Abstract

The effective asset management of real estate is an area of great interest to the building engineering sector as a whole. The determination and programming of maintenance tasks is essential to allow finance entities to establish market order according to a given budget. The process works slowly, and some optimization is generally required. In this paper, two multilayer perceptrons (MLPs) are developed to determine the economic cost of maintenance works in the two types of real estate asset of more interest to building sector: building sites and dwellings. After training using 76 case studies for building sites and 317 for dwellings, the optimal MLP configurations are shown to have 6 and 12 nodes respectively, and the input variables that most influenced their behavior are also determined. Furthermore, the MLPs showed more optimal behavior than models using multiple linear regression. Finally, the MLPs were tested for 15 new case studies for each model, predicting the budgeted costs of the associated maintenance works with deviations of less than 11% compared with the actual value in most cases.