Money Laundering in French Real Estate

Entry type: Single project

Country/area: France

Publishing organisation: L’Obs (one of the largest French magazines)

Organisation size: Big

Publication date: 2022-07-07

Language: French

Authors: Emmanuel Freudenthal, Yann Guégan, Coline Emmel, Youri Van der Weide with contributions by Karine Pfenniger


Emmanuel Freudenthal is a freelance investigative journalist based in Kenya.
Yann Guégan divides his time between freelance work and his position as head of editorial innovation at the political news outlet Contexte.
Coline Emmel is a journalist for Gotham City, a Swiss media dedicated to judicial watch and white-collar crime.
Youri van der Weide is an open-source researcher and trainer at Bellingcat
Karine Pfenniger was a freelance journalist and now works at Forbidden Stories

Project description:

By matching government datasets available online, our cross-border team of journalists uncovered nearly €750 million of properties purchased with shady money, from Paris to the French Riviera.

That is 196 suspicious real estate operations, carried out by 62 foreign individuals, including close family members of dictators (Cameroon, Azerbaijan, Togo…), known money launderers and Russian oligarchs.

Some sales involved French notaries with high-ranking positions in their profession’s accountability institutions.

The team expects this story will likely lead to court cases and prosecutions over the next few years.

Impact reached:

The story was published in the print summer 2022 edition of L’Obs magazine and mentioned on the front page. The magazine is printed to around 200,000 copies. The story was also picked up by French media such as France Inter radio.

The story was widely shared amongst law enforcement agencies. One agency specialised in money laundering prosecutions contacted us to ask us to explain our methodology (we had to turn down a private call for ethical reasons).

Other law enforcement officers showed interest in our work, acknowledging that the methods we used were sound and, sometimes, that they do not have enough resources to replicate them.

In addition, we are aware that several non-profits are planning court cases on the basis of our investigation.

Techniques/technologies used:

Our investigation was built on two government datasets. The first one, published by the French Ministry of Economy and Finance, lists all real estate properties owned by companies in France. It encompasses 16.1 million records. The second one, maintained by the French Institute for Intellectual Property, lists the shareholders and beneficial owners of the 11 million French companies. By combining those two datasets, we were able to find the names of people owning properties in France via a company, which added up to 2.48 millions names.

Nearly all these names were irrelevant because many French citizens own real estate via companies. So our next step was to find the needles in this haystack: the names of people who might be laundering money through French real estate. To do so, we used several datasets of “interesting” names, such as OpenSanctions, Wikidata and others. We then matched these names with our dataset of French property owners.

Eventually, we ended up with a few lists of several hundred names that we looked through by hand, searching the names on Google and weeding our false matches. We still ended up with a list of nearly 200 fairly well-known people who might be laundering money in French real estate. Ultimately, we focused on about 60 of them for practical reasons.

The last step of the research was to file administrative requests for each of the interesting properties that we had found. We made 81 requests to the “Service de la publicité foncière”, a rather obscure government office that keeps track of land ownership in every French department. For each property, the documents that we obtained included purchase price, whether a mortgage had been taken, the names of the sellers and buyers, and the names of the notaries involved in the sale.

Context about the project:

We used a French official dataset of company owners. At the time we were doing our research, this dataset included both shareholders and beneficial owners of french companies. Since 2021, French companies were indeed legally required to declare their beneficial owners. In November 2022, however, the Court of Justice of the European Union ruled that the general public should not be allowed to access registers of beneficial owners, in the name of privacy. As of 1st January 2023, the French government has removed the names of beneficial owners from the database that we had used. Although most of the people we uncovered through our research were direct shareholders of French companies rather than “hidden” beneficial owners, this change will severely limit future investigations like ours. For example, without beneficial ownership information, we would not have found the French properties of Zhanna Volkova, the ex-wife of Putin’s former son in law.

In terms of data analysis, our main challenge was that many people used longer names in official documents than the names registered in datasets of Politically Exposed People. For example “John Smith” and “John Edward Smith” would not lead to a good match if comparing each letter. We tried out several fuzzy matching algorithms before finding one that worked.

Another problem was that we initially thought that we’d get a couple dozen interesting names, at the most. So when we found nearly 200 of them, we were a bit overwhelmed. We realised that we would not be able to work on all of them within our budget so we had to focus on about 60 names. We also collaborated with journalists from the countries where the Politically Exposed Persons came from – although most of our colleagues preferred not to be named in the article, for security reasons.

There was also a high legal risk linked with publishing names of very powerful people while alleging that their investments in French real estate might be money laundering. We are very grateful to L’Obs for having our backs. Fortunately our story was not subjected to any lawsuit.

What can other journalists learn from this project?

This project demonstrated that it is possible to do investigative stories revealing corruption and money laundering by solely using government datasets and documents. This had never been done in France.

Our methodology is highly replicable in other countries where similar datasets exist, which would include much of Europe and dozens of countries around the world. Furthermore, the French datasets that we used are now updated every year, so it is possible to look for new owners every year.

Project links: