A FRAMEWORK TO INTEGRATE BIM WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING-BASED PROPERTY VALUATION METHODS

Property valuation is of extreme importance since variations in the real estate market enormously influence people's life. The main goal of Automated Valuation Models (AVMs) is to calculate the market value of a large number of properties with an acceptable accuracy. The Hedonic Price Model (HPM) is the most widely used AVM for the valuation purposes. Despite its simplicity, ease of use and straightforwardness, HPM lacks the capability to address the non-linear relationships between different value-related factors. Hence, researchers have developed other state-of-the-art property valuation methods based on the advancements in computer science including Artificial Intelligence (AI), Machine Learning (ML), computer vision and deep learning. Design, development, and validation of such advanced AVMs require establishment of a database including data on the different influential factors. Two types of factors are used in the literature, including textual and visual features. Reliable data sources are required for the implementation of AVMs since the accuracy of the provided valuations is definitely linked to the reliability of the used real estate databases. Building Information Modelling (BIM) provides precise information on different components of properties. Although some scholars have tried to use BIM for property valuation, BIM benefits in different valuation procedures have not been fully investigated. Hence, this paper provides a framework that consider BIM capabilities to be integrated with different stages and processes in property valuation, especially in relation to advanced AVMs based on AI and ML.