How Canada’s suburban dream became a debt-filled nightmare

Category: Best data-driven reporting (small and large newsrooms)

Country/area: Canada

Organisation: The Globe and Mail

Organisation size: Big

Publication date: 13/09/2019

Credit: Rachelle Younglai, Chen Wang, Matt Lundy, Murat Yükselir

Project description:

Canadian household debt is at a record high of $2.2-trillion, fueled by a decade-long boom in home-buying. That said, we know very little about who is most at risk. For this project, we aimed to find out where household debt is concentrated, and what it means for Canada. The Globe undertook a multi-month data investigation that examined every aspect of Canada’s most indebted households: from location and family size, to commute and age of neighbourhoods. In doing so, we not only upended existing narratives on household debt, but also pinpointed where early signs of a housing crash could bubble up.

Impact reached:

 For years, the prevailing narrative has been that Canada’s most financially vulnerable people live in downtown Toronto and Vancouver, where detached home prices have rocketed well into seven figures. Turns out that isn’t true.

The Globe and Mail obtained exclusive information from a data analytics firm on the 100 neighborhoods with the highest debt-service ratio, or the percentage of disposable income households used to pay the interest payments on their mortgages, car loans, credit cards and lines of credit. We were surprised by the results: In fact, it’s the commuter cities where people have taken on crushing debt to chase their homeownership dreams, despite those homes being less expensive. The data showed that 98 of the 100 most financially stressed neighborhoods were in the suburbs. 

From there, we undertook a census analysis to see what characteristics were common across these highly indebted neighborhoods. There were many. It wasn’t expensive heritage homes where people had deep financial burdens, but new subdivisions of starter homes that are traditionally better priced. Distressed households were considerably more likely to have larger household sizes, have multiple generations living under one roof, and commute to another city for work. In other words, home buyers have ventured further afield, to areas with scarce jobs, took on pricey car payments, and have rented out spare rooms to make ends meet.

This project — a major systematic look at household debt in the country — was published one month before the Canadian federal election, driving discussion on one of the most important political topics in Canada: home affordability. We offered Canadians valuable context through which to view parties’ pledges. The story stimulated heated discussions among our readers on social media, and a high level of engagement with more than 500 comments on our website. 

Techniques/technologies used:

The analysis part of the project was conducted by using a combination of Excel and programming language R. The Globe collected census profile data for the 100 most indebted neighborhoods and selected more than 200 metrics to trace out the characteristics shared by the neighborhoods. 

The analysis pointed us to clusters of neighbourhoods in three provinces — Ontario, British Columbia and Alberta — and enabled us to distinguish the possible root cause of their high level of indebtedness. We also compared those neighborhoods with respective census metropolitan areas they are located in, to understand if the characteristics are unique to the select neighborhoods or should be ascribed to broader regional reasons. Some of the findings coincided with anecdotes and theories before the start of the project and provided strong support for the reporting followed. 

We used CensusMapper, an API containing complete Canadain census data, and R to plot the communities other than the select ones, leveraging visualization to validate our metrics and build prototypes for our final presentation. The scrolling explainer was built with HTML and CSS. The static graphics were created in QGIS, polished in Adobe Illustrator and then converted into HTML and CSS, using ai2html. Satellite images from Google Earth were used to demonstrate the development of suburban homes in different parts of the country. 

What was the hardest part of this project?

The hardest part was the initial analysis, which needed to prove that high indebtedness was as intense and prevalent as our anecdotal evidence seemed to suggest. Data analysis helped identify key characteristics of neighbourhoods that seemed to contibute to high debt levels and revealed how those factors built up and made the lifestyle no longer sustainable for people in commuter cities, with a higher interest payment and stagnant incomes.  

The analysis also gave reporter Rachelle Younglai a better understanding of the situation and pointed her to the representative families and stakeholders to speak with, with questions on solid ground.   

This led to the other most difficult part of the project: finding real people in the neighbuorhoods who were wiling to share detailed financial information on the record. It was particularly challenging in a community like Brampton where English is not commonly spoken. Younglai interviewed a minimum of 30 homeowners in Brampton, Edmonton and Coquitlam, B.C. Many homeowners were interviewed multiple times for hours at a time. 

The combination of strong, robust data with real human stories was critical in making sure this story landed with maximum impact for our readers. For example, many readers commented on the subject in the lead of the story — it was a perfectly sourced anecdote that drove discussion all on its own.

What can others learn from this project?

A lot of extra value can come from combining disparate datasets: Our original data on indebted neighbourhoods was interesting, and could have been a smaller story in itself, but it was in combining this data with robust census and demographic information that the true insights began to emerge, and gave our reporter fresh insights that led to compelling sources and an unexpected narrative.

Additonally, a lesson we learned is to simply ask for data: many sources, companies and researchers are eager to share their data with trusted journalists, and that can form the basis of a great story.

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