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The purpose of this paper is to offer a framework for computing optimal investment holding periods for real estate portfolios.

The analysis is set within a standard DCF modelling framework and it is shown that it is not adapted to offer sufficient insight into the mechanics leading to optimal holding periods. A richer framework is offered that enables the portfolios terminal value to behave according to a simple diffusion process.

The findings show that optimal holding periods for real estate investment portfolios exist within very precise conditions. The key parameters are the investor's weighted average cost of capital (WACC), the cash flow growth rate during the investment period, and the investment's net initial yield. The key finding is (loosely speaking) that, if the investor's cost of capital is outpaced by (the sum of) the portfolio's net initial yield and the cash flow growth rate, then an optimal holding period exists and can be precisely computed. Numerical examples are provided to illustrate these findings.

Standard financial theory does not specify a consistent methodology for choosing the optimal investment horizon in investment analysis and in particular in discounted cash flow (DCF) modelling. This problem may be particularly acute in real estate investment analysis and valuation, as investment horizons are often arbitrarily chosen. The paper proves that investment horizon may strongly influence net present value.

This paper aims to test the robustness of the trend and volatility estimations for two indices: the classical Weighted Repeat Sales and a PCA factorial index. The estimations are computed from a dataset of Paris commercial properties.

First, two methodologies are presented, and then the dataset. Finally, the impact of the number of transactions per period are tested on the trend and volatility estimates for each index, and an interpretation of the results are given.

The trend and volatility estimates are biased for the WRS index and not for the PCA factorial index when the periodicity increases. Consequently, the level of the index at the end of the computing period is significantly different for various periodicities in the case of the WRS index. Globally, the PCA factorial seems to be more robust to the number of transactions.

As suggested by D. Geltner, commercial properties indices have to be built using repeat sales instead of hedonic indices. The repeat sales method is a means of constructing real estate price indices based on a repeated observation of property transactions. These indices may be used as benchmarks for real estate portfolio managers. But the investors, in general, are also interested in introducing real estate performance in their portfolio to enhance the efficient frontier. Thus, expected return and volatility are the two key parameters. To create and to improve contracts on real estate indices, trend and volatility of these indices must be robust regarding to the periodicity of the index and the volume of transactions.

The purpose of this paper is to demonstrate the impact of lease duration and lease break options on the optimal holding period for a real estate asset or portfolio.

The authors use a Monte Carlo simulation framework to simulate a real estate asset’s cash flows in which lease structures (rent, indexation pattern, overall lease duration and break options) are explicitly taken into account. The authors assume that a tenant exercises his/her option to break a lease if the rent paid is higher than the market rental value (MRV) of similar properties. The authors also model vacancy duration stochastically. Finally, capital values and MRVs, assumed to be correlated, are simulated using specific stochastic processes. The authors derive the optimal holding period for the asset as the value that maximizes its discounted value.

The authors demonstrate that, consistent with existing capital markets literature and real estate business practice, break options in leases can dramatically alter optimal holding periods for real estate assets and, by extension, portfolios. The paper shows that, everything else being equal, shorter lease durations, higher MRV volatility, increasing negative rental reversion, higher vacancy duration, more break options, all tend to decrease the optimal holding period of a real estate asset. The converse is also true.

Practitioners are offered insights as well as a practical methodology for determining the ex-ante optimal holding period for an asset or a portfolio based on a number of market and asset-specific parameters including the lease structure.

The originality of the paper derives from its taking an explicit modelling approach to lease duration and lease breaks as additional sources of asset-specific risk alongside market risk. This is critical in real estate portfolio management because such specific risk is usually difficult to diversify.

The aim of this paper is to use rent and price dynamics in the future cash flows in order to improve real estate portfolio valuation.

Monte Carlo simulation methods are employed for the measurement of complex cash generating assets such as real estate assets return distribution. Important simulation inputs, such as the physical real estate price volatility estimator, are provided by results on real estate indices for Paris, derived in an article by Baroni et al..

Based on a residential real estate portfolio example, simulated cash flows: provide more robust valuations than traditional DCF valuations; permit the user to estimate the portfolio's price distribution for any time horizon; and permit easy values‐at‐risk (VaR) computations.

The terminal value estimation is a core issue in real estate valuation. To estimate it, the proposed method is not based on an anticipated growth rate of cash flows but on the estimation of the trend and the volatility of real estate prices.