green-street’s-automated-valuation-model-sets-new-standard-for-accuracy-and-transparency

Green Street’s Automated Valuation Model Sets New Standard For Accuracy And Transparency

 

Green Street has launched an Automated Valuation Model (AVM), which provides instant valuation estimates for commercial properties and portfolios. The AVM is part of Portfolio Tools – a new suite of products that allows Green Street clients to generate unlimited custom assessments of their property and loan portfolios.

Green Street’s AVM is fueled by the firm’s best-in-class and proprietary data sets on cap rates, market grades, Commercial Property Price Indices (CPPIs), and a robust – and recently expanded – transaction comp database. The model covers the apartment, industrial, office, and retail sectors, and has the ability to value both stabilized and non-stabilized assets. It is a self-service tool that requires as few as five user inputs with optional fields for even greater precision.

“Building an AVM was a very natural evolution for Green Street given property valuations are central to what we do, and a core competency,” said Andy McCulloch, Global Head of Data and Analytics at Green Street. “In designing the Green Street AVM, we wanted to solve for the accuracy and transparency shortcomings in the marketplace. We built the model around three accepted and recognized valuation approaches, feed the model engine with our high-quality data, and provide the detailed breakdown of how each valuation estimate is derived so clients can have visibility and confidence in the results.”

As the latest enhancement to Green Street’s Real Estate Analytics private market solution, Portfolio Tools – which includes the AVM, Portfolio Analytics, and Loan Analytics – was developed to help guide capital allocation decisions, assess and quantify risk, and support portfolio lending decisions and strategy through a self-service and customized experience. Market participants can click here to learn more about Green Street’s new tools and schedule a demo.