Sometimes, when evaluating size and scope of market, it’s useful to look at the volume of traffic for some bellwether sites. In the US real estate industry, those bellwethers would be Trulia, Zillow and Realtor.com.
Here are some graphs of their yearly traffic for 2012 as gathered from Compete.com’s SiteAnalytics product:
Realtor.com unique visits in 2012
Zillow.com unique visits in 2012
Trulia.com unique visits in 2012
Real estate listing aggregation sites, unique visit data comparison
Putting these bits of data into context with one another we can make the following graph and table to get a sense of consumer appetite for real estate listings in the US.
(yes, yes you should be able to click that graph and get a bigger version)
Also, here’s a Google Trends chart to give a sense of general trends on US search data during the same time period.
12 Months beginning Jan 2012 and ending Dec 2012
Data Source: Compete.com
|Leading real estate listing aggregation sites|
By tallying up the total unique visits that Compete estimates went to these three real estate sites we can arrive at a ballpark figure of 368,149,112 unique visits that are interested in real estate listing data. This number seems a little off when we take into account that the US Census Bureau estimates the 2012 population of the US to be 313,914,040.
So now is as good time as any to mention…
When dealing with data it’s always good practice to be open and honest about ways in which the data may be flawed. This helps everyone make better decisions and also helps improve the quality of data gathered in the future.
I don’t include these caveats to undermine the value of this data, rather I want everyone to have as few suspicions as possible about this data and what value it may have for them. It’s far more dangerous to consider data for which no caveats are mentioned.
Here are some caveats to consider when using this data.
This data may not mean what you think it means
Some of these unique visits may not be people interested in real estate listing data. For example, they could be real estate professionals checking to see if their advertising is running, or other people just randomly surfing the internet somehow.
Unique visitors are not people
In web analytics, a unique visitor does not equal an individual person. Instead, a unique visitor is an instance of a piece of hardware: a phone, a tablet, a computer. So, for example, one individual could access one of the sites using a phone, their computer at work, their laptop at a coffee shop and their desktop computer at home. This single individual would generate four “unique visits.”
I suspect that this accounts for much of the cognitive dissonance generated by comparing the total unique visits to the population of the US.
This data is generated via panels, not direct observation
Compete.com has a useful and documented methodology for how it generates traffic estimates. But it is panel-based and is therefore susceptible to all the same weaknesses of panels. For example, it could be that people interested in real estate listing information are over or under represented on the panels.
Aside from gaining access to each of the company’s analytics data, however, this issue is unlikely to be overcome.
I am a human being
It is entirely possible that I made an error somewhere along the way while gathering and presenting this data. I certainly did my best to check and double-check my work. But the fact remains that, as a human, I am fallible.