Location is still the single strongest factor when a home is priced. Two homes of exactly the same size in the same postcode can be 10-20% apart in price, and the difference has nothing to do with square metres or the year they were built. It comes down to where the home is.
If you want to understand how AI assesses location, that is the starting point you need. The models do not treat a neighbourhood as a feeling, but as a dataset of distances, neighbourhood names and registered sales. The street, the neighbourhood, the school catchment area and the distance to grocery shops, public transport and green spaces become figures that can be compared across thousands of homes. Urban versus rural is only the crudest division in that comparison.
Models trained on hundreds of thousands of transactions can estimate a property’s value in seconds, weighing dozens of characteristics at once: floor area, floor level, year of construction and distance to public transport, to name just a few. The amount of data behind each answer is hard to match by hand.
Even so, they do not replace a professional valuation. They complement it. A model gives you a quick, data-driven reference point, but it cannot see what is not in the registers.
This article gives you an insight into how the models actually weigh location, and which aspects of an address they cannot see.
Location and property price: from postcode to residential street
Start with the postcode, and you immediately see how big a role the address plays. Two homes with the same floor area, the same condition and the same year of construction can differ in price by a factor of 3-4, depending on whether they are in Gentofte or in Thisted. The same house, two very different price bands.
Zoom out to the national level and the pattern becomes even clearer. Houses in Copenhagen sell for around 54.781 kr./m², while the national average is 18.626 kr./m². Location and property price are so closely linked precisely because demand concentrates in particular areas. The address is part of the price itself.
But the postcode is only the first layer. Within a single municipality, prices can vary by 30-50% depending on the street, the school and the type of road alone. So you can live in the right municipality and still land on the wrong side of the price range if the address points to a busy road or a less attractive school catchment area.
The last layer is the street and the plot. Even within the same postcode, the type of road and the orientation matter a great deal. A detached house on a quiet residential street is worth more than a similar house facing a busy road, and a south- or west-facing garden sells for more than a north-facing one. Light, quiet and a view are not details the market overlooks. They help set the figure.
This is how the layers stack up: the postcode sets the frame, the municipality and the school catchment area adjust it, and the street and the orientation of the plot settle the rest. It is the same logic an AI model builds on when it weighs an address, only with hundreds of thousands of sales as its basis for comparison.
Automated valuation and location: the data behind the AI answer
Behind every quick answer about an address lie a machine-learning model and a register that feeds the model with figures. That combination is what makes it possible to put a number on location in an AI property valuation.
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BoIQ is a Danish AI-based property valuation tool built on machine learning that draws data from more than 40 sources on Danish properties. The model factors parameters such as location, property type, size, energy label and local environment into its estimate of the value.
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Geomatic supplies one of Denmark’s most widely used AVM solutions (Automated Valuation Model), and several Danish banks use it to price homes. The data comes from official registers such as BBR (the Danish building register) and Matriklen (the Danish cadastral register), so the basis is the same information the authorities themselves hold.
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DinGeo is one place where AVM valuations are available. The platform aggregates several computer-generated valuations, so you can set them side by side and see where they point.
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An AVM calculator is a statistical model that estimates a home’s value from the address alone. Its typical accuracy is within ±20-30% of the actual selling price.
Automated valuation of location is strong when the model can lean on many comparable sales in a well-known area, and looser when the address is unusual or in a thinner market. That is why you should read an AI estimate as a range rather than a definitive answer.
Location as a factor: how AI weighs area, transport, schools and everyday amenities
When a model assesses location as a factor, it is rarely about the distance as the crow flies on a map. In practice, a good location means being close to everyday life: school, nursery, shops and public transport. What counts is the walking distance, and whether the route feels safe for a child to walk. One kilometre along a busy road feels longer than two kilometres along a quiet residential street, and the models are increasingly picking up on that difference.
