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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.

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.

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.

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’.

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.

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.

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 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.

Assesses the future for unions in terms of sectional, attitudinal and behavioural divisions among their membership. Data are drawn from a survey of 200 employees of five organizations carried out in the north east of England in 1992‐93. Develops a typology of different groups among the rank‐and‐file “passive” membership in terms of differing attitudes, and discusses occupational and gender divisions as examples of blocks to union unity. Concludes that unions must seek to identify and cater for the needs of a plurality of groups, but that members can be unified where a common cause is identified.

This paper aims to investigate the stigma effect on property valuation/sale price for remediated residential leaky buildings constructed in New Zealand during the 1990s and 2000s. In particular, the authors want to know whether meeting the regulatory standards for remediation work will totally eliminate the negative stigma effect on remediated properties.

Property transaction data for remediated leaky homes are often limited and not well recorded. Thus, it is very difficult or even impossible to identify those remediated properties in a standard property transaction data set. Moreover, a vast amount of information regarding the nature of property defects, remediation process and method is very difficult to obtain. In this study, members of the Property Institute of New Zealand (PINZ) and the Real Estate Institute of New Zealand were invited to participate in an online website survey. The results were then analysed using the principal component analysis, ordinary least squares and multinomial logit regressions.

This study indicates that for monolithic-clad dwellings, the price discount due to leaky building stigma is significant. Depending on the severity of the leaking problems, this is about 11 per cent on average for general market stigma and an additional 5-10 per cent for post-remediation stigma. The results highlight that meeting the regulatory standards for remediation work cannot totally eliminate the negative stigma effect on remediated properties. The findings are in line with the lemon theory introduced by Akerlof (1970) and robust to individual characteristics of the survey respondent.

General market stigma has been widely researched and documented in the literature. In contrast, there is a lack of research as to whether remediation will eliminate stigma, particularly in the presence of general market stigma. The authors are the first to show that post-remediation stigma can cause value loss in addition to general market stigma based on the lemon theory proposed by Akerlof (1970).