Constructing an Enhanced House Price Index Model: Empirical Evidence
Bibliographic record
Abstract
The objective of this study is to construct an enhanced house price index model in Malaysia.Having reviewed the current model of the Malaysian House Price Index (MHPI), it is currently found that this index is constructed based on the demand-driven variables.Previous studies explained that both macroeconomic factors (income levels, interest rates, and labor market) and supply factors are included in the construction of the house price index.This study begins by examining the determinants of the existing house price index in Malaysia.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 the first quarter of 2008 to the fourth quarter of 2017.The findings reveal that construction cost (CC) and housing loan (HLN) are significant in determining HPI while Overnight Policy Rate (OPR) and land supply (LS) are insignificant with HPI.Then, the housing loan was found to be the most significant variable in determining HPI in Malaysia.Hence, we propose a new enhanced house price index that incorporates new demand and supply variables, by using the Laspeyres approach to calculate the new enhanced HPI.The analysis shows that the enhanced house price index has also recorded the same trend but with a lower value of prices as compared to the current MHPI.This enhanced HPI model may reflect the real situation of the housing market in Malaysia and it is expected to increase the affordability of the society in fulfilling their basic needs.This study may provide evidence for the involved parties to have some policy ramifications to further monitor and take appropriate measures to control the prices of property.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".