Beyond Traditional Methods: Enhancing Cost Escalation Forecasting in Commercial Construction amid Economic Turbulence

The application of cost escalation rates to commercial construction projects has historically been a straightforward task for estimators. However, the onset of the post-COVID-19 recovery period has introduced unprecedented challenges. The surge in inflation, followed by sharp disinflation triggered by one of the most aggressive interest rate hike cycles in Federal Reserve history, has created significant obstacles in forecasting future costs, a situation unfamiliar to many contemporary construction cost estimators. Unlike previous research that predominantly focused on cost indices tracking labor rates and building materials, our study integrates the Turner Building Cost Index, which also accounts for the competitive condition of the marketplace. Although traditional academic forecasting tools may perform well during periods of gradual economic expansion, they often falter amidst recessions or sudden economic shocks. Recognizing the crucial role of the overall economy in future cost projections, our paper rigorously examines current economic conditions and emphasizes concerns stemming from recent monetary policy actions by the Federal Reserve. We introduce an integrated forecasting approach, combing quantitative analysis with qualitative insights from industry experts—a process referred to as decision science analysis. This method allows estimators to incorporate a comprehensive view of the current economic landscape, transcending conventional academic models. Our methodology projects costs across three scenarios: best-case, average, and worst-case. In the best-case scenario, assuming the US economy avoids a recession or sudden economic shock, the annual escalation rate is forecasted at 2.8% over the next 7 years. In contrast, a worst-case scenario characterized by a severe recession could cause a decrease in cost by 13% within 2 years of the index peak. This study underscores the importance of considering macroeconomic conditions during periods of heightened economic uncertainty. Furthermore, it showcases how effective collaboration between industry and academia can yield a robust and comprehensive forecasting approach, adaptable to any economic climate.