homes.co.nz

9.2%

Increase in AVM accuracy

“Not only has Restb.ai helped us with our AVM accuracy, but their tools have helped us develop new product solutions to ensure we continue disrupting the New Zealand property landscape.” – Tom Lintern, Chief Data Scientist – homes.co.nz

Challenge

From publishing listings to helping site visitors find inspiration when remodelling their home, homes.co.nz have spent the last few years at the forefront of innovation in the world of property portals. A recent focus has been to provide accurate home valuations to the thousands of buyers and sellers in the NZ market.

New Zealand’s real estate market has transparent and structured data by law, but homes.co.nz were the first to see it’s potential to better serve home buyers. So, homes.co.nz developed a proprietary valuation model to arm home buyers and sellers with extra valuable information.

Their AVM not only increases transparency and accurately evaluates homes, but it also raises traffic and leads to more visitors to their website. Always looking to break new ground, they asked themselves: ‘how can we make this better?’

“Restb.ai’s technology helped us to better understand the vast database of images that we process a daily basis. We increased our ability to differentiate between properties within similar value areas and expect the accuracy to increase even further!”   Tom Lintern, Chief Data Scientist – homes.co.nz

Project

Imagine an industry leading technology that can accurately determine the condition of a kitchen or a bathroom, just using images and in less than a second. With funding from the EU’s Horizon 2020 vision fund and a team of industry leading computer vision specialists, this is what Restb.ai created.

homes.co.nz knew that most AVM’s have a blind spot when it comes to home valuations: the house’s interior! When they found out about Restb.ai and their room condition technology, they had to test it to understand the impact it could have on their AVM’s accuracy.

Using the turnkey models, homes.co.nz ran a pilot of over ten thousand images to determine how influential Restb.ai’s proprietary room condition analysis model is. All tests included images of kitchens and bathrooms and they received results based on the condition of the rooms.

homes.co.nz graphic

Impact

After several months, the results came in and homes.co.nz noticed significant improvements in the classification of the houses’ interior quality. They found that their AVM has a greater ability to account for the relative difference in a home’s interior quality in a particular area, like a postcode.

In certain regions, the AVM’s median error rate dropped by up to 9.2% and homes.co.nz expects the accuracy to increase even further. Prices are more reflective of the house’s true value and buyers can see more clearly the difference between good and bad condition properties within the same area.

Homes.co.nz is continuing to explore how different bleeding edge technologies can help them better serve their clients, users and stakeholders and Restb.ai are proud to call them partners!

For more examples of how to improve AVM’s, check out our room condition use case
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