Quantifying partnership terms in real estate joint ventures

Abstract

Joint ventures are widely used in real estate investments, and especially so in development projects where partners bring different value to the venture: an Operating Partner who has the desire and operational capabilities to manage the investment but lacks the capital to fund the entire project, and a Money Partner who has the capital, but lacks the expertise and the desire to manage the project. A formal joint venture (partnership) agreement governs the relationship between the Operating Partner and Money Partner in the development project. Real estate investment performance is generally evaluated at the property level (before considering the impact of financing) and then at the venture level (taking into account the impact of financing). Differing real estate investment performance within the venture, due to specific partnership terms, has generally taken a back seat for performance evaluation, and is less of a focus when the investment is performing well.

However, with the current competitive real estate market flooded with cheap financing options, partnership terms between the Operating Partner and Money Partner ought to be scrutinized more carefully, as certain terms can serve as additional sources of return, or "safety net", when dark clouds over the real estate market loom ahead. This paper will focus on partnership terms in a real estate joint venture which can be quantified, discuss the metrics that can be used to evaluate the investment performance of joint ventures, and explain the need to employ probabilistic modelling methods. After setting that context, deterministic modelling methods (Discounted Cash Flow, or "DCF") as well as probabilistic modelling methods (Monte Carlo simulation) will be applied to quantify the impact of relevant partnership terms on a hypothetical real estate development project.

This will be followed by a discussion on how one can use the results of the Monte Carlo simulation alongside traditional DCF with scenario analysis which is more commonly used in the industry. Lastly, the paper will provide a casual narrative from the perspective of a financial analyst who is doing financial modelling from the asset level down to the partnership and partner level and using Monte Carlo simulation analysis.