Title: Data-driven techniques for mass appraisals. Applications to the residential market of the city of Bari (Italy)

Authors: Francesco Tajani; Pierluigi Morano; Marco Locurcio; Carmelo Maria Torre

Addresses: Department of Science of Civil Engineering and Architecture, Polytechnic of Bari, Italy ' Department of Science of Civil Engineering and Architecture, Polytechnic of Bari, Italy ' Department of Architecture and Design, Sapienza University of Rome, Italy ' Department of Science of Civil Engineering and Architecture, Polytechnic of Bari, Italy

Abstract: The need for evaluation models capable of returning 'slender' and reliable mass appraisals of properties belonging to different market segments has been made mandatory by the events that are covering the global real estate finance, because of the emergence of non-performing loans in the banks' balance sheets. In Italy, the non-performing loans have been estimated by the Italian Banking Association equal to about 300 billion euro in 2014. In the present paper, three approaches of data-driven techniques (hedonic price model, artificial neural networks and evolutionary polynomial regression) have been applied to a sample of residential apartments recently sold in a district of the city of Bari (Italy), in order to test the respective performance for mass appraisals. The models obtained by the implementation of the three procedures have been compared in terms of statistical accuracy, empirical compliance of the results and complexity of the functional relationships.

Keywords: data-driven techniques; hedonic price models; artificial neural networks; ANNs; evolutionary polynomial regression; market value; mass appraisals; residential properties; Bari; Italy; real estate finance; non-performing loans; bank loans; residential apartments; house sales.

DOI: 10.1504/IJBIDM.2016.081604

International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.2, pp.109 - 129

Received: 26 Jan 2016
Accepted: 14 Aug 2016

Published online: 17 Jan 2017 *

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