Transfer learning for road-based location classification of non-residential property

This article reveals the method of the property classification by its location relative to highways using transfer learning approach. Solving this problem is crucial for non-residential real estate market value assessment automation in the course of market analysis, pre-trial appraisal and other aspects of making managerial decisions in the field of financing and lending. Instead of a standard approach based on models developed using machine learning libraries and programming, this work considers the use of Google’s Teachable Machine service. This article examines the aspects of initial data preparation, the use of Teachable Machine for model training and the results obtained. The parameters and results of training classification models in different conditions are presented, the classification accuracy is analyzed. The results obtained generally indicate the validity of this approach and recommends it for solving similar problems.