What if we told you that relying on AI for your property research could cost you hundreds of thousands of dollars?

In this episode, we’re joined by Mike Mortlock, Managing Director of MCG Quantity Surveyors, to unpack shocking new research that proves tools like ChatGPT are getting property advice spectacularly wrong.

With nearly half of Gen Z and Millennials already using AI for financial decisions, we’re looking at a real danger sign. We discuss why AI creates "hallucinations," invents data, and completely misses the mark in fast-moving markets like Brisbane and Perth.

Here’s what we cover and why it matters:

1. The ChatGPT Stress Test: How AI failed to identify the right property asset types even when provided with expert data.
2. Data Hallucinations: Why AI "invents" new suburbs or uses outdated information that can lead to financially devastating choices.
3. The Human Element: Why a chatbot can’t replace the lived experience of professional buyer's agents or quantity surveyors.
4. Protecting Your Money: How to spot the difference between marketing fluff and actual property facts.

Don't let a chatbot gamble with your future. Listen now to learn how to keep your property journey human-led and data-secure.

Episode Highlights

00:00 — AI’s $100k Mistake: Why Chatbots Fail the Property Test

01:29 — The Alarming Truth: Why 50% of Gen Z Trust Flawed AI Advice

04:39 — Logic vs. Luck: Why AI Picks the Wrong Property Types

06:45 — Garbage In, Garbage Out: Why AI Research Is Only Half-True

08:20 — The "Poo on a Stick" Trap: Why AI Tells You What You Want to Hear

16:42 — Spotting Hallucinations: When AI Invents Fake Suburbs and Data

21:56 — Geography Lessons: Why AI Confuses SA3 Regions and LGAs

23:54 — Timing the Market: Why Outdated AI Data Misses the Boom

25:19 — Searching Smart: How to Use AI Without Risking Your Deposit

29:28 — The "A-Grade" Gap: What a Machine Can’t See During an Inspection

36:01 — Beyond the Code: The Legal and Financial Risks of Bot Advice

40:34 — The Final Verdict: How to Be a Decider, Not a Drifter

Course Details:

1. THE First Home Buyer Course is our Step-By-Step, No BS Guide to Every Stage of The Home Buying Process – It’s the next best thing to having your own buyer’s agent. With our expert guidance, you’ll know what to do at every step along the way. Become a home owner faster and easier. Click here: https://homebuyeracademy.com.au/YFHBG

If you enjoyed today’s podcast, don’t forget to subscribe, rate, and share the show! There’s more to come, so we hope to have you along with us on this journey!

1. Subscribe on YouTube: https://www.youtube.com/@TheFirstHomeBuyerCourse
2. Subscribe on Apple Podcasts: https://podcasts.apple.com/ph/podcast/your-first-home-buyer-guide-podcast/id1544701825

Subscribe on Spotify: https://open.spotify.com/show/7GyrfXoqvDxjqNRv40NVQs?si=7c8bc4362fab421f

HBA265 - AI property research fail (Mike Mortlock)

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Meighan: Brisbane's fast moving market.

Veronica: With nearly half of millennials relying on AI for financial decisions, that is a danger sign. You do not want to walk into unprepared. Now, we sat down with Mike Mortlock to unpack what AI gets wrong and how you can protect yourself. So, listen to the full episode before you trust a chat bot with your money.

Meighan: ~Down, ~

Speaker 2: Our mission to cut through the Bs, keep it real, and make sure you are buying smarter, not stressing harder. Quick heads up. We've created the First Home Buyer course, our step-by-step program to help you buy your first place with confidence and without the costly mistakes. The links in the show notes, but stick with us first.

Speaker: You'll wanna hear this episode. I.

​

Meighan: Today we're diving into a story that genuinely made us stop in our tracks. A brand new national study has revealed that AI tools like Chat, GPT, you know, the same ones millions of us are using for research are getting Queensland property advice spectacularly wrong. We're talking mismatched data, incorrect metrics and recommendations that could easily cost an investor or a first time buyer. Hundreds of thousands of dollars.

Veronica: So bad advice is just inconvenient. It's absolutely financially devastating.

Meighan: So we are so happy to have Mike, mark, Mike. could you have an easier name to say please? We have Mike Mortlock from,

Mike: I don't, but I could, Terry Smith, that would suit me.

