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AI property valuations: useful tool or false sense of certainty?
By Jarrod McCabe
AI is transforming how property buyers access information. At a minimum, it can deliver demographics, median prices and sale histories in seconds. But when it comes to assessing the value of a specific property, the technology has limitations – and buyers who treat AI-generated valuations as fact, risk making decisions based on flawed data.
That’s not to say AI doesn’t have a place. It does, and it will only get better. But understanding where it currently falls short – and what a physical inspection reveals that no algorithm can – is essential for anyone serious about getting value right.
What AI relies on – and where the data falls short
Sales evidence only works if it’s accurate
AI valuation tools draw on sales databases. So do human valuers. The difference is that a human can spot errors that AI cannot. A $1.1 million sale recorded as $2.1 million or $110,000 is obviously wrong to anyone with local knowledge. AI doesn’t have that filter – particularly the free tools readily available online. Incorrect data in means incorrect valuation out.
The same applies to property details. If a two-bedroom apartment has been recorded as a four-bedroom house, the comparison is meaningless. A human would catch that. An algorithm relying on the data as entered will not.
Not all sales reflect market value
AI tends to treat every recorded transaction as a market sale. Many are not. A family sale where parents sell to a child at a discount is not market evidence. Neither is a divorce settlement where the property transfer reflects a broader financial arrangement across superannuation and other assets. An adjoining owner paying a premium because the property adds significant value to their own holding is equally unrepresentative.
These transactions sit in the same databases as arm’s-length sales. A human can identify them and set them aside. AI – at least in its current form – generally cannot.
Treating all sales as equal
Even among genuine market transactions, AI struggles to account for the attributes that create meaningful differences in value. A corner site with dual street frontage is worth more to a developer than a mid-block equivalent. A north-facing property commands a premium over one facing south. The condition of a building – not just renovated versus original, but the quality and extent of work done – significantly affects price. These are judgements that experienced buyers and valuers make instinctively. AI is improving in this area, but it’s not there yet.
Properties with limited online history present another gap. If a property hasn’t sold for 25 or 30 years – or has never sold at all – there may be very little information available for an algorithm to work with, particularly if the last transaction predated the internet’s role in real estate.
What a physical inspection reveals
Surrounding and adjoining uses
You can’t assess a property’s context from a screen. The proximity of industrial sites, the smell from nearby restaurants or takeaway shops, the noise from a train line a street away, high-voltage power lines overhead – these are things you experience on the ground.
An apartment block next door creating overlooking or overshadowing issues, a derelict property nearby, vacant land with uncertain development potential. All of these influence value and none of them reliably show up in an AI assessment.
Renovation quality and floor plan
Nearly every buyer has inspected a property in person and thought, “That’s not what I expected from the photos.” Sometimes it’s as simple as a coat of paint over cracks. Sometimes it’s the difference between a Bunnings kitchen and bespoke cabinetry. It’s a distinction that matters enormously to value but looks similar enough in a photograph.
Floor plan is arguably even more important. It’s the first thing we look at when a property comes online. Proportions, flow, how bedrooms and living zones connect – these determine how a property lives. A dwelling with the wrong layout or poor internal proportions can be significantly less valuable than one of equivalent size that flows well. AI does not yet capture this nuance reliably.
Position and structural integrity
In apartments and units, position within the block has a material impact on value. Privacy, natural light, noise from driveways or bin storage, aspect and elevation – a ground-floor apartment beside the driveway is a very different proposition from a top-floor unit with a city view. For villa units, front-of-block versus rear changes the buyer appeal entirely. These distinctions require inspection.
Structural integrity is similarly invisible online. Cracks in walls, the smell of rising damp in period brick homes, floors that indicate restumping is needed, downpipes dumping water under the building rather than into stormwater – most people can feel or see these things within minutes of walking through. Photographs are there to capture the positive features of the property, not to focus on any detrimental aspects.
ON GROUND CASE STUDY
When AI got it wrong by $100,000
A client came to us concerned that their property had dropped $100,000 in value within 12 to 18 months of purchase. They were relying on a free website valuation that showed the property well below what they’d paid.
When we investigated, AI had benchmarked the property against a recent sale in the same block. The sale was legitimate, but it was a family transaction, with one relative buying out another at a price well below market rate.
AI treated it as market evidence. It was anything but. Once we explained the circumstances, the client understood, but without that context, they’d have been left believing their investment had gone backwards.
Take home message
AI valuation tools are useful as a starting point, but they are not a substitute for informed human assessment. The data they rely on needs to be accurate, the transactions need to be genuinely market-reflective, and the nuances of floor plan, position, renovation quality and structural integrity need to be assessed in person.
As with most things, you get what you pay for. Free generic tools can provide a rough indication, but they don’t paint the full picture. For decisions of this magnitude, that gap matters.
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