Charles-Olivier Amédée-Manesme

The Impact of Lease Structures on the Optimal Holding Period for a Commercial Real Estate Portfolio

Purpose The purpose of this paper is to exhibit the impacts of lease duration and lease break options on the optimal holding period for a real estate asset or portfolio Methodology / approach We use a Monte Carlo simulation framework to simulate a real estate assets cash-flows in which lease structures (rents indexation patterns overall lease duration and break options) are explicitly taken into account. We assume that a tenant exercises his/her option to break a lease if the rent paid as higher than the market rental value of similar properties. We also model vacancy duration stochastically using Poissons law. Finally capital values and market rental values are simulated using specific stochastic processes. and are also assumed to be correlated. We derive the optimal holding period for the asset as the value that maximises its discounted value. which is the sum of the discounted free cash flows and the discounted terminal Findings We 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 portfolios by extension. We show that. everything else being equal. shorter lease durations. higher market rental value 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. Practical implications Practitioners are insights as well as a practical methodology for determining the ex-ame optimal holding period for an asset or a portfolio based on a number of market and asset specific parameters including the lease structure. Originality / value The originality of the paper derives from 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.

Ex-ante real estate Value at Risk calculation method

The computation of Value at Risk (VaR) has long been a problematic issue in commercial real estate. Difficulties mainly arise from the lack of appropriate data, the lack of transactions, the non-normality of returns, and the inapplicability of many of the traditional methodologies. In addition, specific risks remain latent in investors’ portfolios and thus risk measurements based on market index do not represent the risks of a specific portfolio. Following a spate of new regulations such as Basel II, Basel III, NAIC and Solvency II, financial institutions have increasingly been required to estimate and control their exposure to market risk. Hence, financial institutions now commonly use “internal” VaR (or Expected Shortfall) models in order to assess their market risk exposure. This paper proposes the first model designed especially for static real estate VaR computation. The proposal accounts for specific real estate characteristics such that the lease structures or the vacancies. The paper contributes to the real estate risk management literature by proposing for the first time a model that incorporates characteristics of real estate investments. It allows more precise real estate risk measurements and is derived from a regulators’ approach.

Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach

In this paper, the heterogeneity of the Paris apartment market is addressed. For this purpose, quantile regression is applied – with market segmentation based on price deciles – and the hedonic price of housing attributes is computed for various price segments of the market. The approach is applied to a major data set managed by the Paris region notary office (Chambre des Notaires d’Île de France), which consists of approximately 156,000 transactions over the 2000–2006 period. Although spatial econometric methods could not be applied owing to the unavailability of geocodes, spatial dependence effects are shown to be adequately accounted for through an array of 80 location dummy variables. The findings suggest that the relative hedonic prices of several housing attributes differ significantly among deciles. In particular, the elasticity coefficient of the apartment size variable, which is 1.09 for the cheapest units, is down to 1.03 for the most expensive ones. The unit floor level, the number of indoor parking slots, as well as several neighbourhood attributes and location dummies all exhibit substantial implicit price fluctuations among deciles. Finally, the lower the apartment price, the higher the potential for price appreciation over time. While enhancing our understanding of the complex market dynamics that underlie residential choices in a major metropolis such as Paris, this research provides empirical evidence that the QR approach adequately captures heterogeneity among house price ranges, which simultaneously applies to housing stock, historical construct and social fabric.

Combining Monte Carlo Simulations and Options to Manage the Risk of Real Estate Portfolios

This paper aims to show that the accuracy of real estate portfolio valuations can be improved through the simultaneous use of Monte Carlo simulations and options theory. Our method considers the options embedded in Continental European lease contracts drawn up with tenants who may move before the end of the contract. We combine Monte Carlo simulations for both market prices and rental values with an optional model that takes into account a rational tenant’s behavior. We analyze to what extent the options exercised by the tenant significantly affect the owner’s income. Our main findings are that simulated cash flows which take account of such options are more reliable that those usually computed by the traditional method of discounted cash flow. Moreover, this approach provides interesting metrics, such as the distribution of cash flows. The originality of this research lies in the possibility of taking the structure of the lease into account. In practice this model could be used by professionals to improve the relevance of their valuations: the output as a distribution of outcomes should be of interest to investors. However, some limitations are inherent to our model: these include the assumption of the rationality of tenant’s decisions, and the difficulty of calibrating the model, given the lack of data. After a brief literature review of simulation methods used for real estate valuation, the paper describes the suggested simulation model, its main assumptions, and the incorporation of tenant’s decisions regarding break options influencing the cash flows. Finally, using an empirical example, we analyze the sensitivity of the model to various parameters, test its robustness and note some limitations.

