Germany’s housing market saw a strong boom between 2009 and 2018. While rising prices were visible across the country, one trend stood out: the price gaps between different places grew sharply. Even within single cities or regions, house prices started moving apart—sometimes dramatically. Why did that happen? What made some neighborhoods so much more expensive than others?
This is the question that researchers Leo Kaas, Georgi Kocharkov, and Nicolas Syrichas set out to explore. They looked closely at Germany’s housing boom to understand the causes behind the increasing spatial dispersion of house prices—meaning the growing differences in prices from one location to another.
To do this, they analyzed data from ImmobilienScout24, Germany’s largest housing platform. This gave them detailed information on residential property listings over ten years: prices, housing features, time on the market, and the number of buyer contacts. Importantly, the authors adjusted prices for inflation and housing quality, so they could focus on real differences—not just general market growth or changes in the types of homes being sold.
The results showed a clear pattern: house price differences increased substantially over time, and this trend came entirely from growing differences between postal codes, not within them. In other words, while prices within individual neighborhoods remained relatively stable in relation to one another, the price gap between neighborhoods—and between regions—widened.
To understand why, the authors built and calibrated a housing market model. It included three core components: demand (how much buyers are willing to pay in each area), supply (the value of homes offered by sellers), and search frictions (how easily buyers and sellers find each other, based on factors like market transparency or responsiveness).
They then used the model to break down the observed price increases and growing price dispersion. This approach allowed them to isolate the effects of each factor and assess their importance.
The findings were striking. Demand turned out to be the key driver. It explained 64 percent of the overall rise in house prices nationwide, and 76 percent in Germany’s seven largest metropolitan areas. When it came to the widening of price differences between locations, the role of demand was even stronger—accounting for 98 percent of the increase.
By contrast, supply played a more limited role. It contributed just 16 percent of the national price increase and was mainly relevant in regions where construction activity expanded in response to weaker demand. Search frictions—the difficulties buyers and sellers face in connecting—had only a minor impact on prices overall and a slightly dampening effect on regional price gaps.
To validate the results from their model against outside evidence, the researchers compared their model-based estimates of housing demand and supply to actual demand and supply indicators in different regions. They found that buyer demand in the model lined up with factors such as higher incomes, better credit access, denser populations, and proximity to city centers—areas where people typically want to live. On the seller side, the values in the model corresponded closely with land prices and construction activity. In areas where land was expensive or where construction was limited—either due to space constraints or regulation—sellers tended to place higher values on the homes they were offering. This meant the model didn’t just fit the data mechanically; it reflected real economic differences between regions and helped explain why housing prices evolved differently across the country.
So, what are the implications for policy? The study suggests that if rising demand is concentrated in specific urban areas, then housing supply needs to grow especially in those areas, not just nationally. National averages can hide major local pressures. Measures like simplifying building regulations, increasing land availability in cities, or encouraging higher-density housing could help ease the strain where it’s most acute.
Furthermore, since search frictions had little impact on the rise in spatial price differences, improving real estate platforms or market transparency, though helpful in general, will not address the root of the problem. It is not about how people search, but about where people want to live and the limited housing available there.
In short, this study shows that the housing boom wasn’t just about rising prices across the board—it was about where those prices rose, and why. And if policies aim to reduce inequality in the housing market, they must respond to the geographic concentration of demand.
About the Authors
Leo Kaas
Professor of Macroeconomics at Goethe University Frankfurt. His research focuses on labor markets, housing markets, and the development of macroeconomic models that reflect market frictions.
Georgi Kocharkov
Senior Economist at the European Central Bank and member of the Bulgarian Council for Economic Analyses. He specializes in Macroeconomics, Public Economics and Household Finance.
Nicolas Syrichas
Postdoctoral Fellow at the Free University of Berlin and a member of the Berlin School of Economics. His research focuses broadly on macroeconomic questions which he investigates using a mix of quantitative models and large-scale spatial datasets (Big Data).
