Detecting information transparency in the Italian real estate market: a machine learning approach

Abstract

This research aims to understand how market transparency and data reliability can influence valuation procedures and decision-making processes in the Italian real estate market. Through the analysis of three different real estate markets and the validation of the information collected, this paper’s goal is to understand whether and to what extent the use of asking prices instead of actual purchase and sale prices can lead to valuation errors, increase the uncertainty of valuation, and undermine investment decision-making processes. The research results highlight the primary sources of information opacity in the Italian real estate market, classifying them according to their impact on real estate value. The novelty of this research lies in the integrated use of machine learning techniques, computer programming and multi-parametric valuation procedures to understand and manage information opacity in the Italian real estate market, particularly regarding the estimation of the market value of properties belonging to the residential segment.