AI Use-Cases for the Future of Real Estate

Raghav Bharadwaj

Raghav is serves as Analyst at Emerj, covering AI trends across major industry updates, and conducting qualitative and quantitative research. He previously worked for Frost & Sullivan and Infiniti Research.

AI Use-Cases for the Future of Real Estate

Episode summary: In this episode of AI in Industry, we speak with Andy Terrel, the Chief Data Scientist at REX – Real Estate Exchange Inc., about how AI is being used in the real estate sector today.

Looking ahead ten years into the future, Andy paints a picture of the areas where he believes AI will change the real estate business. Andy explores how marketing in real estate might change in the future with chatbots and conversational interfaces in real estate which are high value per ticket interactions – a process that will likely vary greatly from the chatbot applications we see for smaller B2C purchases (in the fashion sector, eCommerce, etc).

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Guest: Andy Terrel, Chief Data Scientist at REX – Real Estate Exchange Inc.

Expertise: Computer science, Entrepreneurship, 

Brief recognition: Andy earned a bachelor’s degree in Physics and Mathematics from Texas Tech University in 2004. He went on to earn a master’s degree and a Ph.D. in Computer Science from the University of Chicago. He was also a Research Associate Scientist at the University of Texas before serving as the Chief Science Officer for Continuum Analytics and the Chief Technology Officer for Bold Metrics Inc. He has served as Chief Data Scientist at REX since 2017.

Current Affiliations: Advisor at KindHealth, President at NumFOCUS Foundation and Chief Data Scientist at REX.

Big Idea

Andy tells us that data about real estate properties are available online including information like transactions undertaken over a particular home, features and amenities available in a specific home, and more (much of this publicly available data on the MLS is already pulled in and aggregated by sites like Zillow or RedFin).

He states that using a conversational interface to answer questions that prospective buyers or sellers of a real estate property may have can be distilled down to around 60 – 75 most commonly asked questions.

Questions might require a simple reply, such as:

  • “Does the house have a pool?”
  • “How many bedrooms does this house have?”

But more complex questions might be asked of such a system as well:

  • “How much money will I have to spend to fix the roof?”
  • “How many cars fit in the garage?”

Andy explains that answering these more complex questions at scale is a task which is difficult and time consuming for humans to achieve efficiently. While it’s clear that REX hasn’t achieved a full Q-and-A solution for real estate, Andy believes that such a system could eventually be a normal part of the real estate shopping and buying process.

Andy predicts that in the future AI might be useful for more than simply answering a question – it might be helpful in identifying the right contextual questions to ask. For example, information about how old a roof is can potentially lead to more residual information like how old the house was or how well maintained the house was by the previous owners.

Interview Highlights with Andy Terrel from REX

The main questions Andy answered on this topic are listed below. Listeners can use the embedded podcast player (at the top of this post) to jump ahead to sections they might be interested in:

  • (2:57) What’s possible with AI today in the real estate sector?
  • (5:24) What kind of data is collected to aid in finding answers to the more complex questions from customers?
  • (9:08) What role does AI play in marketing for the real estate sectors?
  • (11:21) What are the channels of matching listing variables with buyer/seller variables?
  • (19.20) What are some of the challenges with getting conversational interfaces to click in this space?

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Header image credit: Adobe Stock

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