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A Comparative Study of Machine Learning and Spatial Interpolation Methods for Predicting House Prices

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  • Jeonghyeon Kim

    (Department of Geography, Kyung Hee University, Seoul 02447, Korea)

  • Youngho Lee

    (Department of Geography, Kyung Hee University, Seoul 02447, Korea)

  • Myeong-Hun Lee

    (Department of Geography, Kyung Hee University, Seoul 02447, Korea)

  • Seong-Yun Hong

    (Department of Geography, Kyung Hee University, Seoul 02447, Korea)

Abstract

As the volume of spatial data has rapidly increased over the last several decades, there is a growing concern about missing and incomplete observations that may result in biased conclusions. Several recent studies have reported that machine learning techniques can more efficiently address this limitation in emerging data sets than conventional interpolation approaches, such as inverse distance weighting and kriging. However, most existing studies focus on data from environmental sciences; so, further evaluations are required to assess their strengths and limitations for socioeconomic data, such as house price data. In this study, we conducted a comparative analysis of four commonly used methods: neural networks, random forests, inverse distance weighting, and kriging. We applied these methods to the real estate transaction data of Seoul, South Korea, and demonstrated how the values of the houses at which no transactions are recorded could be predicted. Our empirical analysis suggested that the neural networks and random forests can provide a more accurate estimation than the interpolation counterparts. Of the two machine learning techniques, the results from a random forest model were slightly better than those from a neural network model. However, the neural network appeared to be more sensitive to the amount of training data, implying that it has the potential to outperform the other methods when there are sufficient data available for training.

Suggested Citation

  • Jeonghyeon Kim & Youngho Lee & Myeong-Hun Lee & Seong-Yun Hong, 2022. "A Comparative Study of Machine Learning and Spatial Interpolation Methods for Predicting House Prices," Sustainability, MDPI, vol. 14(15), pages 1-14, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9056-:d:870230
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    References listed on IDEAS

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    1. Jose M. Montero & Beatriz Larraz, 2011. "Interpolation methods for geographical data: Housing and commercial establishment markets," Journal of Real Estate Research, American Real Estate Society, vol. 33(2), pages 233-244.
    2. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
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    1. Szentesi Silviu Gabriel & Pantea Mioara Florina & Trifan Vanina Adoriana & Mazuru Luminița Ioana & Szentesi Noemi Florina Gabriela, 2024. "Standardization of Regression Equation Parameters in the Case of Multiple Linear Regression for an Econometric Model Development to Determine the Price of Apartments," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 2344-2352.

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