The Impact of the 2010 Housing Purchase Restriction on Commercial Housing Prices in Beijing: A Difference-in-Differences Analysis
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the impact of the 2010 housing purchase restrictions on Beijing’s commercial housing prices. The data are monthly from January 2009 to December 2010. A Differences-in-Differences model is employed, with Wuxi serving as the control city. The aim of this study is to determine whether the policy helped to cool down the housing market. The results show that, instead of falling, Beijing’s commercial housing prices increased after the policy. This may be because many people rushed to buy homes before stricter rules took effect, local families were still allowed to purchase more than one property, and the policy signaled that housing would become more limited and valuable. The study suggests that purchase restrictions alone are not enough to control prices; they should be combined with financial tools, such as higher down payments or different mortgage rates, along with supply-side reforms and city-specific policies. The case of Beijing demonstrates that housing policies can lead to unexpected outcomes, highlighting the need for careful design to improve their effectiveness.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it