributed by the development of macroeconomic fundamentals, while others believe the house price growth is due to speculation. The People’s Bank of China (PBC), which is China’s central bank, has implemented policies, for example, rising personal housing mortgage rate and benchmark lending rate to control the growth of house prices, but has received limited effects (Shang, 2009). Those complex phenomena raise the question about what are the key determinants of house price growth in China. In order to find answers to this question, this paper is going to explore the relationship between housing price and a series of variables by four time series regression models using the Ordinary Least Square (OLS) technique based on empirical data from 1st quarter 2000 to 4th quarter 2008. The tested variables include GDP, CPI, land price, bank lending, real benchmark lending rate, real effective exchange rate, and Shanghai Composite Index. It is found that in general, China’s house price growth is highly associated with the improvements of its macro-economic fundamentals. In particular, GDP growth, CPI growth, land price growth, expansion in bank lending and rise in equity prices are positively correlated with the house price growth, while the real benchmark lending rate and the growth in RMB appreciation are negatively correlated with house price growth. Beside, the limited supply of cheap housing, a lack of competition in the land transfer market, and political incentives to local governments also contribute to the increase in house price. Therefore, it is suggested that the Chinese government should not only implement monetary instruments, but policy measures to stabilize house price growth and maintain sustainable development of the domestic housing market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; both teacher heads agree on what is shown here.
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".