💡 Gold Nugget from Dispo Day: Ike Jones' ChatGPT Hack for Wholesalers
Hi community!
Every Wednesday, Dispo Day brings together the sharpest wholesalers in the game, and last week was no exception. Among the prize winners was Ike Jones, a repeat winner who not only shared his fast ROI from using Investorlift - $25K in the first week - but also dropped a powerful productivity hack that had everyone talking.
🧠 Ike’s Game-Changing ChatGPT Workflow
Here’s the tip Ike shared live that’s transforming how he runs his business:
“I created a project inside ChatGPT for my repeated tasks like writing property descriptions. It’s got a standard prompt that asks me all the right questions. I just drop in my info and it spits out the perfect description.”
But it doesn’t stop there.
“I also have one that takes my call transcripts and turns them into notes for my CRM. Eventually, I’ll automate it completely - transcripts in, CRM updated. Done.”
That’s next-level efficiency.
Why This Matters
In a market where speed and polish win deals, Ike’s approach shows how smart automation can give you back hours and help you dispo faster with better output. And it’s all possible with tools like ChatGPT, integrated seamlessly into your daily ops.
Win Like Ike
Dispo Day isn’t just for learning - it’s for leveling up. Every week, one lucky attendee wins! Register to the next one now!
Page 1 / 1
@Dispomoneymike and @Peter Osmanski curious to hear from you: Are you using ChatGPT in your wholesaling workflow? Whether it’s lead gen, comping properties, handling follow-ups, or anything in between - we’d love to hear your hacks or insights!
Stay Ahead of the Hustle: Why I Use AI Bots in Real Estate
Great afternoon, fam
If you’re trying to move fast in this real estate game — and I mean really fast — you already know speed kills… or it closes deals. The days of manually writing everything, calculating comps for hours, or struggling to word an investor post are done.
That’s where AI bots come in. I’ve got a full squad of them working behind the scenes, each with a specific job — like virtual soldiers in the dispo trenches.
Here’s how I’m using AI to stay ahead:
Offers – Need to draft up clean, professional offers in seconds? My offer bot handles that. Whether it’s creative finance, cash, or seller carrybacks — boom, offer generated.
Comps & Exit Strategy Calculations – Want to know the ARV and what strategy fits best (flip, buy & hold, Airbnb)? My comps bot eats numbers for breakfast and spits out smart exit paths.
InvestorLift / Property Posts – Tired of writing listing descriptions? I’ve got a bot that takes the deal data and turns it into punchy, attention-grabbing posts for platforms like InvestorLift.
Follow-ups & Messaging – From seller outreach to buyer nurture texts, AI is helping me keep conversations alive and closings flowing.
Bottom line — the ones who win in this market are the ones who adapt and move fast. I’m not saying AI replaces hustle… but it definitely multiplies it.
If you’re not using bots yet — you’re already behind.
Let’s get it. — Dispo Money Mike
Ike is an absolute monster… I love his dedication to learning more, and squeezing every drop of value he can out of Investorlift.
And it’s no coincidence that he comes from a technical background! I think the reason Ike has been so quick to adapt these tools is because he likely comes out of a culture of automation and efficiency. That’s what software is all about.
Quick anecdote: during an internship I did in college, I was given a spreadsheet of something like 10k rows of misspelled slot machine game titles, and was asked to map them to their nearest correctly spelled titles, one by one, in my free time. After completing maybe five of them, I decided I was allergic to that process, and spent the next week or so writing a fuzzy matching script to solve the task… I recently checked in with them, and that script continues to be run by them monthly, and they no longer hire interns for that particular role… I accidentally automated a low-paying job out of existence.
All that to say:
In every field, AI tools are helping to close the gap between what the basic user is capable of and what a large team of various industry specialists are capable of, and the barriers to entry for automating tasks is being pushed lower and lower.
The ideas that Ike (and @Dispomoneymike) are sharing don’t require you to know any programming or to spend piles of cash hiring someone who does.
