Spatial and temporal variations in residential housing prices in Beijing
Bibliographic record
Abstract
During the past 10 years, the real estate industry in Beijing has been manifesting a strongly growing trend. Researching on the distribution of house prices and their tendencies is helpful to grasp and predict the development of the real estate industry and could be used as reference to city planning. 120 records of housing price data in 2005 to 2006 and open prices in 38 developing projects from the first quarter of 2002 to the second quarter of 2008 were used in this study to analyze the spatial and temporal variations of house price with geostatistical methods and nonlinear regression. Results show that there was a very strong autocorrelation among the house prices in Beijing within the range of about 11 km in 2005 to 2006, which can be well fitted with the spherical model. The isogram of the house prices formed a group of homocentric ellipses, with their long axis extending NW-SE, and the house prices decreased from the center to the periphery. The spatial pattern of house prices in Beijing changed obviously from 2003 to 2006. Although both the spatial patterns for the two periods were homocentric ellipses, the shapes of the ellipses and the directions of the axes changed greatly. And there were more imbalances in 2005 to 2006. The house prices in the Huilongguan-Qinghe residential zone, an example of the typical real estate industry in Beijing, kept growing from 2002 to 2008 and could be fitted with exponential growth model.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".