Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

Real Estate Management and Valuation

Park Keith Key-Ho¹  Lee Changro² 

[1] Department of Geography, Seoul National University, Institute for Korean Regional Studies;Department of Real Estate, Kangwon National University;

关键词: deep learning;    bayesian neural network;    uncertainty;    property valuation;    e37;    l85;    r00;    c11;    c45;   

DOI  :  10.1515/remav-2020-0028

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown