Analysing the Refurbished Smart Phone Market with Machine Learning

Authors : Berrin Beyza Özen, Muhammed Fatih Alaeddinoğlu, Tolga Aydın

Pages : 42-59

Doi:10.16951/trendbusecon.1607949

View : 176 | Download : 540

Publication Date : 2026-01-01

Article Type : Research Paper

Abstract :The refurbished smartphone market has recently attracted attention because of its economic and environmental benefits. In particular, rising environmental awareness and the search for cost-effective alternatives have increased demand for refurbished products. However, the dynamics of this market and its pricing practices differ from those of the new-device market. Price formation depends on several product-specific factors, including device condition and model. Yet, analysing this multi-factor structure and producing accurate price estimates remains challenging for consumers, sellers, and remanufacturers. In this context, machine learning can support high-accuracy price prediction. Developing feature-based price prediction models for refurbished smartphones helps to explain price fluctuations and to estimate a device’s value by considering usage and post-refurbishment condition. In this study, both traditional machine learning and deep learning methods are used to improve prediction accuracy. Model performance is evaluated using MSE, MAE, RMSE, and the R² score. The XGB Regressor achieved the best result among the traditional machine learning algorithms, with an R² of 0.9902. Among the deep learning models, LSTM also performed strongly, reaching an R² of 0.9870.
Keywords : Yenilenmiş Pazar, Akıllı Telefon Fiyatı, Regresyon, Fiyat Tahmini, Sürdürebilirlik

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