During a period of more than seven months, a team of over 25 investigative and data journalists and visualisations experts from 16 European countries, have been investigating the big players in Europe’s residential real estate. We found that the investments into rental flats increased with more than 700% between 2009 and 2020. We have visualised these trends and found that reports of negligence and abusive tactics by corporate landlords are consistent in the cities researched. We also found that often local governments don’t know much about the precise situation in their cities and the investment going into their housing markets.
Politicians, researchers, NGO’s, advocacy groups, and even real estate agents, architects and other housing professionals approached us forour methodology and data. Most notably, the Greens/EFA group at the European Parliament in preparation for a study about the financialisation of housing in Europe, demanding the introduction of transparency registries about corporate landlords. A Member of the Flemish Parliament currently working on this topic contacted our media partner Apache.
Politicians also took our research into parliament. In Norway, there were reactions from politicians at the local and national levels, and the Finance Minister had to answer a question about the topic in Parliament. In Spain,an MP mentioned one of the articles in Parliament.
Other colleagues were inspired by our research. The head of investigations at Al-Araby, a Qatar-based media outlet, contacted us when planning a similar research in Arab countries. A freelancer journalist in Atlanta, US, contacted us to tell us they are using our methodology as inspiration for research there. Dutch De Correspondent would like to follow up on our investigation and methodology to cover the situation in the Netherlands.
Our work was cited in news reports and academic papers. In Norway, several media outlets published editorials about our project (Morgenbladet , Avisa Nordland, Kapital). The project was mentioned in the academic paper, “The value of the city. Rent extraction, right to housing and conflicts for the use of urban space”.
The team members were invitedto talk about the research at (online) conferences and debates, among others Dataharvest – European Investigative Journalism Conference, VVOJ (Dutch-Flemish investigative journalism conference), or the Barcelona Housing and Renovation Forum. Our team members were interviewed for radio and podcast, notablyPUSHBACK talks with Fredrik Gertten and Leilani Farha, the former UN Special rapporteur on the right to adequate housing.
To collect the data we used multiple methods. Where scraping was needed, it was done with Python (requests & beautiful soup or selenium). Much information on the corporate landlords was sourced from yearly reports by a collaborative data collection in spreadsheet templates. Data analysis and cleaning was done mostly in Python (pandas) and excel.
Using open source software focused on the privacy of our data is important to us. The actual collections, as well as the collaborative spreadsheets were therefore hosted in a Next Cloud environment on a server administered by an Arena team member. Our regular meetings were done via open source video meeting software BigBlueButton.
For the publication of more than 30 interactive customizable graphics, we built a framework using d3, mapbox.js and reusable vanilla JS UI components.We had to accommodate a wide range of use cases from single iframes to pages with many graphics embedded directly in the article. We used code-splitting and on-demand loading of libraries to make sure to load as little code as possible while reusing code across graphics.
All graphics were translatable and customizable (colors, highlighting data points, headings and texts) in a purpose-build backend built with Django REST-API with a frontend built using Vue and a UI framework (Vuetify) was used for the backend.
Despite this backend, we made sure that the graphics could be hosted statically to ensure reliable hosting for all participating organizations.
We chose to stay with commonly used types of dataviz, to focus on details (such as adaptive legends for maps that take into account the data visible in the current view of the map) and a good localization (CMS embed, number formatting, idiosyncrasies such as “mil milliones” in Spanish, translatability even of the data, e.g. the transliteration of company names into the greek alphabet).
What was the hardest part of this project?
Finding all the relevant data was a big challenge, as the sector lacks transparency in ownership and consolidated data sets on the city level. Therefore, to build our datasets we had to use a wide range of techniques. Some corporate landlords publish the listings on their website – we have scraped those. Some publish amounts of apartments they own in their yearly reports – we have extracted this data manually. However, some hardly publish anything at all. We have collected dozens of reports on real estate and rental markets from various sources (consultancy companies, banks), but most of these did not cover the cities we needed. In the end, most of our data on the actual names and investments of corporate landlords on the city level from a lengthy negotiations with a private business that collects data on real estate markets (Real Capital Analytics). The context data proved to be equally tricky. Eurostat collects a lot of data that is comparable across countries, but little on the city level. As living conditions and demographics in cities differs from the average country data, we could not use country data. We have dug into all the local statistical offices for context data but not every data point was comparable across the different sources. Methodologies for even seemingly simple measures as average household income proved to be very tricky (median disposable, net disposable, gross disposable, per adult, …). It was even difficult to compare average rental prices. For example in Berlin, rent prices are including water. In the end we managed to make some of these data comparable. We found a dataset on cross-border comparable rental prices for our cities literally the day before the publication.
Translatability and context-specific adaptability of the shared visualisations was also a big challenge (see section above).
What can others learn from this project?
Two elements of this collaboration stand out. By making this reporting cross-border, we could capture an European trend on the local level. Tenants learn that they are not alone: they read about the abusive practices and other troubles they face in their own city, but they can compare to other tenants across Europe. This is crucial, since politicians on European level can step in with regulation measures. This contextualisation was highlighted by the shared visual language of all publications. The shared data visualizations made possible by the custom back-end built by the Innovation Lab at Tagesspiegel allowed for each graphic to be translated and adapted to match the language and the look and feel of the publication medium. As the back-end used the same data for all the graphics, even last minute changes in the source data were not a problem, as they would be if every newsroom would create their own graphics. We have also commissioned an illustrator to create a series of illustrations for all the articles. The colour scheme of the illustrations was also adaptable. – in harmony with the visualisations.
The second element was the post-publication knowledge directory (see at the “main link to your project”) we have created. Here we collected all the articles that were published within the project (68 up to date), and we also share our methodology, limitations of the available information, and a data catalogue. Here we describe for example the data availability and comparability, what to pay attention to, what are the problems we encountered, but also share links to resources elsewhere. As we were getting many questions about our methodology, this directory makes it easier for any journalist,s or researcher or student of journalism to pick up the topic and further develop the reporting and research themselves.