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University of San Diego real estate team wins first place in 2024 ARGUS University Challenge

University of San Diego real estate team wins first place in 2024 ARGUS University Challenge

TORONTO — The 2024 winners of the 15th annual ARGUS University Challenge were recently announced by Altus Group Limited, a provider of asset and fund intelligence for commercial real estate.

Teams of aspiring real estate professionals from leading universities across the world competed for scholarship awards by identifying the top commercial real estate investment opportunities, indicates a release.

They presented their investment case based on a hypothetical real estate opportunity. Over 85 students from 20 universities competed in the challenge.

First place went to the University of San Diego and the team consisting of Kevin Dixon, Dillard Harvey, Hunter Loncar, Grant Neeley and Sandis Seale. The professor is Charles Tu.

International Real Estate Business School (IREBS) – University of Regensburg received second place. The team includes Lars Baur, Michele Cirillo, Clara-Maria Hamberger, Antonia Radmacher, Konstantin Muratov and professor Wolfgang Schaefers.

Third place went to Cornell University and the team of Chloe Xiang, Rohit Vilas Kharche, Kai Yang, Abdullah Syarifuddin and professors Daniel Lebret and Chris de Mestre.

Baruch College was awarded fourth place. The team included Aditya Agarwal, Sixi Chen, Shubham Jadhao. The professor was Zoey Tang.

The teams were evaluated on their ability to leverage ARGUS Enterprise to present a summary investment recommendations based on a hypothetical case study. The AE valuation model is based on a discounted cash flow methodology and includes factual and assumptive inputs and its capabilities range from property and portfolio valuation analysis to financial modelling and scenario analysis reporting, a release states.

Each team prepared a report encompassing a comprehensive analysis of the property’s performance for the next 10 years, considering building, pre-letting, financing, development and market data, along with personal assumptions.

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