How is AI transforming corporate real estate

High costs challenge AI adoption.

With AI and generative AI expected to significantly impact the real estate sector over the years, several barriers have been seen hindering its progress, according to industry experts.

Ariel Shtarkman, Vice Chair for Finance at Urban Land Institute Hong Kong and Managing Partner at Undivided Ventures, emphasised the financial burden of adopting AI technologies. “First of all is the initial high costs. Yes, you have a lot of free tools available, but when you look at our organisations in general, especially large organisations, you will need to invest quite significantly to make sure all your workflows are streamlined, and then you need to also train your staff.”

Alongside the financial costs, Shtarkman pointed out that resistance to change is a significant barrier. “Adopting a new way of working and new technology is not intuitive,” she noted. “Of course, with that comes also resistance to change.”

Managing data in corporate real estate

Data quality and availability are other major challenges, particularly in the Asia Pacific market. “With AI, the most important thing is the quality of data. AI can give you wonderful responses and wonderful data mining when you know what you're putting in, and you can rely on the data you're putting in,” Shtarkman explained.

Mike Davis, Managing Director of Occupier Services, Asia Pacific at Colliers, echoes Shtarkman’s concerns about data challenges. “Comprehensive data is needed to maximise benefits and is necessary for accurate benchmarking and trend forecasting,” Davis said. 

“Transparent data is important across commercial real estate firms and business lines, which are often not standardised and also have proprietary information. So this will need to undergo a transformation to realise AI's full potential.”

Despite these challenges, AI is already changing interactions in real estate deals. “There's an opportunity for machine learning and generative AI to automate workflows and produce market analysis to provide insights on rents, prices, vacancies, and other market trends,” Davis noted. “One area where we're making strides is in our portfolio AI platform, where within minutes of loading a company's data and plan, we can compare machine recommendations against the baseline of companies already established.”

Investor interactions and data security

For Shtarkman, AI also plays a significant role in interactions with investors. “We invest in early-stage technologies for the built environment with the aspect of sustainability,” she said. “On the investor side, we're still exploring those AI solutions that can help us to streamline back office operations.”

Data security and privacy are paramount. “Cyber security of AI will continue to be a big challenge, but then the solutions will come from two different parts of the industry– private sector and regulation,” Shtarkman emphasised.

Both Shtarkman and Davis stress the importance of governance and regulation. “Governance is the biggest topic facing the advancement of AI across all industries,” Davis stated. “Clients and providers need to create data governance programs that focus on minimising risk, ensuring accuracy and consistency, and enabling more effective data utilisation.”