Understanding the customers’ perception of the value of constituent characteristics of a good is among the key questions in any pricing strategy. Hedonic pricing allows such an analysis and is frequently applied in economic fields. Although it is regarded as a benchmark in its original form, the availability of new data sources and the development of machine learning techniques created a space for further improvement. In this study, we propose a general framework for applying machine learning tools to enhance the hedonic pricing model in several directions. We do this, first, by adding image and text sources to conventional data and then by applying an advanced nonparametric prediction model. Lastly, we use model agnostic analysis to uncover new pricing factors and unravel complex relationships that could not be captured by conventional models.