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Sustainability and Risk in Real Estate Investments: Combining Monte Carlo Simulation and DCF

Sustainability and Risk in Real Estate Investments: Combining Monte Carlo Simulation and DCF

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Sustainability and Risk in Real Estate Investments: Combining Monte Carlo Simulation and DCF

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  1. Sustainability and Risk in Real Estate Investments: Combining Monte Carlo Simulation and DCF European Real Estate Society 20th Annual ConferenceVienna, Austria July 3-6, 2013 Erika Meins, Center for Corporate ResponsiblityandSustainability (CCRS) atthe University ofZurich Daniel Sager, Meta-Sys AG, Zurich

  2. What is this Study about? Sustainability and risk: Identifying the relative contribution of sustainability citeria to property value risk in an investment value perspective Rating: results are used for risk-based weighting of a sustainability rating Practical use: The rating summarizes how sustainability features affect the risk of specific properties and is used as a basis for real estate investment decisions

  3. Table of Contents Measuring Sustainability Operationalization of Sustainability Quantifying the Effect of Sustainability Criteria on Risk Results Example of Application Conclusion Literature Acknowledgments, Funding

  4. I. Measuring Sustainability The challenge Concept Multidimensional / Unidimensional Criteria / Features Measurement Weighting

  5. I. Measuring Sustainability Our answer: Economic Sustainability Indicator* Concept: main focus: economic / secondary focus: social and environmental Assisting private investors (private interest) Unidimensional Criteria / Features: Selected according to Meins (2010) Measurement: subjective probabilities / damages Weighting: Riskbased * developed in a joint effort of CCRS atUniversity of Zurich with representatives of Swiss real estate sector and government

  6. I. Measuring Sustainability Market Value and Investment Value (Worth) Market Value: Given by actual perception of return perspective and risk of market participants (immediate, short run) Investment Value (Worth): Depending on individual situation / perception of investor Risks related to sustainability not present in historical series («structuralinterruption»)

  7. II. Operationalization of Sustainability Criteria – Subindicators – Coding Coding Storeyheight 1 = >2.74m 0 = 2.54m – 2.74m -1 = <2.54m Subindicators 1.1.1 Floor plan 1.1.2 Storeyheight 1.1.3 Acessibilitywiring / pipes / buildingservices 1.1.4 Reserve capacity wiring / pipes / building services

  8. II. Operationalization of Sustainability Criteria - Subindicators – Risk Estimates** *Demand reductions - Irreversible: reductionrevenue in % - Reversible: Capital expenditure CHF/m2 ** Risk estimates: expert panel estimated likely changes in demand within 30 years

  9. III. Quantifying the Effect of Sustainability Criteria on Property Value Risk Monte Carlo Simulation • The Question: • How to assess “future” risks, and how to separate them from risks already accounted for in market discount rates ? • The Answer: • Explicitely model all risks and simulate the full possible distribution of values. (Spirit of Present Value Distribution Model (Hughes, 1995)).

  10. III. Quantifying Sustainability Monte Carlo Simulation for Investment Appraisal • Determine an appraisal model • Determine (objective or subjective) probability distributions of future outcomes • Separate important from unimportant variables in appraisal model • Based on the sensitivity of the result with regard to the variable • identify and describe correlations of future outcomes • (Savvides 1994)

  11. III. Quantifying Sustainability Valuation Model (1) where gross rental income (O-M) operating – maintenance cost M maintenance cost Capex capital expenditure discount rate (equity financed, not WACC) t time index

  12. III. Quantifying Sustainability Monte Carlo Simulation for Investment Appraisal • Typical Swiss Apartment Building as reference object • Benchmarks of Real Estate Investment Data Association (REIDA) • 100 periods • 20’000 simulation runs • Discount rate = riskless rate

  13. III. Quantifying Sustainability One simulation, 2 ESI sub-indicators

  14. III. Quantifying Sustainability Result of Monte Carlo Simulation, ESI sub-indicator 31

  15. III. Quantifying Sustainability DerivingtheWeights (I) Calculate standard deviation of valuations / mean of valuations (II) Discount Rate = riskless rate + (I) * Sharpe Ratio (2) where weight of ESI factor x discount rate calculated based on simulation with ESI factor x discount rate calculated based on simulation without ESI factors N number of ESI factors

  16. IV. Results 4 most importantsubindicators limiting depreciation: low consumption of thermal energy (29.3%) good access to public transportation (16.3%) sufficient day light (9.6%) generous story height (6.3%) Account for almost two thirds of the total measured risk.

  17. IV. Results Application* to Portfolio of Swisscanto** Figure 4: IFCA portfolio with 129 properties worth over CHF 1’200 Mio* * application under www.esiweb.ch ** Swiss institutional investor

  18. Value as is Capital Exepnditure V. Example of Application Immogreen Value renovated In 1‘000 CHF Actual state Renovation 1 Renovation 2 Reconstruction

  19. V. Example of Application Immogreen II Actual state Renovation 1 Renovation 2 Reconstruction

  20. VI. Conclusion Value Attempt to found a sustainability rating in financial theory (basis for integrating sustainability to risk management and portfolio theory (Krysiak, 2009). Links Monte Carlo simulations to a DCF to assess the impact of changing market conditions related to sustainability on the estimated worth (Lorenz & Lützkendorf, 2011). Allows managers to make informed decisions between risk and expected benefits when managing real estate investments sustainably. The results can also be used as a risk documentation for valuation or for reporting purposes, as postulated by (Lorenz & Lützkendorf, 2011).

  21. VI. Conclusion Future Research Further develop modeling of subjective probabilities and damages Riskless discounting of simulations including all risks Transformation of present value distribution to risk measure Extension to other real estate sectors

  22. Literature

  23. Acknowledgments & Funding The authors would like to thank UrsFaes (UBS Global Real Estate Switzerland), Kurt Ritz (PricewaterhouseCoopers Switzerland), Hans-Peter Burkhard and Urs von Arx (both CCRS, University of Zurich). The research in this article is funded by EPImmo, Inrate, Reuss Engineering AG, SEK-SVIT, Steiner AG, SUVA and Zurich Cantonalbank.