Akademik Veri Yönetim Sistemi
CONSTRUCTION MANAGEMENT AND ECONOMICS, cilt.42, sa.9, ss.787-801, 2024 (ESCI)
In order to prepare proper budgets, practitioners endeavor to forecast future cost fluctuations. In recent years, the COVID-19 pandemic and the Russia-Ukraine war have caused cost overruns in most construction projects, and there has been no effort to analyze the effects of such situations on cost forecasting performances. This paper aims to investigate the impact of unexpected conditions, such as pandemics and wars, on the construction cost forecasting performances of widely used univariate time series models. For this purpose, statistical forecasting models and two widely used machine learning techniques are employed for the construction cost forecasting of nine European economies in two scenarios: a stable period (2017–2020) and a fluctuating period (2020–2023). The results showed that the success of the models varied by region and period. Unexpected global conditions cause significant errors, especially in the medium and long term. There are significant increases in forecast errors between stable and fluctuating periods. This study provides comprehensive experimental results to determine the cost-forecasting risks of construction projects under unexpected global conditions. The investigation is limited to the European economies, which are mostly developed countries with low inflation rates. Therefore, the results should also be validated in underdeveloped and developing countries.