Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

Lee Changro () and Park Keith Key-Ho ()
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Lee Changro: Department of Real Estate, Kangwon National University
Park Keith Key-Ho: Department of Geography, Seoul National University, Institute for Korean Regional Studies

Real Estate Management and Valuation, 2020, vol. 28, issue 4, 15-23

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.

Keywords: deep learning; Bayesian neural network; uncertainty; property valuation (search for similar items in EconPapers)
JEL-codes: C11 C45 E37 L85 R00 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:remava:v:28:y:2020:i:4:p:15-23:n:2

DOI: 10.1515/remav-2020-0028

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