An enhanced house price index model in Malaysia: Maqasid Shariah perspectives
Bibliographic record
Abstract
The house price index (HPI) has become an important tool for measuring housing market performance in Malaysia. Having reviewed the current model of the Malaysian House Price Index (MHPI), it was discovered that HPI is constructed based on demand-driven variables. Previous studies alluded that both macroeconomic and supply factors should be incorporated into the HPI construction to obtain a better reflective indicator. Therefore, this study aims to construct an enhanced HPI model in Malaysia. The time series analysis technique known as the autoregressive distributed lag model (ARDL) was adopted to analyse the short and long-run dynamics between the variables. The existing determinants of HPI were first examined. The data employed in this study was quarterly data from the first quarter of 2008 to the fourth quarter of 2018. The findings revealed that the construction cost and home financing are significant in determining HPI, while the overnight policy rate and land supply are insignificant. Hence, a new enhanced HPI incorporating new demand and supply variables was developed using the Laspeyres approach. The analysis shows that the enhanced HPI has also recorded the same trend but with a lower price value than the current MHPI. This enhanced HPI model is able to contribute a better indication of the real housing market situation in Malaysia, which is more reflective of the macroeconomic fundamentals and housing market variables. The implementation of price benchmarks is another contribution as it is consistent with the goals of Shariah to create justice and fairness in financial transactions and serves as a mechanism to prevent people from facing distress and hardship. This study has an important policy implication for the involved parties to have some policy ramifications to further monitor and take appropriate measures in controlling property prices.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".