Unlocking visual data to enhance the accuracy of AI-enabled mass valuation of urban houses: An Australian city case study

Despite earlier praises of hedonic price models (HPMs) in predicting the prices of residential properties, stakeholders in the property market have raised concerns regarding inaccuracy in automated valuation models (AVMs) using conventional HPMs. Furthermore, despite significant advancements in integrating artificial intelligence and image data into AVMs, research in the Australian context is absent. This paper investigates how image data capturing visual features of properties could enhance valuation accuracy using the data of actual sales of 34,399 properties from 2018 to 2020 across 128 urban suburbs in Brisbane, the third largest city in Australia. We develop a convolutional neural network model to extract visual features from large-scale data of 320,000 street-view and aerial-view images. We develop fusion models to integrate these additional visual features in HPMs. Using several experimental designs, our preferred fusion models generate a reduction of 28.35% in root-mean-square errors. Less predictive errors of AI-enabled AVMs through the use of visual data would enhance business confidence for AVMs' end-users for investment, reporting, risk management, tax estimation, and urban planning purposes.