Artificial intelligence for mass appraisals of residential properties in Nicosia: mathematical modelling and algorithmic implementation

A recent study in property valuation literature, indicated that the vast majority of researchers and academics are focusing on Mass Appraisals rather than on further developing the existing methods. Researchers are using a variety of mathematical models from the field of Machine Learning and Artificial Neural Networks, which are applied to real estate valuations, with high accuracy. On the other hand, it appears that the professional valuers do no use those sophisticated models on their daily practice, using essentially the traditional 5 methods. At that point, authors deal with the ethical question that arises and that is whether those models can replace the judgment of the individual valuer. As in many other aspects of scientific research, and in particular in artificial intelligence applications, human intelligence is still dangerous to be replaced by machine intelligence (like i.e. the self-driving cars). Despite the fact that those models are proved to be extremely accurate in academic test cases, in real-world applications, they cannot be used without the audit of an experienced valuer. The aim of this work is to investigate the capabilities of such models and how they can be used in order to improve valuer’s work.

[1]  Milwida M. Guevara Real Property Taxation in the Philippines , 2004 .

[2]  N Oreskes,et al.  Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences , 1994, Science.

[3]  Stephen Cave,et al.  Ancient dreams of intelligent machines: 3,000 years of robots , 2018, Nature.

[4]  J. Bryson Robots should be slaves , 2010 .

[5]  Margarita M. Lenk,et al.  An Exploration of Neural Networks and Its Application to Real Estate Valuation , 1995 .

[6]  Michael A. Osborne,et al.  The future of employment: How susceptible are jobs to computerisation? , 2017 .

[7]  Tom M. Mitchell,et al.  What Can Machines Learn, and What Does It Mean for Occupations and the Economy? , 2018 .

[8]  Alexander M. Millkey The Black Swan: The Impact of the Highly Improbable , 2009 .

[9]  David H. Autor,et al.  Why Are There Still So Many Jobs? The History and Future of Workplace , 2015 .

[10]  Taher Buyong,et al.  RESIDENTIAL PROPERTY VALUATION USING GEOGRAPHIC INFORMATION SYSTEM , 1998 .

[11]  J. Grange A scientific appraisal , 2002 .

[12]  Diofantos G. Hadjimitsis,et al.  Accuracy measurement of Random Forests and Linear Regression for mass appraisal models that estimate the prices of residential apartments in Nicosia, Cyprus , 2018, Advances in Geosciences.

[13]  Thomas Dimopoulos,et al.  An artificial intelligence algorithm analyzing 30 years of research in mass appraisals , 2019 .

[14]  N. Bakas Numerical Solution for the Extrapolation Problem of Analytic Functions , 2019, Research.

[15]  Spyros Makridakis,et al.  Forecasting and uncertainty: A survey , 2016, Risk Decis. Anal..

[16]  Alan Edelman,et al.  Julia: A Fresh Approach to Numerical Computing , 2014, SIAM Rev..

[17]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[18]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .