Visible Hand or Crippled Hand: Stimulation and Stabilization in China’s Real Estate Markets, 2008–2010
Bibliographic record
Abstract
The burst of housing bubbles in 2008 triggered the worst economic crisis in the United States since the Great Depression. Financial globalization has exacerbated the contagion and a worldwide crisis soon followed. As a major trading country, China depended heavily on export markets in the United States and Europe; therefore its economy experienced serious setbacks. After the double digit growth in 2007, the Chinese real estate market started to take a nose dive. In the first quarter of 2008, the average house price in 70 major cities grew 11 percent, but by the fourth quarter the growth rate slowed down to only 0.5 percent. The first quarter of 2009 even saw a decline of 1.1 percent, the first drop since 2000. The parallel between the United States and China, however, stopped there. While the American housing market continued to be sluggish after a steep decline, the housing market in China rebounded dramatically. After two consecutive quarters of negative growth, house prices rose again in the third quarter of 2009 and double-digit growth reappeared by the first half of 2010. In April 2010, for example, the average house price grew by 12.8 percent, the fastest rise since 2000! The national average actually understated the extent of price hikes in the hottest real estate markets, for example, Shenzhen (18 percent), Hangzhou (17 percent), Wenzhou (22 percent), Haikou (53 percent), and Sanya (52 percent). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".