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

2. 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. I.Measuring Sustainability II.Operationalization of Sustainability III.Quantifying the Effect of Sustainability Criteria on Risk IV.Results V.Example of Application VI.Conclusion Literature Acknowledgments, Funding 3 Table of Contents

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

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: Risk based * developed in a joint effort of CCRS at University of Zurich with representatives of Swiss real estate sector and government 5

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 («structural interruption») 6

7. Sustainability Criteria 1. Flexibility and polyvalence 1.1 Flexibility of use 1.2 Adaptability to users 2. Resources consumption and greenhouse gases 2.1 Energy and greenhouse gases 2.2 Water 2.3 Building materials 3. Location and mobility 3.1 Public transport 3.2. Non motorized traffic 3.3 Location 4. Safety and security 4.1 Location regarding natural hazards 4.2 Building safety and security measures 5. Health and comfort 5.1 Health and comfort II. Operationalization of Sustainability Criteria – Subindicators – Coding 7 Subindicators 1.1.1 Floor plan 1.1.2 Storey height 1.1.3 Acessibility wiring / pipes / building services 1.1.4 Reserve capacity wiring / pipes / building services Coding Storey height 1 = >2.74m 0 = 2.54m – 2.74m -1 = <2.54m

8. II. Operationalization of Sustainability Criteria - Subindicators – Risk Estimates** *Demand reductions - Irreversible: reduction revenue in % - Reversible: Capital expenditure CHF/m2 8 SubindicatorStates of Nature (in 30 years) ProbabilitiesDemand Reductions* 1.1.1 Floor planNo change10%CHF/m2 or % revenue Small change40%15 CHF/m2 Medium change40%60 CHF/m2 Maximum change10%125 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)). 9

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) 10

11. III. Quantifying Sustainability Valuation Model 11

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 12

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

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

15. III. Quantifying Sustainability Deriving the Weights 15

16. IV. Results 4 most important subindicators 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. 16

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

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

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

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). 20

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 21

22. Literature 22

23. Acknowledgments & Funding The authors would like to thank Urs Faes (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. 23