In H1 2017, y-o-y rental appreciation ranged between 8-17% in several cities

Office rents in National Capital Region (NCR) and in Mumbai, traditionally the centres of economic and political power in the country, are more or less stable. However, rents have been rising in the IT-focused cities of Bengaluru (Bangalore), Pune, Hyderabad and Chennai where the y-o-y change in H1 2017 ranged between 8-17% in select micromarkets. In H1 2017, Bengaluru and Hyderabad both witnessed single digit vacancy levels hovering between 8-9% due to a dearth of quality Grade A stock, thus leading to increased rental values. “With the demand for quality, Grade A supply continuing to be high, especially in the preferred and most active micro-markets, rentals continue to stay closer to the higher end with no visibility of any correction in the near to medium term” said Shaju Thomas, Director, Office Services at Colliers International (Chennai)

According to Colliers Research, this trend should persist for at least the next three years. Hence, landlords will need to be careful so as not to force out tenants in the IT-focused cities by raising rents excessively. In our opinion, Grade A developments and buildings with high accessibility will continue to command premium rents. We suggest that developers should continue to focus on such buildings, and preferably incorporate new features in building managements systems and alternative workplace solutions. “From a real estate perspective, it is imperative to look at reducing cost and at the same time creating a differentiator by introducing automation in building management systems. Firms need to be viewed as ‘in tune with the times’, ‘future ready’ and ‘next generation, technology friendly’ to attract talent and high-tech enterprise clients” added Ritesh Sachdev, Senior Executive Director, Occupier Services at Colliers International India.

Most metro cities in India have been plagued by infrastructure issues since decades and the situation has worsened considerably with increased population in these cities. “It is particularly worth highlighting that workers in India typically face a daily commuting time to the office of one to two hours each way in most large cities. In the long term, AI will allow driverless cars and excellent real-time communication with the office, so that time will not be wasted. However, at least for the next few years, that long commuting time represents a major loss of productive time for the Indian economy” says, Surabhi Arora, Senior Associate Director, Research at Colliers International India.

Colliers Research has investigated the implications of lengthy commuting time at a very local level. In particular, seven bottlenecks or “pain points” along the Outer Ring Road (ORR) which is one of the preferred locations for IT companies in Bengaluru (Bangalore) have been identified. We estimate the aggregate economic loss per annum from hold-ups or accidents at these bottlenecks at INR31.2 crore (INR312 million, or about USD5 million). This is not a huge amount, but enough to be significant when combined with the loses on other such stretches across the country and additional economic losses from relatively slow internet connectivity and high electricity costs due to frequent power cuts.

Every year, Mumbai experiences flooding due to heavy rains and appalling condition of infrastructure in the city. As per the estimates of a 2015 report by the UN Office for Disaster Risk Reduction (UNISDR), flooding in India causes an average economic loss of $7 billion annually, with Mumbai – India’s financial capital, possessing a total wealth of $82 billion – contributing substantially to the losses.

About infrastructure, we believe that legacy IT systems may turn out to be a major drag on automation and adoption of AI, both in India and elsewhere. Many older IT systems are not very intelligent, and getting them to work with systems which are intelligent is a process that could easily take several years. To sum up, India has a long way to go to create an AI favourable scenario for a seamless adoption of AI technologies.

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