<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

Seoul National University, Institute for Korean Regional Studies

Real Estate Management and Valuation

This work is licensed under the Creative Commons Attribution 4.0 International License.

Although deep learning-based valuation models are spreading throughout the real estate industry following the artificial intelligence boom, property owners and...

Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

Online veröffentlicht: 12. Dez. 2020

Seitenbereich: 15 - 23

DOI: https://doi.org/10.1515/remav-2020-0028

Schlüsselwörter
deep learning, Bayesian neural network, uncertainty, property valuation

© 2020 Changro Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

Online veröffentlicht: 12. Dez. 2020

Seitenbereich: 15 - 23

DOI: https://doi.org/10.1515/remav-2020-0028

Schlüsselwörterdeep learning, Bayesian neural network, uncertainty, property valuation

© 2020 Changro Lee et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
deep learning, Bayesian neural network, uncertainty, property valuation