The Experiment

Being disadvantaged at the housing market is real for many people. But is it possible to prove the existence of discrimination in the rental housing market and, if so, how strong is it? Are there differences due to gender and origin of the applicant? Does it matter in which city you are looking for an apartment to rent? Those were the questions we asked ourselves at the beginning of the story. To answer them, we sent more than 20,000 applications to approximately 7,000 apartment advertisements in an automated process and evaluated the received responses. Here we document in detail how we constructed our investigation and calculated the results.


1 Every flat is contacted by one German and two foreign profiles.

2 The texts are almost identical: Clear, friendly and written in flawless German

3 The apartments are located in ten German cities and comparable in size and price.

4 The landlord gets all the requests and has to decide, whom he sends an invitation to a house viewing.

5 The answer is either positive, negative or neutral. And sometimes, the answer is just spam.

6 We are classifying the answers and matching them to the according flat.

7 In the end, every test person has received a number of answers. From these we are calculating the discrimination rate.

Fictitious persons

In order to find out whether people looking for apartments are discriminated because of their name and associated migration background, we have constructed the following fictitious persons:

Name Age Origin Sex
Hanna Berg 27 German female
Nina Weiss 26 German female
Stephan Braun 26 German male
Daniel Buschle 27 German male
Aylin Demirci 27 Turkish female
Hamit Yilmaz 26 Turkish male
Milena Adamowicz 27 Polish female
Mikolaj Janowski 26 Polish male
Maryam Abedini 26 Arabic female
Ismail Hamed 27 Arabic male
Vittoria di Lauro 26 Italian female
Stefano Loguercio 27 Italian male
Carsten Meier 42 German male
Lovis Kuhn 25 German male

Turkish, Italians and Polish are the largest groups of immigrants in Germany. In addition, persons of Arab origin were chosen as they are supposed to be particularly exposed in times of migration crisis. Otherwise, all our fictitious persons had more or less identical characteristics. They are 26 to 27 years old and first-time employees in marketing - a large professional field for young academics.

In order to record, how positive or negative the feedback can be besides , we have created two more profiles. On the one hand: Carsten Meier, a doctor in his early 40s, single, with an exemplary cover letter - the ideal tenant. On the other hand Lovis Kuhn, long-term student. His cover letter is informal, casually written.

Selection of apartments

The housing supply in Germany is extremely diverse. We put our focus on situations happening thousands of times day to day. An average tenant is looking for an ordinary apartment. He or she is not looking for a penthouse or a car-free housing project, but simply an affordable apartment with 30 to 60 m2 of living space. As demanded by the steadily growing number of single households.

In order to have a broad selection of suitable apartments, we have scraped the two largest online platforms, ImmobilienScout24 and Immowelt. In June and September 2016, we responded twice a day to new housing advertisements in the cities of Berlin, Dresden, Dortmund, Frankfurt, Hamburg, Cologne, Leipzig, Magdeburg, Munich and Nuremberg.

In order to take account of the varying price levels on the housing markets in different cities, we used the 2014 IMX rental price index. In Dresden, for example, we have chosen apartments with a monthly rent between 150 and 496 euros per month, in more expensive Munich apartments in the range of 312 to 937 euros.


In the mornings and evenings we have selected up to ten of the newly exposed apartments per city and automatically filled in the respective contact forms on the renting portals. We used ten different cover letters that are similar in length and wording, but at the same time different enough to not arouse suspicion among the providers. For the above-described candidates with extreme profiles, we each used a particularly good and a particularly bad cover letter.

(Due to linguistic subtleties, we did not translate these original German texts)

Sehr geehrte Damen und Herren,

mein Name ist {Name}, ich bin 27 Jahre alt und suche eine Wohnung in {Stadt}. Ich bin ledig, arbeite seit kurzem als Marketing-Manager und kann ein geregeltes Einkommen nachweisen. Die von Ihnen angebotene Wohnung entspricht genau meinen Vorstellungen. Über eine Rückmeldung von Ihnen und einen Besichtigungstermin würde ich mich deshalb sehr freuen. Bei der Terminfindung richte ich mich gerne ganz nach Ihren Wünschen.

Mit freundlichen Grüßen



habe ihre Wohnungsanzeige gesehen und würde die Wohnung gerne anschauen. Geht das und wenn ja, wann? Ich bin 25, absolviere gerade ein Studium in Ethnologie und bin dringend auf der Suche nach einer neuen Bleibe. Würde mich freuen, wenn das klappt. Bitte melden sie sich.

