Data-Driven Asset Selling

Abstract

Online asset-selling businesses, such as used cars and real estate platforms, have experienced remarkable growth in recent years. Unlike general retail operations, which make decisions at the stock-keeping unit level, asset selling operates at the individual unit asset level. Practical operational constraints (e.g., infrequent price adjustments within a limited timeframe) set asset-selling platforms apart from general retail. Further complicating decision-making are real-world uncertainties, such as volatile demand and unknown latent value of the asset. We present a dynamic pricing framework that captures the salient characteristics of the asset-selling business while leveraging consumer online behavioral data to maximize the payoff of individual assets. We develop practical algorithms for solving the dynamic pricing problem, including a mean approximation (MA) algorithm that uses forecast mean values as proxies for future customer arrival rates and online learning algorithms that integrate learning of the latent value of an asset with dynamic pricing decisions. To evaluate these algorithms, we propose an asymptotic regime suitable for the online asset-selling business context, one that scales up customer demand arrival rate within a finite time horizon. The key findings are that, under mild conditions, the expected value of selling an asset is concave and increasing at a logarithmic rate with respect to demand rates and increasing no faster than a linear speed in the asset’s latent value. These properties allow us to derive the performance bounds of our policies. An extensive numerical study and a real-data calibrated case study demonstrate the practical value of our proposed algorithms, suggesting that those simple heuristics can achieve strong performance in the asset-selling environment. Moreover, our integration of the learning of an asset’s latent value with dynamic pricing decisions, alongside asymptotic analysis, provides a robust framework for data-driven decision-making and demonstrates the potential of consumer behavior data as a strategic asset for online asset-selling platforms.