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

Jan-Peter Kucklick (), Jennifer Priefer (), Daniel Beverungen () and Oliver Müller ()
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Jan-Peter Kucklick: Paderborn University
Jennifer Priefer: Paderborn University
Daniel Beverungen: Paderborn University
Oliver Müller: Paderborn University

No 112, Working Papers Dissertations from Paderborn University, Faculty of Business Administration and Economics

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.

Keywords: information asymmetries, real estate appraisal; SEC theory; Machine Learning; Geographic Information Systems, Computer Vision (search for similar items in EconPapers)
JEL-codes: C45 R00 R32 (search for similar items in EconPapers)
Pages: 43
Date: 2023-06
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:pdn:dispap:112

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