“Data is the new oil,” said Clive Humby, the British mathematician and data entrepreneur. The quote not only underlines the importance of data in business operations and the world economy but also notes the necessity of deriving value out of data by turning information into insights.
Today, almost every industry’s success is data-dependent. The real estate industry has also jumped on this bandwagon, and many organisations are already reaping the rewards of data-informed decisions.
Here’s how big data is transforming the real estate industry.
How big data works in real estate
Big data refers to complex and massive data sets that help predict industry trends, consumer behaviour, and business outcomes. These forecasts and estimates drive intelligence-based decision-making, enhancing resource optimisation, strategic planning, and cost-efficiency in business.
In the real estate industry, big data analytics is spurring the growth of both residential and commercial real estate.
Big data relies on machine learning algorithms that aggregate and interpret vast amounts of data. This data is automatically collected through access to application programming interfaces (APIs) and the integration of multiple databases. Once collected, patterns and trends are extracted from this raw data, resulting in market predictions that benefit both buyers and developers.
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Big data as feature-rich tech solution
Big data analytics offers numerous advantageous features to the real estate sector worldwide.
Unlike traditional methods that count on historical trends and professional expertise, big data provides real-time, accurate information, enabling businesses, developers, and investors to assess risks better and identify lucrative opportunities. It enhances the efficiency of operations, improves decision-making processes, and maximises the scope of profit, allowing real estate firms to be more agile and responsive to market demands.
Developers can also use big data to identify high-potential areas for investment by cross-referencing relevant data sets, such as infrastructure development plans and demographic trends. It improves property evaluations by incorporating current supply and demand, sales volume, property characteristics, and construction costs into AI models and predictive algorithms. Further, it boosts property development by pinpointing land parcels for high-yield investments and tailoring amenities to resident needs.
Moreover, realty analytics facilitate risk mitigation. From construction-specific risks to possible regulatory hurdles, big data is a window into problems that may occur while developing a real estate project or purchasing land, helping builders and investors determine the long-term viability of the real estate investment. Considering insights drawn from big data may also help buyers choose the right insurance for their properties.
For example, someone living in Delhi, which falls under seismic zone IV, would require greater structural and content damage coverage than someone living in Bengaluru, which has areas divided between seismic zones II and III. Predictive analyses can highlight these critical points while building or buying a property, ensuring secure and profitable investments.
Likewise, it can suggest the lowest possible cost of project development, gauge the geographical advantage of the project’s location, optimise space management, and increase efficiencies in buildings. Further, builders, brokers, financiers, and other real estate stakeholders use real estate data analysis to find the best time for property sale or purchase, identify ideal tenants for leasable spaces, negotiate effectively, and fine-tune promotional efforts.
Big data is expected to be a game-changer in the rapidly growing Indian real estate market and its diverse consumer base. The country’s real estate sector is estimated to reach $1 trillion in market size by 2030, adding 13% to its GDP by 2025, according to a report by IBEF.
Additionally, big data can help real estate companies analyse vast amounts of data from various sources, such as government records, social media, and market trends, to make informed decisions. This is crucial in a market with complex regional variations in property values, customer preferences, and regulatory frameworks.
Overall, big data integration into India’s real estate sector offers a competitive edge, driving growth and innovation while ensuring transparency and fairness in deals. For instance, comprehensive market data contributed to improvement in India’s rank on the Global Real Estate Transparency Index for the past eight years, from 2014 until 2022.
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Challenges on the way
The room for errors while interpreting data is one of the challenges around using big data in real estate. Intelligent and error-free data aggregation is necessary to derive workable insights from the analysis of raw data.
Next, data storage and security are central to maintaining data integrity—the bedrock of transparency and reliability of market data.
Third, unlike their Western counterparts, many big data companies in India still feed data manually into the database, which can compromise data legitimacy. A shortage of professionals specialising in real estate analytics may also hamper the progress that can be achieved by implementing data-backed strategies. Therefore, real estate companies should choose organisations with advanced analytics capabilities.
Speaking futuristically
As per a MarketsandMarkets report, the global real estate analytics market may rise to $13.4 billion by 2024, with a projected compound annual growth rate (CAGR) of 19.8%.
The real estate industry stands to gain even more precise insights with the off-and-running development of data science and machine learning. It will lead to even more well-thought-out decisions, resulting in favourable outcomes and guided readiness for future challenges.
(Srinivasan Gopalan is Co-founder and Chairman of ArisUnitern RE Solutions Pvt Ltd and Co-founder of Aris Infra Solutions Pvt Ltd.)
Edited by Kanishk Singh
(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)
