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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 20th Annual ConferenceVienna, AustriaJuly 3-6, 2013

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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

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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

Table of Contents

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I. Measuring SustainabilityThe challenge

Concept

Multidimensional / Unidimensional

Criteria / Features

Measurement

Weighting

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I. Measuring SustainabilityOur answer: Economic Sustainability Indicator*

Concept: main focus: economic / secondary focus: social and environmentalAssisting 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

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I. Measuring SustainabilityMarket 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»)

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Sustainability Criteria

1. Flexibility and polyvalence1.1 Flexibility of use

1.2 Adaptability to users

2. Resources consumption and greenhouse gases2.1 Energy and greenhouse gases

2.2 Water

2.3 Building materials

3. Location and mobility3.1 Public transport

3.2. Non motorized traffic

3.3 Location

4. Safety and security4.1 Location regarding natural hazards

4.2 Building safety and security measures

5. Health and comfort5.1 Health and comfort

II. Operationalization of SustainabilityCriteria – Subindicators – Coding

Subindicators 1.1.1 Floor plan1.1.2 Storey height1.1.3 Acessibility wiring / pipes / building services1.1.4 Reserve capacity wiring / pipes / building services

Coding Storey height 1 = >2.74m 0 = 2.54m – 2.74m-1 = <2.54m

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II. Operationalization of SustainabilityCriteria - Subindicators – Risk Estimates**

*Demand reductions- Irreversible: reduction revenue in %- Reversible: Capital expenditure CHF/m2

Subindicator States of Nature(in 30 years)

Probabilities Demand Reductions*

1.1.1 Floor plan No change 10% CHF/m2 or % revenue

Small change 40% 15 CHF/m2

Medium change 40% 60 CHF/m2

Maximum change 10% 125 CHF/m2

** Risk estimates: expert panel estimated likely changes in demand within 30 years

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III. Quantifying the Effect of Sustainability Criteria on Property Value RiskMonte 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)).

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III. Quantifying SustainabilityMonte 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)

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III. Quantifying SustainabilityValuation Model

(1)

where

gross rental income(O-M) operating – maintenance costM maintenance costCapex capital expenditure

discount rate (equity financed, not WACC)t time index

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III. Quantifying SustainabilityMonte 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

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III. Quantifying SustainabilityOne simulation, 2 ESI sub-indicators

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III. Quantifying SustainabilityResult of Monte Carlo Simulation, ESI sub-indicator 31

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III. Quantifying SustainabilityDeriving the Weights

(I) Calculate standard deviation of valuations / mean of valuations

(II) Discount Rate = riskless rate + (I) * Sharpe Ratio

(2)

where

weight of ESI factor xdiscount rate calculated based on simulation with ESI factor xdiscount rate calculated based on simulation without ESI factors

N number of ESI factors

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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.

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IV. ResultsApplication* 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

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V. Example of ApplicationImmogreen

-1 = poor 0 = median +1 = good

1 Flexibility of Use and Polyvalence 2 Use of Resources and Greenhouse Gases 3 Place and Mobility 4 Security 5 Health and Convenience

Value as is

Capital Exepnditure

Value renovated

In 1‘000 CHF

Actual state Renovation 1 Renovation 2 Reconstruction

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V. Example of ApplicationImmogreen II

Actual state Renovation 1 Renovation 2 Reconstruction

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VI. ConclusionValue

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).

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VI. ConclusionFuture 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

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Literature

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Hoesli, M., Jani, E. and Bender, A. (2005), Monte Carlo Simulations for Real Estate Valuations. Hughes, W. (1995), “Risk Analysis and Asset Valuation: A Monte Carlo Simulation Using Stochastic

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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.