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How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market

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  • Potrawa, Tomasz
  • Tetereva, Anastasija

Abstract

Understanding the customers’ perception of the value of constituent characteristics of a good is among the key questions in any pricing strategy. Hedonic pricing allows such an analysis and is frequently applied in economic fields. Although it is regarded as a benchmark in its original form, the availability of new data sources and the development of machine learning techniques created a space for further improvement. In this study, we propose a general framework for applying machine learning tools to enhance the hedonic pricing model in several directions. We do this, first, by adding image and text sources to conventional data and then by applying an advanced nonparametric prediction model. Lastly, we use model agnostic analysis to uncover new pricing factors and unravel complex relationships that could not be captured by conventional models.

Suggested Citation

  • Potrawa, Tomasz & Tetereva, Anastasija, 2022. "How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market," Journal of Business Research, Elsevier, vol. 144(C), pages 50-65.
    • Handle: RePEc:eee:jbrese:v:144:y:2022:i:c:p:50-65
    DOI: 10.1016/j.jbusres.2022.01.027
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    2. Ye Ye & Baichen Jiang & Binyao Ning & Xinjean Lim & Lijia Hu, 2023. "Does Price Matter in Mainland China? Examine the Factors Influencing Broiler Chicken Purchase Intention," Sustainability, MDPI, vol. 15(4), pages 1-16, February.
    3. Ou, Yunbei & Bailey, Nick & McArthur, David Philip & Zhao, Qunshan, 2024. "The Price Premium of Residential Energy Performance Certificates: A Scoping Review of the European Literature," OSF Preprints bm9xk, Center for Open Science.
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    6. Cankaya, Burak & Topuz, Kazim & Delen, Dursun & Glassman, Aaron, 2023. "Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents," Omega, Elsevier, vol. 120(C).

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