Technology helps companies in many ways. One way in particular, where technology has made a real difference for people and businesses, is in the real estate sector. Within the last few years, finding an apartment to rent
Just a few years ago, finding an apartment meant actually doing the legwork, meeting with brokers, and visiting properties. In fact, just finding out the “right price” in a neighborhood was quite a challenge as there was no transparency. However, technology has changed everything — and transformed the industry in leaps and bounds.
Today, we have drones climbing up to a certain floor height to show us what the view from a property that is yet to be constructed will look like (exactly) — and augmented reality helping people fit-out furniture they can’t (really) touch in rooms that haven’t been built yet.
However, one of the most exciting technologies in the real estate space that is companies are experimenting with and are still hush-hush about is artificial intelligence (AI).
To understand how AI can help further transform the real estate space and deliver an exciting user and customer experience (UX and CX), Tech Wire Asia caught up with PropertyGuru CTO Manav Kamboj.
“We leverage AI to deliver relevance, insights, and quality to property seekers,” said Kamboj.
Since 2016, the company started experimenting with AI-driven solutions, particularly around property recommendations.
True, AI-powered recommendation engines have been around for quite a while now. In fact, they’ve propelled companies like Netflix and Amazon far ahead of their competitors and are often referred to as one of the defining technology milestones for these companies.
However, in the real-estate space, AI-powered recommendation engines are slightly different as the end product, the property, will only be sold once and hence, a property portal can’t operate in the same way.
“It’s why in the property space you need to take that simple filtering algorithm and add deep learning in terms of what are the kind of property pictures that the customer has been inclined to view more, or is there a specific set of keywords like (great view or higher floor) that the customer hasn’t categorically expressed but you’re learning from user behavior,” explained Kamboj.
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