Econometric Modeling of Construction Cost Estimation: A VECM-Based Approach for Forecasting Price Fluctuations in Türkiye
Bibliographic record
Abstract
The construction sector in Turkey is highly influenced by price fluctuations, economic crises, and external shocks. In particular, the major earthquake in 2023 accelerated the increase in construction costs, revealing that price volatility in Turkey is significantly higher compared to other OECD countries. This study develops a forecasting model for construction costs using the Vector Error Correction Model (VECM). A total of 16 periods of data were collected from the first quarter of 2021 to the last quarter of 2024. The selected variables include total cost, labor cost, material cost, iron prices, and the Consumer Price Index (CPI). The VECM model was applied to analyze long-term economic relationships and short- term fluctuations in construction costs. The results were compared with traditional forecasting methods, demonstrating a lower margin of error in the VECM-based predictions. These findings highlight the necessity of data-driven modeling for more reliable cost estimations in the construction industry. The study provides a methodological framework for improving cost forecasting, particularly during periods of high inflation and market uncertainty. By leveraging econometric modeling, decision- makers in the construction sector can enhance financial planning and risk management. Future research may focus on expanding the model with additional economic indicators, testing its applicability in different regions, and developing long-term forecasting strategies to improve predictive accuracy.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".