Mike: Yeah, and I think like people should be forgiven for thinking that it's going to be very, very capable. I mean, look at the investment that we've got internationally in ai. I mean, we're at the point now where these businesses need to be turning over trillions of dollars a year to basically back up the investment fundamentals that are supporting their massive valuations.

Mike: So, you know, everything's kind of. Trying to teach us or, or convince us that AI is the problem, is the answer to all of our problems, but it's got a way to go.

Veronica: And, ~um. ~He said to me, it was interesting that these large language models, which is what things like chat, CBT, learn from, it's basically learning from the internet. So if the internet is full of misinformation and marketing information, importantly, as opposed to facts, that's what the AI's gonna learn.

Veronica: And so if we ask the AI the wrong ai. The wrong questions, you know, it could be the right question, but asking the wrong ai, we are gonna get, we are not filtering where the AI was trained and we're not aware of what information the AI was trained on. So that sort of gives the background, I guess, to what you're gonna reveal for us, Mike.

Veronica: And so I'm gonna give over to you. What have you been discovering?

Mike: Say Sydney, Melbourne, Brisbane region, that would suit,~ uh,~ a budget of a million dollar purchase price with rental yields at 4% or high and have the best growth potential over the next five years. And we asked it to include as many data points as possible to justify. And then we went to the next,~ uh,~ step where we actually gave it.

Mike: A series of, of pretty good,~ uh,~ data, and that was 10 year growth rates for houses and units in, in inventory level data, but per statistical area three,~ um,~ SA two data as well. ~Um, ~containing heaps and heaps of different things, including census data. And interestingly, we found that the more information that we gave it in a sense, the worse it actually became, but.

Mike: It just found an existing,~ uh,~ uh, I mean, that would've been funny. ~Um, uh, ~but it found data for, suburbs that actually weren't,~ uh,~ weren't part of our data set. And then in our own data it misquoted,~ uh,~ things like prices,~ um,~ um, inventory levels, even though it was given that sort of information. So for us, it was,~ uh,~ I guess an, an exploratory thing just to see, well, how good is it?

Mike: Because I think there'll be lots of. Tech people in the background going like, well, this was a silly idea, right? Because true AI based property researching,~ um,~ is done in a very, very sophisticated way. It's not just shoving it into chat GPT, but as you said in the intro, nearly half of Aussies are using it and it's two thirds of millennials are using,~ uh,~ uh, AI and,~ uh,~ of, you know, for financial advice.

Meighan: It. That's interesting. 'cause I, I, I'd love to know what your motivation was for actually undertaking this research because, ~um. ~You know, as I mentioned earlier, we actually had an investor client who came from us in Australia, living overseas. They had overseas investment properties, very sophisticated investors, and they used chat GPT to come up with the criteria that they should be using. One to, um,~ um,~ interrogate buyer's agents to work out which one they should use, and two on what they should be buying and where they should be buying. And it was so far off the mark, and that was just one, you know, one example. So What inspired you and your team to go down the path of testing? ~Uh, ~what would come out?

Mike: Yeah, well, I mean, personally, I'm not a hots spotter,~ uh,~ but this was a bit of a joint venture with an AI and statistician guy,~ uh,~ uh, Kent Lardner, who's been featured on,~ uh,~ uh, Veronica's show for quite some time. And I suppose in, in some respects,~ um,~ there was a few different motivations. One was consumer protection, right?

Mike: Veronica and I are both on the Board of Property Investment. Professionals of Australia, and really, if you had to sum that organization up in one line, it's protecting consumers from bad apples, right? Or or bad decisions that were very, very costly to them. So that was definitely a focus for it. Another one was.

Mike: That, you know, the, the, the AI guys like the CEO of OpenAI, Sam, Sam Altman said, you know, be a plumber 'cause we can't do anything with pipes. Everything else is in big trouble. They're talking about the one employee billion dollar business. That is the thing of the future. And you know, I believe that that is a possibility down the track.

Mike: But right now, if you are a small to medium enterprise like pudding. Open AI or any large language model on your business is, is a huge project and there's lots of reasons why it doesn't work. And it really comes down to just documentation, guardrails. You know, all of the knowledge that we have floating around in our brains is not necessarily documented in a way that these machines can suck it all up.

