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Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices

Author

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  • Yunjong Kim

    (Financial Supervisory Service, 38, Yeoui-daero, Yeongdeungpo-gu, Seoul 07321, Korea
    These authors contributed equally to this work.)

  • Seungwoo Choi

    (Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
    These authors contributed equally to this work.)

  • Mun Yong Yi

    (Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

Abstract

In this paper, we propose a novel procedure designed to apply comparable sales method to the automated price estimation of real estates, in particular, that of apartments. Apartments are the most popular residential housing type in Korea. The price of a single apartment is influenced by many factors, making it hard to estimate accurately. Moreover, as an apartment is purchased for living, with a sizable amount of money, it is mostly traded infrequently. Thus, its past transaction price may not be particularly helpful to the estimation after a certain period of time. For these reasons, the up-to-date price of an apartment is commonly estimated by certified appraisers, who typically rely on comparable sales method (CSM). CSM requires comparable properties to be identified and used as references in estimating the current price of the property in question. In this research, we develop a procedure to systematically apply this procedure to the automated estimation of apartment prices and assess its applicability using nine years’ real transaction data from the capital city and the most-populated province in South Korea and multiple scenarios designed to reflect the conditions of low and high fluctuations of housing prices. The results from extensive evaluations show that the proposed approach is superior to the traditional approach of relying on real estate professionals and also to the baseline machine learning approach.

Suggested Citation

  • Yunjong Kim & Seungwoo Choi & Mun Yong Yi, 2020. "Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices," Sustainability, MDPI, vol. 12(14), pages 1-19, July.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5679-:d:384722
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    References listed on IDEAS

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    Cited by:

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    3. Jungsun Kim & Jaewoong Won & Hyeongsoon Kim & Joonghyeok Heo, 2021. "Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    4. Elena Bykowa & Maria Skachkova & Ivan Raguzin & Irina Dyachkova & Maxim Boltov, 2022. "Automation of Negative Infrastructural Externalities Assessment Methods to Determine the Cost of Land Resources Based on the Development of a “Thin Client” Model," Sustainability, MDPI, vol. 14(15), pages 1-29, July.
    5. Moritz Stang & Bastian Krämer & Cathrine Nagl & Wolfgang Schäfers, 2023. "From human business to machine learning—methods for automating real estate appraisals and their practical implications [Vom Vergleichswertverfahren zum maschinellen Lernen – Methoden zur automatisi," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 9(2), pages 81-108, October.
    6. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.

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