Helsinki city subsidizes some apartment buyers trough a housing price control system called Hitas. But no one, not even the city itself, really knows who lives in these apartments. In this ground-breaking piece HS acquired and analysed multiple data sets and found out that almost 1 out of 5 apartments are most likely used against their intended purpose. The investigation also found out that average Hitas buyers are richer than average working people and showed how Hitas apartments really are significantly cheaper than those on the open market.
The legitimacy of Hitas system has been widely disputed throughout years. However, before our investigation no one really knew who exactly benefited from the system, i.e. who owned or lived in these subsidized apartments. Our investigation showed that almost 1 out of 5 apartments are likely used against their intended purpose. The apartments are rented out, not used as the owner’s own aparment as intended. Also, our prior investigation showed how Hitas regulations are often bypassed, and one person owns more than one Hitas apartment.
Immediately after the publication of the story the deputy mayor of Helsinki thanked HS for the investigation and acknowledged that the city will use the findings of the story to help the city to revamp the Hitas system.
Eight months later, in November 2020, the city council decided to terminate the whole Hitas system in two years and replace it with a fairer system.
Our goal was to show these unique and complex findings with couple of easy to understand, informative scrolly tell charts while simultaneously highlighting the most important findings in writing.
We acquired four kinds of data sets: one from Helsinki city that listed all the owners of the Hitas apartments. Secondly, we bought two sets of address data to be able to check where the owners actually live. Thirdly, we sourced manually public tax records and found out how much the owners of the Hitas apartments earn. Fourthly, using Python we scraped an online housing market service to get price information about new apartments around Helsinki.
We compiled and analysed these data sets using both Python and Excel.
What was the hardest part of this project?
The hardest part was that we were on completely new terrain: no one had done anything like this before. We knew what we wanted to do: we wanted to investigate if Hitas system served the purpose it was created for. The hardest part was to come up with a solid and feasible plan: what kind of data we could get and how that data could help us answer our question. The analysis process was burdensome too, but after we had carefully made a plan, the analysis phase was just hard work to be done.
What can others learn from this project?
The power of combining different data sets to reveal information not known before, and the importance of maiking a solid plan before diving into analysis.