  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.
