An Enhanced House Price Index Model in Malaysia: A Laspeyres Approach
Bibliographic record
Abstract
The purpose of this study is to develop an enhanced house price index model in Malaysia. At the same time, it attempts to examine the determinants of the existinghouse price index in Malaysia. Review of thecurrent Malaysian House Price Index (MHPI) model shows that this index is constructed based on demand driven variables. Past studies explained that both macroeconomic factors (income levels, interest rates, labor market) and supply factors are included in constructing the house price index. Therefore, this study aims at providing evidence on the determinants of the House Price Index (HPI). This study employs the Autoregressive Distributed Lag Model (ARDL) to discover the short and long-run dynamics between the variables. The study considers the quarterly data from first quarter 2008 to fourth quarter 2015. The analysis shows that supply and institutional factors are significant in determining the HPI. Hence, we propose anew enhanced house price index incorporating new demand and supply variables. By using LaspeyresApproach, the new enhanced HPI has been calculated. The findings revealed that MHPI was found to have a long run significant relationship with employment, Overnight Policy Rate (OPR), Consumer Price Index (CPI), land supply and housing loan while construction cost is not significant in determining the MHPI
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".