Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/18526
Title: Sensitivity analysis of machine learning models for the mass appraisal of real estate. Case study of residential units in Nicosia, Cyprus
Authors: Dimopoulos, Thomas 
Bakas, Nikolaos P. 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: General valuation;Cyprus;Artificial intelligence;Mass appraisals;Real estate;Algorithms;Mathematical models;AVM;CAMA
Issue Date: 2-Dec-2019
Source: Remote Sensing, 2019, vol. 11, no. 24, articl. no. 3047
Volume: 11
Issue: 24
Journal: Remote Sensing 
Abstract: A recent study of property valuation literature indicated that the vast majority of researchers and academics in the field of real estate are focusing on Mass Appraisals rather than on the further development of the existing valuation methods. Researchers are using a variety of mathematical models used within the field of Machine Learning, which are applied to real estate valuations with high accuracy. On the other hand, it appears that professional valuers do not use these sophisticated models during daily practice, rather they operate using the traditional five methods. The Department of Lands and Surveys in Cyprus recently published the property values (General Valuation) for taxation purposes which were calculated by applying a hybrid model based on the Cost approach with the use of regression analysis in order to quantify the specific parameters of each property. In this paper, the authors propose a number of algorithms based on Artificial Intelligence and Machine Learning approaches that improve the accuracy of these results significantly. The aim of this work is to investigate the capabilities of such models and how they can be used for the mass appraisal of properties, to highlight the importance of sensitivity analysis in such models and also to increase the transparency so that automated valuation models (AVM) can be used for the day-to-day work of the valuer.
URI: https://hdl.handle.net/20.500.14279/18526
ISSN: 20724292
DOI: 10.3390/rs11243047
Rights: © 2019 by the authors.
Attribution-NonCommercial-NoDerivs 3.0 United States
Type: Article
Affiliation : Neapolis University Pafos 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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