The course builds upon the latest developments in urban economics and data analytics, with application to housing real estate.
The first part of the course deals with the housing demand and discusses the following questions:
How to figure out empirically how much people are willing to pay for different attributes of a dwelling and the location? How can housing managers use this information? Do population groups differ in their housing preferences? How to forecast future residential demand in a location?
The second part of the course deals with housing supply, housing market dynamics and policies. Following questions are discussed:
How do housing demand and supply interact in dynamics? Where is new construction the most profitable? What are the effects of the policies restricting new construction? How does gentrification work?
In both parts of the course different empirical techniques are explained and discussed that allow to answer the questions mentioned before. These include: hedonic price models, multiple regression analysis, regression discontinuity, quasi-experiment, etc.
The course makes use of active learning. In weekly tutorials, students present and discuss with each other high quality applied articles that use big data to solve real world problems connected to housing markets. Furthermore, students perform a practical data assignment, to get a hands-on experience with applying the learned techniques to real housing market data.