Abstract We present an application preference, list-based framework to Hungarian universities, which allows different type of flexible aggregation, and hence, analysis and clustering of application data. A novel mathematical method is developed by which preference lists can be converted into scored rankings. The proposed approach is demonstrated in the case of Hungary covering the period of 2006–2015. Our method reveals that the efforts to leverage the geographical center–periphery differences did not fulfil the expectations of policy makers. Also, it turns out that a student's top preference is very difficult to influence, while recruiters may build their strategy on the information of the first but one choice.