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Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal

Author

Listed:
  • Jan-Peter Kucklick

    (Paderborn University)

  • Jennifer Priefer

    (Paderborn University)

  • Daniel Beverungen

    (Paderborn University)

  • Oliver Müller

    (Paderborn University)

Abstract

Information systems have proven their value in facilitating pricing decisions. Still, predicting prices for complex goods remains challenging due to information asymmetries. Beyond Search qualities that sellers can identify ex-ante of a purchase, these goods possess Experience qualities only identifiable ex-post. While research has discussed how information asymmetries cause market failure, it remains unclear what benefits Search and Experience qualities offer for information systems that enable pricing on online markets. In a Machine Learning-based study, we quantify their predictive power for online real estate pricing. We use Geographic Information Systems and Computer Vision to incorporate spatial and image data into a Machine Learning algorithm for price prediction. We find that these secondary use data can transform Experience qualities to Search qualities, increasing the predictive power by up to 15.4%. Our results suggest that secondary use data can provide valuable resources for improving the predictive power of pricing complex goods.

Suggested Citation

  • Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023. "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations 112, Paderborn University, Faculty of Business Administration and Economics.
    • Handle: RePEc:pdn:dispap:112
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    information asymmetries; real estate appraisal; SEC theory; Machine Learning; Geographic Information Systems; Computer Vision;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis
    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General

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