Compass True North
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Compass True North

Likely-to-Sell Recommendations for Real Estate

Foster Provost, Panos Ipeirotis, Eda Kaplan, and Nate Rentmeester

Why Likely-to-Sell recommendations in CRM?

Why are CRM recommendations relevant to the business? Experienced real estate agents often tell us that they generate the majority of their new business via their social and professional relationships. However, consider this seeming paradox: The National Association of Realtors reported that agents get only about 30% of their business from repeat customers and referrals. Also, 90% of homebuyers claim that they would use their agent again or recommend their agent to others. However, it turns out that only a small minority do! “One of the major reasons homeowners do not rehire their real estate agent is that they simply cannot remember their agent’s name.” What’s more, 75% of recent sellers contacted only one agent before finding the agent they worked with to sell their home.

  1. Likely-to-sell recommendations directly address Compass’s strategic goal of helping our agents grow their business. Ideally, following our suggestion, the agent will reach out to the likely-to-sell contact; when the contact becomes ready to sell, they will not forget the relationship with our agent. And if the contact happens to be on the verge of wanting to sell right now, the agent may capture that sale immediately. (Read to the end for some success stories!)
  2. Providing automatic recommendations via the Compass tech platform was straightforward because the recommendations could be integrated seamlessly with the Compass CRM. The tech platform integrates the data on homes and homeowners, the AI algorithms, the agents’ contacts, and the user interface to make outreach smooth and efficient. Agents receive these recommendations as part of their standard workflow.
  3. We already had access to the data we needed to apply machine learning to “train” the AI models for estimating likelihood to sell, and (equally importantly) the data to evaluate the models.
  4. The data science work done for this product has significant “option value” — it can be leveraged for future products: seller likelihood models can improve prospecting, farming, targeted marketing, and other tasks.
  5. Individuals most likely to sell also may be among the most likely to refer other likely-to-sell customers (due to homophily — the social principle that birds of a feather do indeed flock together).
  6. The Likely-to-Sell CRM recommendations can kick-start a program introducing a variety of business-enhancing recommendations for regular communication between agents and their contacts. How about identifying contacts that currently rent and are likely to buy? Those who visit the Compass website and seem to be interested in moving? Individuals in an agent’s sphere of influence who provide the best referrals?

So, what exactly are these “AI-driven” Likely-to-Sell recommendations?

We make a Likely-to-Sell recommendation when one of an agent’s contacts is estimated (by our AI system) to be among the most likely individuals in the region to sell their home.

  1. the AI algorithm that estimates the likelihood-to-sell of any home, also known as “the model,”
  2. the decision-making system that uses the likelihood-to-sell estimations to choose contacts for recommendations, and
  3. the (separate) AI algorithm that creates the model from data — also known as “the machine learning system.”
  • Details about the property (bedrooms, bathrooms, square footage, etc.)
  • Time since the last sale, and frequency of past transactions for the property
  • Home value appreciation; home value compared to others in the neighborhood
  • Mortgage status and estimated equity held in the home
  • People movement data (percent of owners, renters, how often they move)

How are likelihood predictions turned into recommendations?

What is happening to create the recommendations?

  • Is the home one of the most-likely-to-sell in the local area?
  • What is the “base rate” of selling in the area?
  • Are the top-decile homes much more likely to sell than the average home?

Do these recommendations work?

Understanding the impact of likely-to-sell recommendations is a long game. Any individual recommended homeowner may not be ready to sell immediately, but starting work on strengthening the relationship with them now may pay off down the line. Furthermore, our agent may receive other sorts of value from the outreach — such as new referrals.

  • “I was in the CRM to look up a few clients to contact them when I noticed the likely to sell feature. I contacted a client that I was pretty sure didn’t need to sell her home. Just checking in with her since it had been awhile. Turns out, she does need to sell it! We will be listing her home. It’s a starter home and will be in demand when we come to market. We have several buyers looking for a home like this one. This is a great feature to help focus our efforts. Love it!” — Kristine Halverson
  • “I LOVE this feature. Just this past week, I reached out to one of the people that I was notified may be ready to sell. I reached out to her and will likely be listing her house in the coming weeks. I see no reason why this won’t help me win more business in the coming years!” — Todd Brunsvold
  • “I was a bit skeptical about this feature at first. The system shares some names with me and some make sense, while others, well, I thought — how can this be — I just sold them a house 3 years ago —so I did not really act upon it. And a couple of days later, low and behold this client calls asking to see a house. I was SHOCKED!!! We are now actively looking to find them a larger home and then sell their current home. I have no clue why the Compass System thought they were likely to sell, but now I am a believer!” — Sally Marcelli

Stay tuned for more!

We have a series of blog posts — some out already, some in the works — that will dig into our Likely-to-Sell recommendations in more detail and depth, for those of you who are interested in more of the AI/data science details. For example, the next post in the series presents the machine learning system that creates the AI algorithms (models) that estimate seller likelihood. We also will explain the comprehensive evaluation platform that allows us to improve the predictive models continuously and have confidence that the new-and-improved models will improve the recommendations. (For you AI aficionados: we will get into stuff that you won’t get in your typical machine learning class! For example, the machine learning post shows why different homes were estimated as being likely to sell.)



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The AI@Compass Team is building the AI to support the first end-to-end tech platform for real estate. This is our story.