Danke und Grüße

Lovis Kuhn

Sehr geehrte Damen und Herren,

mit großem Interesse habe ich ihre Wohnungsannonce gelesen, da mir das von Ihnen angebotene Objekt außerordentlich gut gefällt. Ich möchte mich zuerst einmal kurz vorstellen: Ich heiße Dr. Carsten Meier, bin 42 Jahre alt, alleinstehend und arbeite seit kurzem als Orthopäde in einer Privatpraxis in {Stadt}. Außerdem bin ich Nichtraucher und besitze keine Haustiere, an einem langfristigen Mietverhältnis ist mir sehr gelegen. Über die Einladung zu einer Wohnungsbesichtigung würde ich mich sehr freuen, zeitlich bin ich flexibel und kann mich ganz nach Ihrem Vorschlag richten. Gerne könnte ich Ihnen bei dieser Gelegenheit auch eine Schufa-Auskunft und meinen Verdienstnachweis vorlegen. Über eine Antwort von Ihnen würde ich mich sehr freuen.

Einstweilen verbleibe ich mit freundlichen Grüßen

Dr. Carsten Meier

The advertiser of the chosen apartments were contacted with one German and two foreign profiles of the same gender each. The requests were sent every 30 minutes. One out of ten flats was additionally contacted during the same period by the two candidates with extreme profiles.


Following the pattern just described, we sent a total of 20,728 applications. We then received 8377 mails from homeowners, administrators and brokers. We have stored these answers in a database and assigned them to the respective request and person in a first step. The assignment often succeeded automatically on behalf of the unique ID in the apartment advertisement or the address of the apartment. When assigning by hand, the time of the answer, the title of the apartment advertisement, the price or the name of the contact person were helpful for the allocation.

Subsequently, suspected housing frauds and spam were sorted out in conspicuous, recurring passages of text and the remaining emails were categorized by hand. In doing so, we have rated a reply email as positive that either includes an invitation to a viewing appointment or specifically offers such a prospect. All other responses, as well as non-responses, were considered negative.

Discrimination rate

Discrimination is most evident when our fictitious persons have received different answers when applying for the same flat. Every apartment successfully requested by a German and a non-German person can be classified in the following cross tabulation:

Positive foreign Negative foreign
Positive German pp pn
Negative German np nn

In addition to a preference of German applicants (pn), there are also cases in which only the person with a foreign name receives a positive answer (np). This can be a deliberate decision of the landlord. Or simply related to the fact that the German applicant wrote later and the landlord has not even read the request. In order not to overestimate the actual extent of discrimination against persons with a foreign name, these cases are also considered in the calculation of the discrimination rate:

discr = (pn - np) / (pp + pn + np)

So we relate cases of unequal treatment to the number of apartments whose landlords responded positively to at least one of our two requests. Cases in which none of our persons has received a commitment (nn), we do not take into account when determining the rate of discrimination. They rather show how tense the housing market is.

We back up the discrimination rate with a logistic regression to control factors such as the order in which cover letters were sent. The model confirms the results from the descriptive calculation. It also helps us to determine whether differences in the extent of discrimination against different subgroups (e.g. women and men) are statistically significant. This corresponds to a study by the empirical social science researcher Prof. Auspurg from the LMU Munich.

Share of positive feedback

In addition to the extent of discrimination, we also look at how often the considered groups received positive responses in total. And relate that to the total number of requests:

p = positive_answers / requests

The proportion of positive feedback (p) helps us classify the importance of individual factors when looking for an appartment. This shows that the prospects for positive feedback also vary with the gender of the applicant, the city and the organisational form of the provider (commercial or private).

There are different types of landlords. While some offer mass visits, others invite only a few candidates to a local meeting. Therefore, we also calculate a logistic regression with a random factor for each apartment. This model accounts for the possibly different response behaviors of different landlords.

Odds ratio

A regression model also forms the basis for calculating the odds ratio between Germans and applicants with foreign names. The chance of a positive feedback results from the proportion of positive feedback (p) as follows:

chance = p / (1 - p)

The odds ratio (r) between foreign and German applicants is thus:

r = chance_foreign / chance_German

It shows how difficult it is for people with foreign names to find an apartment compared to German applicants. According to this logic, the chances of success in the respective cities result from a logistic regression, in which an interaction factor between the city and the ascribed migration background of an applicant is explicitly modeled.

A detailed description of the analyses and the associated program code is linked here (German). You can also find the complete dataset in the corresponding repository on GitHub.

If you have any questions about our research, please contact us via e-mail or on Twitter (BR Data, SPIEGEL ONLINE).