ABSTRACT The study predicts construction hiring by considering socioeconomic conditions, political elements, and extreme weather events. The research aims to create a predictive model to help construction companies plan future hiring levels more effectively. By analyzing historical data on construction hiring and related variables, the model forecasts hiring demand at both national and state levels, allowing contractors to adjust their hiring plans and ensure job security and industry stability. The methodology combines deep learning algorithms with ensemble learning to process diverse datasets, including state‐specific features and time‐dependent variables. The anticipated outcome is a robust predictive framework to alert companies to market disruptions well in advance, moving from a reactive to a proactive approach in managing workforce dynamics. This research contributes to the resilience of the construction workforce, ultimately enhancing job security and stability within the industry. The model outperformed the more commonly used auto‐regressive models by achieving a lower overall mean absolute error (MAE) in predictions 6, 12, and 24 steps ahead, and the feature importance results highlight similar patterns among important construction markets in the United States.
Ashtab et al. (Mon,) studied this question.