Exploring the Elements of Housing Price in Malaysia
Bibliographic record
Abstract
This informative study explores the elements of housing price in Malaysia and found the price is affected by many factors. This study of great practical significance has shown that house prices are determined by the demand for attributes, not only of the dwelling units themselves, but also of the region in which the units are located. There are many structural, neighborhood and locational attributes that could have brought impacts on house prices (Chin, Chau, & Ng, 2004). Structural characteristics, location-specific factors, and neighbourhood characteristics may define various sub-markets. Sub-markets may be defined by structure type (e.g. single-family detached, row house, town home, and condominium), by structural characteristics (property age housing consumers may have strong preferences for newly constructed properties or for historic properties), or by neighbourhood characteristics (e.g., public education and public safety). Results show that the housing price in Malaysia evidently depends on population, demand and supply, location, physical characteristic, accessibility, developer, cost of material and income. It is also influenced by neighbourhood factors as people nowadays will likely choose a better neighbourhood. These factors determine whether the housing price will be high or low. In summary, the government must take an active role to monitor and take appropriate measures to control property prices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".