Prediction of Bidding Rate of Residential Auction Items using Model Stacking
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Recently, as interest in real estate auctions has grown, research on predicting the bidding price has been actively underway. The hedonic price model, one that is traditionally used to evaluate the value of real estate, uses simple linear regression and has the disadvantage of delivering a poor predictive performance. To compensate for this, recent research using machine learning or deep learning has been actively conducted in the real estate sector. It was found that the predictive performance of machine learning surpassed that of the traditional hedonic price model in predicting the bidding rate. In this paper, we try to find a method that performs better than the current machine learning model while predicting the bidding rate. In previous studies, Bagging and Boosting methodologies, among ensemble methodologies, have been used. In this paper, we use the model stacking method to predict the bidding price of a real estate auction.
Abstract
Recently, as interest in real estate auctions has grown, research on predicting the bidding price has been actively underway. The hedonic price model, one that is traditionally used to evaluate the value of real estate, uses simple linear regression and has the disadvantage of delivering a poor predictive performance. To compensate for this, recent research using machine learning or deep learning has been actively conducted in the real estate sector. It was found that the predictive performance of machine learning surpassed that of the traditional hedonic price model in predicting the bidding rate. In this paper, we try to find a method that performs better than the current machine learning model while predicting the bidding rate. In previous studies, Bagging and Boosting methodologies, among ensemble methodologies, have been used. In this paper, we use the model stacking method to predict the bidding price of a real estate auction.
참고문헌 (14)
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