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Reducing information asymmetry in used-car markets by using machine learning models
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Resumo(s)
Information asymmetry in used-car markets results from knowledge differences between buyers and sellers about used cars. Naturally, someone who owns a used car for a certain period, develops a deeper understanding of the real value opposed to someone who did not. The goal of this work is to attempt to reduce information asymmetry in used-car markets by using state-of-the-art machine learning models. With data provided by a Polish used-car online marketplace, a price range estimation as well as a point estimation will be made for every car. A Median Absolute Percentage Error of 7.86%and Target Zone of 58.38% are achieved.
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Palavras-chave
Information asymmetry Machine learning Price range estimation Used-car markets