An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment

Abstract

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system-based approach to estimate prices for residential properties. The data set consists of historic sales of houses in a market in the Midwest region of the United States and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine-tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

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