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Feeling discouraged about finding the right assistant for your team? Are you worried about how to pay your current assistant(s) due to NAR’s elimination of buyer agent compensation through the listing agent? Are you afraid about what will happen if the portals and lead generation companies shut down your lead pipeline in July when the new FCC regulations go into effect?
National Administrative Professionals Day, sometimes known as Admin or Secretary’s Day, is coming up the last week in April. If finding, training and paying for an IRL admin seems out of reach for you right now, a generative AI assistant may be your dream solution to meeting these challenges.
Before the advent of AI, the best advice for agents building a team was that their first two hires should be administrative, usually a transaction coordinator and assistant. With all the upheaval from the commission lawsuits, the FCC written consent laws, constricted inventory and high interest rates, finding two capable assistants to help you is more difficult and costly than ever.
Enter the generative AI assistant
The big shift that every agent, team and brokerage should take right now is to start working with generative AI. Forget about all the advice you have seen over the last year. The new generative AI tools will transform the business dramatically over the next 12-18 months.
Best of all, these new tools will be what some experts have called “armchair” AI. You can ask the AI the best way to query it (give it a command), where it will produce the best answers.
Better yet, many of these new tools are being designed to respond to voice commands to be virtually identical to talking to a human assistant. However, it’s way more intelligent, requires little or no training, and completes tasks in seconds.
Big data, machine learning, vs generative AI
To understand the amazing pivot that AI has recently made from what MIT Professor David Edelman calls “Vanilla AI” to “true” generative AI, as ChatGPT calls it, you must first understand the differences between “Big data,” “machine learning,” and “generative AI.”
Here’s how ChatGPT defines these differences between Big data, Machine learning, and Generative AI:
Big data: It’s a vast amount of information that, when analyzed, can reveal trends, like what homebuyers are looking for in a neighborhood or when it’s the best time to sell.”
Zillow routinely produces market reports based on the analysis of its data to reveal trends. One of the best examples is their Consumer Housing Trends Reports.
Machine learning: “This is like teaching a computer to become an expert in identifying what makes a house valuable. You show it many examples of houses, tell it which ones sold for more and why, and over time, it learns to predict the value of a house on its own. It’s a type of AI that gets better with experience.”
Whether it’s Zillow, Realtor.com, or Trulia, they all use machine learning to predict the value of houses. ChatGPT also said Compass is using machine learning to optimize their CRM.
Generative AI: “Imagine an artist who can create a masterpiece from just a few words of description. Generative AI is like that artist but in the digital world. It can generate new content, whether it’s images, text, or even realistic voices, based on your instructions. It’s not just copying; it’s creating something new, like designing a house never built.”
According to Edelman, only a tiny fraction (2 percent to 3 percent) of today’s AI applications use generative AI, including 97 percent to 98 percent of all AI in real estate.
Vanilla AI virtual assistants
When I asked ChatGPT to use Python (its generative AI) to identify the top AI virtual assistants, it came back with the following list:
Gabbi.AI offers real estate agents a comprehensive AI assistant that nurtures leads; responds to clients (chatbot); tracks important conversations; responds to texts, phones and emails; can book showings and manage tasks.
Luke is an AI-powered personal assistant that helps agents manage customer interactions, streamline tasks, arrange meetings and set reminders. It can automate responses, schedule tours, collect prospect data and research market trends. There currently is a waitlist to join Luke.
This AI is aimed at the investor market and can provide lists of off-market properties, research contact information, do custom mailing, provide MLS and county comparable sales. It also aids agents in organizing and optimizing their prospecting and follow-up tasks.
These tools, like ChapGPT 3.5, are limited in what they have been trained to do. They can do an amazing number of things, but they’re not true generative AI. For a list of 11 Vanilla AI tools, visit XARA.
What kind of tool does this look like for my team?
I recently interviewed Michael Martin, the founder of Sidekick, a new generative AI Assistant that handles much of what you can do with two administrative assistants at much less than paying two salaries.
Sidekick has just emerged from its public beta. It is currently available to about 500,000 agents nationally through their MLS (and they’re quickly adding more MLSs). What Sidekick can do with minimal direction is remarkable.
“There are a number of different activities that every professional industry has to go through when it comes to research, data analysis, looking at properties, running comps, coming up with marketing strategies, sending emails, managing calendars and scheduling,” Martin said.
“These activities are really important, but you don’t have time to do them. Typically, you have to hire administrative support, but it’s hard to find one or even two people that are good at all those different things.”
Examples of tasks AI assistants can help agents accomplish
- Manage your calendar and inbox (setting up appointments, sending emails)
- Search the MLS for listings and perform a market analysis
- Create CMAs/run comparable sales
- Perform data analysis and create/interpret spreadsheets
- Generate listing descriptions based on photos
- Generate social media strategies and content with dynamic hashtags
- Trainable: Requires less training than a human with specific rules
Case study:
A major challenge in pricing high-rises is determining the premiums between different floors, views and a variety of other differentiating factors. While an agent who really knows the building may be able to do this, it’s still hard to quantify. This is where generative AI not only identifies the differences in these premiums but can also spot other factors that make a given location in the building more or less desirable.
One of Martin’s clients in Manhattan had been tracking the sales of one of the major apartment (co-op) buildings for many years. When she entered the data into Sidekick, it came back with two very useful findings:
- Sidekick identified how much the premium would be for a seventh-floor unit as compared to the second-floor (or any other location in the building).
- Sidekick also determined how many units in the “C” line of the building differed in terms of price per square foot vs. those in the “F” line.
Wrapping up: Do your homework
Many agents are intimidated by this technology. It’s essential to do your research and learn and explore which option is best for your team, and to price out which models to see how you and your team could benefit from using this tech.
Unfortunately, there’s a lot of bad advice and uncertainty about these new technologies. As a result, people are hesitant about trying them. To overcome any reluctance you may have, Martin recommends the following:
“I think the most important thing is to adopt a mindset of curiosity and experimentation so you can wrap your arms around these technologies. Once you do, the value will become obvious, and you will probably ask yourself, ‘Why didn’t I do this sooner?’
Remember that this tech can be an efficiency lifesaver, freeing up time for you to work on your human relationships and grow your business. Don’t be afraid to learn new things; you may find that the solution you have been searching for is just a few clicks away.
Bernice Ross, president and CEO of BrokerageUP and RealEstateC
