Forecasting Residential Real Estate Prices via Machine Learning for Taizhou City of Zhejiang Province in China
Bibliographic record
Abstract
The Chinese real estate market has grown at such a fast rate over the last several decades, up to the present decline patterns that began at the end of 2021. As a result, projecting future property prices has become more challenging for investors and the government. This is a result of the state of the economy right now. In this work, we examine monthly residential property prices for Taizhou City, Zhejiang Province, China, using Gaussian process regressions with a variety of kernels and basis functions, spanning from April 2015 to the July 2024. Our forecasting activities employ estimated models, which we train using a combination of cross-validation and Bayesian optimisations for endowing the constructed model with good flexibility. The models that were created were successful in precisely predicting the prices from September 2022 to July 2024 outside the sample. These models had a relative root mean square error of 0.1120%, a root mean square error of 19.1482, a mean absolute error of 14.3875, and a correlation coefficient of 99.986%. It is plausible that our findings may be used alone or in combination with further projections to formulate conjectures about fluctuations in the residential real estate prices and conduct additional policy analysis, which will help investors and policymakers in better understanding the evolving market condition.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".