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.
Highlights
Machine learning algorithms belong to the wider thematic area of Artificial Intelligence, with applications in Healthcare [3], Automotive [4], Finance and Economics [5], Military [6], Advertising [7], Image Recognition [8], and so forth
Machine learning models are highly non-transparent and it is difficult to completely understand what affects the value of a particular property the most
We defeat this issue by detailed sensitivity analysis for each predictor, by utilizing and comparing four machine learning models
Summary
Machine Intelligence imitates human perception, by utilizing mathematical models that compete against humans to deliver certain tasks such as the assessment and analysis of a studied system and predictions of out-of-sample observations. The accomplished tasks can be highly complex, based on mathematical models which simulate a physical, social, financial and so forth, system of study [1,2]. Machine learning algorithms belong to the wider thematic area of Artificial Intelligence, with applications in Healthcare [3], Automotive (self-driving cars) [4], Finance and Economics (predictions, assets management) [5], Military (drones capable of autonomous action) [6], Advertising (predict/quantify the behaviour of customers) [7], Image Recognition [8], and so forth. A bibliometric study of Artificial Intelligence Algorithms in Mass Appraisals Research [11] revealed that complex methods are increasingly considered in Real Estate predictions, in contrast to the well-established five methods for valuations
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