Developing a forecasting model for real estate auction prices using artificial intelligence

Jun Kang, Hyun Jun Lee, Seung Hwan Jeong, Hee Soo Lee, Kyong Joo Oh

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


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.

Original languageEnglish
Article number2899
JournalSustainability (Switzerland)
Issue number7
Publication statusPublished - 2020 Apr 1

Bibliographical note

Publisher Copyright:
© 2020 by the authors.

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Management, Monitoring, Policy and Law


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