The quality of a prediction model is evaluated through statistical metrics that capture different aspects of prediction accuracy. The most important metrics in the context of predictive analytics are R², MAPE, and MAE.

R² (coefficient of determination) indicates what proportion of price variation is explained by the model. An R² of over 90 percent means: the model captures over 90 percent of the systematic price differences. MAPE (Mean Absolute Percentage Error) measures the average percentage deviation between prediction and actual price. MAE (Mean Absolute Error) gives the average absolute deviation in euros. The combination of these metrics provides a differentiated picture: R² measures explanatory power, MAPE measures relative accuracy, and MAE measures the economic relevance of the deviation.