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Almost every sector today is embracing technology. One of the ways in which technology is enabling a better quality of services by automating time-consuming yet straightforward tasks, thereby allowing humans to focus on more important and complex matters. In the following article, we look at how this philosophy applies to the real estate sector. In particular, how real estate is benefitting from the massive advances in AI.

Traditionally, when you want to buy a new property, say a house, you contact a real estate agent so that they can show you the best available houses according to your requirements and budget constraints.

In the pre-internet era, there used to be an information asymmetry. Meaning, most of the knowledge about a transaction resided with the middleman, in our case the agent. With the advent of the internet, this asymmetry started breaking. Anyone, in any part of the world, was able to access the details about the prices of real estate in a particular location. However, this wasn’t enough. There was a ton of information available, which in turn led to users browsing through websites, but unable to decide on an option. This is where AI takes this process a step ahead. One of the key advantages of using AI is getting personalized recommendations. Recommender systems, a popular AI application is used to filter out a few of the thousands of possible options.

As a result, you save time, energy as well as get an idea of how would your dream property look like and what price would you pay for it.

Skyline, An Israeli company, founded just last year, but with offices in one of the world’s most expensive real estate markets, New York, Skyline AI just raised a $3 million Seed round in March from top VC firm Sequoia Capital. The startup claims its platform can tell real estate investors what properties offer the best return by ingesting tons of data from more than 130 sources, taking into account over 10,000 different attributes on every property, going back as much as 50 years on every multi-family property in the United States.

If you are into Machine Learning competitions, you must have heard or even participated in the House Price Prediction Challenge. A popular competition, here the participants have to train a model that can predict the prices of the house based on many parameters relating to the locality, house size, house age and many more. Famous as a competition, this idea has a lot of business value in it. Pricing is perhaps the most critical deal breaker/maker in the real estate business. Methods like Machine Learning, Time-Series forecasting, can be used to evaluate the price of property not only today but also in the future.

London-based Proportunity, which raised $1.7 million last year, might be an even more attractive acquisition for a company like Zillow in the future. Founded in 2016 by a pair of Romanian entrepreneurs, Proportunity claims its machine learning algorithms can accurately forecast which homes, and even neighborhoods will experience the most significant bump in value over a specified period.

Computer Vision, a broad field in AI that deals with extracting information from images has one promising application in the real estate sector. The structural properties, which include any damages can be monitored using Machine Learning techniques. Just a few images of the structure can give you quick results as to how healthy your future house is.

In India, startups like Housing.com have reinvented the way people in India hunt for rentals and buys or sells properties. The startup used infused technology and innovation into the business with its verified listings, availability of maps and location descriptions, search widgets, high-quality pictures, refined search through filters and much more. More startups like 99 acres and commonfloor are on a similar mission to simplify real estate.

If you wish to learn about more AI trends, do give the below article a read-

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