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ADGEO | Articles | Volume 45
Adv. Geosci., 45, 377-382, 2018
https://doi.org/10.5194/adgeo-45-377-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Adv. Geosci., 45, 377-382, 2018
https://doi.org/10.5194/adgeo-45-377-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  29 Nov 2018

29 Nov 2018

Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus

Thomas Dimopoulos et al.
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Cited articles  
Antipov, E. A. and Pokryshevskaya, E. B.: Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics, Expert Syst. Appl., 39, 1772–1778, 2012. 
Benjamin, J. D., Guttery, R. S., and Sirmans, C. F.: Mass appraisal: An introduction to multiple regression analysis for real estate valuation, Journal of Real Estate Practice and Education, 7, 65–77, 2004 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
Dimopoulos, T. and Moulas, A: A proposal of a mass appraisal system in Greece with CAMA system. Evaluating GWR and MRA techniques. The case study of Thessaloniki Municipality, Open Geosci., 8.1, https://doi.org/10.1515/geo-2016-0064, 2016. 
Liu, X., Deng, Z., and Wang, T.: Real estate appraisal system based on GIS and BP neural network, T. Nonferr. Metal. Soc., 21, s626–s630, 2011. 
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Short summary
The paper examines a machine learning algorithm (Random Forests) in comparison with Multivariate Linear Regression, for a data-set of 3500 transactions of residential apartments in Nicosia District in Cyprus. The methodology suggested, indicated high accuracy of the Random Forests Method, that can be applied in automated valuation models and CAMA systems.
The paper examines a machine learning algorithm (Random Forests) in comparison with Multivariate...
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