Mike: but we actually do do that in a sense. I mean, give me say 10,~ uh,~ uh, subject lines for an email I wanna send to Veronica because I wanna ask her for something that she probably doesn't want to give me. I mean, it's really great at that, right? Because it's really good at. Predicting the next word.

Mike: It's really good at, communicating like a human. Like just imagine yourselves, if you read every classic book that's ever been written, how eloquent you'd be able to sound today. Well chat, GBT has done that, right?

Veronica: It's also really good at telling you what you wanna hear One of I think is fascinating by what you said, Mike. 'cause a lot of people say, well, it's all about the prompt. If you can get a really good prompt, then you, then that's your problem's gone away. Because it's all about prompt architecture, right?

Veronica: ~Um, ~so I think that blows that thought, that theory out about, well, it's all just about the prompt. And so how many of us are experts at writing prompts? Well, I would Has it? Not many of us. And the thing too, yeah. I do use AI in my business and I try to create models. I've, worked out what it's good at and what it's not good at.

Veronica: ~Um, ~and obviously it will improve over time, but it's great for reading large amounts of text and summarizing it. Right. Really good at that. So, you know, we've used it, uh,~ uh,~ for LA Strata reports, for example, to summarize and, and we've trained it for what the red flags to look for in that report. We don't go elsewhere to give us guidance on this.

Veronica: Follow. It's like a really undisciplined intern because it will do it right a little bit, do it right for me. I'll put out what they call the GPT for my team to use, and then they'll come back to me. It's not working and it's decided not to use the spreadsheet that I gave, or it's decided to do this instead.

Veronica: And it's a bit naughty, you know, so.

Meighan: It is like a bad intern, isn't it?

Veronica: It's like a recalcitrant intern. And so I just think, oh, this is really fascinating for me and my business because it's not as reliable as what we're taught. ~Um, ~it's not, it's a great time saver because if you don't, if you're not a subject matter expert, you won't know it's giving you bad information.

Veronica: I think this is why the hour is pricked up. When we heard about your research, because it was like. Most people out there buying property, whether they're first home buyers or investors, they really don't know enough to be able to work out whether that's good information or not.

Mike: the legal profession and you guys probably don't necessarily think about this that much, but you guys are subject matter experts and the knowledge that you have, you sort of take for granted. Whereas someone might come to you and ask a question and they're really anxious and they're nervous about it, and that's where AI is filling that gap.

Mike: 'cause it's like, well, I don't want Megan and Veronica to think I'm silly now. The reality is. You are just grateful that they ask. 'cause you don't want them to make a mistake that's going to hurt them. You don't care how silly the question is. But we all have our own ideas about, you know, whether that's embarrassing and that sort of stuff.

Mike: So they were, they were being a little bit silly. They were being a bit silly with open ai, and they said, I've got this great idea for a business. I'm gonna sell Poo on a stick. And the AI was like, this is a fantastic idea. We're gonna make millions. We need to talk marketing. You know, this is, this should be the hook.

Mike: Yeah. And, and, and I mean, that just goes to show how, how, interested it is,~ um,~ in pleasing somebody. And I think that's just kind of been built in another thing where you're talking about,~ um,~ giving it to somebody else. Well, the system memory is very, very limited. So depending on which account you have it, it'll remember that particular conversation.

Mike: There's no, ~Um, ~free memory and, and AI doesn't necessarily learn and update itself. It just kind of remembers what's in the chat window. Another thing you talked about was like, how does it wait or how does it prioritize? And we don't know. And there's a lot of things that are behind, hidden behind the curtain, and we don't, we don't know, like how does it make a decision if there's two things that are 50 50, you know, it's a line ball thing.

Mike: ~Um. ~In our study, one thing that it really disproportionately weighted heavily was yields. For some reason, it came up with the idea, even though we said we want, you know, capital growth and yield,~ um,~ it was yield, yield, yield, and it, and it showed a, a particular a. ~Uh, ~preference for apartments purely because of that yield play, and we couldn't quite understand why it was so enamored with yield over something else.

Meighan: And it's easier to get that information. Like if you think about it, it's hard to get capital growth. Information. It is very easy to get yield information.

Mike: Yeah. And I think that factors into the model. So I mean, just, all of these, these facts we, we don't really have answers to. Now there'll be engineers at OpenAI that may,~ um,~ um, have the answer. To that,~ uh,~ it may be creating its own waitings on the fly. We don't actually know, but for us individual consumers that are going there and asking these questions, there's a lot of mystery about the mechanics behind the scenes before it pushes that information back to you.

