Abstract
A huge effort has already been made to prove the existence of housing market segments, as well as how to utilize them to improve valuation accuracy and gain knowledge about the inner structure of the entire superior housing market. Accordingly, many different methods on the topic have been explored, but no universal framework is yet known. The aim of this article is to review some previous studies on data-driven housing market segmentation methods with a focus on clustering methods and their ability to capture market segments with respect to the shape of clusters, fuzziness and hierarchical structure.