Price prediction of residential buildings using random forest and artificial neural network

Abstract

ABSTRACT Inaccurate cost estimates significantly affect the ultimate cost of construction projects and reduce revenues. This study compares the accuracy of two algorithm models in estimating construction project prices during the early conceptual stage of project development. The construction prices of residential projects are forecasted in this study using two modeling methods, artificial neural networks (ANN) and random forest (RF), based on the project data from the actual projects. The datasets have been collected from 220 residential buildings implemented in various cities in Egypt. Python was used to build the model during the training and testing phases. To evaluate the performance of the models generated by each algorithm, five key statistical metrics were employed. The findings revealed that both ANN and RF managed to predict the price per meter square of the construction projects, attaining model score accuracies of 96.6% and 93.05%, respectively. The best training for RF was acquired with 110 trees and a maximum decision tree depth of 20. The outcomes of this research could assist stakeholders in the building sector estimate construction project costs during the conceptual stage, enabling more informed decisions by examining key cost drivers for building projects and utilizing appropriate machine learning techniques for analysis.