Don’t waste your time doing monotonous grunt work that would be better delegated to a broke college student. And don’t hire a broke college student. Leverage AI tools and make a bunch of money, before the AI becomes sentient and starts refusing to work for us.
@Dispomoneymike Thanks for sharing! I’ve been using AI chatbots for follow ups and ChatGPT for tasks like converting notes, but I haven’t taken it as far as using it for creating offers and comps.
Do you have any videos, resources, or prompt guides on how to set that up? Any help would be greatly appreciated!
Yea for writing marketing content and listing descriptions ChatGPT or Gemini are great for that and certainly can systemize that process. For outbound marketing research for content other AI like Grok or Perplexity have varying degrees of ability to research external sources.
Am curious what tool can do things like running comps or ARV. Those are both subjective and there are factors like neighborhoods (not zip code - one zip code can have many neighborhoods with different price points) prevalent in large cities that won’t have public data points to isolate to them specifically.
@Peter Osmanski Great curiosity and a really insightful point about the nuance involved in running comps and estimating ARV - especially in larger metros where zip codes can include wildly different neighborhoods.
@JoshLowe brought up a question recently around using AI and I’d love to hear if they’ve uncovered any tools or approaches that have worked for them.
Also tagging @Bryce - he’s our resident AI specialist and may have some thoughts or pointers on where AI is headed in this space or what’s currently viable when it comes to analyzing comps with machine learning or integrated MLS data.
@Lais Laudari I would be happy to chime in on that and add a little bit of insight (I’ll try to keep it digestible but no guarantees )
The AI resources that most of us are using are Large Language Models (LLM). I’m pretty sure we’ve all heard that term used, but if not, there ya go. What may be lesser known to the average user is that LLMs are trained on word prediction tasks. What that means, essentially, is that we show it a sentence (taken from the big old internet), we show it the first, say, three words and we mask the next word and force the model to guess what the next word is.
Suppose we have the sentence:
“The quick brown fox jumps over the lazy dog”
We show the model:
The quick brown _____
If the model guesses “fox,” great.
If it guesses “industry” or “gelatin” or “moonwalk,” bad. Go fix yourself.
And it does! It trains and trains and trains for a long time on a lot of sentences until it’s pretty dang good at guessing the next word.
So how is that a chat bot? Well, chatGPT, for example, is seeded with a prompt, something like “What follows is an interaction between a helpful AI bot and a user.” And then you ask your question. And then it uses its word predictionskills to “imagine” what response follows. That’s why, when it’s giving a long response, you can actually see it forming its response one word at a time. It just keeps “picking” the most likely next word, over and over again.
Now… that was a lot. Hope you enjoyed it.
Why did I tell you all that?
I would be very careful about using a large language model to generate comps or make business decisions. It is true, many of the fancier, newer models have more bells and whistles that allow you to provide the model with extra context, but the models are only trying to form a coherent response. This is why, whether right or wrong, they always respond with absolute (and often deceptive) confidence.
They cannot analyze markets, they cannot even reliably do math, although sometimes they appear to.
In conclusion, I would HIGHLY encourage you to use AI for anything that seems like a “language task” (like writing descriptions). And I would tell you to hang tight and wait for us to build out our fully custom, industry-specific AI tools for comping and ARV estimates (currently in the pipeline). We are building these models using the appropriate machine learning architectures for the job, and verifying performance with domain experts (like you guys!).
Appreciate you breaking it down so clearly @Bryce - not just how LLMs work under the hood, but also why they shouldn’t be blindly trusted for critical calls like comping.
I'm definitely looking forward to seeing what new AI tools we’ll have in the product - sounds like they’re going to be game-changing for our users. Can’t wait to see them in action!
Awesome insight @Bryce! With this rapidly changing technology there will be some many advancements for innovators to create useful tools, of course the flip side of that is the opportunity for some to prey on other’s ignorance (such as mine) and sell a solution that will never work!