Can You Trust AI for Property Research? The Hidden Risks First Home Buyers Need to Know
When you're buying property in Australia, the stakes are high, and the margin for error feels razor-thin. So it's no surprise that more buyers are turning to AI tools like ChatGPT to help with suburban research, price comparisons, and early-stage decision-making. On the surface, it feels efficient, affordable, and impressively confident—almost like getting instant investment property advice without paying for it.
But confidence doesn't equal accuracy. And when it comes to property, relying on the wrong information can easily become a six-figure mistake.
In this episode, Veronica Morgan and Meighan Wells are joined by Mike Mortlock, Managing Director of MCG Quantity Surveyors, to unpack new research that puts AI through a real-world property stress test. What the study revealed was confronting. From inaccurate recommendations to completely fabricated data, the findings highlight serious AI property research risks that most buyers don't realise they're exposed to—especially when they don't yet have the experience to question the output.
Here's what we cover, and why it matters:
🤖 The AI "Hallucination" Trap in Property Research
One of the most alarming issues uncovered in the research is the prevalence of data hallucinations in property analysis. In simple terms, this is when AI confidently generates information that isn't just wrong—it doesn't exist at all.
In Mike's study, ChatGPT didn't just misinterpret property data; it invented suburbs, misreported market metrics, and filled in gaps with assumptions that sounded professional but had no grounding in reality. This is particularly dangerous for first home buyers, because the answers often feel logical and well-reasoned.
If you've ever wondered, "Is ChatGPT reliable for property research?" this episode delivers a clear answer: not without human oversight. AI doesn't know when it's wrong. It doesn't flag uncertainty. And when buyers treat its responses as facts rather than starting points, those hallucinations can quietly steer decisions toward poor-performing assets or unsuitable locations.
📉 Why Outdated Property Data Can Cost You Thousands
Another major issue is outdated property data. Most AI tools rely on historical datasets with a built-in knowledge cutoff. In slow-moving industries, that might be manageable. In real estate, it's a problem.
We unpack why AI fails in fast-moving property markets like Brisbane and Perth, where prices, demand, and days on market can shift dramatically in a matter of weeks. When AI references old property data, it often misses turning points—like demand surges, shrinking stock, or rapid price recalibration.
The result? Advice that's already obsolete by the time you act on it. Buyers following that guidance can find themselves overpaying, chasing properties that no longer exist, or misjudging growth potential entirely.
⚖️ Suburb Research Errors and the Missing Human Context
Many buyers use AI to speed up suburb research—but that shortcut often creates new problems. Suburb research errors are common when nuance is removed from the process.
AI struggles to interpret boundaries like SA3s versus LGAs, and it can't assess micro-level factors that materially affect livability and value. A chatbot doesn't know the difference between a flood-prone pocket and a premium street two blocks away. It can't assess school catchments, street noise, or local buyer sentiment.
This is where the dangers of using AI for house hunting become clear. Property decisions aren't just data-driven — they're context-driven. Human professionals combine numbers with lived, local insight. AI can't replicate that.
🧠 How to Fact-Check Property Suburb Data Properly
With so much conflicting information online, it's easy to feel overwhelmed. One of the most practical parts of this episode is learning how to fact-check property suburb data without drowning in spreadsheets or opinion pieces.
We break down how to cross-reference multiple data sources, identify reliable indicators, and filter out marketing-led narratives. Rather than relying on a single tool, buyers are encouraged to validate insights using verified property data, local expertise, and clear buying criteria.
This is the same structured approach taught in the First Home Buyer Course—simplifying decisions without cutting corners that could lead to regret.
🛡️ Using AI Without Putting Your Property Future at Risk
Technology isn't the enemy—but it's not a crystal ball either. While property data and AI tools can support early-stage learning, they shouldn't replace experienced guidance.
Buying property in Australia still comes down to fundamentals: strategy, context, timing, and risk management. This episode shows how to use AI as an assistant—not a decision-maker—and why investing in quality investment property advice can protect you from becoming another cautionary tale.
🎯 By the end of this episode, you'll understand the real risks of AI-driven property advice, know how to spot unreliable or hallucinated data, and feel more confident making decisions based on verified facts and human expertise—not a chatbot's best guess.
