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Frustrated by finding the right assistant for your team? Are you worried about how to pay your current associates now that NAR has eliminated buyer agent compensation through the listing agent? Are you worried about what will happen if portals and lead generation companies shut down your lead pipeline when new FCC regulations take effect in July?
National Administrative Professionals Daysometimes called Executive or Secretarial Day, comes during the last week of April. If you can’t seem to find, train, and pay IRL administrators right now, generative AI assistants might be your ideal solution to these challenges.
Before the advent of artificial intelligence, the best advice for agents building their teams was that their first two employees should be administrative staff, typically a deal coordinator and an assistant. With the upheaval brought about by commission litigation, FCC written consent laws, tight inventory, and high interest rates, finding two capable assistants to help you is more difficult and expensive than ever.
Enter the generative artificial intelligence assistant
The big shift every broker, team, and brokerage firm should make now is to start using generative artificial intelligence. Forget all the advice you saw last year. New generative AI tools will dramatically transform business over the next 12 to 18 months.
Most importantly, these new tools will be what some experts call “armchair” artificial intelligence. You can ask the AI the best way to query (give it a command) and it will produce the best answer.
Even better, many new tools are designed to respond to voice commands, just like talking to a human assistant. However, it’s smarter, requires almost no training, and can complete tasks in seconds.
Big data, machine learning and generative artificial intelligence
To understand the recent astonishing transformation of artificial intelligence from what MIT professor David Edelman calls “ordinary artificial intelligence” to what ChatGPT calls “real” generative artificial intelligence, you must first understand “big data” and “machine learning” and “machine learning”. and “generative artificial intelligence.”
Here’s how ChatGPT defines the difference between big data, machine learning, and generating artificial intelligence:
Big Data: This is a wealth of information that, when analyzed, can reveal trends, such as what homebuyers are looking for in a neighborhood or when is the best time to sell. “
Zillow regular production market report Reveal trends based on analysis of its data.One of the best examples is their Consumer Housing Trends Report.
Machine learning: “It’s like teaching a computer to become an expert at identifying home values. You show it many examples of homes, tell it which homes sell for more and why, and over time it learns to predict the value of homes on its own. This It’s an artificial intelligence that gets better with experience.”
Whether it’s Zillow, Realtor.com or Trulia, they all use machine learning to predict the value of a home. ChatGPT also said that Compass is using machine learning to optimize their CRM.
Generative artificial intelligence: “Imagine an artist creating a masterpiece with just a few sentences of description. Generative AI is like that artist, but in a digital world. It can generate new content based on your instructions, whether Images, text, even realistic sounds. It’s not just copying; it’s creating something new, like designing a house that was never built.”
According to Edelman, only a small percentage (2% to 3%) of today’s AI applications use generative AI, including 97% to 98% of all AI in real estate.
Vanilla AI Virtual Assistant
When I asked ChatGPT to identify the top AI virtual assistants using Python (its generative AI), it returned the following list:
Gabbi.AI provides real estate agents with a comprehensive artificial intelligence assistant that can nurture leads; respond to customers (chatbot); track important conversations; respond to text messages, calls and emails; can book viewings and manage tasks.
Luke is an AI-powered personal assistant that helps agents manage customer interactions, streamline tasks, schedule meetings and set reminders. It can automatically respond, schedule tours, collect lead profiles and research market trends. There is currently a waiting list to join Luke.
Targeted at the investor market, the AI can provide off-market property listings, research contact information, conduct customized emails, and provide MLS and county comparable sales.It also helps agents organize and optimize their prospecting and follow-up tasks.
These tools, such as ChapGPT 3.5, are limited in their trained capabilities. They can do a lot of things, but they are not truly generative artificial intelligence. For a list of 11 Vanilla AI tools, visit Kara.
What kind of tool is this for my team?
I recently interviewed Michael Martin,founder partnera new generative artificial intelligence assistant that can handle most of the work that two administrative assistants can do, for far less than paying two salaries.
Sidekick just came out of open beta. Currently, approximately 500,000 agents nationwide have access to the service through their MLS (and they are rapidly adding more MLSs). Sidekick can do amazing things with minimal guidance.
“Every professional industry has to go through many different activities when it comes to researching, analyzing data, visiting properties, running comparisons, developing marketing strategies, sending emails, managing calendars and schedules,” Martin said.
“These are really important activities, but you don’t have time to do them. Typically, you have to hire administrative support staff, but it’s hard to find one or even two people who are good at all these different things.”
Examples of tasks that AI assistants can help agents complete
- Manage your calendar and inbox (set appointments, send emails)
- Search the MLS for listings and perform market analysis
- Create CMA/Conduct comparable sales
- Perform data analysis and build/interpret spreadsheets
- Generate listing description based on photos
- Generate social media strategy and content using dynamic tags
- Trainable: Requires less training than humans with specific rules
case analysis:
A major challenge in pricing high-rise buildings is determining the premium between different floors, views and various other differentiating factors. While an agent who truly knows the building may be able to do this, it’s still difficult to quantify. Here, generative AI can not only identify differences in these premiums but also discover other factors that make a given location in a building more or less ideal.
One of Martin’s clients in Manhattan had been tracking sales of a prime condominium (co-op) building for several years. When she entered the data into Sidekick, it returned two very useful findings:
- Sidekick determines what the premium is for a seventh-floor unit compared to the second floor (or any other location within the building).
- Sidekick also determined the number of units with different price per square foot in the “C” and “F” lines of the building.
Summary: Do your homework
Many agents are intimidated by the technology. Research, learning, and exploring which options are best for your team and which models are priced is necessary to understand how you and your team can benefit from using this technology.
Unfortunately, there is a lot of bad advice and uncertainty about these new technologies. Therefore, people are hesitant to try them. To overcome any reluctance you may have, Martin suggests the following:
“I think the most important thing is to adopt a mindset of curiosity and experimentation so that you can embrace these technologies. Once you do that, the value will become obvious and you may ask yourself, ‘Why didn’t I do this sooner? ? “
Remember, this technology can be a productivity savior, freeing up time for you to focus on relationships and growing your business. Don’t be afraid to learn new things; you may find that the solution you’ve been looking for is just a few clicks away.
Bernice Ross, President and CEO Brokerage UP and Real EstateC
