Rapidly rising rents are creating financial strain for tenants, driving out small businesses and local restaurants, and weakening social cohesion. To counter these developments, cities and municipalities need meaningful data that enables science-based policymaking. In a new case study led by Dr. Fabian Braesemann, Associated Researcher at the Einstein Center Digital Future and researcher at the University of Oxford, a team of researchers examines how housing preferences in Vienna shifted during the COVID-19 pandemic—and what other cities can learn from this to keep housing affordable.

The study was recently published in the journal PLOS One. Under the title "How have urban housing preferences developed in response to the COVID-19 pandemic? A case study of Vienna", the research team—including scientists from the ECDF and economists from the Austrian think tank Agenda Austria—analyzes the impact of the pandemic on rent levels in Vienna. Their work shows how data can help cities better understand and manage the dynamics of urban housing markets. The qualitative and quantitative analysis is based on more than 120,000 listings from one of Austria’s largest housing platforms. The researchers identify three key developments that are also relevant for cities like Berlin or Munich:

Crises reshape housing preferences
The pandemic changed how tenants evaluate housing: instead of central location and public transport access, features like a separate home office, balconies, or multiple bathrooms have become more important. As the home became a workplace, new demands emerged—reflected clearly in rent trends.

Cities need structural diversity
Vienna was better able to meet increased demand for larger homes with outdoor space through development in its outer districts. The result: while rents rose in those areas, prices in the city center remained relatively stable. This dynamic highlights the importance of a diverse urban structure for resilience to external shocks like a pandemic.

Real-time data enables responsive action
The study shows that understanding rental dynamics requires high-resolution, spatially detailed data—average rent levels alone are not enough. Real-time data analysis provides cities with a crucial basis for evidence-based housing policy. To respond effectively to changing demand, urban planning tools must work both in the short and long term. This means that data-driven urban development needs to become a routine part of housing policy—as both an early warning system and a strategic guide. At the same time, the researchers caution against overestimating short-term trends: not every pandemic-related effect will have lasting impact. What's needed, they argue, is a combination of technological tools, social housing, and smart regulation—not isolated, piecemeal measures.

What does this mean for cities in Germany and beyond?
“Urban housing markets are complex systems,” says Fabian Braesemann. “If we want to understand and actively shape rent developments, we need data-driven insights—and the ability to flexibly adjust policies to shifting preferences.” The researchers recommend strengthening digital capacities within city administrations, building partnerships with platform providers to improve data access, and favoring targeted support measures over blanket interventions like rent caps. Another key focus should be on social housing and cooperative housing models, which can help ensure stable and fair rent levels in the long run—not through market distortion, but through social innovation and targeted support for especially burdened households.