Price Prediction Model of Demand and Supply in the Housing Market
Bibliographic record
Abstract
Over recent years, the imbalance between housing demand and supply, particularly in the high-cost housing segment, led to the rapid increase in the house prices. This paper has applied the standard theory of consumer demand and supply supplemented using content analysis method to explain the trend of housing demand and supply of housing market in Malaysia. Sampling in the quantitative content analysis is carried out to achieve the objective. Property Market Status Report in the NAPIC website provide a series data for total housing demand and supply for any house type of terrace, detached, cluster and townhouse in the price range between RM50,000 to RM300,000. All data provided cover from the first quarter until the fourth quarter across the year 2006 to 2015 specifically in Peninsular Malaysia only. Each level of the house price has a different equilibrium price so that developers can use it as an indicator based on the housing type. This research will promote ways to achieve the sustainabiliy in construction output overall so that the scholars can improve the equilibrium price model proposed in order to make the Malaysian housing become an affordable.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".