In The Netherlands the waiting times for social housing are long. Yet nobody knew how long exactly: somehow no part of the government keeps tabs on this. So we set out to find the exact waiting time for social housing, for every single one of the 355 municipalities in the country. We requested data from the 300+ social housing corporations in the country, to find that in at least a quarter of the municipalities you’ll need to wait for more than 7 years. In some parts of the country waiting times were as high as 20 years – or more.
Our investigation added to the national society wide debate about the current housing crisis, its consequences and possible solutions.
Following our addition to the debate the housing crisis was high on the agenda of the newly formed political coalition. Who, when presenting themselves merely weeks ago, installed a minister of housing as part of the Ministry of Interior and Kingdom Relations.
We published across different media – online news article, online interactive, radio reportage, tv-broadcast – on the last Saturday of april 2021. Come Monday the new insight on exact waiting times for every municipality dominated the frontpages of all newspapers – both national and regional, as well in local and regional broadcasts. As the national public news broadcast, our reporting reaches a great audience. Yet by sharing the outcomes and data with regional news outlets, our impact further increased.
Our results can be seen in an interactive that can be personalized on a municipality level, an explainer-video on Youtube, and on our site and newsapp, reaching over hunderds of thousands unique visitors. The articles were viewed 400.000+ times, the interactive has had 400.000+ unique visitors, and the YouTube video has 280.000+ unique views. Together with our television- and radiobroadcasts, which usually reach millions of people.
Most of our research consisted of classic journalistic slog e-mailing the 300+ corporations responsible for social housing throughout the country. Many of them did not answer our e-mails, so we ended up calling those corporations, explaining what we were trying to do and asking them for data.
Once corporations agreed, we had to get the numbers out of the files they sent us (PDF’s, Word documents, annual reports and emails) and into our spreadsheet. For data analysis purposes we mostly used Google Spreadsheets and Python Pandas – though most of it was spreadsheet based.
The Netherlands is made up of a multitude of municipalities and in most of them the social housing is dealt with by multiple corporations. We therefore needed to calculate weighted average waiting times, to make sure we had a good understanding of the situation in every municipality. (We used a threshold of 75% of all social housing in a municipality; if we had less than 75% of results for a given municipality, we showed no data.)
For our interactive we wanted to provide readers/visitors with more contextual data; these numbers, mostly open data, were also collected and edited in the spreadsheet.
The interactive was built using the Nuxt.js and designed with Adobe XD. For graphics Adobe illustrator was used, and After Effects for animations.
What was the hardest part of this project?
The hardest part of this project was the sheer number of unknown unknowns we had to overcome, and the vast number of sources needed to gain insight into waiting times for social housing.
The Netherlands is made up of a multitude of municipalities and in most of them the social housing is dealt with by multiple corporations.
Every organization had a slightly different definition of waiting time, active and passive. We ended up resolving this with personal contact – explaining our needs to many organizations.
What can others learn from this project?
It’s always worth asking simple questions even when people think the answer is obvious. Everyone “knew” that waiting for social housing was a matter of years and years, instead of months. Yet nobody knew exactly how long the wait was.
We found out why nobody knew soon enough: the information needed to get to an average waiting time on a municipality level was scattered among hundreds of organizations.
Second lesson for others – one story begets many more. By building an interactive website where users need to fill in their municipality, we ended up with 355 different stories: one story for every municipality. So instead of spending months apparently working on one production, think of it as time well spent telling 355 different stories.
And finally, note how this story did not start with a simple downloaded dataset. Some data-driven investigation require you to build your own dataset, e-mailing and calling hundreds of sources.