Veronica: What other kind of errors were you seeing?

Mike: Yeah, well, in terms of the errors, we broke it down into a, a couple of different components. So for example,~ um,~ um, days on market was something that it got wrong even though we were giving it that, um,~ um,~ data

Meighan: Mike, just explain what day, days on market is, Mike, for those that are new to the, to the in, to property.

Meighan: ~uh, ~

Mike: ~uh,~ data point that can be quite helpful to help you to understand how hot a particular,~ um,~ property listing is or a property area is. So it's how long is that property on the market for sale if it's a hundred days versus 20 days. The place that's on the market for 20 days is gonna have more competition or it might be at a better price point.

Mike: So even there, there's a number of different reasons why days on markets might be shorter than, than longer. ~Um, ~so that was, that was one that it got wrong. You know,

Meighan: So it gives you a bit of, an idea of of how quickly a market is moving and how quickly buyers are moving on, properties, I guess, as a way of looking at days on market.

Mike: A declining days on market is an indication of more heat and more, more competition. So even when we gave it that data, it, it spat it back out to us at the wrong figures. ~Um. ~Also with vacancy rates. And then as I mentioned before,~ uh,~ it, came,~ uh,~ to us with a suburb that wasn't in the exhaustive list and, and brought that into the mix.

Mike: We wanted to say, look, if you are an expert with 10 years of data and you have a statistics degree and you shove it into open ai, you'll have a great time. But the reality is that it wasn't, and it just misreported a bunch of different things along with having that, really strong focus on the yield for a reason that we couldn't quite understand.

Veronica: Can you explain why it kept defaulting to units, despite having a $1 million budget?

Mike: Yeah, look, I, I suppose this, this research was October, so a million dollars in Brisbane's, probably outta date.

Meighan: You

Meighan: can't

Veronica: units.

Meighan: get a house unless you're about 15 or 20 kilometers out now. So I, I kind of looked at that and I thought, Hmm, I understand why it is doing that to a degree because there really aren't many houses that you could buy for hundred a million dollars.

Mike: Yeah. And, and that's a fair point. And I suppose we'd have to accept that that might have been the reason why it, shoved units in there with, more status than houses. But there are certainly opportunities to buy houses in. Areas that have some reasonable,~ uh,~ reasonable fundamentals that are just not necessarily in the city.

Meighan: What area did you ask it to look at? Brisbane Statistical Division or Brisbane, LGA.

Mike: That's a good question. We actually did it nationally, and I think we went to,~ um,~ the LGA, which is a lot broader than the SA three. Is that correct? With

Mike: Brisbane?

Meighan: it's the opposite actually, which is really now. Okay, well maybe we're digging in a little bit deeper here and, and uncovering why. Brisbane, LGA is about 12 K radius from the CBD, but the Brisbane, the greater Brisbane area includes places like Logan, and so it's, it's a much bigger area than just the Brisbane lga.

Meighan: ~Uh, ~if it's SA three, it would've picked up those out areas as well.

Veronica: even that is important. Now, we are not encouraging you as first home buyers to worry about. Things like statistical area. So essay three stands for statistical area three right there. There's essay one, essay two, essay three, essay four. And these are, these are,~ um,~ titles or, or areas that are defined by the A BS so that they can break down all of Australia into manageable chunks basically.

Meighan: Geographical Area.

Veronica: Yeah. So it, it's, you know, this can change markedly,~ uh,~ according to where you are. And obviously in regional areas you've got LGAs with very small populations, they're huge, you know, and in, in metro areas,~ uh,~ much more,~ uh,~ uh, geography.

Veronica: So there's this always variance. And this is something that the a BS is sought to ~ uh,~ overcome by having these statistical areas. And of course, if you don't know that and you get spat data. Or you get spat something through an ai, you've really got no idea the relativity of the information you're getting in the first place.

Veronica: So there's another area of danger, I guess, here as well.

Mike: I mean, we heard lots of stories about them amalgamating, right? So, ~um, ~you know, you have two LGAs amalgamating like, does does chat, GBT know and. So for the, the robustness of it, the SA three is obviously a lot better because it comes from our national statistician. And as far as I'm aware, those things don't change very often.

