Predictive model for corrective maintenance costs: Empowering decision-making in building renovation

The prediction of corrective maintenance costs is significant given the predominant use of a corrective maintenance approach in building maintenance. Accurately estimating the costs associated with corrective maintenance at an early stage holds substantial implications for cost allocation, maintenance budgeting, cost effectiveness, and efficient planning, all of which are vital factors contributing to the overall success of building maintenance. However, the utilization of historical data to predict future maintenance costs remains underutilized. To contribute to this gap, this study aims to develop a prediction model for the number of building defects and their associated costs based on past data of defects and building age. The study encompasses 40 buildings and employs regression analysis to develop a predictive model. The predictive model was coded in Python to validate and ensure logical outputs and alignment with expected outcomes while also utilizing the Pearson product-moment correlation coefficient between variables and model output accuracy. The resulting model can provide logical outcomes, enabling accurate predictions of corrective maintenance costs for each building. Furthermore, it assists decision-making regarding cost considerations, such as determining whether an aging building should be renovated or if repairing specific defects based on a corrective approach is more beneficial. In summary, this study contributes to enhancing maintenance planning and informed decision-making, providing significant benefits for maintenance cost estimation, and building renovation decisions.