Taking advantage of the richness of Danish register data, we examine the extent to which regulatory policies—such as rent control and waiting lists—lead to inefficient matches between households and housing. In other words, housing with specific characteristics may not be allocated to the households that value it the most, resulting in significant welfare losses.

Building on previous research from the U.S. and the Netherlands, we contribute to the current state-of-the-art methodology in several ways. First, we simplify the empirical approach by using linear demand models instead of non-linear ordered probit models, significantly reducing computational time. Second, we relax certain assumptions from previous methodologies to ease the computation of counterfactuals. Finally, our data enables us to analyze various housing market regulations in greater detail.

Overall, this project improves existing methodologies and provides new insights into the impact of market regulations on welfare loss. The findings are highly relevant to the ongoing academic and policy debate on the role, scope, and extent of public housing in the housing market.

UrbanLab has access to an extensive Dutch data set providing the exact locations of all Dutch shops for each year 2003-2024. With the Dutch data set we are in a unique position to study spatial clustering of shops both within shops of the same type and between shops of different types. 

In the project we will develop new incisive statistical point process methods to quantify the clustering. The obtained results will be used to investigate economic theories regarding industry clustering concerned with optimal location of shops considering competition, customer availability, logistics etc. 

More specifically we are developing so-called semi-parametric Markov point process models that enable modelling and assessment of interactions between shops while controlling for large scale sources of clustering such as population density and urban structure.