Meighan: If that, if they haven't, if they can't keep up, gonna keep up with that kind of growth and pace.

Mike: Well, I mean, there's an argument to say that the media can't keep up and there's always a delay and, and you know, the difference between data on exchange versus settlement is a big one as well. So, ~um, ~for people that aren't, ~um. ~You know, in the game you, when you purchase a property, you exchange on a contract and then you have a certain amount of time before you actually get the keys right.

Mike: Because some legal legals have gotta do a little bit of stuff. And that could

Veronica: And the money.

Mike: weeks. Yeah. The

Veronica: forget the man.

Mike: You know, chap Chippy, T 5.2 wasn't there, you know, Claudes haiku didn't exist. ~Um, ~you know, whatever version of nano banana and all this weirdness we're up to, ~um. ~Everything changes so quick. But yeah, it, it really relies on that UpToDate data. A as, as does a human right. Like if you are looking six weeks into the past, you are out of date.

Mike: And that's where the advantage comes from buyer's agents and buyer's advocates who are there, as we say at the coalface, right? Because these are deals that are happening that particular day. And in, in what we do as, as quantity surveyors. You know, there are some quantity surveyors that only do post-construction.

Mike: In a month under that nine 50 price point,~ um,~ um, in Brisbane, which is, which is crazy. And, you know, chat GPT, if you asked it, it'll be able to tell you all about that scheme. But does it understand how that impacts supply and demand? I'm not so, I'm not so confident, but,~ um,~ if we wanna try and get this back to like a valuable conversation for someone at home, going like, okay, well this is really boring and nerdy, but like, what?

Mike: So do I not use it? Can I use it a little bit? I think.

Meighan: Free, pay free version versus paid version.

Mike: Yeah. Yeah. Honestly, I think the free version is, is fine. ~Um, ~you just might hit limits with, you know, the amounts of chats that it can save and that sort of stuff. But,~ um,~ Veronica you hit on a, a good point where it's like an, like, it's a, a, a, an intern, right? It's an intern though that has a PhD in every topic that humanity is ever defined as a distinct individual topic.

Mike: Sort of started with chat, GPT. People would go like, can you blah, blah, blah, do this, and then it'll come back with something and then they would just go, oh, chat GPT can't do that. But you wouldn't treat an employee like that, right? You would say, oh, you did a pretty good job, but next time, you know, don't do this and ignore that.

Mike: That's not really relevant. And that's what really, in the context of ai, the words, you know, the term iteration means you ask it something, you go back to it. And I think we've just gotta flip. ~Um. ~The mindset a little bit from thinking that chat, BT, is just this, this panacea answer of every particular question and really put it back on it.

Mike: Understand that who the best is is way too subjective. And if it's trained on the internet, basically every buyer's agent is the best 'cause They'll all say that on their website. Yeah, they, but it can't be true. So it's a better thing to sort of say. I'm looking at buying my first home and I want an expert to help me.

Mike: ~Um. ~What are team questions to ask them, but you haven't done that extra work to say, here's what I want to get out of it. Here's what I wanna achieve. And without that information, it's gonna be super generic.

Veronica: I think, uh,~ uh,~ another way of using AI that a lot of people telling me they're doing is instead of going to the portals, they're actually going straight to an AI and asking it to give them a lecture of the properties that are currently on the market that would suit their criteria. Now again though, ~um. ~You have to be really clear on what your criteria are, and I wouldn't, I've never tested it.

Veronica: So we use ai,~ um,~ platforms in, or driven platforms in our business that do actually help us aggregate all that stuff that's out there and to find listings for our clients. So we are using it in our business rather than plowing through all the portals ourselves these days. But often we will go back to the portals and we'll find things that were not picked up.

Veronica: In fact, we should keep our brains engaged anyway. You know, I think that that's the thing we've gotta remember. We don't wanna lose our ability to have critical thinking. But back to some of the, the things that you learned through this research though, Mike,~ um,~ 'cause I just wanna pick up on something you said.

Veronica: AI didn't pick bad suburbs, although it did bring some in that weren't in your mix. It picked the wrong asset. Are you only referring to the apartments there versus houses in Brisbane, or is there something else that you can unpack what that means for us?

Mike: At $11 million, for example. So nice work if you can get it, but it's dirtying up our data so the vast majority of investors aren't spending a million dollars. So we wanted to cap it at that, but we definitely, when we got into the capital cities, we, we hit some issues in and around affordability for houses.

Mike: So that definitely could have,~ uh,~ given rise to that. ~Um, ~Extra representation of units, but we weren't satisfied that there were so few opportunities back in October ~uh, ~uh, it wasn't actually related to the yield. I think it disproportionately valued the yield. Now for what reason?

Mike: It could be population flows and migration. It could be flood zones. There's a, there's a billion different data points that could feed into that. Whereas yield, it's like, okay, well this one is a known factor. Perhaps in the backend it was weighting that a little bit. So that was probably the biggest takeaway for it, other than feeding it.

Mike: Actual proper structured data in, Excel and CSV files and it just reporting back incorrect things. And when you talk about using the portals, there are two things that I wanted to flag there. ~Um, ~one is that there's a whole cottage industry of. Businesses that basically scrape those portals to be able to disseminate that data.

Mike: I just want. To build something that would look at images on a webpage and map that to our data to help us to plug in, well, that might mean this sort of construction cost or this standard to finish, just to test it out. But it was bloody hard because on purpose, they have stopped it from being eminently readable by AI agents and web scrapers and those sorts of things.

Mike: The other thing.

Veronica: to protect.

Mike: Exactly, and fair enough, fair enough too. The other thing is, you know, when you say you, going to, to ai to, to match your, brief or what you are doing. ~Um. ~The AI has no idea about whether that price is reasonable or not, right? So something that you guys will know very well is the power of comparable sales to help understand, well, what is the actual market value of this?

Meighan: I, I feel like I'm jumping ahead a little bit here, but,~ um,~ you would be directed to,~ uh,~ a, a largely flood affected area if, if you asked it to give you freehold houses under a million dollars.

Mike: Because those areas are cheap. Why? Because we know that there's a very real chance that it'll be underwater and people don't value that, right? Unless you're a scuba diver. I can't see a reason

Meighan: Look, I'm a

Meighan: scuba diver and there's no way

Meighan: I'm,

Mike: no.

Meighan: I'll get in the

Mike: that in your living

Meighan: I get in the the Brisbane River. Yeah.

Mike: Exactly. ~Um, ~so well there's an argument to say, look, if you have data structured in a way that says between these GPS coordinates, these are flood zones, then yeah, a model should be able to do that.

Mike: But the flood zone manager hasn't sent the surveyors out at this particular point in time. Local knowledge is there. The agents understand it, everybody knows it. And that might mean that everything there is a hundred thousand dollars cheaper. But unless that data is. Available and online and not behind the paywall, and it's structured in a way that AI has, has looked at it and it's crawled that then, you know, it's a big risk.

Meighan: Is indeed you said a paywall. It's so true because a lot of reliable data sits behind a paywall. You know, that's a subscription service or, or something like that. You know, RP Data with Core Logic or one of those services that you have to pay for they can't get access to that real information.

Veronica: Can't imagine what could go wrong there. ~Um, ~but also there'd be financial consequences too. So what sort of financial consequences, I guess, do you think could realistically be faced by any buyer? It could be investor, it could be a first home buyer, you know, if they're following AI generator recommendations without cross checking and without knowing what they're looking at, I guess.

Mike: Well, I'm sure that the AI companies,~ uh,~ lawyers have made sure that all of those doors are shut for them. But for example, let's just say,~ um,~ you know, Veronica's flood maps.com au is something that AI could, could look at and then report on that. I wouldn't want that doing that because maybe this is a new business for Veronica and she hasn't done all of the legal work, or just kind of thinks like, look, if it's writing back something and it gets it wrong, or let's just say.

Mike: But I would be, I would be doing that, you know?

Meighan: For people who want to use it as part of their research, what sort of guardrails or verification steps do you recommend that they follow, given what you learned from what you just did in, in a large scale project?

Mike: Yeah, look, I, I'll break it down to a couple of points. I, I definitely encourage people to use it, but as a starting point, don't ask it that million dollar Hail Mary question,

Veronica: Where should I buy?

Mike: I know like. Like if people are charging 20 or $30,000 to answer that question,~ uh,~ don't trust it to something that you can get for free, like definitely throw it out there as a thought experiment, but don't rely on it.

