Rapid Modelling of Machine Learning in Predicting Office Rental Price

Abstract

This study demonstrates the utilization of rapid machine learning modelling in an essential case of the real estate industry. Predicting office rental price is highly crucial in the real estate industry but the study of machine learning is still in its infancy. Despite the renowned advantages of machine learning, the difficulties have restricted the inexpert machine learning researchers to embark on this prominent artificial intelligence approach. This paper presents the empirical research results based on three machine learning algorithms namely Random Forest, Decision Tree and Support Vector Machine to be compared between two training approaches; split and cross-validation. AutoModel machine learning has accelarated the modelling tasks and is useful for inexperienced machine learning researchers for any domain. Based on real cases of office rental in a big city of Kuala Lumpur, Malaysia, the evaluation results indicated that Random Forest with cross-validation was the best promising algorithm with 0.9 R squared value. This research has significance for real estate domain in near future, by applying a more in-depth analysis, particularly on the relevant variables of building pricing as well as on the machine learning algorithms.