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New ways to measure tenant preferences


New ways to measure tenant preferences

Tenant preferences are usually measured using classic methods such as tenant surveys or evaluations of search subscriptions. Structures and changes can be read better and more precisely from the data of household-specific applications and effective rentals. In addition, completely new insights can be gained through so-called “Revealed Preferences”.

In the course of a joint blog post with Christian Kraft and Daniel Steffen from the Lucerne University of Applied Sciences, our insightLab made initial evaluations of the revealed preferences. The demand data comes from emonitor and was obtained from applications for first-time letting projects in the city of Zurich over the past three years.

Christoph Craviolini and Marius Wehrle, 31. March 2021

The provision of living space is becoming an ever greater challenge due to the changes and the existing social complexity in the competitive housing market. In order to do justice to this, owners, planners and the public sector should know exactly the locations and their residents as well as the demand on the market.

From a market perspective, the transparency of the preferences of apartment hunters leads to a more efficient allocation of living space. The following applies: Inquiring, observing and disclosing are basically the three possible approaches for measuring preferences. A detailed discussion of the survey methods can be found in the translated version of the original blog post.

The gold standard: data from specific rental processes

Data from effective application processes, such as those generated by emonitor, reveal the effective preferences, so-called “revealed preferences”, of those making inquiries in the housing market. Due to the potential review of the data through reference inquiries from landlords and employers, they are also of particularly high quality.

The data from specific letting processes also make it possible to analyze the “revealed preferences” according to population groups, income structures or age and to determine the mix, financial sustainability or density of use in a targeted manner. By comparing them with available apartments, the supply for individual population groups can also be observed. Any shortages or overhangs can thus be recognized early. The population structure of the surrounding perimeter can also be used to show deviations between existing population structures and thus to identify current developments at an early stage.

First analytics for the city of Zurich

The figure below gives a brief insight into the first evaluations of the application data in the vicinity of the city of Zurich. The demand data comes from emonitor and was obtained from applications for first-time rental projects over the past three years. You can find further evaluations in the original blog post on the real estate blog of the Lucerne University of Applied Sciences and Arts.

Beitragsbild HSLU
Figure 1: Preference regarding living space (in m2); Source: emonitor insightLab

Figure 1 shows the preferences regarding the living space (in m2). The darker the color, the greater the demand in a certain phase of life for a certain size of apartment. On the right-hand side, the proportion of the respective life phases in the total of applications is shown. There are clear differences in the preferences according to life phase. For example, most couples between 19 and 34 years of age are looking for an apartment with around 80m2, while couples between 35 and 59 years of age are looking for larger apartments with 100m2.

Demand monitoring

These first analysis’ show the considerable potential of application data for the analysis of socially differentiated tenant preferences and the monitoring of changes in demand.

The emonitor AG data pool is currently focused on Zurich, but is growing every day across Switzerland. Together with emonitor, the Lucerne University of Applied Sciences and Arts is planning to use this data to develop a demand monitor for systematic analysis and benchmarking at market level.

Are you interested in further information about this method or the underlying project? Then contact Christoph Craviolini or Daniel Steffen directly and read the original blog post on the real estate blog of the Lucerne University of Applied Sciences and Arts.

Christoph is one of the co-founders of emonitor. He is an expert in the field of urban development, housing and the housing market. Since his research at the Universities of Zurich and Fribourg and ETH Zurich, he has been fascinated by the topic of “housing”.


Christoph Craviolini, Co-Founder & Head of Data Analytics

On the first page of his PhD thesis in theoretical quantum dynamics one reads: 

«Every time I see a math word problem, it looks like this: If I have 10 ice cubes and you have 11 apples. How many pancakes will fit on the roof? Answer: Purple because aliens don’t wear hats.»


Dr. Marius Wehrle, Head of emonitor insightLab

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