Empirical Analysis of Factors Influencing Residential Property Prices in Malaysia
Bibliographic record
Abstract
This study concerns the factors influencing the prices for residential properties in Malaysia as well as their relationship towards residential property prices. The data collected and analysed in this research is from quarter one year 2000 to quarter four year 2015. Various determinants have been identified namely country population, Gross Domestic Product, household income, inflation and lending rates in this research. The time-series analysis methodologies adopted in this research are the Augmented Dickey-Fuller (ADF) and Philip-Perron (PP) test for unit root, Johansen and Juselius Cointegration Test, Granger Causality Test for Vector Error Correction Model (VECM) and also Variance Decomposition (VDC). In this study, these two variables, population growth (POPGROWTH) and inflation measured by the consumer price index (CPI) were found has a significant and positive effect towards the price of residential properties in Malaysia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".