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Equites first in concluding green loan with RMB for African real estate sector

Equites Property Fund and RMB said yesterday they had successfully concluded the first green loan in the sub-Saharan African real estate sector. Picture: Karen Sandison/African News Agency(ANA)

Equites Property Fund and RMB said yesterday they had successfully concluded the first green loan in the sub-Saharan African real estate sector. Picture: Karen Sandison/African News Agency(ANA)

Published Sep 30, 2021

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EQUITES Property Fund and RMB said yesterday they had successfully concluded the first green loan in the sub-Saharan African real estate sector.

The facility would enable Equites to unlock ongoing environmental benefits. RMB acted as sole arranger and sustainability agent for the R225 million green loan.

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Equites is the only specialist logistics Real Estate Investment Trust (Reit) on the JSE. It has a footprint in South Africa and the UK.

Equites chief financial officer Laila Razack said sustainability was central to their real estate strategy and they required a green loan to fund the development of two high-specification IFC EDGE (Excellence in Design for Greater Efficiencies) certified green buildings, in an attractive industrial node, Equites Park Meadowview, for key tenants Altron and Digistics.

“Equites has been focused on sustainability for several years, in alignment with our dedication to offer our tenants high-quality logistics assets that support both our tenants’ and our commitment to sustainability.

“The recognition by RMB of the sustainable build of our new high-specification properties in Equites Park Meadowview enables Equites to continue to meet our goal of ensuring all our new properties are certified to internationally recognised green building standards in both South Africa and the UK,” she said.

EDGE is a globally recognised certification which brings speed, market intelligence and an investment focus to green building certification in over 170 countries globally.

The EDGE engine has a set of city-based climate and cost data, consumption patterns and algorithms for predicting the most accurate performance results.

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