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.
However, 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 |
|
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.
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 #.
At 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.







Teresa Boardman 5:45 am on September 21, 2009 Permalink |
Thanks for the weenie food. I love this post and I think I have about 4 posts for the weenie because of it.
Dustin 8:46 am on September 21, 2009 Permalink |
It’s about time for me to do something to earn weenie status…
Toby Boyce 5:58 am on September 21, 2009 Permalink |
What a level of work Dustin! I’m not surprised by the list of names, great people doing great things.
Dustin 8:48 am on September 21, 2009 Permalink |
I’m not really surprised by many of the names… but I think that’s the point. For people on the inside it should be a “no duh” moment when they see a list of 50 influential people. It’s really geared toward giving people new to real estate twitter a great list of people to follow!
Missy Caulk 6:30 am on September 21, 2009 Permalink |
Thanks Dustin I could never do stat’s like this. But, makes me smile.
Susie Blackmon 6:39 am on September 21, 2009 Permalink |
Wow, Stefan’s list has been fun to read about (and great to be included on as well).
Love your numbers method too Dustin. I, like many others, work hard to be respected among my peers, and am always most grateful for recognition of my efforts.
Thanks BTW for your assistance re the ‘IDX’ provider. Did get a call Friday, thanks to you, with a promise on an answer Friday but alas, heard nothing. Third time is the charm.
Have a great day.
Dustin 8:49 am on September 21, 2009 Permalink |
Susie… You rock! Despite the fact that those IDX guys need you more than you need them, it sounds like you might have to keep bugging them a bit!
Kris Berg 7:08 am on September 21, 2009 Permalink |
And you did all of this analysis when?
Very impressive, Dustin. Thanks for the mention, but at 93% I feel sort of like I cheated on a test. As for the 100 percenters, you nailed it.
Maureen Francis 7:18 am on September 21, 2009 Permalink |
Thanks Dustin! Your math brain is much bigger than mine.
That “follow all” button on Stefan’s list is sending my twitter notifications soaring…
Dustin 8:50 am on September 21, 2009 Permalink |
I’m definitely missing the “follow all” button, huh? Wouldn’t be hard to throw all these people in that same app, but I think I rather the people had to work a bit!
Dale Chumbley 12:15 am on September 22, 2009 Permalink |
Dustin, your list doesn’t need a “follow all” button since these are all people I already follow! Top class individuals!!! In fact, I’ve personally met, hung out with, enjoyed the company of 45 out of the 50.
Dustin 12:21 am on September 22, 2009 Permalink |
Interesting way to look at it… I can remember meeting all but 2 of the people on the list (I won’t name names since it’s quite possible I’ve met all of them!).
Dustin 7:21 am on September 21, 2009 Permalink |
You’all rock! No surprise it was actually a lot of fun to put together.
I woke up thinking I should try this out for other “communities.” I’m almost positive none of the other communities will be nearly as strong (in terms of having a large group of people that almost everyone in the group follows), but I think it might almost make the results for local areas and/or other topics just as interesting.
Randy Barnes 7:25 am on September 21, 2009 Permalink |
Ok – Im geeky and degree in Econ, but reading the process made me a little dizzy. Thanks for doing that for us as many of us can take this and run from here. Kudos. rb
Dustin 7:28 am on September 21, 2009 Permalink |
LOL! Every once in a while, the geek in me, definitely comes out.
Bill Lublin 7:34 am on September 21, 2009 Permalink |
Dustin- I knew you were smart, but this is very impressive – To create your own indices and correlate them so effectively is awe inspiring. Like Teresa (though without her wit and articulation) I see the stuff of new blog posts in this ground breaking effort – Thanks so much for this breakthrough!
Dustin 8:30 am on September 21, 2009 Permalink |
Let me know if you want to geek out a bit… I’d be happy to pass along the spreadsheet I used!
