The validation of house price value remains a critical task for scientific research as well as for practitioners. The following paper investigates this challenge by integrating textual-based information contained in real estate descriptions. More specifically, we show different approaches surrounding how to integrate verbal descriptions from real estate advertisements in an automated valuation model. By using Airbnb listing data, we address the proposed methods against a traditional hedonic-based approach, where we show that a neural network-based prediction model—featuring only information from verbal descriptions—are able to outperform a traditional hedonic-based model estimated with physical attributes, such as bathrooms or/and bedrooms. We also draw attention to techniques that allow for interrelations between physical, locational, and qualitative, text-based attributes. The results strongly suggest the integration of textual information, specifically modelled in a 2-stage model architecture in which the first model (recurrent long short-term memory network) outputs a probability distribution over price classifications, which is then used along with quantitative measurements in a stacked feed-forward neural network.