Neural Networks for Automated Valuation Prediction for Collectible Cards

Dominic Wood; Umer Sufan; Cesar Villamil; Dr Richard Wood

doi:10.18535/ijsrm/v12i10.ec04

Abstract

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.

References

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[refR-1] Wang, Z., Chen, Q., & Lee, S. J. (2023). Prediction of NFT Sale Price Fluctuations on OpenSea Using Machine Learning Approaches. Computers, Materials & Continua, 75(2).Google Scholar ↗

[refR-2] Khajanchi, A. (2003). Artificial neural networks: the next intelligence. USC, Technology Commercalization Alliance. http://www. usc. edu/org/techalliance/Anthology2003/Final_Khajanch. pdf.Google Scholar ↗

[refR-3] Duan, J. (2019). Financial system modeling using deep neural networks (DNNs) for effective risk assessment and prediction. Journal of the Franklin Institute, 356(8), 4716-4731.Google Scholar ↗

[refR-4] Teng, H. W., & Lee, M. (2019). Estimation procedures of using five alternative machine learning methods for predicting credit card default. Review of Pacific Basin Financial Markets and Policies, 22(03), 1950021.Google Scholar ↗

[refR-5] Wood, D., Sufan, U., Villamil, C., & Wood, R. (2024). Predicting Counterfeits from Smartphone Multi-Images Using Deep Learning. Valley International Journal Digital Library, 26474-26484.Google Scholar ↗

[refR-6] Dobrovolny, M., Soukal, I., Salamat, A., Cierniak-Emerych, A., & Krejcar, O. (2021). Forecasting of the Stock Price Using Recurrent Neural Network–Long Short-term Memory.Google Scholar ↗

[refR-7] Stahl, F., & Jordanov, I. (2012). An overview of the use of neural networks for data mining tasks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(3), 193-208.Google Scholar ↗

[refR-8] Gan, C., Limsombunchao, V., Clemes, M., & Weng, Y. (2005). Consumer choice prediction: Artificial neural networks versus logistic models.Google Scholar ↗

[refR-9] Wood, D., Sufan, U., Villamil, C., & Wood, R. (2024). Optical Image Processing using Eulerian Amplification. Valley International Journal Digital Library, 1484-1495.Google Scholar ↗

[refR-10] QUINTIN-JOHN, S. M. I. T. H., & Valverde, R. (2021). A perceptron based neural network data analytics architecture for the detection of fraud in credit card transactions in financial legacy systems. WSEAS Transactions on Systems and Control, 16.Google Scholar ↗

[refR-11] Kausar, M., Ishtiaq, M., & Hussain, S. (2021). Distributed agile patterns-using agile practices to solve offshore development issues. IEEE Access, 10, 8840-8854.Google Scholar ↗

[refR-12] Kausar, M., & Al-Yasiri, A. (2015, July). Distributed agile patterns for offshore software development. In 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE (p. 17).Google Scholar ↗

[refR-13] Kausar, M., Muhammad, A. W., Jabbar, R., & Ishtiaq, M. (2022). Key challenges of requirement change management in the context of global software development: systematic literature review. Pakistan Journal of Engineering and Applied Sciences.Google Scholar ↗

[refR-14] Vaithianathan, M. (2024). Real-Time Object Detection and Recognition in FPGA-Based Autonomous Driving Systems. International Journal of Computer Trends and Technology, 72(4), 145-152.Google Scholar ↗

[refR-15] Kausar, M., & Al-Yasiri, A. (2017). Using distributed agile patterns for supporting the requirements engineering process. Requirements Engineering for Service and Cloud Computing, 291-316.Google Scholar ↗

[refR-16] Cena, J., & Harry, A. (2024). Blockchain-Based Solutions for Privacy-Preserving Authentication and Authorization in Networks.Google Scholar ↗

[refR-17] Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2023). Comparative Study of FPGA and GPU for High-Performance Computing and AI. ESP International Journal of Advancements in Computational Technology (ESP-IJACT), 1(1), 37-46.Google Scholar ↗

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[refR-19] Vaithianathan, M., Patil, M., Ng, S. F., & Udkar, S. (2024). Integrating AI and Machine Learning with UVM in Semiconductor Design. ESP International Journal of Advancements in Computational Technology (ESP-IJACT) Volume, 2, 37-51.Google Scholar ↗

[refR-20] Kausar, M. (2018). Distributed agile patterns: an approach to facilitate agile adoption in offshore software development. University of Salford (United Kingdom).Google Scholar ↗

[refR-21] Seenivasan, D., & Vaithianathan, M. Real-Time Adaptation: Change Data Capture in Modern Computer Architecture.Google Scholar ↗

[refR-22] Mazhar, N., & Kausar, M. (2023). Rational Coordination in Cognitive Agents: A Decision-Theoretic Approach Using ERMM. IEEE Access.Google Scholar ↗

[refR-23] Wang, J. (2021). Impact of mobile payment on e-commerce operations in different business scenarios under cloud computing environment. International Journal of System Assurance Engineering and Management, 12(4), 776-789.Google Scholar ↗

[refR-24] Wang, J., & Zheng, G. (2020). Research on E-commerce Talents Training in Higher Vocational Education under New Business Background. INTI JOURNAL, 2020(5).Google Scholar ↗

[refR-25] Xiao, G., Lin, Y., Lin, H., Dai, M., Chen, L., Jiang, X., ... & Zhang, W. (2022). Bioinspired self-assembled Fe/Cu-phenolic building blocks of hierarchical porous biomass-derived carbon aerogels for enhanced electrocatalytic oxygen reduction. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 648, 128932.Google Scholar ↗

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