Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea

Jungsun Kim, Jaewoong Won, Hyeongsoon Kim and Joonghyeok Heo
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Jungsun Kim: Real Estate Artificial Intelligence Research Institute, Seoul 06651, Korea
Jaewoong Won: Department of Real Estate, Graduate School of Tourism, Kyung Hee University, Seoul 02447, Korea
Hyeongsoon Kim: Seoul Appraisal Co., Ltd., Seoul 06654, Korea
Joonghyeok Heo: Department of Geosciences, University of Texas-Permian Basin, Odessa, TX 79762, USA

Sustainability, 2021, vol. 13, issue 23, 1-14

Abstract: The accurate estimation of real estate value helps the development of real estate policies that can respond to the complexities and instability of the real estate market. Previously, statistical methods were used to estimate real estate value, but machine learning methods have gained popularity because their predictions are more accurate. In contrast to existing studies that use various machine learning methods to estimate the transactions or list prices of real estate properties without separating the building and land prices, this study estimates land price using a large amount of land-use information obtained from various land- and building-related datasets. The random forest and XGBoost methods were used to estimate 52,900 land prices in Seoul, South Korea, from January 2017 to December 2020. The models were also separately trained for different land uses and different time periods. Overall, the results revealed that XGBoost yields a higher prediction accuracy. Whereas the XGBoost models were more accurate on the 2020 data than on the 2017–2020 data when analyzing residential areas, the random forest models were more accurate on the 2017–2020 data than on the 2020 data. Further analysis will extend the prediction model to consider submarkets determined by price volatility and locality.

Keywords: land price; prediction modeling; machine learning; ensemble; random forest; XGBoost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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