Research project: Housing Search in a Digitalized World
The internet has come to play an important role in housing search. Particularly online housing platforms have drastically changed the way in which individuals search for housing. This research project analyses buyer search and decision-making using novel big data from a large digital housing platform in the Netherlands. The use of novel big data makes it possible to empirically investigate housing search from a buyer perspective in more detail than before. It opens unique opportunities to empirically test to which extent prevailing housing search theories fit the new digital context. Importantly, the innovative nature of the big data makes it possible to relate online search behaviour to real market outcomes.
The current housing affordability issues and supply side shortages pose an important societal challenge. This innovative project provides a major contribution to the field by providing important new insights to the housing search literature and a better understanding of the housing market. The findings of the research project at the interface of housing economics and data science provide relevant societal benefits as it can contribute to market regulation, housing legislation, and housing policy.
The selected PhD candidate will have the opportunity to contribute to solutions to one of the most important current societal challenges and work on the key issues at the frontier of housing market research. The research findings based on unique big data will contribute to shaping the future research agenda and housing market policy.The position comprises the following tasks:
Conduct innovative research on the topic of online housing search and decision making;
- Write a PhD Thesis;
- Publish in academic journals;
- Present papers at international conferences;
- Actively disseminate the research findings;
- Organise and participate in seminars, workshops and conferences of the programme;
- Teach in the programmes of the Department of Economics;
- Follow PhD courses based on an individual training plan.