The hedonic price model is a popular method to estimate the implicit prices of observed attributes of a property. However, the inputs to the model are only numerically quantified information. This study quantifies the unstructured qualitative statements contained in the written descriptions from the Multiple Listing Service (MLS) data. These statements contain unstructured text describing the features and setting of the house, providing important but typically unused qualitative information. Our approach is unique in that we use the qualitative information to classify these words into eight groups that reflect previously unmeasured housing quality. The purpose of the study is to test whether these previously unmeasured attributes of the property have an impact on the selling price of the property and its time on the market. The dataset consists of 5,160 home sales in Ames, Iowa between the second quarter of 2003 and the second quarter of 2015. Our findings show that the role of unstructured qualitative text varies; some are redundant to the quantitative information already in the models and have no effect, while others, particularly those reflecting the quality of the structure, represent unique information and are important predictors in determining housing prices and the time on market.