Housing demand in Malaysia / Sharena Mohd Nur
Bibliographic record
Abstract
This research consists three main objectives which is to explain factors that determines impact toward household demand toward a house, to investigate the relationship between housing demands toward income per capita, price of house itself, population in Malaysia, and BLR and last but not least is to determine the main factors that influences household demand toward housing in term of geographic factor (population), income per capita and housing characteristic (housing market price) and BLR. This study will help researchers to understanding the housing demand in Malaysia and other variable that changes the consumers demand toward dwelling unit. Hence, other variable that influence consumers demand toward housing demand is Base Lending Rate (BLR), housing price index, per capita income, and population. Based on those three variables, there will be variables that mostly influence and effect consumers decision toward a dwelling units. Moreover, this research are using secondary data in which data are collected on websites and to add more, cross-sectional data are being chosen because in this research are using panel data that deal other countries data such as Malaysia, Thailand, Australia and Canada data. From four countries, each countries data collected starting from year 2005 up to 2013. The statistical software which is Eview7 is being used in order to analyze and to generate the data. The result from the multiple regression analysis shows only housing price index give positive relationship toward housing demand and this result has supported by previous research by (Stein, 1995), who state a positive connection between the dwelling unit price and consumer housing consumption for an housing demand in term of single house unit. Other variable such as BLR, per capita income and population shows negative relationship toward housing demand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".