In Spotlight: TEAL - Terra Economics and Analytics Lab
Using data and AI to radically improve title verification process for property
Terra Economics and Analytics Lab (TEAL) is doing for real estate what the Credit Information Bureau (India) Limited (CIBIL) score did for banking and financial institutions.
CIBIL assists financial institutions in accurately assessing lending risks and brings individuals into the formal credit economy.
TEAL aspires to do the same for the real estate market; to develop a standardised, recognisable profile for each property based on land and property records.
Any transaction relating to or involving a property requires title verification to establish the rightful legal owner and extensive due diligence to ensure that it is free of encumbrances. This is a critical process that everyone has to contend with for a range of things, whether you need to take a loan for or against a property when a property is transferred to legal heirs in matters of succession, and commonly while buying and selling any property.
The parameters for due diligence are extensive, costly and require significant manual intervention. In small ticket-size loans, lawyers may either be able to check limited parameters or not do any checks at all. This is reflected in the high volume of litigation related to land or property (it is estimated that more than two-thirds of all civil cases in India are related to land or property).
On the intent behind TEAL
“A lot of times we don’t even have proper infrastructure or connectivity, let alone the ability for a survey or a value/ title search clerk or a lawyer to go physically and inspect it (the property). That’s a very costly thing. Because margins are so low, a lot of times those don’t get funded by established players. They get informal financing or something, but the idea here is to make it cleaned up, accessible, ready, data-driven, independent and standardized to whatever extent we can. So that’s the intent.”*
Seeing this as an economics, legal and data problem all in one, Kshitij, Rohan and Shreyas started TEAL, a single searchable platform for property titles, to build transparency over the legal risk that is an inherent part of the real estate market.
TEAL’s innovation lies in using big data and sophisticated machine learning models to simplify this complex legal process of title verification.
How TEAL works
TEAL relies on land records digitised by different government authorities and available in the public domain to collect as many different pieces of information about a property as possible into a single platform.
This includes multiple data points from disparate sources, including title information from state governments who register property transactions, property tax records and mutations registered that are maintained by the municipality, checking for special permissions and unauthorised constructions from development authorities specific to a state, checking for ongoing litigations across High Courts, District Courts, Revenue Courts, Tribunals, geospatial data and cadastral maps that can be collected, and many variations of requirements depending on which urban or rural area in which state of India we are talking about.
TEAL processes large volumes of unstructured data from several sources using machine learning models to make sense of it.
Currently, TEAL is dealing with more than a hundred million properties across sixteen states, over 330 districts, eight vernacular languages, and some remarkably obscure terminologies dating back to the Mughal era. It has resulted in the team creating libraries and data dictionaries internally to make sense of the big and varied Indian address problem and arrive at some form of standardisation.
All this is to make the records easily searchable to users. Their first user groups are banking and financial institutions, and they have secured the buy-in of credible and prominent actors such as ICICI Bank, Kotak Mahindra Bank, Aadhaar Housing Finance, Tata Capital and Bajaj Finserv to use their platform for property due diligence.
A complementary part of their innovative approach is that they don’t limit themselves to high-value transactions or commercial real estate only, for which high-skilled professionals are hired, and extensive due diligence already happens. Instead, they find applicability in affordable housing finance because that is where risks are highest. Here, the time-cost benefit of intensive diligence doesn’t add up, and no market solutions work effectively. This segment urgently needs a title verification process that is quick, independent and trustworthy. TEAL’s approach is to grade every single property that they can.
“Our intent was to go where most of the volume is there but also where a lot of the need is there,” says Kshitij. “For example, in Delhi, we have a lot of data for unauthorised colonies, for places where you don’t even have a formal title, but you may have ancillary data that can help you get there or triangulate who the owner is by using property tax records in some areas. They may not have a proper title, but some municipal service or utility payments will be made there.”
This is how TEAL solves the breakdown of trust that happens when good information is unavailable.
Notes:
*Kshitij Batra, co-founder of TEAL, in conversation with Ritvik Lukose during Agami Prize 2022.
Edited by Jahnavi Jayanth, Keerthana Medarametla and Supriya Sankaran.
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