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Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach AbstractAlthough 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Real Estate Management and Valuation de Gruyter

Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

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Publisher
de Gruyter
Copyright
© 2020 Changro Lee et al., published by Sciendo
ISSN
1733-2478
eISSN
2300-5289
DOI
10.1515/remav-2020-0028
Publisher site
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Abstract

AbstractAlthough 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.

Journal

Real Estate Management and Valuation – de Gruyter

Published: Dec 1, 2020

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