An index to forecast housing returns

This research demonstrates the substantial benefits obtained by modeling housing price using a- repeat sales factorial model. In particular, the model is able to give accurate forecast of housing returns on a short or medium run. The index is built-up by determining the weight of 9 economic and financial indices (rental index, short or long-term rate, inflation, stocks index, REITs index, population, Disposable income, population and CPI) to explain capital returns and then to represent housing prices dynamics. The index is computed on Paris housing market from transactions. Mainly the results provide empirical evidence of the ability of the model to forecast short and mid-term changes of the housing prices and more importantly of the housing returns dynamics. Also, the proposed model makes possible to analyze deeply the basic elements that govern the housing market. The developed model also offers applications to regulation and credit risk

Market heterogeneity, investment risk and portfolio allocation: Applying quantile regression to the Paris apartment market

Purpose The purpose of this paper is to address the heterogeneity of real estate assets with regard to investment risk measurement, with Paris’ apartment market as a case study. Design/methodology/approach Quantile regression is used to handle the fact that willingness to pay for housing attributes may vary greatly over both space and asset value categories. The method is alternately applied on central and peripheral districts of Paris, or “arrondissements”, with hedonic indices built for nine deciles over a 17-year period (1990-2006). Portfolio allocation is subsequently analysed with deciles being the assets. Findings The findings suggest that during the slump, peripheral districts show better resilience and define the efficient frontier while also exhibiting a lower volatility. In addition, higher returns are observed for lower-priced apartments, both central and peripheral. During the recovery and boom stages of the cycle, the highest returns are experienced for the cheapest apartments in central locations, whereas upper-priced, centrally located units yield the lowest returns. Originality/value The originality of this research resides in the application of quantile regression in a real estate investment and risk management context. The methodology may raise individual investors’ and practitioners’ attention, especially index providers’.

Commercial leases, terms and options in the light of game theory

In this paper, a lease rate valuation model is developed whereby the market rent is subject to binomial movements. We then consider a commercial leasing game where different renters, large and small, find a location in different buildings owned by competing landlords operating under a rent negotiation process. When supply for office space exceeds demand from large firms, the latter always pay an advantageous rent. We demonstrate theoretically that small firms are better off in a building containing a large firm and thus will accept to pay a premium to lodge in such buildings rather than opting for a lower rent in a building occupied by small firms. The conditions leading to opposite results are also discussed in the paper. Using data from New York City, Chicago and Los Angeles, we discuss lease rates in light of the predictions of our theoretical model.

Segmenting the Paris residential market using a Principal Component Analysis

This paper aims to generate homogeneous submarkets in terms of price behaviour for Paris through a Principal Component Analysis. The database, which is provided by the Chambre des Notaires, includes cases spread over a 17 year period, that is from 1990 to 2006. This period offers three sub-periods during which prices are going down, recovering and booming. For each transaction, housing descriptors are available to construct the hedonic index. As most of large cities, Paris is divided in administrative boroughs (“arrondissements”) and neighbourhoods ‘”quartiers” where prices evolve differently during slump, recovery and boom periods. Prices moves over time are measured using hedonic indices. A cluster analysis highlights a segmentation according the size effect and price volatility. It also shows that over or under-performance of various districts compared with the global index is dependent on the period. In particular, one of the major contributions of this research is to highlight the existence of a twofold residential dynamics in the Paris region, with the central boroughs clearly parting from outlying ones with respect to apartment price appreciation over time.

The pricing of embedded lease contracts options

In all the major asset class, implied volatility is obtained from the quotations of the derivatives market. However, in real estate, derivatives market is almost inexistent and volatility pricing face many difficulties. This paper proposes a methodology to determine implied volatility of real estate market based on options embedded in lease contracts. Leases are generally agreed for long term with possible options to leave in favour of the tenant during the course of the lease. Tenants that have no break-options during the life of their lease expect either to pay a lower rent or to receive more gratuities (rent free, financial incentives...) than those with options to leave. We built up a model that allows estimating implied volatility based on the leasing transactions by analogy between lease options and financial options.