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Investors beware: ‘Lazy’ buyer’s agents are costing Aussies millions
A property expert has urged investors to be aware of “lazy” buyer’s agents who cut corners through hotspots generated by artificial intelligence (AI), risking thousands in losses.
Hotspotting director Terry Ryder has warned indolent “buyer’s agents” to stop relying on AI-generated hotspot predictions to help their customers invest in property.
“Lazy practitioners who call themselves ‘buyer’s agents’ are asking ChatGPT to tell them the best place in Australia to buy. I fear for the investors who rely on ‘advice’ from such people,” Ryder said.
According to Ryder, relying on AI has become widespread among inexperienced practitioners, who do not understand local market details, such as street, suburb, or area levels, which are essential for choosing the right locations.
He said that attempting to bypass the thorough research needed to pinpoint future hotspots by leaning on AI generalisations will inevitably fail and could be very costly for investors.
“At the risk of appearing like a dinosaur in the digital age, I doubt the people trying to make AI do the work for them will ever succeed because real estate has too many subtle and intangible elements.”
“Asking a bot to scour the metaverse and provide the next hotspot is inviting a presentation on the great seething mass of misinformation that pervades real estate.”
Ryder’s warning followed a recent analysis by MCG Quantity Surveyors, which found that ChatGPT’s suburb recommendations were incorrect in over half of the tested cases, even when using carefully curated datasets.
Using a $1 million budget, the study asked ChatGPT to list suburbs that meet key investment criteria and found that AI struggled with basic property research, producing statistically unreliable recommendations.
The report tested AI across beginner to advanced settings, yielding mixed results, with its performance declining in the “deep research” mode.
The research showed that AI consistently favoured unit selections and focused narrowly on yield metrics, leading to repeated errors in days-on-market and 12-month price changes, and, occasionally, even fabricated locations.
While some suburb suggestions aligned with industry recommendations, the analysis found that AI frequently underestimated pipeline supply and inventory risks, critical factors for medium-term capital growth.
Study authors said that oversight was particularly problematic in areas with an oversupply of certain types of apartments.
“In this vast nation, with so many different scenarios playing out at any point in time, there are always options to buy well in locations with good future prospects,” Ryder said.
“A smart buyer will consider all of Australia as their market and never have blind faith in a basic suburb list that AI has produced.”
“Treating AI as a search engine or property research tool merely invites it to regurgitate all the misinformation on real estate that is pervasive in the property investment industry,” he concluded.
