Rising costs and falling fees are putting pressure on margins for assets managers...

Rising costs and falling fees are putting pressure on margins for assets managers in the commercial real estate world and beyond. At the same time, commercial real estate managers have an additional stressor –soaring construction costs and rising land prices, which are emerging as critical issues for the sector, according to PWC’s Emerging Trends in Real Estate 2019 survey. While there’s no silver bullet for these issues, more commercial real estate managers are finding that digital technologies can help them to be more agile and accurate through data-driven investment decision making. The thinking is that digital technology presents a real opportunity to lower operational costs and risks. It also can provide a competitive advantage in delivering superior returns. And as a result, we’re finding that more commercial real estate managers are investing in these companies.

The digital difference

Digital platforms with advanced analytics and AI present disruptive opportunities for all fund managers. New data sources and analytical tools are improving the quality of research while reducing the cost. At the same time, more powerful predictive tools are better targeting front office investment activities and digital technology is automating many of the most mundane operational tasks in the mid and back office. Most asset managers are experimenting with digital labs, hiring data scientists, and testing the use of alternative data.

But alternative asset managers continue to be challenged byCRE market turns AI, machine learning to bolster margins – Real Estate Finance & Investment legacy processes and systems, and many firms have underinvested in creating and enabling a data driven culture. Across the investment sector – not least in the real assets space – there is continued reliance on 30-year old systems like Excel and PowerPoint. This makes a sudden leapfrog to AI and Machine learning impossible. So, before you invest millions of dollars in AI and Machine Learning, ask yourself this question. What is the foundational layer you need? And what do you need if you focus on real assets like infrastructure, real estate, energy or power?

The fundamentals

For a real assets investor, there are two fundamental layers. The first is financial data centralization, ensuring that disparate data sources across multiple systems, companies and spreadsheets are brought together into a single source of truth. Without this, there is no foundation to analyse the data or anticipate what lies ahead. This source of truth must capture real team data locked up in the thousands of excel based financial models that exist on everyone’s desktop.

Secondly, you need a collaborative workflow that works across all the functional organizations where you have systemized workflows in place. This will allow your organization to collaborate in a sufficiently modern way. Capturing and understanding patterns of workflow is essential to see what parts of the process can benefit from AI and machine learning.

With a strong data foundation that is able to capture and execute financial data, reports, tasks and decisions anywhere and anytime, asset and investment managers can begin to take advantage of advanced technologies.

If the investment community is to survive any upcoming recession, then creating a strong data repository that has inherent transparency and traceability must come first. While no one can control every outcome, spending more time proactively scenario planning against a robust data foundation will afford asset and investment managers the opportunity to anticipate and plan. In turn, this builds confidence and trust in the analysis, predictions, and returns.