05 Jul 2022
CEO Michael Robinson blamed the company’s struggles on numerous factors ranging from COVID-19 to rising energy prices. As a major US corporation operating in a growing sector, it’s likely many perceived them to be a low-risk investment. But ultimately, as they did in 2019, creditors will likely be forced to choose between forgiving significant amounts of debt or face little to no return on their initial investment.
For property lenders, real-estate owners, and investors alike, case-studies like these highlight, not only the volatility of the modern-day marketplace and the necessity of easy access to accurate data but the consequences of managing highly concentrated portfolios in a time of increased market unpredictability. Whether linked by sector, geography, or the utilisation of certain technologies, when lenders invest too heavily in a concentrated portfolio, they leave themselves susceptible to the consequences of operating with a high degree of concentration risk. This can lead to significant losses, portfolio liquidity, and even bankruptcy. Already at a disadvantage due to the information disparity between lenders and borrowers, it is essential lenders utilise new solutions to continually monitor and manage portfolios.
With so much noise out there, easy access to reliable data is crucial in ensuring tenant profiles are not overly concentrated in a particular risk area. This is why the widespread adoption of an AI data organisation and management programs, including Recognyte’s own DataScout and ActiveEstate are vital in dealing with concentration risk. By working in collaboration with AI, lenders can identify the attributes that carry relatively high concentration risk.
The ever-growing unpredictability of the modern marketplace makes already difficult decisions for real estate even harder. But with the utilisation of accurate data combined with AI analytical tools, the risk to large-scale portfolios can be minimised.
