Determinants of housing supply: Case of Kuala Lumpur and Johor / Anis Nurhidayah Anuar
Bibliographic record
Abstract
This study focuses on the determinants of housing supply especially in Kuala Lumpur and Johor. Many houses are being built to fulfill the customer's needs. Housing provision has played an important part of the government social policy in Malaysia. Housing as economic goods has some special characteristics that make it different from other kinds of properties. The objective of the study is to determine the factors that affect the housing supply and to examine whether there is any relationship that will exist between the factors and housing supply. The determinants of housing supply that will use in this study are house price, construction cost and interest rate. To analyze all these factors that will determine the housing supply, this study will take Kuala Lumpur and Johor as the sample population. The time horizon is from 3rd quarter of 2002 until 2nd quarter of 2006 which is five years. It will take the quarterly data. This study used multiple regressions to examine the relationship that exist between house price, construction cost and interest rate towards the housing supply. This method will determine whether independent variable has relationship or not with the dependent variable. In the previous study has said that it is so difficult to see the relationship that will exist between all the variables. Based on the study that has been conducted the result that get from this study were, there are no significant relationship between housing supply and housing price in Kuala Lumpur and Johor. There are significant relationships that exist between construction cost and gross national income with the housing supply in Kuala Lumpur and Johor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".