Agent Ace: Using Algorithms To Help You Find A Real Estate Agent

Story by Benjamin F. Kuo


In the age old world of real estate, it's often referrals, friends, door to door visits, and sometimes shopping carts which help connect home buyers and sellers with local real estate agents. However, Los Angeles-based Agent Ace ( thinks is has a much better way to find the best real estate agent for you: software algorithms. We spoke with Mazen Fawaz, founder and CEO of the startup, to learn why the company thinks it can do a much better job of finding you a real estate agent, by digging through home sales data.

How does Agent Ace work?

Mazen Fawaz: We match homebuyers and sellers to the highest performing real estate agents for their transaction. That match is made mathematically. We mine historical home sales data, years and years of data, and determine who is the highest performer for a given type of house. We look at things like price range and geography, and type of house, and look at all agents who have done similar transactions and determine who has the highest probability of success. In other words, we help you find the agent who will sell your house the fastest and for the most money, based on their stats. It's much like the stats on a baseball player.

Why do you need a service like this, rather than the traditional process of asking around for recommendations?

Mazen Fawz: The result is really an economic impact on your deal. These people do sell homes faster, and sell them for more money. They often have exponentially more experience in whatever you want to tackle. If you want to buy a house at between $800,00 and $1 million in Venice, there is a guy who has represented more buyers and has more success than anybody else. It's a function of experience. What some of these successful agents do, is they work in a niche and are highly specialized. We can read that in the data. It gives you lower risk, and you have less risk of buying the wrong house, or paying too much. On the sales side, you can sell your home faster and for more money. The person your mother's hairdresser knows just can't qualify. They might mean well, but they are not really qualified to judge agent performance.

What's your background, and how did you get into this? Mazen Fawaz: My background early on was in the energy trade. However, I started developing commercial real estate, and in doing so was hiring lots of real estate agents to help me acquire properties. I would go through twenty of them, before I found one that knew what they were doing, and even then I was barely satisfied. I wish I had some data and had some background, so I could find the agents who has the relevant experience, and find historically the guys who had done deals like I wanted to do. I didn't want to have to teach them how to do what I wanted, I needed someone with experience. So, I started looking for that data, but found out the transactional data in commercial real estate is not very good. I stumbled into residential real estate, and found that there are excellent historical records there, which are very solid, and the number of transactions is massive. There are five million transactions a year in the U.S. recorded in the residential market.

How do agents react to your service?

Mazen Fawaz: They love it. The only agents we connect with, are the ones who we find through our data. A customer comes in, tells us what they want to do, that they ant to buy a house for this much money or sell this house. We come up with the top three agents, and reach out to the first one. We don't have a relationship with that agent, and what we are doing is delivering a deal to them in their sweet spot, in their geographic are, in their price range, with the type of home and type of deal that is perfect for them. We've gotten a very positive reaction, and we have lots of agents who are doing things like sending us flowers, champagne, and all sorts of stuff. It's a very happy situation. The customer is very happy, the agent is very happy. We're growing very quickly because it's working out for every party involved.

What's the business model here for you?

Mazen Fawaz: We connect a customer and agent. If a deal ensues , and if a closing comes from the effort, we participate in the commission. We're only paid if there's a closing, that's how our interests are aligned. We want to find the agent who will most likely sell for the most money, and we are only paid a percentage of the agent's commission.

Can you make a business from those referral fees?

Mazen Fawaz: In real estate, referral fees have a very specific connotation. We actually are participating in the representation of the customer, which is less common. Referrals are super common, and broker to broker referrals happen all the time. We'll do that in certain cases, but we also participate in the representation of the customer, to make suer that the agent is responsible. We're in the deal as a licensed broker, and we're in forty four states now.

Speaking of markets, what is your strategy for geographic coverage?

Mazen Fawaz: We're growing very, very rapidly both geographically and in terms of revenue. The multiplier for all of that is geographic coverage. We are operational in about 45 percent of the U.S., and will be in about 75 percent in the next sixty days. By Q1 of next year, we expect that to be about 90 percent. That's not an easy task. It requires sort of an arduous administrative and licensing process, as well as extracting data in all of those markets. It's not one data feed, it's many, many data feeds, all of which have to be dealth with. There are many data systems, which are disparate, and you have to speak different languages to operate with those, so we can take all that data and combine it all into one. That's been a very length administrative and technology process. We've been growing our team quickly, to deal with that new data as it is added to our pile.

What are your next big plans, and what's your big challenge now?

Mazen Fawaz: We are growing very, very quickly. The response to our service is not necessarily surprising, although it's early. However, we are growing revenue much faster than expected. We're also growing from the demand perspective, which is sooner than we expected, so we're trying to keep up in terms of hiring programmers, engineers, and growing the team. That's our biggest challenge, for sure. We're hiring a world class team to expand our coverage nationally, and instead of offering this service to hundreds of people a month, offering it to thousands and thousands. The goal is really to build the team to handle the demand we're experiencing right now.



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