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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.

This paper aims to show that the accuracy of real estate portfolio valuations and of real estate risk management can be improved through the simultaneous use of Monte Carlo simulations and options theory.

The authors' method considers the options embedded in Continental European lease contracts drawn up with tenants who may move before the end of the contract. The authors combine Monte Carlo simulations for both market prices and rental values with an optional model that takes into account a rational tenant's behaviour. They analyze how the options significantly affect the owner's income.

The authors' 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.

Some limitations are inherent to the authors' model: these include the assumption of the rationality of tenant's decisions and the difficulty of calibrating the model given the lack of data in many markets.

The main contribution of the paper is both by accounting for market risk (Monte Carlo simulations for the prices and market rental values) and for accounting for the idiosyncratic risk (the leasing risk).

The purpose of this paper is the development for a conceptual framework with regard to the risk management of real estate positions as foundation for transaction decisions. In this context, the current market environment and legal obligations are the main drivers for market participants to improve their risk management practices. Based on this environment, a practical but science backed model is outlined.

The paper uses a conceptual approach based on the existing literature to develop a practical decision support system. In addition, the current risk management best practices are outlined to illustrate the corporate and methodological foundation for the decision support system.

The conceptual model development reveals a clear necessity for the supplementation of price to value measures. Additional measures are derived from theoretic considerations based on Monte Carlo Simulation approaches to the risk management of property investments. These additional risk metrics support investors in order make risk-appropriate decisions.

The resulting decision support system can be applied to the risk management of transaction decisions. Here, the model can be applied in any investment decision to support portfolio management considerations from a comprehensive risk management perspective. Investors can implement the system as part of their transaction procedure.

The existing body of literature mainly focuses on macroeconomic ratios in the context of decision support. In contrast, the present paper reveals a corporate decision support system, which is supposed to foster decisions of market agents especially with regard to potential price and value divergences and tightening legal obligations.

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.

Traditional methods of portfolio valuation have been criticised for their inability to adapt sufficiently in an increasingly complex market, providing, according to the critics, simplistic and inaccurate analysis for the client. This paper, while accepting the need to adopt more rigorous valuation techniques in certain areas, argues that new techniques alone will not produce better answers. When adopting an increasingly data‐rich quantitative approach, the valuation is only as good as the information to which the valuer has access. Where information is historic, lacking or distanced from the market, the resulting valuation may be meaningless. Only by being close to the marketplace can the valuer accurately reflect market fluctuations in the valuation and thus provide accurate and precise advice for the client.

An acute need exists for a practical quantitative risk management‐based real estate investment underwriting methodology that clearly helps guide decision making and addresses the shortcomings of discounted cash flow (DCF) modeling by evaluating the full range of probable outcomes. This paper seeks to address this issue.

The simulation‐based excess return model (SERM) is an original methodology developed based on an application of Monte Carlo simulation to project risk assessment combined with the widely practiced DCF modeling. A case study is provided where results of the modeling are compared with traditional DCF risk models and with prior projects with known outcomes.

This paper lays out a practical method for stochastic quantitative risk management modeling for real estate development projects and illustrates that for identical projects risk‐adjusted returns derived with the use of SERM may differ significantly from returns provided by traditional discounted cash flow analysis. SERM corrects serious shortcomings in the DCF methodology by incorporating stochastic tools for the measurement of the universe of outcomes. It further serves to condense the results of Monte Carlo simulations into a simplified metric that can guide practitioners and which is easily communicational to decision makers for making project funding decisions.

SERM offers a simple, practical decision‐making method for underwriting projects that addresses the limitations of the prevailing methodologies via: stochastic assessment of the range of outcomes; interdependence of input variables; and objective risk premium metrics.

This paper presents an original methodology for making project‐funding decisions for real estate development projects that is based on Monte Carlo simulation combined with DCF analysis. The methodology presented here will have value for real estate developers, investors, project underwriters, and lenders looking for a practical and objective method for project valuation and risk management than is offered by traditional DCF analysis. A review of literature did not reveal analogous methodologies for risk management.

The risk management of transitory risk for real assets has gained large interest especially in the past 10 years among researchers as well as market participants. In addition, the recent regulatory tightening in the EU urges financial market participants to disclose sustainability-related financial risk, without providing any methodological guidance. The purpose of the study is the identification and explanation of the methodological limitations in the field of transitory risk modeling and the logic step to advance toward a stochastic approach.

The study reviews the literature on deterministic risk modeling of transitory risk exposure for real estate highlighting the heavy methodological limitations. Based on this, the necessity to model transitory risk stochastically is described. In order to illustrate the stochastic risk modeling of transitory risk, the empirical study uses a Markov Switching Generalized Autoregressive Conditional Heteroskedasticity model to quantify the carbon price risk exposure of real assets.

The authors find academic as well as regulatory urgency to model sustainability risk stochastically from a conceptual point of view. The own empirical results show the superior goodness of fit of the multiregime Markov Switching Generalized Autoregressive Conditional Heteroskedasticity in comparison to their single regime peer. Lastly, carbon price risk simulations show the increasing exposure across time.

The practical implication is the motivation of the stochastic modeling of sustainability-related risk factors for real assets to improve the quality of applied risk management for institutional investment managers.

The present study extends the existing literature on sustainability risk for real estate essentially by connecting the transitory risk management of real estate and stochastic risk modeling.

The purpose of this paper is to examine the cross-sectoral collaboration between conservation and economic appraisal, and to process a financial analysis for private owners of a built heritage.

The methodology applied addresses the financial analysis of restoration through a discounted cash flow analysis, together with a life cycle costing. Costs and revenues are both analysed in this paper. Some energy-saving measures are applied to cut running costs and decrease the energy required by the building, using as reference the “Guidelines for improving energy efficiency in cultural heritage” drafted by MiBACT, which considers the respect of restoration principles. In order to increase revenues, part of the building is rented. The attractiveness of the investment opportunity is valued through the calculation of the net present value of cash flows, the payback period and the internal rate of return.

The paper offers a simple strategy for the planning of cost-revenues, preventively allowing verification if the conservation is economically feasible and if the owners can afford the operation. The strategic planning will give the owners the chance of maintaining the property of their building and achieve a proper restoration on it.

The novelty of the paper is the study of cooperation between conservation and economic valuation, but also the focus on a specific portion of twentieth-century heritage, the war-wounded houses, which represent a widespread patrimony, on which it is not clear how to operate yet.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐17; Journal of Property Investment & Finance Volumes 8‐17; Property Management Volumes 8‐17; Structural Survey Volumes 8‐17.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.