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Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence

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  • Jun Kang

    (Graduate Program in Investment Information Engineering, Yonsei University, Seoul 03722, Korea)

  • Hyun Jun Lee

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

  • Seung Hwan Jeong

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

  • Hee Soo Lee

    (Department of Business Administration, Sejong University, Seoul 05006, Korea)

  • Kyong Joo Oh

    (Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea)

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.

Suggested Citation

  • Jun Kang & Hyun Jun Lee & Seung Hwan Jeong & Hee Soo Lee & Kyong Joo Oh, 2020. "Developing a Forecasting Model for Real Estate Auction Prices Using Artificial Intelligence," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:7:p:2899-:d:341749
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    References listed on IDEAS

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