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

    Changro Lee   Affiliation

Abstract

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. https://doi.org/10.3846/ijspm.2021.14293
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Mar 12, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abidoye, R. B., & Chan, A. P. (2017). Artificial neural network in property valuation: application framework and research trend. Property Management, 35(5), 554–571.
https://doi.org/10.1108/PM-06-2016-0027

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.
https://doi.org/10.3844/jmssp.2012.419.434

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. https://doi.org/10.1016/j.eswa.2019.02.033

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. https://doi.org/10.2478/rgg-2018-0011

Bourassa, S. C., Hamelink, F., Hoesli, M., & MacGregor, B. D. (1999). Defining housing submarkets. Journal of Housing Economics, 8(2), 160–183. https://doi.org/10.1006/jhec.1999.0246

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. https://doi.org/10.3846/ijspm.2019.6372

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. https://doi.org/10.3846/ijspm.2018.5226

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.
https://doi.org/10.1038/nature14541

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. https://doi.org/10.3846/ijspm.2020.12742

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. https://doi.org/10.1137/090771806

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.
https://doi.org/10.3846/1648715X.2015.1101626

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. https://doi.org/10.18276/sip.2016.45/1-25

Jiang, Y., & Shen, J. (2013). Weighting for what? A comparison of two weighting methods for measuring urban competitiveness. Habitat International, 38, 167–174.
https://doi.org/10.1016/j.habitatint.2012.06.003

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. https://doi.org/10.1109/JPROC.2015.2501978

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

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. https://doi.org/10.1080/09599916.2013.781204

Mooya, M. M. (2016). Real estate valuation theory. Springer Books. https://doi.org/10.1007/978-3-662-49164-5

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. https://doi.org/10.1504/IJBIDM.2013.059263

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. https://doi.org/10.1016/j.measurement.2020.107693

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. https://doi.org/10.1007/s00138-018-0922-2

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. https://doi.org/10.3923/ajsr.2015.182.194

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. https://doi.org/10.1007/978-981-13-3329-3_45

Tadeusiewicz, R. (2011). Artificial intelligence applied to the intelligent buildings. In 6th International Congress on Intelligent Building Systems (pp. 1–11).
https://www.academia.edu/39590553/Artificial_Intelligence_Applied_to_the_Intelligent_Buildings

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. https://doi.org/10.1007/978-3-030-28377-3_30

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). https://doi.org/10.1061/9780784413135.098

Wilkinson, S. (2014). The preliminary assessment of adaptation potential in existing office buildings. International Journal of Strategic Property Management, 18(1), 77–87.
https://doi.org/10.3846/1648715X.2013.853705

Zimmermann, J., & Eber, W. (2014). Consideration of risk in PPP-projects. Business, Management and Education, 12(1), 30–46. https://doi.org/10.3846/bme.2014.03