PREDICTING LAND PRICES AND MEASURING UNCERTAINTY BY COMBINING SUPERVISED AND UNSUPERVISED LEARNING

Abstract

Despite the popularity deep learning has been gaining, measuring the uncertainty within the result has not met expectations in many deep learning applications and this includes property valuation. In real-world tasks, however, rather than simply requiring predictions, assurance of the certainty of the predictions is also demanded. In this study, supervised learning is combined with unsupervised learning to bridge this gap. A method based on principal component analysis, a popular tool of unsupervised learning, was developed and used to represent the uncertainty in property valuation. Then, a neural network, a representative algorithm to implement supervised learning, was constructed, and trained to predict land prices. Finally, the uncertainty that was measured using principal component analysis was incorporated into the price predicted by the neural network. This hybrid approach is shown to be likely to improve the credibility of the valuation work. The findings of this study are expected to generate interest in the integration of the two learning approaches, thereby promoting the rapid adoption of deep learning tools in the property valuation industry.