Projects submitted to the Data Journalism Awards competition

Right here you will find a list of all the projects submitted to the Data Journalism Awards competition.  

No DSS: Most flat shares refuse benefits claimants
Country: United Kingdom
Organisation: BBC News Onine
Investigation of the year
Team Members
Daniel WainwrightPaul BradshawPete Sherlock
Project Description
With rising rents and a shortage of housing, the BBC England data unit uncovered “naked discrimination” against housing benefit claimants trying to find a house to share.Using a web-scraper to compile the data, we analysed over 11,000 separate listings on and found how landlords were more likely to accept people with dogs than they were “DSS” tenants.Just a few hundred across London and 18 other big cities were prepared to accept people on benefits.Aiming to raise an issue affecting an underserved audience, we also compared the situation with Ireland, which has seen changes to equality laws and spoke to a woman who had ended up having to lie to her landlady just to keep a roof over her and her son’s heads.Personalised data content was also provided via a look-up tool allowing people to see how affordable it was to rent in their area. We also visualised our data by way of a simple chart and infographic.The story furthered public understanding of the difficulties faced by people who are on benefits moving into private rented accommodation, in the wake of a shortage of social housing.It also reflected the situation faced by landlords, who find their insurance can bar them accepting people on housing benefit as well as accepting asylum seekers or students.
What makes this project innovative?
The technique used meant we gathered data that was effectively sitting hidden in plain sight to prove a point that had previously been made anecdotally, but was never supported by hard statistical evidence. We used Python to write a scraper that would pull in the data from adverts for tenants, picking out whether or not they specified "No DSS" - the term for excluding people on housing benefits and whether or not they were prepared to accept people with pets.
What was the impact of your project? How did you measure it?
The piece was read 600,000 times on the day of publication and picked up by other BBC News outlets.Engaged time on the story, measured using Chartbeat, showed people spent an average of over a minute which is well above the average for a news story.
Source and methodology
We wrote a scraping code in Python which then scrolled through adverts on and went through listings for various cities, stating whether or not they specified "no DSS" and/or "no pets". This enabled us to show that far more landlords were prepared to accept tenants with pets than they were those on housing benefit.The data was publicly available, but not in a format that anyone could analyse and easily interrogate until we scraped it and put it into a CSV.It was verified by manually checking samples at random in order to ensure the scraper had worked as planned.
Technologies Used
We used to write the scraper, in Python, and then downloaded the compiled data into a CSV in Excel. The data is hosted on GitHub.