Modelling the Drivers of Housing Price using Autoregressive Distributed Lag – Error Correction Model (ARDL-ECM) in Indonesia
Bibliographic record
Abstract
This study aims to design a model tand to estimate the effect of market fundamentals (macroeconomic drivers) on housing prices in Indonesia. The identification of macroeconomic drivers helps the government utilize these macroeconomic indicators to control housing prices in accordance with the current situation. Therefore, the contribution of this study is to analyse how is the housing price in Indonesia. The analytical tool used in this study is the Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM). The variables used in this study are the residential housing price index, real loan interest rates, and the unemployment rate with the observation period starting in the first quarter of 2010 - fourth quarter of 2019. The process of establishing the ARDL-ECM was carried out through a series of tests on research data. Based on the ARDL-ECM estimation results, it was found that in the short-term real loan interest rates had a negative and significant effect on housing prices, while in the long-term real loan interest rates and unemployment rates had a negative and significant effect on housing prices. These results indicate that real interest rates and unemployment rates as macroeconomic drivers can affect housing prices so that they can be utilized by policy makers, specifically through monetary policy (interest rates) and fiscal policy (unemployment rate).
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".