A Weighted Average Price to Better Measure Price Variation
When measuring, in aggregate, the price of all residential
properties in a given area and for a given period of time, it
is important to remember that the price is always
influenced by the distribution of transactions in terms of
both geography and property category. But more than the
average price itself, real estate market analysts are more
interested in knowing the rate of change in average price
between two periods of time. Thus, when we measure the
evolution of property prices from one period to another, we
cannot be sure that we’re correctly measuring variations in
price if the distribution of sales is not identical between the
two periods. If this is not the case, variations in price may
be solely attributable to a change in the distribution of
sales. The QFREB has therefore developed a simple
calculation that can prevent such changes from distorting
variations in price.
Regional Disparities in Property Prices
The geographic distribution of sales is particularly
important as prices can differ significantly in the different
geographic areas. For example, with property prices being
much higher in British Columbia than in Québec, it is
evident that the share of total sales concluded in each of
these two provinces will influence the national average. For
example, a decrease in sales in British Columbia and an
increase in sale in Québec would have a downward
influence on the average price of Canadian properties.
Now, let us consider an example that is specific to Québec.
Let’s calculate the price variation between January 2009
and January 2010, supposing that in January 2009, 500
transactions were concluded in the Montréal Census
Metropolitan Area (CMA) at an average price of $250,000
and 500 sales were concluded in the rest of the province at
an average price of $150,000. The provincial average price
would be $200,000. Now suppose that in January 2010,
the average price does not change, neither in Montréal nor
in the rest of the province. However, this time, 600
transactions were concluded in the Montréal area and 400
in the rest of the province. The provincial average price
then rises to $210,000i. We therefore obtain a price
variation of 5%, which leads us to conclude, incorrectly,
that prices have increased in the province. However, the
increase is due solely to the fact that the Montréal share of
total provincial sales rose from 50% to 60%.
To get around this problem, the QFREB calculates a
weighted average price, which consists of maintaining fixed
weightings that are representative of the proportion of sales
concluded in each CMA (Gatineau, Montréal, Québec City,
Saguenay, Sherbrooke and Trois-Rivières). In other words,
each CMA always carries the same weight in the
calculation of the provincial average price. The weightings
were determined based on each region’s share of Centris®
transactions among the provincial total, based on a
reference periodii. The period we used goes from 2008 to
2012 inclusively.
Three Property Categories, Three Different Weights
Within each CMA, the average property price is also
influenced by the breakdown of sales among single-family
homes, condominiums and plexes (2-5 dwellings). When
the proportion of these property categories change
between two periods, the price variation will be affected. A
change in the market share of condominiums is a good
example. Let’s take the case of the Québec City
metropolitan area, where the proportion of condominium
sales increased from just under 15% in 2000 to 24% in
2012. Because condominiums generally sell for less than
single-family homes and plexes, the increase in the
proportion of condominiums has pulled the average price
downward in the Québec City area over the years. To
correct this situation, within each of the six metropolitan
areas, we have also applied a fixed weighting for each
property category. Once again, these reflect each property
category’s share of Centris® transactions among total
residential sales in its area during the period of 2008 to
2012. These weightings are applied to the entire series,
making it easier to compare the different periods.
Comparing Apples with Apples
In summary, for each metropolitan area, we obtain a
weighted average price by using fixed shares for each
property category. We then calculate a weighted average
price for the whole province by assigning a fixed weighting
to each metropolitan area.
This way, changes in average price between two periods
better reflect of reality. The benefit of using the weighted
average price is that it ensures that price variations are not
simply due to a change in the makeup or geographic
distribution of salesiii.
(600 x $250,000)+(400 x $150,000)
i
$210,000 =
ii
It is as though the distribution of sales for each month reflects that of the reference period.
1,000
It is also a similar approach to the one used by Statistics Canada to measure inflation, by tracking the evolution of prices of a fixed basket of consumer
goods over time.
iii