Skyrocket your real estate portal with computer vision and image recognition

23 June 2020 | 5min read

Many amazing technologies have come out of artificial intelligence, but computer vision is starting to stand out as one of the most popular, promising, and exciting branches of this field. Why? Because when utilized correctly, it has the power to greatly diversify markets and improve user experiences.

In a world where rapid technological change is the new normal, opting out of computer vision technology is no longer an option. According to Omdia, the computer vision market is expected to reach over 33 billion US dollars by 2025. This growth and increased adoption are expected to reach all industries, with real estate being no exception.

In fact, the real estate industry is already utilizing computer vision to identify and categorize different real estate related images. Let’s take a look at how computer vision is shaping the real estate industry today and into the future.

You will read about the next topics:

  • Image recognition vs computer vision
  • 3 benefits of computer vision for online real estate
  • Computer vision real estate use cases

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Image recognition vs computer vision

The terms image recognition and computer vision are often used interchangeably, but they actually have distinct definitions. Image recognition is a part of computer vision, but computer vision encompasses much more than just image recognition. Image recognition works by analyzing patterns and pixels within the image to find objects. By contrast, computer vision is the mechanism through which all image data can be “seen” and processed – it goes beyond just looking for patterns of pixels.

If this distinction seems a little subtle to you, then consider this. People use approximately two-thirds of their brain for image processing. While smell is the dominant sense for dogs, and hearing for bats, for humans, it’s sight. If we were to just recognize objects within the images we see, then our sight wouldn’t be remarkable. Recognizing that a car is in front of you is useful, but you also need to process everything in the image for it to become actionable information. How fast is it traveling in relation to its environment? Are there any hazards I need to be aware of?

3 benefits of computer vision for online real estate

Improved long-tail targeting

Time is a valuable commodity in the digital age, and people don’t want to spend several minutes browsing listings to find something that meets their needs. Today, prospects aren’t typing “New York, three-bedroom apartment” into Google. Instead, they are more likely to type “Three-bedroom apartment New York child-friendly with Central Park view”. Prospects expect to get a meaningful result that includes only the listings that contain all the keywords in the search term.

With real estate room detection technology powered by AI-based computer vision, this becomes a reality. This technology leads to thousands of custom listings that are optimized for specific keywords, and this can dramatically increase the visibility on Google.

Improve search engine results

The search engine results on the real estate portal itself can be improved with image metadata. You can avoid the time-consuming task of writing all those tags and image descriptions yourself, and redirect your time to more creative endeavors. The more accurate and in-depth the metadata is, the more accurate, comprehensive, and meaningful the search engine results will be.

Improving your metadata also helps drive the right prospects to your website and empowers them to find the listings that best suit their needs. Your visitors will be able to conduct a search using a broader range of keywords like “balcony”, “fireplace”, “garden”, renovated bathroom”, and more. This is really about connecting the right prospects with the right listings. When you attract uninterested parties to a listing because the metadata is too vague, no one wins. The user feels frustrated and the real estate company is extremely unlikely to get a sale.

Searching on more keywords is something Zoopla was already doing back in 2010. The goal of improving metadata isn’t new, but it has stood the test of time and continues to be a major focus for real estate companies today.

Fraud detection

Duplicate listings of images can harm the user experience and diminish the quality of your real estate portal. There are only so many duplicate listings a user will click on before feeling frustrated and coming to the conclusion that their time will be better spent elsewhere. Hiring a team of employees to manually remove duplicate listings is both time-consuming and expensive, and the more listings you have, the more time and money it will cost.

Image recognition enables you to find duplicate listings and duplicate images in a certain listing by the power of Artificial Intelligence. Once you find these listings, you can remove them easily and improve the user experience in a matter of clicks.

Computer vision real estate use cases

Real estate room detection

Understanding the type of room being displayed in a given photo can be incredibly valuable. The huge volume and turnover of images on real estate portals make it impossible to do this manually.

Knowing if a picture is tagged as front yard, kitchen or bathroom can be the difference between amazing user experience and complete confusion. Computer vision enables you to automatically classify rooms such as bedroom, bathroom, backyard, front yard, living room, and kitchen.

Armed with this information, you can ensure that the first picture on a listing is always the front yard, living room, or whatever else you think is the highest priority for your customers. This is just one example, but the possibilities are endless.

Real estate image object detection

Computer vision can go way beyond classifying an image, it can delve into all the small details of the room to provide even more value. When computer vision is utilized in real estate, it becomes possible to identify key features in the image that will deliver increasingly useful information to the user.

For example, it’s possible to identify the levels of daylight, whether the room has a fireplace, if there are hardwood floors, and much more. This information could be translated into specific keywords to improve the searchable metadata.

Real estate room condition analysis

It should come as no surprise that the condition of a room can vary greatly between listings. Some people are more comfortable with the idea of buying a “project” property, while others want something they can move into and feel comfortable in from day one. And of course, some prospective buyers will purposefully seek out renovation projects for development opportunities.

Kitchens and bathrooms tend to be the most important to buyers when it comes to condition. With computer vision technology, these rooms can be automatically tagged as “To be renovated” or “New Kitchen/bathroom”.

Detecting the condition of certain rooms is a great way to source ideal properties for renovation or narrow down the list of suitable properties for buyers.

Another possibility is to show advertisements for kitchen renovations on properties that have a kitchen that needs to be renovated.

Compete with computer vision

With thousands of listings going up every day, competition is fierce in the real estate industry and it’s becoming increasingly difficult for real estate companies to stand out from their competitors.

One of the ways companies can stand out is by competing on user experience. Computer vision for real estate portals can provide an amazing and seamless buying experience.

We, at Co-libry, create solutions based on AI and data. Request a free demo today and see for yourself how computer vision can improve your business.


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