Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence
J. KANG, Hyun Jun Lee, Hyun Jun Lee, Seunghwan Jeong, Hee Soo Lee, Hee Soo Lee, Kyong Joo Oh
Abstract
The real estate auction market has become increasingly important in the financial, economic and investment fields, but few artificial intelligence-based studies have attempted to forecast the auction prices of real estate. The purpose of this study is to develop forecasting models of real estate auction prices using artificial intelligence and statistical methodologies. The forecasting models are developed through a regression model, an artificial neural network and a genetic algorithm. For empirical analysis, we use Seoul apartment auction data from 2013 to 2017 to predict the auction prices and compare the forecasting accuracy of the models. The genetic algorithm model has the best performance, and effective regional segmentation based on the auction appraisal price improves the predictive accuracy.