ED attaches Delhi-NCR realty group's assets worth ₹2,348 crore

New Delhi, The ED on Monday said it attached hundreds of acres of land and unsold real estate inventory in Delhi-NCR, apart from some residential units in Goa, worth ₹2,348 crore as part of a money-laundering investigation against realty group WTC Faridabad Infrastructure Private Limited and other WTC Group entities.

The federal probe agency issued a provisional attachment order under the Prevention of Money Laundering Act to attach these properties.

The action has been taken in "a massive real estate fraud involving WTC Faridabad Infrastructure Private Limited and other entities of the WTC Group, headed by Ashish Bhalla", the Enforcement Directorate said.

The attached assets include about 159 acres of licensed and unlicensed land, along with unsold real estate inventory across Delhi, Gurugram, Faridabad and Noida, as well as residential properties in Goa and commercial premises in Delhi-NCR, it said.

These assets are worth ₹2,348 crore, the agency said.

The money-laundering investigation stems from more than 30 FIRs registered by the Faridabad Police in Haryana and the Delhi Police's Economic Offences Wing against the company and its promoters on charges of cheating, fraud and criminal conspiracy.

The agency claimed more than 12,000 investors were duped by Bhalla and his group companies through a "well-orchestrated" scheme by promising assured returns on investments in plots and commercial spaces under the WTC brand.

More than ₹2,700 crore was collected from investors across states such as Haryana, Uttar Pradesh, Punjab and the Union Territory of Chandigarh, it said.

However, a "significant" portion of these funds was "diverted" and "siphoned off" by Bhalla and never used for real estate development.

Additionally, a substantial amount was illegally transferred abroad to entities based in Singapore, which are "beneficially owned" by close family members of Bhalla, it alleged.

The ED arrested Bhalla in March as part of the probe.

This article was generated from an automated news agency feed without modifications to text.