A recent scientometric study by DImopoulos and Bakas (2018), on the relevant to Mass Appraisals and Property Valuations literature, revealed a transformation from traditional statistics to more complex numerical models, computational procedures, and automated methods, from the field of Artificial Intelligence. The study was based on an adequate pool of papers, constituted in Scopus database, utilizing a machine learning algorithm developed from one of the authors, for multidimensional scaling and clustering of the keywords found in the papers’ database, the authors and their cooperation and the co-occurrences of the references in the papers studied. The time-series of the most frequent keywords are also computed, demonstrated that the evolution of the mass appraisals research.
Accordingly, we have developed and comprehensively tested a variety of mathematical models for Real Estate Datasets, always trying to accomplish the best possible accuracy and generalization in the predictions. Novel techniques, such as text mining can provide supplementary data, which combined with artificial intelligence algorithms are able to enhance the prediction accuracy, decrease the error metrics and supply the user with a model which generalizes in a best possible manner. Below is attached a relative document we preseed in RICS, Cyprus.
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