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.Monte Carlo Simulations ; Real Estate Portfolio Valuation ; Break options ; Lease Structure ; Options

DATA ASSIMILATION ON A FLOOD WAVE PROPAGATION MODEL : EMULATION OF A KALMAN FILTER ALGORITHM

International audienceThis study describes the assimilation of synthetically-generated river water level observations in a flood wave propagation model. For this approach to be applied in the framework of real-time flood forecasting, the cost of the data assimilation procedure, mostly related to the estimation of the background error covariance matrix, should be bound. An Ensemble Kalman Filter (EnKF) algorithm is applied, with a steady observation network, to demonstrate how the assimilation modifies the background correlation function at the observation point. It is shown that an initially Gaussian correlation function turns into an anisotropic function at the observation point, with a shorter correlation length-scale downstream of the observation point than upstream, and that the variance of the error in the water level state is significantly reduced downstream of the observation point. The covariance matrix resulting from the EnKF is then used as an invariant background error covariance matrix for a series of successive Best Linear Unbiased Estimation (BLUE) algorithms which emulate an EnKF at a lower cost. This study shows how the background error covariance matrix can be computed off-line, with an advanced algorithm, and then used with a cheaper algorithm for real-time application

Proper use of the modified Sharpe ratios in performance measurement : rearranging the Cornish Fisher expansion

Performance analysis is a key process in finance to evaluate or compare investment opportunities, allocations, or management. The classical method is to compute the market or sub-market returns and volatilities, and then calculate the standard performance measure, namely, the Sharpe ratio. This measure is based on the first two moments of a return distribution. Therefore, a significant weakness of this method is that it implicitly assumes that the distribution is Gaussian (if it is not Gaussian, the approach may lead to a bad fit). In fact, risk comes from not only volatility, but also from higher moments of distribution such as skewness and kurtosis. The standard method to resolve this issue is to use the modified Sharpe ratio; this method replaces the classical Sharpe ratio volatility with the value at risk. The latter is computed using the Cornish Fisher expansion, a tool based on the first four moments of return distribution. This methodology, however, may present a major pitfall: in some cases, quantile functions do not stay monotone. In this paper, we show how this tool can be used effectively through a specific procedure, rearrangement. We compare various metrics using rank correlation, and demonstrate how and in which cases the proposed procedure delivers ranking different from the standard Sharpe ratio ranking. Furthermore, we show how our technique offers better distribution approximations and is therefore a more useful performance metric. Institutional investors may find the technique proposed here useful in that it allows for considering non-normality in performance analysi

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

Parametric Kalman filter for chemical transport models

A computational simplification of the Kalman filter (KF) is introduced – the parametric Kalman filter (PKF). The full covariance matrix dynamics of the KF, which describes the evolution along the analysis and forecast cycle, is replaced by the dynamics of the error variance and the diffusion tensor, which is related to the correlation length-scales. The PKF developed here has been applied to the simplified framework of advection–diffusion of a passive tracer, for its use in chemical transport model assimilation. The PKF is easy to compute and computationally cost-effective than an ensemble Kalman filter (EnKF) in this context. The validation of the method is presented for a simplified 1-D advection–diffusion dynamics

Data assimilation with the 1D hydraulic code Mascaret and multi-dimensional coupling between Telemac and Mascaret

Water Qualit

Ensemble-based data assimilation for operational flood forecasting – On the merits of state estimation for 1D hydrodynamic forecasting through the example of the “Adour Maritime” river

This study presents the implementation and the merits of an Ensemble Kalman Filter (EnKF) algorithm with an inflation procedure on the 1D shallow water model MASCARET in the framework of operational flood forecasting on the “Adour Maritime” river (South West France). In situ water level observations are sequentially assimilated to correct both water level and discharge. The stochastic estimation of the background error statistics is achieved over an ensemble of MASCARET integrations with perturbed hydrological boundary conditions. It is shown that the geometric characteristics of the network as well as the hydrological forcings and their temporal variability have a significant impact on the shape of the univariate (water level) and multivariate (water level and discharge) background error covariance functions and thus on the EnKF analysis. The performance of the EnKF algorithm is examined for observing system simulation experiments as well as for a set of eight real flood events (2009–2014). The quality of the ensemble is deemed satisfactory as long as the forecast lead time remains under the transfer time of the network, when perfect hydrological forcings are considered. Results demonstrate that the simulated hydraulic state variables can be improved over the entire network, even where no data are available, with a limited ensemble size and thus a computational cost compatible with operational constraints. The improvement in the water level Root-Mean-Square Error obtained with the EnKF reaches up to 88% at the analysis time and 40% at a 4-h forecast lead time compared to the standalone model

Un nouveau paradigme de la dynamique des rendements immobiliers parisiens

Cet article fait suite au travail de Baroni, Barthélémy et Mokrane [2008, Revco] dans lequel les auteurs développent un modèle factoriel permettant d’expliquer la dynamique des prix des biens immobiliers résidentiels à Paris et sa proche banlieue par un ensemble de variables économiques et financières prédéfinies. Le présent article s’attache à mettre en exergue les changements récents du poids de ces facteurs explicatifs. Les principaux résultats de l’article sont d’une part que le modèle développé par Baroni, Barthélémy et Mokrane [op. cit.] garde sa capacité explicative, et d’autre part, que le poids des facteurs a nettement évolué ces dernières années et par suite que le marché immobilier résidentiel parisien est entré dans un nouveau paradigme. Notamment, l’article montre que l’impact desloyers sur le rendement en capital immobilier s’est récemment renforcé au détriment des taux d’intérêt.This article follows Baroni, Barthélémy and Mokrane [2008]. In this work, the authors propose a factorial model to explain the price dynamics of Paris and its suburbs based on a set of predefined economic and financial variables. The article seeks to bring out the recent changes on the weight of explanatory factors. The main results are first that the model developed by Baroni, Barthélémy and Mokrane [op. cit.] keeps a good explanatory power over time and second, that factors weights largely change during the past years. So, it is now possible to assert that Paris housing market has entered a new paradigm. In particular, the article shows that the impact of rents on real estate capital return has recently increased in opposite with the one of the interest rates

Thick-film multi-DOF force / torque sensor for wrist rehabilitation

A complete six–degree-of-freedom (6-DOF) force / torque sensor has been designed and fabricated for wrist rehabilitation applications, with the focus laid on simple, straightforward manufacturing processes. This paper details the mechanical design, 3D-modeling, manufacture and characterisation of the sensor. Compared to previous work, this design has the advantage of simple, fully planar machining, and the load-sensing elements all lie on the same plane, making the device compatible with single-film deposition or a foil bonding process. The sensor was machined from steel, and the piezoresistive load-sensing bridges were deposited using thick-film technology. We used commercial thick-film materials in a first time. A new lead-free materials system compatible with low processing temperatures (<700°C) will shortly replace the commercial one and is expected to eventually also be adaptable to aluminium substrates