The effects of macroeconomic variables towards housing price in Malaysia / Nur Farahin Abdul Rahman
Bibliographic record
Abstract
Over the past few years, house price in Malaysia has experienced significant price expansion and this situation became a worrying that causes distress especially to the current lenders. This study were an attempt to determine the macroeconomic variables that affected to house price in Malaysia and explored the relationship between the variables with house price. Only three macroeconomic variables such as Gross Domestic Product (GDP), Inflation Rate (CPI) and Population (Pop) were taken into this study with House Price Index (HPI) as dependent variable. This study consists of 127 observations of quarterly data for each variable starting from 1985 first quarter until 2016 third quarter. All data of HPI, GDP, CPI and Pop were taken or got from DataStream and then there were tested by using EViews8. Those data were analyzedusing descriptive analysis, unit root test, correlation matrix and regression analysis. It were then being analyzed using test on assumption such like normality test, test on variance error term, test on serial correlation of error term (BG Test) and multicollinearity (VIF Test). The results showed only GDP and Pop were positive and significantly related to HPI while CPI was not significant with HPI and only two hypotheses were rejected while the other one was failed to reject. A conclusion were made by given some proofs and evidence from the previous research or journal. Besides that, a few recommendation and limitation were listed in this study for uses and references to the future researchers.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".