Predicting land prices and measuring uncertainty by combining supervised and unsupervised learning

    Changro Lee   Affiliation


Despite the popularity deep learning has been gaining, measuring the uncertainty within the result has not met expectations in many deep learning applications and this includes property valuation. In real-world tasks, however, rather than simply requiring predictions, assurance of the certainty of the predictions is also demanded. In this study, supervised learning is combined with unsupervised learning to bridge this gap. A method based on principal component analysis, a popular tool of unsupervised learning, was developed and used to represent the uncertainty in property valuation. Then, a neural network, a representative algorithm to implement supervised learning, was constructed, and trained to predict land prices. Finally, the uncertainty that was measured using principal component analysis was incorporated into the price predicted by the neural network. This hybrid approach is shown to be likely to improve the credibility of the valuation work. The findings of this study are expected to generate interest in the integration of the two learning approaches, thereby promoting the rapid adoption of deep learning tools in the property valuation industry.

First published online 23 February 2021

Keyword : supervised learning, unsupervised learning, property valuation, land prices, uncertainty, principal component analysis, neural network

How to Cite
Lee, C. (2021). Predicting land prices and measuring uncertainty by combining supervised and unsupervised learning. International Journal of Strategic Property Management, 25(2), 169-178.
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Mar 12, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.


Abidoye, R. B., & Chan, A. P. (2017). Artificial neural network in property valuation: application framework and research trend. Property Management, 35(5), 554–571.

Amri, S., & Tularam, G. A. (2012). Performance of multiple linear regression and nonlinear neural networks and fuzzy logic techniques in modelling house prices. Journal of Mathematics and Statistics, 8(4), 419–434.

Bao, W., Lianju, N., & Yue, K. (2019). Integration of unsupervised and supervised machine learning algorithms for credit risk assessment. Expert Systems with Applications, 128, 301–315.

Bazan-Krzywoszanska, A., & Bereta, M. (2018). The use of urban indicators in forecasting a real estate value with the use of deep neural network. Reports on Geodesy and Geoinformatics, 106, 25–34.

Bourassa, S. C., Hamelink, F., Hoesli, M., & MacGregor, B. D. (1999). Defining housing submarkets. Journal of Housing Economics, 8(2), 160–183.

Budie, B., Appel-Meulenbroek, R., Kemperman, A., & WeijsPerree, M. (2019). Employee satisfaction with the physical work environment: the importance of a need based approach. International Journal of Strategic Property Management, 23(1), 36–49.

Chiang, T. Y., & Perng, Y. H. (2018). A new model to improve service quality in the property management industry. International Journal of Strategic Property Management, 22(5), 436–446.

Chollet, F. (2018). Deep Learning mit Python und Keras: das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITPVerlags GmbH & Co. KG.

Conway, J. (2018). Artificial intelligence and machine learning: current applications in real estate [Master’s thesis]. Massachusetts Institute of Technology, Cambridge, Massachusetts.

Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521(7553), 452–459.

Guo, J. Q., Chiang, S. H., Liu, M., Yang, C. C., & Guo, K. Y. (2020). Can machine learning algorithms associated with text mining from internet data improve housing price prediction performance? International Journal of Strategic Property Management, 24(5), 300–312.

Halko, N., Martinsson, P. G., & Tropp, J. A. (2011). Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions. SIAM Review, 53(2), 217–288.

Hsu, Y. H., & Juan, Y. K. (2016). ANN-based decision model for the reuse of vacant buildings in urban areas. International Journal of Strategic Property Management, 20(1), 31–43.

International Association of Assessing Officers. (2013). Standard on ratio studies. IAAO.

Jasiński, T., & Bochenek, A. (2016). Prognozowanie cen nieruchomości lokalowych za pomocą sztucznych sieci neuronowych [Apartment prices forecasting by the artificial neural networks]. Studia i Prace WNEiZ US, 45, 317–328.

Jiang, Y., & Shen, J. (2013). Weighting for what? A comparison of two weighting methods for measuring urban competitiveness. Habitat International, 38, 167–174.

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, 104(2), 444–466.

Kucharska-Stasiak, E. (2013). Uncertainty of property valuation as a subject of academic research. Real Estate Management and Valuation, 21(4), 17–25.

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.

Mazur-Dudzińska, A. (2014, 12–16 maja). Sztuczne sieci neuronowe w modelowaniu zjawisk zachodzących na rynku nieruchomości [Application of the artificial neural networks to the real estate market analysis]. In XVIII Międzynarodowa Szkoła Komputerowego Wspomagania Projektowania, Wytwarzania i Eksploatacji (pp. 381–388), Szczyrk, Polska. Stowarzyszenie Inżynierów i Techników Mechaników Polskich.

McCluskey, W. J., McCord, M., Davis, P. T., Haran, M., & McIlhatton, D. (2013). Prediction accuracy in mass appraisal: a comparison of modern approaches. Journal of Property Research, 30(4), 239–265.

Mooya, M. M. (2016). Real estate valuation theory. Springer Books.

Morano, P., & Tajani, F. (2013). Bare ownership evaluation. Hedonic price model vs. artificial neural network. International Journal of Business Intelligence and Data Mining, 8(4), 340– 362.

Morano, P., Tajani, F., & Torre, C. M. (2015). Artificial intelligence in property valuations: an application of artificial neural networks to housing appraisal. Advances in Environmental Science and Energy Planning, 23–29.

Mrówczyńska, M., Sztubecki, J., & Greinert, A. (2020). Compression of results of geodetic displacement measurements using the PCA method and neural networks. Measurement, 158, 107693.

Patel, A. A. (2019). Hands-on unsupervised learning using Python: how to build applied machine learning solutions from unlabeled data. O’Reilly Media, Inc.

Peterson, S., & Flanagan, A. (2009). Neural network hedonic pricing models in mass real estate appraisal. Journal of Real Estate Research, 31(2), 147–164.

Poursaeed, O., Matera, T., & Belongie, S. (2018). Vision-based real estate price estimation. Machine Vision and Applications, 29(4), 667–676.

Sampathkumar, V., Santhi, M. H., & Vanjinathan, J. (2015). Evaluation of the trend of land price using regression and neural network models. Asian Journal of Scientific Research, 8(2), 182–194.

Sandbhor, S., & Chaphalkar, N. B. (2019). Impact of outlier detection on neural networks based property value prediction. In Information systems design and intelligent applications (pp. 481–495). Springer.

Tadeusiewicz, R. (2011). Artificial intelligence applied to the intelligent buildings. In 6th International Congress on Intelligent Building Systems (pp. 1–11).

Talaga, M., Piwowarczyk, M., Kutrzyński, M., Lasota, T., Telec, Z., & Trawiński, B. (2019, September). Apartment valuation models for a big city using selected spatial attributes. In International Conference on Computational Collective Intelligence (pp. 363–376). Springer.

Wang, X., & Zhang, J. (2013). Principal component analysis of influencing factors of the development of China’s real estate market. In ICCREM 2013: Construction and Operation in the Context of Sustainability (pp. 1027–1035).

Wilkinson, S. (2014). The preliminary assessment of adaptation potential in existing office buildings. International Journal of Strategic Property Management, 18(1), 77–87.

Zimmermann, J., & Eber, W. (2014). Consideration of risk in PPP-projects. Business, Management and Education, 12(1), 30–46.