1. Automated valuation ma­chines/ Price estimators: The price of a house is influenced by various factors like location, brand, size, amenities, construction quality, age, facing, interiors etc. To have a rule based approach to determine which factor is influencing price how much is almost impossible beyond a point, and in such cases, machine learn­ing based approach scores really high. If you train your models with huge number of listings data with price, the system will automatically figure out what is best for a given input data.

2. Fraud content detection in listings: In a country like India, where there is no central source of listings (MLS), it is very important to have a quality control on the list­ings quality to deliver a higher qual­ity experience. To verify millions of listings on the platform manually is non-scalable. The machine learning approach has really proved to be a boon in such a case wherein we have trained millions of listing data (im­age and other content) and reached a level where the identification of real estate images, their classification started happening automatically. Even on the non-image listing con­tent the NLP along with ML works really well.

3. Lead scoring: This is a classic problem across verticals. The good thing about real estate is if the jour­ney is longer, the user provides many signals before transacting a proper­ty, which enables us to the lead scor­ing in an effective way. If one com­bines the click stream data on the website along with CRM data col­lected at various states of journey in sales, then a very effective lead scor­ing system can be developed which can be used to effectively utilize the sales productivity.

4. Sentiment analysis on user gen­erated content: Real estate is such a big decision that before going for transaction users discuss and measure the decisions on various parameters. In case you have a platform, which has a lot of UGC, then you can use machine learning to come up with the sentiment analysis of a given de­velopment, neighbourhood, develop­er, etc. This can be further leveraged for better decision making and repu­tation management.

There are many such applications of ML/AI in real estate e.g. Under­standing the context of conversation from a recorded audio conversation, Automatic response by bots on real estate queries based on the knowledge base across platforms etc. So far, we have just explored the obvious use cas­es, but the possibilities are immense.

I am really excited with the pos­sibilities and avenues these new tech­nologies are opening to address some of the core issues of information asym­metry, trust deficit, pricing stand­ardization etc. in real estate. If used effectively, this can turn out to be a real game changer towards the larger benefit of better customer experi­ence. The music is on and the future is bright.