Neural Networks for Automated Valuation Prediction for Collectible Cards
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This paper presents a novel approach to automated valuation prediction for collectible cards, specifically Pokémon cards, by leveraging recent advancements in neural networks, machine learning, and computer vision. Using a proprietary dataset from over 1.2 million online auctions between 2022 and 2024, we develop a convolutional neural network (CNN) to predict card prices based on both visual and textual information. Our method focuses on generating price predictions along with estimations of potential prediction errors. Results show that while machine learning-based valuations are more accurate than traditional hedonic models, they remain less precise compared to expert auction house estimates. The study underscores the potential of neural networks in valuation and the limitations posed by market dynamics and expert biases.
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Copyright (c) 2024 Dominic Wood, Umer Sufan, Cesar Villamil, Dr Richard Wood
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