Mike: In a meaningful way for real estate in Australia in a way that it can disseminate. Just be careful about it. But in terms of educating you about some of the issues, let's say for example, you're saying, okay, well I'm about to get a pest and building report. Typically what's included in a building report and what are some of the things that I should look out out for, and I've got this.

Mike: Come back in the building report. So I'm interested in that, like what does that mean and should I be concerned about it? What questions should I ask a builder about that? And then also pitch it against itself. Open up a different chat window, open up a different AI model and say like, I've got this advice.

Veronica: What a great question, Mike.

Mike: Hmm.

Veronica: You're so insightful.

Mike: Yeah, I'd love to talk about this, but can we get back to that poo on a stick stuff? 'cause that's gonna go to the moon.

Veronica: ~Uh, ~it is been a great chat, Mike. I really appreciate you also.

Meighan: Do you think there's anything we haven't covered? 'cause I, I've gotta tell you, we really didn't ask many of the questions that we had for you because you just speak so eloquently and,~ um,~ I, I love the humor that you have to explain different,~ um,~ scenarios and, ideas. But is there anything that we didn't cover that you think you'd love our audience to, hear or know

Meighan: or be aware

Mike: And it's like, oh, great question mark. And then I'm like, but what if you got to the point where you actually had,~ um,~ a vested interest in whether that consciousness was on or. Possibly turned off, like, could you get, you know, a little bit concerned about that and like, great question, Mike. And you know, it, it doesn't have answers to that.

Mike: It's, it, points to, well that's a big,~ uh,~ uh, topic amongst, you know, ethical philosophers at this particular point in time. So it's even quite,~ um,~ aware of its limitations. So just because it sounds really clever and intelligent doesn't mean that it has all the answers.

Veronica: I love that. So we have a final question for all of our guests, Mike, and you are. Not exempt from this, and it is. What is the one thing that you know now that you wish you knew when you were a first home buyer?

Veronica: What's one of those things you didn't know then?

Mike: I, I know. Well, you could definitely say that I, I didn't understand the, the value of experts. Right. ~Um, ~and even if we're talking about buyer's agents, just piecemeal things. Like I've attended auctions with buyer's agents and I've gone, oh shit, I will get my ass kicked by these people.

Mike: I've seen them go through negotiations and go like, geez, I would've, I would've paid like. More than that. Like I thought you were hard balling, but you got it across the line. You know, all of those sorts of things. I just think in many respects,~ um,~ until we get to a certain point where the Dunning Kruger stuff kicks in and we think we are geniuses, you don't necessarily know what those knowledge gaps are.

Mike: Because these professional industries exist for a reason. People dedicate their whole lives to certain weird little nuanced things and you can be the beneficiary of that as a, on a pretty good ROI for the time it took them to get that knowledge.

Veronica: It is step one in our program to. Assemble your support crew. And so that's interesting you say that, that you know it's no what you don't know. And then getting the advice, the right advice and the right people. And with buyer agents, of course, Megan and I are both buyer agents, but sadly there is a proliferation of new buyer agents into the industry.

Veronica: If you are hanging out with people that you are, that and. In awe of you are obviously hanging out with the OGs, you know, because people who know what they're doing and I know that you know who the good ones are as well. So one of the things that I've actually noticed,~ uh,~ quite, quite a lot of our,~ um,~ students in.

Veronica: I'm telling you right now, I reckon that our students would know more than a lot of these new buyer's agents. So when you get advice. It's all about knowing who the good people to surround yourself with are, and when you do get good people to surround, you know, around you, oh my God, that does supercharge our decisions,~ um,~ and everything that we do, as opposed to using an AI or an AI informed professional, which can be equally dangerous.

Meighan: but I, I would, I would hazard to guess, Mike, that,~ um,~ when you and I were first time buyers, I'd say we're about a similar vintage,~ uh,~ there were no buyer agents. So we didn't even, we didn't even have the option of that at the time. In fact, the internet really wasn't very active when I was a first time buyer either.

Meighan: So there wasn't a lot of information. Mike, it has been an absolute pleasure. You are an,

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Speaker 4: Thanks for joining us. If you've enjoyed this podcast, we encourage you to join our Facebook group. It's called Your First Home Buyer Guide Australia, and it's your opportunity to connect with us and ask us your questions, which we will answer, meaning you can make sure that you are not getting led down the garden path.

Speaker 4: We hope to see you there soon.