predicting the operation and maintenance costs of condominium properties in the project planning phase: an artificial neural network approach

the decisions made in the planning phase of a building project greatly affect its future operation and maintenance (o&m;) cost. recognizing the o&m; cost of condominiums’ common facilities as a critical issue for home owners, this research aims to develop an artificial neural network (ann) o&m; cost prediction model to assist developers and architects in effectively assessing the impacts of their decisions made in the planning phase of condominium projects on future o&m; costs. a regression cost prediction model was also developed as a benchmark model for testing the predictive accuracy of the ann model. six critical building design attributes (building age, number of apartment units, number of floors, average sale price, total floor area, and common facility floor area) which are usually available in the project planning phase, were identified as the input factors to both models and average monthly o&m; cost as the output factor. 55 of the 65 existing condominium properties randomly selected were treated as the training samples whose data were used to develop the ann and regression models the other ten as the test samples to compare and verify the predictive performance of both models. the study results revealed that the ann model delivers more accurate and reliable cost prediction results, with lower average absolute error around 7.2% and maximum absolute error around 16.7%, as compared with the regression model. this study shows that ann is an effective method in predicting building o&m; costs in the project planning phase. keywords: project management, facility management, common facilities, cost modeling.