Increasing Profits from Real Estate Leasing: Flexible Strategies based on Market Conditions

Abstract

The increasing use of modern data analytics is changing decision making processes in the commercial real estate industry. Advances in data analytics present opportunities for commercial real estate owners and managers to increase profits by integrating market cycles into leasing strategy. This research presents a model that exploits readily available data to simulate market volatility and uncertainty, inform leasing strategy, and make better decisions about lease durations offered. We compare the results of applying three different leasing strategies: consistent 5-year, consistent 10-year, and variable based on understanding of relative positioning within the market cycle. For comparative analysis of these strategies, Monte Carlo simulation via Julia is used to run 10,000 trials for each strategy, calculating the range of outcomes that could occur with each leasing strategy over the life of an asset. It is found that leasing with market knowledge is most optimal of the three strategies examined as it increases profits. The results suggest that incorporating knowledge of relative position within the market cycle to determine optimal lease length creates opportunity for increased profits from leasing. Given the increasing availability of real estate data, future research is directed at exploring different lease duration strategies and the use of real data feeding the simulation to make better models.