Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

• Autorzy: Changro Lee , Keith Key-Ho Park
• Języki publikacji: EN

Abstrakty

EN

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 Gangnamgu, 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. (original abstract)

Słowa kluczowe

EN

Neural networks; Real estate valuation; Bayesian network

PL

Sieci neuronowe; Wycena nieruchomości; Sieci bayesowskie

• Czasopismo: Real Estate Management and Valuation
• Rocznik: 2020
• Numer: vol. 28, iss. 4
• Strony: 15--23

Twórcy

autor

Changro Lee

• Kangwon National University

autor

Keith Key-Ho Park

• Seoul National University

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Identyfikatory

DOI

doi.org/10.1515/remav-2020-0028