New Method Sharpens Canada's Open Housing Data

By Steven Pearce, Cross-Econ

June 4, 2026 7:30 AM EST

3 min read

Most Canadian local housing data exists in vast national database platforms, requiring significant navigation, time, and expertise to surface anything meaningful locally. Instead of one national database, Cross-Econ created an accessible bilingual webpage for every community that instantly displays relevant local housing data. It includes powerful data tools to support rapidly comparing trends across communities, time periods, and housing categories. That required carefully adapting national housing data, which is complicated by month-to-month noise, fundamentally different variable types, and inconsistent reporting periods across communities.

Interval and Aggregation Controls

The first goal was to make it easy to view housing data not just at the monthly level, but also aggregated to quarterly and annual intervals. Many housing variables are noisy month-to-month, and quarterly or annual views reveal trends more clearly. Alongside the interval controls, users can toggle between two aggregation modes: SUM, which shows the total activity within a period, and Monthly Average (M. AVG), which distributes that total into a comparable monthly rate by quarter and year.

Flow vs. Stock Variables

The more fundamental challenge is that not all housing variables are the same type of measure. Variables like starts, completions, and absorbed units are flow variables. They measure activity within a period, so summing them across months or quarters is meaningful. Variables like units under construction and unabsorbed inventory are stock variables. They represent a count at a point in time, not activity within a period. Summing stock variables across periods would count the same housing units multiple times, which is not meaningful. For this reason, stock variables are restricted to monthly average calculations regardless of which aggregation button is selected.

Quarterly vs. Monthly Reporters

The last major challenge is that the data source reports most smaller municipalities quarterly rather than monthly. Monthly values were conditionally calculated from quarterly observations. For flow variables at the monthly interval, the quarterly total is divided by 3 and distributed across each month of the quarter, converting the quarterly total into an estimated monthly rate. For stock variables at the monthly interval, the quarterly observation is simply carried across all three months of the quarter unchanged, since dividing a point-in-time count by 3 would be incorrect.

All of this conditional logic is hopefully made clear through the chart title, legend subheadings, tooltip labels, and inline notes. Please comment any questions or suggestions for improvements on our LinkedIn posts or at community@cross-econ.ca.

Logic Summary by Variable Type, Reporter Frequency, Aggregation, and Interval

Monthly reporters, flow variables (starts, completions, absorbed units):

• M + SUM: raw monthly value
• M + M.AVG: raw monthly value
• Q + SUM: sum of 3 monthly values
• Q + M.AVG: average of 3 monthly values
• 1YR + SUM: sum of 12 monthly values
• 1YR + M.AVG: average of 12 monthly values

Monthly reporters, stock variables (under construction, unabsorbed inventory):

• M: raw monthly value
• Q: average of 3 monthly snapshots
• 1YR: average of 12 monthly snapshots
• SUM is restricted, only M.AVG is shown

Quarterly reporters, flow variables:

• M + SUM: quarterly value divided by 3, extended across all 3 months
• M + M.AVG: quarterly value divided by 3, extended across all 3 months
• Q + SUM: quarterly value (single observation)
• Q + M.AVG: quarterly value divided by 3
• 1YR + SUM: sum of 4 quarterly values
• 1YR + M.AVG: sum of 4 quarterly values divided by 12

Quarterly reporters, stock variables:

• M: quarterly observation carried across all 3 months as is
• Q: quarterly observation as is
• 1YR: average of 4 quarterly snapshots
• SUM is restricted, only M.AVG is shown

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