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The following is a guest post from Gabriel Anderson.  Gabriel has started a company called Revongo, and he’s working to bring a more efficient marketplace to the real estate industry.  

Gabriel is a maker.  He’s a maker who is trying to create something to improve the lives of the doers, to improve your life.  That’s just the type of thing we like to highlight here on  This is an intro to what Gabriel and his team have cooking, in his own words. We think it is worth your attention so check it out. . . 


retso robotBig data analytics has changed everything from the business of scouting for baseball talent (think “Moneyball”), to Wall Street trading ruled by algorithms by so-called quant funds, to getting you from New York to LA through an AI auto-pilot.

And soon, if I and my team have our way – we’ll use AI (artificial intelligence) to match the right consumer, to the right agent at the right price, and put clients on a path to you as simple as Fidelity’s “Turn Here” path.

Let’s not mince words… I want to free agents from having to be professional marketers, and get you back to the business of transacting.  You’re welcome.

In The Beginning

Back in October 2011, my co-founder and I got together on the phone.  We were both coming out of previous real estate ventures and looking to build and innovate something new… something that would change the world.

We were both at a point in our lives where we wanted to make a life… not just a living.  We wanted to “put a dent in the universe” and help push society forward.

Kind of a lofty goal… but if you’re gonna build a company, why not be audacious, right?

We discussed a couple of ideas, and really got excited about this notion of using data, AI (artificial intelligence) and ML  (machine learning) to build a platform of ready, willing and able sellers that were ready to transact, and matching them w/ real estate professionals that were the best candidates for that particular property.

Essentially we were talking about building a more efficient marketplace for the real estate business.

We had a couple of iterations, until we finally had the beginnings of an idea.

But an idea in and of itself is worthless… I wanted data.  So, we pulled a list of over 250 expired listings, newly listed properties and their listing agents and I literally cold called (i.e. – begged) people to interview them and see if we had the beginnings of a true business idea.

And the responses were right in line with our business assumptions:

  • 87.6% of consumers would like objective data when deciding who to hire to list and sell their property BUT it was not the deciding factor.  Clients also needed subjective client satisfaction scores, that gave them that “warm and fuzzy” feeling (Hint:  that was our first Eureka moment).
  • 71.6% of consumers would be comfortable using an agent recommendation tool using AI with relevant, public information to help them hire a real estate agent (after I explained what the hell AI was of course).
  • 82.4% of consumers were willing to pay a premium to their real estate agent, for specialized knowledge especially if that knowledge lead to the sale of their property for more money, better terms, and fewer days on market (shown through objective analysis).
  • 80% of real estate agents enjoy activities relating to transacting and doing real estate deals (including negotiations, showing properties, etc.) as opposed to activities related to prospecting for clients (including marketing strategies, listing appointments, etc.).

In God We Trust… Everyone Else Must Bring Data

When we first started looking at patterns in real estate agent data, we noticed 3 types of agents:

  1. Top Producers
  2. Up-and-Comers
  3. Lifestyle Agents

What was even more interesting though, is underneath that we found 361 micro-niches in the real estate agent business.

And this is a key find.  Smart AI looks at more than crude data points (avg sales price, avg DOM, SP/LP, etc.) but rather benchmarks them to the particulars of a specific property.

In other words, how many properties have you listed or represented buyers in, that are similar to the subject property?  And how were your metrics with those types of properties?

That’s good, clean, predictive AI.  That’s powerful data analytics!

If You Must Compete, Compete Intelligently

Real estate is an experiential business.  The more you transact the more knowledge you have and the more value you bring to each transaction.  Make sense?

So why does the agent that’s done 48 deals in the last 12 months earn the same as the agent that’s done 18 deals in the same time?

Holding all other things constant (I think I’ve addressed the specialization argument already), that makes no sense and it’s a losing proposition for everyone.

With solid and transparent data a more efficient marketplace is created.  One where clients can find exactly what they’re looking for, and pay for it.

The more you transact the more value you add to your clients and the more you’re compensated for this value.  It’s a winning proposition for everyone.

  • The client wins because they can make a clear distinction between price and quality – and decide what’s most important to them.
  • The real estate agent that has more experience wins because their experience is rewarded with more money.
  • And the agent with fewer transactions wins because they have a real way to compete on pricing, and build up that experience that garners a premium.

I’ll Show You Mine, If You Show Me Yours

So what does this product look like?

It’s a consumer facing platform, that agents have a portal to.  Let’s call it a consumer-facing Open-Source MLS if you will.

There are some tough challenges that need to be addressed, and we’re excited to tackle them.  Several that come to mind:

  1. Data Accuracy – GIGO (garbage in, garbage out) means we need agents to upload data directly.  But first and foremost, the previous data has to be cleaned up.  We’ll do it.  Without accurate data nothing works properly.
  2. The Consumer Revenue Model – The consumer needs to pay.  They should have the ability to access information for free, but for agent data, matching and other in-depth analysis they should pay for the service.  This eliminates the conflict of interest of having our revenue derived from agents.  Anytime the revenue and the customer are disconnected, funny things happen.
  3. Team Accounts, and Off-Market Transactions – because agents are uploading data directly (and it’s not being pulled from the MLS) things like what to do in the case of teams, or new developments, or trustee sales can be solved.

But we think we have some solutions to these problems, by building an independent platform.  It can’t be built on top of existing systems… we have to build from scratch.

A Big Enough “Why”

I started building this product with a specific person in mind.  The best agent I ever had was incredible.  She knew CC&R’s inside and out, had her law degree and knew contracts like the back of her hand, and could negotiate the hell out of a deal.

But she left the business in 2011, because as good as she was an agent she was horrible at marketing herself.  She thought just being a good Realtor was good enough.  She wasn’t good at door knocking, and she despised sending annual calendars.  She liked real estate, and loved her clients.

I built this company for her, and all the other kick-ass agents out there.  We’re going to make it happen. As God is my witness, I’m hell-bent on making it work.

To find out more you can check out our blog for updates, or visit us at


Photo courtesy of Lisa Archer.  Thanks Lisa!