Advancements in technology have long helped financial services firms boost analytical capabilities, gain insights into market activity and seize new investment opportunities. Private equity firms, however, have been slower to respond to tech trends, especially when compared to their hedge fund or venture capital peers, relying in many cases on outdated infrastructure.
But PE has been profoundly shaped by innovations in recent years, with real estate funds in particular benefiting from the advent of automated tools and fully digitalized products to help expedite operations and customize investor services. More managers have taken steps to embrace biometrics and paperless reporting, or install highly advanced cybersecurity protections and encryption to protect data and fully secure networks. Virtual assistance helps investors navigate daunting PE forms, and drones assess the entire landscape of properties to help inform investment decisions. But, it has been algorithms that have recently assumed a critical role in PE real estate, helping to identify commercial properties for potential deals.
PE firms can use algorithms to quickly gather and analyze large volumes of data to dramatically improve the deal-vetting process for investors. They can narrow the pool of potential investment properties, filtering out poor candidates based on size, location, geography and other factors. They can also save firms from relying on a small force of analysts to hunt for the best deals in a more manual, labor-intensive process.
Such algorithms are particularly critical for PE firms that target smaller markets. Plum real estate deals in prime markets like New York or San Francisco could eventually lead to lucrative payouts for the bigger firms that pursue them — but deep pockets can also be an impediment. Large PE and hedge fund players focusing solely on generating fees get stuck, trapped in multibillion-dollar deals with no return. As a fund grows and scales, big deals typically have much lower returns.