Predicting Repair Costs of Residential Facilities Using Deep Learning Algorithms

This research focuses on developing a deep learning-based framework to forecast maintenance expenditures within the residential sector. To maintain building value, resident safety, and energy efficiency, consistent facility maintenance is indispensable. This necessity is especially heightened given the recent increase in the construction of supertall and high-performance buildings. However, estimating repair costs for residential facilities is challenging due to the diverse building types, ownership structures, and occupancy patterns compared to other property uses. Therefore, this research proposes a deep-learning model to establish a highly reliable and scientific method for estimating repair costs using empirical data gathered from actual residential facilities. Among the deep learning algorithms, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) were adopted to develop models and optimize them through a fixed split. The framework and results of this paper facilitate the prediction of maintenance costs for residential facilities, which can contribute to budget planning, long-term facility management, preventive maintenance, resource management, and advanced decision-making. Moreover, it will contribute to the advancement of facility management of residential facilities.