Rental price index forecasts of residential properties using Gaussian process regressions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Since the Chinese real estate market has expanded so quickly over the past 10 years, investors and the government are both quite concerned about projecting future property prices. Design/methodology/approach This work aims to investigate monthly rental price index forecasts of residential properties for ten major Chinese cities from 3M2012 to 5M2020 by using Gaussian process regressions with a diverse variety of kernels and basis functions. The authors conduct forecast exercises through use of Bayesian optimizations and cross-validation. Findings With relative root mean square errors spanning the range of 0.0370%–0.8953%, the constructed models successfully forecast the ten price indices from 6M2019 to 5M2020 out of sample. Originality/value The findings might be used independently or in combination with other projections to create theories about the trends in the rental price index of the residential property and carry out additional policy analysis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it