Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach
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)
- Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424.
- Conway, J. (2018). Artificial intelligence and machine learning: current applications in real estate. Master's Thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts
- French, N. (2001). Uncertainty in property valuation: The pricing of flexible leases. Journal of Corporate Real Estate, 3(1), 17-27. https://doi.org/10.1108/14630010110811454
- French, N., & Gabrielli, L. (2004). The uncertainty of valuation. Journal of Property Investment & Finance, 22(6), 484-500. https://doi.org/10.1108/14635780410569470
- Gal, Y., & Ghahramani, Z. (2016). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059).
- Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452-459. https://doi.org/10.1038/nature14541 PMID:26017444
- Graves, A. (2011). Practical variational inference for neural networks. In Advances in neural information processing systems, 2348-2356.
- Hasenclever, L., Webb, S., Lienart, T., Vollmer, S., Lakshminarayanan, B., Blundell, C., & Teh, Y. W. (2017). Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server. Journal of Machine Learning Research, 18(1), 3744-3780.
- Harper, R., & Southern, J. (2019). A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat. arXiv preprint arXiv:1902.03043.
- Jang, H., & Lee, J. (2019). Generative Bayesian neural network model for risk-neutral pricing of American index options. Quantitative Finance, 19(4), 587-603. https://doi.org/10.1080/14697688.2018.1490807
- Johnson, A. E., Ghassemi, M. M., Nemati, S., Niehaus, K. E., Clifton, D. A., & Clifford, G. D. (2016). Machine learning and decision support in critical care. Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 104(2), 444. https://doi.org/10.1109/JPROC.2015.2501978
- Joslin, A. (2005). An investigation into the expression of uncertainty in property valuations. Journal of Property Investment & Finance, 23(3), 269-285. https://doi.org/10.1108/14635780510599476
- Kendall, A., & Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems, 5574-5584.
- Kucharska-Stasiak, E. (2013). Uncertainty of property valuation as a subject of academic research. Real Estate Management and Valuation, 21(4), 17-25. https://doi.org/10.2478/remav-2013-0033
- Mallinson, M., & French, N. (2000). Uncertainty in property valuation-The nature and relevance of uncertainty and how it might be measured and reported. Journal of Property Investment & Finance, 18(1), 13-32. https://doi.org/10.1108/14635780010316636
- Meszek, W. (2007). Uncertainty phenomenon in property valuation. International Journal of Management and Decision Making, 8(5/6), 575-585. https://doi.org/10.1504/IJMDM.2007.013419
- Neal, R. M. (2012). Bayesian learning for neural networks (Vol. 118). Springer Science & Business Media.
- Seoul. (2017). Seoul Statistics. Department of housing policy.
- Wang, Y., & Blei, D. M. (2019). Frequentist consistency of variational Bayes. Journal of the American Statistical Association, 114(527), 1147-1161. https://doi.org/10.1080/01621459.2018.1473776
- Welling, M., & Teh, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th international conference on machine learning (ICML-11) (pp. 681-688).