Nick Bastian 7:38 am on September 21, 2009 Permalink |
Dustin, you always amaze me with your work! Thanks so much for the mention. Influential? I’m not so sure. Having fun meeting a lot of great people? Yep.
ines 7:47 am on September 21, 2009 Permalink |
I am sitting here cracking up! don’t know if it has to do with trying to get your thought process or because you took the time to do this – LOL!!! You are the funniest!! ….and you know I *HEART* @tyr even if he is one of the geekiest people I know
Dustin 8:31 am on September 21, 2009 Permalink |
Anything I can do to make you smile…
BuzzBuzzHome 7:58 am on September 21, 2009 Permalink |
Great list, and really great to find 50 new people to follow up on in real estate!
I must admit, your methodology got a bit much to read but the just of it sounded good! Awesome that it was based on “recommendations by peers”.
Dustin 8:34 am on September 21, 2009 Permalink |
You did a better job summarizing it than I did! If I could get 2 sentences, it would probably be something like this:
I used a small set of people within a community to create a much larger list of all the “likely” influential people in a community. I then tracked who all the people in the “larger” list were following to create an “influence” or “rank” of all the top people within the community.
However, the result is almost exactly what you said… a list of people as “recommended by peers”.
BuzzBuzzHome 8:41 am on September 21, 2009 Permalink |
Then again, my only issue is that I don’t think people actually use Twitter to “listen” to other people. Once you are following over 500 people, it is hard to actually keep up-to-date on what people are talking about; then follow above 1,000 and it is really impossible.
I have switched how I use twitter to be based on the “search” button, and by directly going to the 5 or so people that I like to check out daily for some news.
As such, it becomes hard on Twitter to actually be “recommended by peers”. But, again, that is when you are following over 500…
Dustin 8:52 am on September 21, 2009 Permalink |
Not sure the number is 500, but I completely agree that once people follow too many it becomes nearly impossible to “listen” to people…
Nick Bostic 7:59 am on September 21, 2009 Permalink |
Thanks for considering me a “thoughtful person”, I always thought it just took me a long time to form sentences due to the hangover
From what I remember from my old college Decision Sciences classes, your method seems valid and is very well explained, thank you for the (typical) great work!
Dustin 8:38 am on September 21, 2009 Permalink |
yeah… There’s plenty of room for bias to creep in, but the methodology is good enough for the job!
The biggest factor is the initial 10 people I choose… But I’m pretty sure (although only testing would confirm) that as long as the 10 people fit the criteria I mentioned (i.e. following 100 to 1000 and being followed by 1000+) & were within the “real estate” community, then the results would be nearly the same.
Matt Fagioli 8:02 am on September 21, 2009 Permalink |
My apparently feable mind is now smoking; overheated in an attempt to follow that process! Dude, you crack me up.
Glad the @tyr supercomputer spit out @mattfagioli at the end somewhere
Dustin 8:38 am on September 21, 2009 Permalink |
Anything I can do to keep you mind smoking.
Frances Flynn Thorsen 8:22 am on September 21, 2009 Permalink |
Dustin, This is an extremely impressive calculation. Thanks for sharing the nuts and bolts of the process. Wow!
Dustin 8:45 am on September 21, 2009 Permalink |
Your “nuts and bolts” comment reminded me that I forgot to mention one more “sorting” issue.
After sorting for “peer rating”, the list is sorted by the time when the user signed up for Twitter. In other words, Andy was not only one of 10 people who scored 100%, but he was also the 1st out of that group to sign up for Twitter.
Probably more interesting than the 1st however, is the “last” person in that list because it shows that Rob Hahn was the last of the people to sign up on Twitter who has managed to get all of the 44 people from step 5 to follow him!
Jay Thompson 8:58 am on September 21, 2009 Permalink |
What’s the “spread” between when Andy signed up and when Rob did? And who’s the longest (I suspect Andy) and shortest tenured?
(Yeah, I could look it up, but sounds like you already have the data…)