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

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.