Four things come up again and again when the local area has to be turned into figures. Schools within walking distance push the value up, and so do grocery shops close by. Public transport within reach is something buyers pay a premium for, and green spaces and nature add value to a home. A home close to a school, shops, nature or transport is therefore typically worth more, and that is the logic behind every AI property valuation.
Nature premiums along the lakes, on the other hand, are something calculators consistently underestimate. In Silkeborg, Sejs-Svejbæk and Virklund have the strongest price growth, driven by Aarhus commuters’ interest in natural surroundings, and in Sejs-Svejbæk prices for detached houses are 18.000-28.000 kr./m². It is one of the places where an automated estimate often comes in below what the market actually pays for the view and the tranquillity.
In Birkerød the picture looks a little different. Here the average price of detached houses is 42.274 kr./m², and the single most important factor is S-train line A, which takes you to Nørreport in around 20 minutes. The quality of the natural surroundings and a strong school system also push prices up, but it is the commuting time that underpins the price. Two areas, two different weightings, and both show why the models do not treat location as one single quantity.
Property value and location: what AI cannot see
An AI estimate is a quick reference point, but it is not a final price. An online property calculator based on AVM is typically accurate to within ±20-30% of the actual selling price. A professional valuation by an estate agent, by contrast, lands within ±5-7% in 80% of cases. That difference decides whether you are holding an educated guess or a figure you can act on.
Some things the model simply cannot see. AI cannot assess unique factors such as the home’s actual condition, the view, noise from the neighbours or the quality of a renovation. The model does not know whether the kitchen is from IKEA or Bulthaup, and it has no idea that a heat pump has been installed or a new roof put on. Two homes with the same BBR data can therefore be far apart in real value without the estimate picking it up.
An algorithm uses all the available data. That includes atypical sales, forced auctions and family sales at a reduced price, and they can skew the estimate in either direction. A local estate agent sorts through the same data and uses only the most relevant comparisons, so that a single skewed sale does not spread through the whole valuation.
That is why a local valuation looks at the neighbourhood more narrowly and more precisely. The estate agent compares with actual sales in the neighbourhood within the last 6-12 months and within a radius of 1-2 kilometres. It is a different method from drawing on a national average, and it captures the factors the algorithm overlooks.
Property value and location are still linked in a way that no model fully calculates. AI sees the street, the postcode and the distances. It does not see the view from the first floor, the noise from the neighbours or the craftsmanship that went into the renovation. Those things take a person who has stood in the home and knows the local market.
The two complement each other. An AI estimate gives you a quick, data-driven starting point, and a professional valuation gives you the precision that figures alone cannot deliver. Use the estimate to get your bearings, and let the local valuation sharpen the picture before you make a decision.
AI and property prices: how to use the answer wisely
Use the AI estimate to see the direction rather than to set an amount. AI-based valuations are good indicators of general price trends and the market level in an area, but they do not replace a professional valuation of the individual home. Read the answer as a guide to how the market in the area is moving, and keep it separate from the question of what that particular home is worth.
This is where the municipal average falls short. An average does not take into account the specific location, condition, layout or plot, so the result is not a valuation but a starting point. Two homes in the same municipality can share an average figure without being alike in real value, because one is on a quiet residential street with a south-facing garden and the other faces a busy road. The figure describes the setting, not the home.
That very difference is worth holding on to, because it determines how you read the signals in the AI answer. Brikk helps home buyers and sellers understand what the location really means for the specific home, and how the location signals in the AI answer should be interpreted, so that a figure for the area is not mistaken for a price for your address.
Before you make a decision, you should combine the AI estimate with the factors the model cannot read:
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Always compare the AI estimate with actual sales in the neighbourhood within the last 6-12 months.
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Assess the home’s actual condition.
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Check the energy label.
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Look into the local planning conditions.
AI gives you a quick, data-driven starting point for the location, but the decisive value lies in what a human eye adds. If you want to get from the figure to what the location really means for your particular home, this is where Brikk comes in.
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