Artificial intelligence (AI) and machine learning are revolutionizing technology all around the world. Both are pivotal in shaping most of the forthcoming innovations in every possible field.  AI, in layman terms, makes machines perform complex tasks, associated with human minds, in a smart manner. Machine learning makes computers (machine) analyse and solve problems by teaching it basic logic and making the machine intelligent enough to learn on its own, as it progresses to solve different variants of the problem.

When it comes to integrating AI, the real estate industry is not far behind. From how property seekers search for property to providing buyers with relevant information to analysing property prices – all the stages of property buying are now powered by AI and machine learning. Let’s understand how an online property portal uses these technologies to help property seekers find a home.

Whenever a customer, who is in the initial stages of property hunt, comes to a property portal the first point of interaction with him, is the search option. All the legacy systems which enabled this search were restrictive in nature. These systems only allowed customers to search on the basis of defined set of parameters say locality and property type.

But, imagine a scenario where one of the critical requirements for a customer is to have an elevator in the building because of his aging parents. To help in such situations, free text search has been established using AI and machine learning. Now customers can use a search query with a free flowing text which is unique to their property needs like “2BHK apartments in Chennai with 1200-1400 square feet area between 40 to 50 lakhs with elevator facility”. The search results will return the best properties that matches these criteria. This feature helps property dealers understand the customer’s requirement and priority better.

Text based search engines have further expanded to provide ‘search query suggestions’. It is an intelligent tool which learns, analyses and lists possible searches based on the user search query patterns in the past. The system learns the frequently used search criteria that are often used together and forms real time suggestions for buyers.  After each word that the customer types their best possible suggestions dynamically changes which simplifies the search process.

Another area where AI and machine learning are used during the property search cycle to enhance user experience and give property seekers recommendations in a platter is personalisation. Online realty portals personalise the property search experience for users by giving them property recommendations and relevant content recommendations.

With lakhs of properties in the market it’s difficult for a property buyer to filter relevant properties that suits their needs. Machine learning helps solve this issue. Based on the customer’s activities on the website the machine finds similar users and analyses their search patterns and comes up with a set of property recommendations for the buyer. The machine also learns what properties the user doesn’t like and uses that signal to tweak the recommendations.

Another aspect of personalisation is content recommendation. Every buyer has a multitude of questions during their property buying journey. These queries change depending on the stage of the property buying cycle. At the early stage of property search, a buyer might have doubts about where to buy a property or the buying process or details on loans, but during the last stage of property search he might be looking for types of sale deeds, home loans assistance etc. The recommendation engines analyses the property search patterns and smartly identifies relevant news articles, reports, localities of interest, property buying tips and other relevant and timely information for the buyer.

Using AI and machine learning, machines are now able to understand and analyse unique customer requirements and they are equipped to give them customised property solutions. In this digital age real estate has moved way beyond the traditional property search techniques and evolution in technology is only making the process simpler.

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