An AI model learns the relationships between object characteristics and actually achieved prices from historical transaction data. These include properties such as age, condition, market segment, and historical price development. The model detects patterns hidden in the mass of data and derives a price prediction for new, not yet valued objects.

The quality of the prediction depends on the data foundation: the more historical transactions are available and the more consistent the data is, the more precise the prediction becomes. In this project, 10 years of transaction data with thousands of orders per day were available. This breadth and depth enabled a model precise enough for binding price commitments.