Expect the unexpected: housing price bubble on the horizon in Malaysia
Bibliographic record
Abstract
The growth of financial market has taken centre stage in today’s world economy. It takes a quarter of a second to change the whole dynamics of an economy. The moment an asset price bubble and burst occurs, the whole economy may collapse. This paper makes an attempt to investigate the existence of housing price bubble by taking Malaysia as a case study. In Malaysia, the housing market is in its boom, naturally housing prices are sky high. There is no consensus in the literature about what is a housing price bubble. The method applied in this study are the standard time series techniques of cointegration, long-run structural modelling, vector error correction, variance decomposition method. To our knowledge, this is the first study on housing bubble based on demand and supply side variables, for a period of 17 years of data. Our findings tend to indicate that variables are cointegrated and market tends to correct any disequilibrium that exists over time. The results also imply that house prices are on the rise. The policy implications are that, though housing prices bubble and burst are not imminent, the upward pressures on housing prices, might require more sustainable measures within the current housing boom period.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".