THE COMPARISON OF HEDONIC REGRESSION AND ARTIFICIAL NEURAL NETWORK IN THE DEVELOPMENT OF MASS APPRAISAL MODEL Case Study: Residential Property in Surabaya City

Abstract

The appraisal of land and building tax objects in Surabaya City has been carried out manually, so it is prone to bias, human error, and inefficiency in terms of time and energy. Therefore, it is necessary to develop a mass appraisal system based on a property price prediction model in accordance with the real estate market structure. The traditional approach to mass appraisal is based on multiple regression analysis, which, despite having a long history in the appraisal world, cannot address interactions between variables such as nonlinearity. This deficiency was then overcome by introducing an Artificial Intelligence approach based on an artificial neural network (ANN), which has the ability to learn on its own and generalize solutions. Specifically, this study aims to build a mass appraisal model for residential property using (i) multiple regression analysis and (ii) artificial neural network, as well as (iii) determining the best mass appraisal model. The result of the study shows that ANN performs better than multiple regression analysis in predicting the value of residential property in Surabaya City.