ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
This addresses the need for trustworthy automated pricing in the used car market, benefiting buyers and sellers, but is incremental as it builds on existing methods by adding uncertainty quantification.
The paper tackles the problem of uncertainty quantification in used car pricing models by introducing ProbSAINT, which provides accurate point predictions comparable to state-of-the-art boosting techniques and principled uncertainty estimates, with experiments showing high accuracy on instances where the model is certain.
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics. With the recent surge in online marketplaces and increased demand for used cars, accurate pricing would benefit both buyers and sellers by ensuring fair transactions. However, the transition towards automated pricing algorithms using machine learning necessitates the comprehension of model uncertainties, specifically the ability to flag predictions that the model is unsure about. Although recent literature proposes the use of boosting algorithms or nearest neighbor-based approaches for swift and precise price predictions, encapsulating model uncertainties with such algorithms presents a complex challenge. We introduce ProbSAINT, a model that offers a principled approach for uncertainty quantification of its price predictions, along with accurate point predictions that are comparable to state-of-the-art boosting techniques. Furthermore, acknowledging that the business prefers pricing used cars based on the number of days the vehicle was listed for sale, we show how ProbSAINT can be used as a dynamic forecasting model for predicting price probabilities for different expected offer duration. Our experiments further indicate that ProbSAINT is especially accurate on instances where it is highly certain. This proves the applicability of its probabilistic predictions in real-world scenarios where trustworthiness is crucial.