The Integration of Residential Real Estate Market and Stock Markets : Assessment from ARDL Approach
Bibliographic record
Abstract
This thesis examines the long-run and short-run relationship between residential real estate market and stock market in Malaysia during the period of 1988-2004. I take the perspective that real estate prices are the driving forces of stock prices given the fact that the purchase of residential property is an important investment decision to an individual investor. Individual investors are expected to adjust their financial assets allocation based on the changes in house prices with the purpose to maximize their investment utility. Terrace House Price Index and High-Rise Unit Price Index were used as proxies for residential real estate market given the trade-off nature between these properties for an individual investor when come to investment decision. By using Autoregressive Distributed Lag (ARDL) cointegration procedure, the results suggest that residential real estate and stock market are not cointegrated. Further test was also executed by using the All House Price Index as a proxy for residential real estate. The results remained the same where residential real estate market is found to be segmented from the stock market. An exclusian of a variable (Consumer Price Index) that was highly correlated with other independent variables in the ARDL model also does not change the results. This would indicate that investors could diversify their portfolio by investing in the residential real estate and the stock market.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".