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

Jennifer Priefer Author-1-Name-First: Jennifer Author-1-Name-Last: Priefer (), Jan-Peter Kucklick Author-2-Name-First: Jan-Peter Author-2-Name-Last: Kucklick (), Daniel Beverungen Author-3-Name-First: Daniel Author-3-Name-Last: Beverungen () and Oliver Müller Author-3-Name-First: Oliver Author-3-Name-Last: Müller ()
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Jennifer Priefer Author-1-Name-First: Jennifer Author-1-Name-Last: Priefer: Paderborn University
Jan-Peter Kucklick Author-2-Name-First: Jan-Peter Author-2-Name-Last: Kucklick: Paderborn University
Daniel Beverungen Author-3-Name-First: Daniel Author-3-Name-Last: Beverungen: Paderborn University
Oliver Müller Author-3-Name-First: Oliver Author-3-Name-Last: Müller: Paderborn University

No 138, 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, such as houses, remains challenging due to information asymmetries that obscure their qualities. Beyond search qualities that sellers can identify before a purchase, complex goods also possess experience qualities only identifiable ex-post. While research has discussed how information asymmetries cause market failure, it remains unclear how information systems can account for search and experience qualities of complex goods to enable their pricing in online markets. In a machine learning-based study, we quantify their predictive power for online real estate pricing, using geographic information systems and computer vision to incorporate spatial and image data into price prediction. We find that leveraging these secondary use data can transform some experience qualities into search qualities, increasing predictive power by up to 15.4%. We conclude that spatial and image data can provide valuable resources for improving price predictions for 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: C53 D82 R31 (search for similar items in EconPapers)
Pages: 48
Date: 2025-05
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