Abstract
Abstract Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and investors continue to doubt the accuracy of the results. In this study, we specify a neural network for predicting house prices. We suggest a standard feed-forward network with two hidden layers, and show that it is sufficiently reasonable to apply its prediction to real-world projects such as property valuation. In addition, we propose a Bayesian neural network for describing uncertainty in house price predictions while providing a means to quantify uncertainty for each prediction. We choose Gangnam-gu, Seoul for the analysis, and predict house prices in the area using both networks. Although the Bayesian neural network did not perform better than the conventional network, it could provide a tool to measure the uncertainty inherent in predicted prices. The findings of this study show that a Bayesian approach can model uncertainty in property valuation, thereby promoting the adoption of deep learning tools in the real estate industry.
Highlights
Deep learning tools, such as neural networks, are used extensively in a variety of fields, such as medicine, healthcare and automobile business, conventional deep learning tools for regression and classification have not paid due attention to model uncertainty (Gal & Ghahramani, 2016)
This study suggests a Bayesian approach to model uncertainty in property valuation, promoting the adoption of deep learning tools in real estate
This study proceeds from a review of uncertainty in property valuation and Bayesian probability theory related to uncertainty; the third section explains the data used and network architectures
Summary
Deep learning tools, such as neural networks, are used extensively in a variety of fields, such as medicine, healthcare and automobile business, conventional deep learning tools for regression and classification have not paid due attention to model uncertainty (Gal & Ghahramani, 2016). Representing model uncertainty, is indispensable for applications in the real-world (Ghahramani, 2015). We develop the model necessary to represent uncertainty in deep learning and apply it to property valuation. This study is the first attempt to describe uncertainty in property valuation through a Bayesian deep neural network. Deep learning-based valuation models are gaining in popularity nowadays, but they are not sufficient to convince property owners and investors to trust the results (Conway, 2018). This study suggests a Bayesian approach to model uncertainty in property valuation, promoting the adoption of deep learning tools in real estate. This study proceeds from a review of uncertainty in property valuation and Bayesian probability theory related to uncertainty; the third section explains the data used and network architectures. The main results and implications are provided in the fourth section, and a summary of the study and conclusions are presented
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