In the previous post, Jonathan told you about the Connect Create 48 hour developer competition we were invited to be in at the Inman Connect conference last week. I have to say that we planned well and worked hard, and it turned out fantastic. I’m going to go through the why, what and how of the project.
We really wanted to do something different, something that would be useful, and something that no one has done. What we decided to do is an experiment in transparency. This is something the real estate industry may or may not be ready for, but I’ll bet the consumers are.
Let’s say I’m Joe. I have a home that I want to sell. I want top dollar, but I don’t want it to take 6 months to sell. So what do I do? Do I call the agent that has been sending me those nice pads of paper with her face on it, drive around the neighborhood to see who has the most for sale signs, search Google to see who has the best looking website or who is the best blogger, or do I talk to my friends to see who they used? All of those ideas sound okay to me, but how do I know any of these agents are good at what they do? I can interview them, but that won’t give me the complete picture on how good they really are. All that will tell me is how good of a personality they have and how well they can sell me on themselves. At this point, I, Joe, am really wishing that there was a way to easily find and evaluate an agent statistically the way employees or sports stars get evaluated.
This is where our experiment comes in.
Since we work with MLS data day in and day out for our IDX solutions, we that we should lead with our strength and show people an example of what we can do if there were no data use rules. We started with the data from one of the MLSs that we work with in southern California. And yes, we asked permission. We pulled in agent and office information, open houses, all information on the active, pending, withdrawn, canceled, expired, and sold listings over the last 10+ years, and all of the change history for every single one of those listings.
We then analyzed that data to create a number of algorithms that give us ranking statistics that we could use for each agent. Some of these statistics include the following:
• Popularity: How many homes have they sold in the last 180 day, 1 year, and 2 year periods?
• Salesmanship: What was the average number of days on the market
• Knowledge of market: How many times did they drop the price from the initial listing?
• Experience: How long have they been a member of the MLS?
• Ability to negotiate: How close was the last list price to the final sales price?
• Diligence: On average per listing, how many open houses do they have, how many photos do they load in, and how lengthy are their descriptions?
• … and more.
Enough of me telling you about it. Let me show you what we did.
Here I can search for agents that have had a transactions in a city or for a specific agent or office. Assuming I don’t know anyone out there, I am going to search for agents in Laguna Beach.
Up come results for the city. First off all of these agents have had at least one transaction in the city in the last 3 years
I’m sure you noticed the ASR Star Scores next to each agent. We thought it was important that there was an easy way to get an idea of who is statistically a better agent rather than throwing a bunch of number at them right off the bat. So we surveyed about two dozen agents, brokers, and home buyers to determine what the most important statistical factors were. We then weighted those factors appropriately when we assign a score to an agent in each area so that no one single factor can override all of the other factors. Finally, we used those factors to rank the agents and their transactions against each other so that their transactions in other areas or in other price ranges didn’t factor into the star ratings that are shown.
Once you find an agent who you want to know more about, you can simply click on their name and it will take you to their details page. Here you can view the statistics we were able to derive from analyzing their past performance. The details page also includes a list of their sold properties and the details of each.
Something else to keep in mind is that most agents are not good everywhere, so we included the ability to drill down into different cities, areas and tracts to see their specific score for those areas.
As you can see, the possibilities here are endless. This is just what we were able to accomplish is 48 hours. Imagine if we had even longer. We could add the ability for agents to claim their profile and add some additional information about themselves. We could also add the ability to save favorites and print our reports on the agents they like best. Or maybe we incorporate a user rating system too, like what HAR or Redfin has done. We could tailor it for multiple different purposes as well. Who knows, maybe there are some brokers that want to use it to evaluate their agents or MLSs that want to provide it to their membership?
In all reality this idea may or may not ever progress past the stage it’s in now. It all depends on interest of the right people. That’s not what this competition was about though. It was to have a good time, come up with something creative that solves a problem, and to hopefully inspire others to keep our industry moving forward.
According to the California Association of REALTORS’ most recent consumer study, some of the consumer’s top complaints are that the agent didn’t negotiate hard enough for them or that their house didn’t sell quick enough. Could providing this type of information to them help them find an agent that is best suited for their needs or possibly reduce their unrealistic expectations?