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Discover how GenAI is transforming real estate by accelerating development, enhancing customer engagement and enabling smarter decisions with EY insights.
In the latest episode of EY India Insights podcasts, Chaitanya Seth, Partner, Real Estate, EY-Parthenon India shares insights on generative AI (GenAI) reshaping the real estate sector – from accelerating land-to-launch cycles and enabling smarter product decisions to transforming sales, customer engagement and construction management. He also shares use cases that are already delivering value for real estate developers. The conversation also highlights how developers can move from experimentation to scaled adoption through strong leadership commitment, robust data foundations and operating model transformation.
Key takeaways
GenAI can help accelerate land-to-launch decisions by analyzing market trends, competition and customer insights and create stronger value propositions.
Personalized customer engagement powered by GenAI and Agentic AI can enhance lead conversion, sales productivity and marketing effectiveness.
AI-driven project monitoring can improve schedule predictability, cost control and risk management through predictive insights across construction lifecycles.
Developers can unlock significant efficiency gains by using GenAI to automate RFP analysis, call audits, training and reporting.
Successful GenAI adoption requires strong leadership commitment, a clear roadmap and strong data foundations.
GenAI can help developers turn diverse market insights into compelling value propositions that accelerate sales and improve launch success.
For your convenience, a full text transcript of this podcast is available on the link below:
Welcome to the new episode of EY India Insights podcast, where we bring you perspectives on the trends shaping businesses, industries and the economy. I am Pallavi, your host for today. In this episode, we explore how generative AI (GenAI) is transforming the real estate sector – from smarter decision making and faster project launches to improved customer engagement, construction efficiency and enterprise value creation.
To discuss this further, we are joined by Chaitanya Seth, Partner, Real Estate, EY-Parthenon India. Chaitanya will be sharing insights on how real estate companies can move from experimentation to scalable adoption and what leaders need to do now to stay ahead in a rapidly evolving market.
Hi, Chaitanya, thank you for joining and welcome to the podcast.
Chaitanya
Thank you for having me, Pallavi.
Pallavi
How do you see GenAI playing a role for real estate developers and what impact can it bring to the real estate sector?
Chaitanya
I see GenAI becoming a true transformation catalyst for the industry. To understand that better, let us understand what the industry is witnessing today.
The industry is witnessing a significant shift in its priorities. We see an intense competition now, which means that as a developer, I need to have an accelerated speed and effectiveness of my launches. We see an increase in construction cost – both in the material and the labor, which means that I need to ensure that my construction cycles are optimized and I have far more control and predictability of my schedule, my budget and my risk.
We clearly see that there is a shift from what used to be sellers’ market to buyers’ market now, which means that I need to be closer to the customer and need to understand my customer better. The imperatives around experiences have amplified in the last few years.
Lastly, the industry is shifting from being semi-organized to becoming more organized now. We see Real Estate (Regulation and Development) Act, 2016 or RERA and GST. We see a lot of developers getting listed; capital markets, private equity (PE) money coming in, which means that a developer needs to have a faster turnaround, more transparency, and far more automation predictability in the business. So, all these are the imperatives which the industry is facing, which leads to the fact that we need a catalyst to accelerate and transform all these lifecycles. GenAI can actually help have an accelerated land-to-launch cycle, more successful, more effective.
It can help us do better and faster selling by reimagining the entire operating model – the way I reach out to my channel partners, the way I do my marketing effectiveness and the way I run my referral and loyalty campaign programs. It can actually help us drive personalization at scale, which means that I can give far more effective and curated customer experience, from lead booking to booking to position and post position.
The last would be the ability of technology to look at how I can optimize my schedule and my cost. That is again a big lever, which GenAI can play across use cases within procurement, contracts and monitoring, which can actually allow us to have a faster turnaround time, more efficiency and better profitability at the end of the day.
Pallavi
Could you also share some GenAI use cases with our listeners?
Chaitanya
Let us look at the four themes I talked about and let us take one or two use cases on each one of them.
We talked about land-to-launch. When a developer is looking to launch a project, one problem to be solved is of value proposition. What should I launch? Will I be able to sell effectively?
To find the answer, the developer will understand what the competition is offering, he will talk to channel partners, look at a few research reports, demand-supply data, registration data, understand the velocity in the last three months to a year in the market and the team will sit and analyze the data.
Now, what if we have a market scan bot which allows him to accelerate and give a far more comprehensive view? This means that one can ingest 7-10 brochures of competitors, which I go to the channel partner with and feed in the entire demand-supply data, velocity data including movement of 1BHK, 2BHK or 3BHK in the market and price points at which they are selling in the market.
One can feed in the intel gathered from channel partners, whatever information is available and it will collectively be analyzed. The bot can give a starting point in terms of what can be a potential value proposition in terms of the configuration, size, amenities and pricing recommendation. And that, to me, can actually give the developer a 30% to 50% head start.
For example, if I am looking to understand my layout – what should be the size of the kitchen? That is one differentiator I want to create based on the market research I conducted. One simple thing is to even look at the visuals or different op docs and understand in terms of what the competition is offering; it becomes so easy and convenient for a developer to look at.
