Modelling of Malaysia House Price Index
Bibliographic record
Abstract
In Malaysia, house price is considered high at a certain part of the country causing the lower and middle groupsunable to purchase a house. The aim of this study is to study the trend of the House Price Index and to identify the suitable model of the Malaysia House Price Index. The data was obtained from the Valuation and Property Services department (JPPH), Department of Statistics Malaysia and Bank Negara. The data was collected over 10 years from 2010 to the first quarter of 2019. Box-Jenkins methodology is applied in determining the best Autoregressive Integrated Moving Average (ARIMA) model of the House Price Index (HPI) in Malaysia. The general finding of this study is that the HPI shows an upward trend for the past nine years but slightly drops in the first quarter of 2019. This study has found out that ARIMA (1,2,1) is the best model for the HPI since it has the smallest value of AIC, BIC and Hannan-Quinn.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".