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Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach

Author

Listed:
  • Lee Changro

    (Department of Real Estate, Kangwon National University)

  • Park Keith Key-Ho

    (Department of Geography, Seoul National University, Institute for Korean Regional Studies)

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.

Suggested Citation

  • Lee Changro & Park Keith Key-Ho, 2020. "Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach," Real Estate Management and Valuation, Sciendo, vol. 28(4), pages 15-23, December.
    • Handle: RePEc:vrs:remava:v:28:y:2020:i:4:p:15-23:n:2
    DOI: 10.1515/remav-2020-0028
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    References listed on IDEAS

    as
    1. Yixin Wang & David M. Blei, 2019. "Frequentist Consistency of Variational Bayes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1147-1161, July.
    2. Wieslaw Meszek, 2007. "Uncertainty phenomenon in property valuation," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 8(5/6), pages 575-585.
    3. Huisu Jang & Jaewook Lee, 2019. "Generative Bayesian neural network model for risk-neutral pricing of American index options," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 587-603, April.
    4. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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    More about this item

    Keywords

    deep learning; Bayesian neural network; uncertainty; property valuation;
    All these keywords.

    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services
    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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