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The AI Promise For Indian Real Estate

Abhinav Aggarwal, Head – IT, Mahindra Lifespaces

With more than 15 years of professional experience, Abhinav’s forte lies in aligning all things 'technology' to organizational objectives and goals, thereby delivering on business results.

Just like the way the internet changed the world in the last 20 years, AI & ML are no longer buzzwords, and will change the way the world functions in the coming two decades. Organizations with the foresight and patience to adopt AI & ML could emerge as front runners.

Just as nothing good in life comes cheap, AI programs will need more resources than budgeted and will not yield results in the short term. The trick will lie in the ability and willingness of organizations to identify genuine AI use cases, to make investments in AI resources, to action the insights that emerge from such applications and do all things right by DATA – knowing what to collect, how to collect, where to store and how to use - for AI & ML to fire effectively. All the above factors also explain the slow adoption of AI & ML in the Indian real estate space.

The sector has taken a while to evolve from family-run conventional enterprises to professionally managed organizations. Developers have had to spend considerable time in getting basic ERP, CRM or HRMS tools in place, before even considering new-age technology.

Data was not king for the longest time. Both data collection and data nurturing have been huge gaps for our industry. There was a strong belief that product, pricing and location were enough to drive sales. Given the homogeneity of the real estate product and competitive nature of sales today, developers are increasingly realizing that rich, actionable data on consumer behavior, buying patterns and demographics etc. plays a huge role in the sales process.

Real estate is talent-starved when it comes to AI/ML. While investments in brand partnerships or aggressive marketing plans have traditionally found traction, AI-focused hiring has not seen commensurate rigor.

But there is good news for us. NLP is probably the one AI (ML) concept which has found instant traction very early on. It seemed to be a quick win for business teams: bots answering historically known questions equals instant online traction. Then came unfounded panic - one wrongly

answered question would make visitors change their mind about buying! Thus, a human being taking over the interaction after one failed NLP response, inadequate monitoring of chat analytics and lack of calibration of ‘A’ in the Q&A caused regression to web forms. However, NLP is making a resurgence in these COVID times.

Interested home buyers wish to ask free-flowing questions, not just in English but also in regional languages. The use of NLP in text/voice bots could play a significant role in converting online visits to bookings, and in running critical CRM operations (welcome process, demands & payments, service requests). The success of any NLP-powered chatbot will depend less on the software and more on sustained efforts to improve the bot’s accuracy in answering questions.

Indian real estate now appreciates the under-utilized mountain of data it has been sitting on. Being a high involvement process, a real estate purchase leads to multiple customer touchpoints (datapoints). This is further accentuated by the pace at which the industry’s landscape is evolving – regulation, GST, customer awareness, price movements, revenue recognition.

The list of AI use-cases in real estate is mushrooming and it is up to us to find what’s relevant for us and create a meaningful program

AI tools with the ability to model sales forecasts, cash flows, buying patterns and consumer behavior are gaining traction. Equipping Sales teams to convert deals faster than before – is a sought-after result of AI application. We can potentially pre-equip Sales teams with actionable insights – a better understanding of what the customer needs, positive/negative influencers, buying behavior, etc. Such interactions promise to be more meaningful and consequently create long-lasting customer relationships. Of course, GDPR/PDPR will rightfully limit the amount and kind of data which can be collected and used.

Like NLP, Augmented reality (AR) has made a resurgence in Indian real estate. Developers are adding AR based virtual walkthroughs during online customer meetings to enable views of the entire project, amenities, views from inside the apartment, outside views from the apartment et al. Customers can experience the site from the safety of their homes.

AI Use-cases in Indian Real Estate
Building Management & Home Security Systems:
It can algorithmically predict issues with energy & water consumption, elevator & fire safety, HVAC and infrastructural issues. The need to ensure efficiency in utility spends and resident safety have taken paramount importance.

Building Measurement: Measurement of apartment dimensions/area using AR

Design Elements: AR/VR based addition of design elements like furniture etc. to an apartment layout to facilitate discussions between interior designers and homebuyers.

CCTV Analytics: CCTV Analytics at construction sites to point out instances of non-compliance – construction workers not wearing helmets/safety jackets/safety harnesses, entry into hazardous zones, safety net deployment, etc.

The list of AI use-cases in real estate is mushrooming and it is up to us to find what’s relevant for us and create a meaningful program to institutionalize such projects, make investments in the right resources and have the patience to execute (and the freedom to fail)! Any meaningful results from AI, ML can only emerge over a period of time – once the machine has learned what you need it to learn.

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