The present work is the second in aseries of studies in which we are going to present an unbiased picture on the attractivity of universities. In doing so we confined our study to Hungary, where we have access to all annual application data of students to universities and colleges. Our first study presented an unbiased one-dimensional preference list of higher educational institutions, schools and study programs alongside a bunch of methods to produce such preference lists. In the present work we report on the first results of the second stage of our project in which we investigate students’ choice of further studies. Our database contains more than a million application entries, covers student scores, place of residence, and GDP per capita and employment data of their regions of residence. Similar economic data have been collected about the institutions as well as their indicators of academic excellence. We incorporated into the database the distance between students’ places of residence and colleges as well. Classical and novel econometric methods are used from logistic regression and gravity models to neural networks. The study reveals some common patterns of students’ choices and striking differences between different fields of studies. Among other results it has beenfound that the most preferred place of study is selected with much care while descending on the preference list the choice is less and less sophisticated. To the best of our knowledge this article is one of the few attempts to analyse the behaviour of student mobility: an estimation of the quantitative direct impact of several determinants for student flows.