Leveraging consumer behavior and macroeconomic factors to increase real estate investment potential
Bibliographic record
Abstract
The Indonesian real estate market has shown promising development in recent years, attracting investors seeking lucrative opportunities. This research aims to increase the success rate of real estate investments in Indonesia by examining the connection between macroeconomic factors and consumer behavior. This study employs vector autoregression (VAR) models to investigate the influence of macroeconomic data on the Indonesian house price index (HPI). We intend to determine the primary determinants of the HPI by analyzing various macroeconomic indicators, including GDP growth, CPI, crude oil, interest rates, and the unemployment rate. In addition, the research investigates the role of consumer behavior in Indonesian real estate searches. Real estate developers and investors can gain valuable insight into the effect of consumer preferences, motivations, and decision-making processes on demand functions by understanding consumer preferences, incentives, and decision-making processes. A comprehensive analysis of historical data and econometric modeling techniques are employed to accomplish research objectives. The research is conducted using pertinent macroeconomic indicators, real estate market data, and consumer surveys.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".