Some Thoughts on Agent Reviews

Agent reviews are coming. Actually, they’ve been here for quite a while already, but haven’t reached the tipping point yet where consumers rely on them and the industry embraces them.

Some recent attempts to provide agent reviews have face significant pushback from the industry, including an attempt by to rank agents largely based on productivity. It makes sense that productivity, by itself, wouldn’t be the best measure for ranking agents. If that measure was used to rank restaurants, McDonald’s would be the top restaurant in many cities.

This debate reminded me of a conversation I had with a woman who worked for a real estate broker. She responded to e-leads by phone up to 10pm at night, qualified them, then connected them to an appropriate agent based on the information she learned from them in the e-lead and phone call. As you can probably imagine, her e-lead conversion rates were incredibly high due to her fast response rate and personal touch.

I think it’s safe to assume that she didn’t dole out each of those leads to the most productive person in her company. Instead, she connected those leads to the most relevant agent based on that prospect’s interests. The broker makes money if the deal closes, so it’s in her interest to make the best connection possible. How can this be done online?

Think about Yelp. When people use a service like Yelp to find a restaurant, what variables are they using to make a decision? Do they expect to see the restaurants that serve the most people at the top of the list? Probably not. Instead, people seem to be triangulating a combination of location, type of service, value, and availability. Yelp uses a combination of check-ins, quantity of reviews, and quality of reviews to rank restaurants. Notice that only 1/3 of that a direct measure of productivity (Check-ins. Granted, you’ll have more opportunities to gain more reviews if you serve more people).

Agent reviews, done well, will help narrow the gap between looking at lists of alphabetically sorted agents and what the woman described above does within her company. Systems that help prospective buyers & sellers identify agents who work in appropriate areas, types of real estate, styles of home, with an approach to real estate that syncs with the prospects personality should help both consumers and agents since it gives both more confidence that they’re not wasting each other’s time.

Where will these reviews reside? Aggregator sites? Yelp? Google? Broker sites? Other? Who’ll win the how and where questions is less clear than why, what, or when.

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  1. Jim Straughan October 9, 2014 at 3:20 pm

    Perhaps realtor reviews are coming perhaps not.
    However like any other internet based service they will be open to manipulation and thus meaningless for the most part.
    I think the writer is a little naive to think that the lead converter women would send the leads to the most relevant
    realtor in the office.
    Having worked for large franchises such as Sutton.Homelife,Century 21 and Realty Executives and small independant offices I can say from experience that all follow the same script .It is the friends and relatives and high producers that get the office leads not the most deserving realtor in terms of experience relative to the listing based on any criteria .
    Small town like Komoka or City office in London Ont it is the same!

  2. Ed Kohler April 21, 2015 at 12:55 pm

    @Jim, it looks like reviews have gotten some traction over the past year. Zillow’s a good example of that. While manipulation is a possibility, when I search Yelp for, say, the best brew pubs in Minneapolis, the results look like what I’d expect as somewhat of an expert in that subject. The only reason people would manipulate online reviews is because people do find them valuable. Which then leads to review sites policing reviews in order to maintain the credibility of their sites.

    I agree that politics play a role in dishing out leads. But, just because merit isn’t the sole factor doesn’t mean that a broker is going to risk giving a qualified lead to someone who can’t close a deal. Algorithms involving people can get complex, but are still better than nothing.