So, the ability to look at varied sets of information, different questions and find meaningful answers and then ideate internally to create a value proposition, which can be compelling in the market – that is a simple but powerful use case because developers know that if they put up a launch which is not a right product in the market, they will take longer to sell.
Second example is of sales acceleration. The entire journey from pre-sales, which is a contact center wherein the customers are being reached out to make a site visit. That is one micro journey about which we can talk, which can be completely automated and transformed using Agentic AI and GenAI put together. You can use Agentic AI to make calls to the customers and you can have a GenAI coach to train human agents on how to go about it. You can have Gen AI, which can actually customize your entire collateral. So, it is not that customer A, B or C will get similar collaterals. While the information might be the same but the way it is being served can be more personalized. For example, if I am an investor or an end-user or an upgrade housing buyer, the proposition to me would be different and my collateral can be served in that manner. This allows smarter targeting.
So, GenAI can actually help me in coaching, dead lead harvesting, drive propensity modeling and place calls to customers and pursue them to make a site visit. It can do a 100% call audit, which allows me to assess the right training needs for pre-sales callers and make them more effective. So, efficiency can go up, scale can go up and personalization can go up significantly.
I pick up the third example from a construction site, which enhances the ability to look at the health of the project. Health of a project can broadly be looked at through scheduled cost and risk as the major parameters.
Today, developers have access to multiple information sources such as MS Word or an MS Excel daily progress report or a Venn model. It can be a MS project file or an Enterprise Resource Planning (ERP), but ability to actually synthesize the information and make those decisions and create a predictive model out of it – that is the skill which AI and GenAI I can create, which can give you a view in terms of what is the predictive health of my project and across its lifecycle.
So, even if I want to look at the fact that I have rolled out an Request for Proposal (RFP) in the market, there are seven or eight vendors which are responding to that RFP – all would reply with their own set, some inclusions, some exclusions, their own specs, making changes to even to the standard terms and conditions I would have offered – the tool can help you summarize and make a very easy comparison.
I am giving you a few examples of low hanging fruits which can be quickly deployed, allowing developers to start using them at an initial stage and later amplify the use case and make it part of the entire operating model. So, these are a few examples of the use cases about which I can talk.
Pallavi
What does an organization need to do in order to embark on this GenAI journey?
Chaitanya
Honestly, the industry has been a slow adopter of technology. It was the pandemic that pushed the acceleration of using digital or technology to a reasonable extent. So, the mindset has to be the starting point. We need to see this as a true investment and not as a cost. The first shift that we need to make to make this journey successful is that it has to be top-driven. There has to be a belief that this is a true catalyst which can activate disruption. There are significant potential benefits which it can unlock. We have talked about it in our thought leadership also that it can deliver 2X to 3X of increase in the enterprise value if you are able to embed it into the overall operating model.
So, first is the mindset – that is something which the industry will need to think of this as a changing, transformation catalyst. To me, it is no longer a choice; it is a strategic lever which developers need to embark upon.
Second is they need to have a clear strategy or a roadmap in place with well-defined business case. Which use case would work? What kind of impact will it have? And you create a center of excellence for GenAI, which has a healthy mix of people from both technology side and the business side, so that GenAI becomes far more aligned to business objectives, which can keep on rolling out the use cases, pilot them, scale them, amplify them so that they can deliver the business benefit.
At the same time, we also need to think through our data – how robust is our data; how well-shaped it is; how are current technologies being leveraged? We have seen many examples where data was being in great shape, which leads to garbage in, garbage out at the end of the day.
But yes, GenAI has the beauty of looking at unstructured data, images, structured data to make sense. But yes, data has to be at the core, which also needs to be thought through and data strategy is important to make this entire journey successful.
And then obviously you need to look at the talent, how you bring talent up to speed. Today, new people who are joining the industry are far more familiar and open to new technologies. So, it is also about training people and making them aware about it. And this will also lead to some change of operating model.
I gave an example of pre-sales – how my lead to walk-in journey can be transformed using Agentic AI + GenAI. We may have to rethink in terms of the people we are deploying, the training that we are providing to them and the output that we are expecting. So, right from the developers’ or promoters’ mindset to a well-defined strategy backed by business case, laying a strong data foundation, relooking at the way my entire operating model needs to be looked at in terms of my processes, my people, my technology – all when put together can make a lot of difference in making this journey successful.
Pallavi
That brings us to the end of this episode. A big thank you to you for joining us and sharing all your perspectives on the impact of GenAI and the use cases and overall, how GenAI is reshaping the future of real estate in India.
Chaitanya
Thank you for the great conversation.
Pallavi
Thank you very much. And to all our listeners, thanks for joining. As the sector becomes more data-driven, intelligent and customer focused, the real opportunity lies not just in adopting GenAI, but in embedding it across the real estate value chain to drive speed, efficiency and long-term value.
Thanks for listening. Until next time, this is Pallavi, signing off.
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