50 Most Influential Real Estate People on Twitter

Yesterday, Stefan Swanepoel published a list of 100 influential and interesting people within real estate. It’s an interesting list and got a few of us (myself included) a bunch more followers.

twitter-logo_000However, set aside for a minute that he missed a whole bunch of influential people (which he is already revising) the reality is that a lot of people on his list just aren’t that interesting (and many border on being twitter spammers).   If you’re a real estate professional new to twitter and you started following some of those people, I can only imagine twitter would start looking like a big wasteland of crappy tweets.

However, I think a list of influential people could be a really good thing, especially for people new to twitter…  I’ve had this idea for measuring “twitter influence” within a community, and Stefan’s project finally pushed me to build a prototype.   The idea is to measure, as objectively as possible, the influential people within a twitter community.

My theory and calculations are described below, but first, here’s the list:


Name

Twitter

Peer Rating
Andy Kaufman AndyKaufman 100%
Dustin Luther tyr 100%
Rudy Bachraty trulia 100%
Jeff Turner respres 100%
Teresa Boardman TBoard 100%
Kelley Koehler housechick 100%
Jay Thompson PhxREguy 100%
Daniel Rothamel RealEstateZebra 100%
Ginger Wilcox gingerw 100%
Robert Hahn robhahn 100%
Brad Nix bnix 98%
Jeff Corbett JeffX 98%
Heather Elias hthrflynn 98%
Nicole Nicolay nik_nik 98%
Mike Simonsen mikesimonsen 98%
Jeff Bernheisel JBern 98%
Joseph Ferrara jfsellsius 95%
Jonathan Washburn JonWashburn 95%
Pat Kitano pkitano 95%
Drew Meyers drewmeyers 95%
Marc Davison 1000wattmarc 95%
Jim Cronin RETomato 95%
Matt Fagioli MattFagioli 95%
Brad Coy BradCoy 95%
Mike Price mlbroadcast 95%
Nick Bostic nbostic 95%
Dan Green mortgagereports 95%
Kim Wood KimWood 95%
Todd Carpenter tcar 95%
Mike Mueller MikeMueller 95%
Sherry Chris BHGRE_Sherry 95%
Derek Overbey doverbey 95%
Ricardo Bueno Ribeezie 95%
Loren Nason lorennason 93%
Ines Hegedus-Garcia Ines 93%
Jim Duncan JimDuncan 93%
Jason Sandquist JasonSandquist 93%
Dale Chumbley DaleChumbley 93%
Missy Caulk missycaulk 93%
Kris Berg KrisBerg 93%
Brad Andersohn BradAndersohn 93%
Maureen Francis MaureenFrancis 93%
Lani Rosales LaniAR 93%
Stacey Harmon staceyharmon 93%
Bill Lublin billlublin 93%
Eric Stegemann EricStegemann 93%
Judy M realestatechick 93%
Joel McDonald joelrunner 93%
Reggie Nicolay Cyberhomes 93%
Morgan Brown morganb 91%
Mariana Wagner mizzle 91%
Paul Chaney pchaney 91%
Jim Marks jimmarks 91%
FrancesFlynn Thorsen FrancesFlynnTho 91%
Benn Rosales BennRosales 91%
Nick Bastian RailLife 91%

For those interested, here’s how I calculated the influential people within the real estate community.

Step 1: Starting with Stefan’s list, I took 10 people in real estate who were following between 100 and 1000 people AND had more than 1000 people following them.  My logic here is that I was looking for active twitter users (i.e. it’s hard to get over 1000 followers without being active) who pay attention to who they follow (i.e. they don’t “autofollow” or “mass” follow people).  I was explicitly *not* looking to start with a list of the most influential people, but rather use some thoughtful people within the community to jump start the process.   As you’ll hopefully see, the people don’t really matter much in terms of the final results, but here they are anyway: jburslem, RETomato, 1000wattmarc, robhahn, spencerrascoff, hthrflynn, JeffX, nbostic, PoppyD, ardelld. (note: Stefan’s list didn’t include enough people that matched my criteria, so I ended up grabbing a few people out of my twitter stream who did).

Step 2: Using the Twitter API, I created a list of ALL the people these 10 people are following.  At this point, everyone is just a number and I won’t see anyone’s twitter name until the very last step.

twitter #s

Step 3: I put all of these twitter IDs in a big list and used a pivot table to give me a count by ID #.

followeeAt this point, I have a pretty good list of people within the real estate space.  I think it’s pretty safe to say that if someone was “influential” (on Twitter) in real estate, then they’d be on the list of 4000+ people this process created… and most likely near the top since they’re likely being followed by this group if they’re influential.    However, it’s time to expand the scope way beyond these 10 people.

Step 4: Now I took EVERYONE who was being followed by at least 8 of those 10 people (45 total) and looked at ALL the people they followed.  Because some of these people were following thousands (sometimes tens of thousands!), this turned out to be a huge amount of data… although it all fit nicely in an excel spreadsheet, so I kept going.

Step 5: Starting with a base of people who were being followed in step 3 (4000+), I did a count to see how many times those people were being followed in the HUGE lists that were created in Step 4.  (The idea here is that if someone was “influential” they would have at least shown up in the 4000+ IDs that were generated in Step 3 and now I was just counting how many times they showed up within this list of 45 people)

Step 6: I then sorted this list and based on the number of followers that any given ID had, I gave it a “peer” ranking that is simply the total number of followers divided by 44.  A peer ranking of 100% means that out of the people created in Step 4, 44 were following that person. A ranking of 91% meant that 40 were following that person.

Step 7:  I sorted the list, used Twitter’s API to reverse lookup people’s usernames (and real names), and copy-and-pasted the results above.

It’s also worth noting that I *could* take this list further and displayed the “top 100″ or “top 200″, in which case we would have caught some great names that just didn’t make the cut (David Gibbons, Joel Burslem, Hilary March, Ben Martin, Susie Blackmon, Kevin Tomlinson, and Stefen Swanepoel come to mind), but I had to stop somewhere, so I decided to stop at 50 (although since 7 people tied for 50th, there’s actually 56 people on the list!).  Nonetheless, if there’s interest, it’d be pretty easy to expand the list…

Final thoughts

What I really like about this approach is that it’s completely determined by our real estate peers.  Like it or not, there’s no better indication of your twitter influence than the “vote” your peers give you when they follow you… and while a “total” follower count is meaningless in terms of influence within a group, if you look at the “influentials” in a relatively objective way (as I’ve done here) and track who they are following, the result is a very non-spammy, highly influential group of people within the real estate twitter community.