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mortgage overload

Leanne Poda, 42, with her kids in their Calgary home.Todd Korol/The Globe and Mail

Canadian housing prices have soared since the Great Recession.

The result? Many families are forced to pony up substantial sums in order to fulfill their home ownership dreams, leaving some house poor and vulnerable to financial shocks.

How pervasive is the problem? It's a question we've probed in this Globe and Mail project – and in doing so, we've uncovered several stories of Canadians feeling pinched by hefty mortgages.

But first, we needed data.

To that end, in late October we published the Real Life Ratio calculator, developed by Globe personal finance columnist Rob Carrick with input from accredited financial planners. The tool tells readers what percentage of their take-home pay is being used for key monthly expenditures (eg. mortgages, car leases, daycare, etc.) and ultimately, whether they're financially sound or overextended.

We also used the tool to collect reader data to inform our stories. (Don't worry: This was disclosed in the preamble to the calculator. Reader participation was strictly voluntary.)

The response was overwhelming. More than 50,000 submissions flooded in within a few days of calculator being published.

After eliminating anonymous and duplicate submissions, we narrowed the responses down to 1,318.

Here's what we found out:

  • The average RLR score was 60.53. Thus, on average, our respondents were spending 60.53 of their after-tax monthly household income on key expenses, leaving nearly 40 per cent for other costs.
  • The median RLR score was roughly the same at 60.12. For what it’s worth, the median RLR score for every submission – all 50,676 responses – was 60.91.
  • Nearly 17 per cent of our respondents were under extreme financial stress or on the cusp. Those in the extreme category have an RLR score of 86 and up (6.9 per cent of our respondents), while those on the threshold have an RLR score of 76 to 85 (10 per cent of our respondents).
  • The average monthly household take-home pay was $8,187. The median was $7,000.
  • The average monthly mortgage payment was $1,815, while total monthly housing costs – after accounting for insurance, utilities and other expenses – averaged a shade over $2,600. On the second measure, the median was $2,360.
  • Close to half of our respondents were in their thirties. Their average RLR score (61.8) was higher than those for other age brackets. Nearly one in five thirtysomething respondents were under extreme financial stress or on the threshold.
  • Nearly one-third of respondents were paying for daycare, at an average monthly rate of $975. Some were paying thousands on a monthly basis.

To arrive at these conclusions, we needed to clean up the data. Why? Because the data were very, very messy.

For one, any time a reader clicked "Submit" we collected their input. As we've learned from publishing previous tools, readers like to resubmit different data to assess other scenarios. This time was no exception.

Secondly, readers could submit their data anonymously. Thus, it was impossible to determine which submissions were coming from the same person.

However, some readers left additional personal details (name, e-mail address, personal details, etc.) if they were open to a Globe reporter contacting them for this project, or wanted the opportunity to receive financial advice from certified planners. This helped us immensely.

Here's how we filtered the responses:

  • We narrowed the data to those who left personal information: either an e-mail address or a name and hometown.
  • We eliminated responses with an RLR score of 100 and greater and 10 and under. (Responses at the margins typically signified an error or typo was made by the user.)
  • We limited the respondents to self-described homeowners (rather than prospective buyers) who make monthly mortgage payments (ie. not homeowners who have already paid off their mortgage).
  • For those who submitted multiple times, we took their first response.

We narrowed the responses significantly, but in doing so, the remaining data were of much higher quality, giving us a stronger foundation for our reporting.

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