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Sustainability and risk

Sustainability and risk Purpose– This paper aims to identify the relative contribution of sustainability criteria to property value risk. Design/methodology/approach– A discounted cash flow (DCF) model is used to assess the effect of a given set of 42 sustainability sub-indicators on property value. The anticipated demand for each sustainability sub-indicator is described by four future states of nature. Their impact on costs or revenue is estimated and included in the model. Subjective probability distributions describe the occurrence of the future states of nature. Monte Carlo simulations of the DCF model are then used to estimate the impact of an individual feature on the risk (volatility) of the property value distribution. Findings– The results for Switzerland show that “use of thermal energy” (29.3 per cent), followed by “access to public transportation” (16.3 per cent), “day light” (9.6 per cent) and “story height” (6.3 per cent) have the highest single impact on property value risk. Practical implications– The results are used for a risk-based weighting of a sustainability rating. The rating illustrates how sustainability criteria affect the risk of specific properties and are used as a basis for real estate investment decisions. Originality/value– In this paper, an effort is made to rigorously ground sustainability ratings in financial theory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of European Real Estate Research Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1753-9269
DOI
10.1108/JERER-05-2014-0019
Publisher site
See Article on Publisher Site

Abstract

Purpose– This paper aims to identify the relative contribution of sustainability criteria to property value risk. Design/methodology/approach– A discounted cash flow (DCF) model is used to assess the effect of a given set of 42 sustainability sub-indicators on property value. The anticipated demand for each sustainability sub-indicator is described by four future states of nature. Their impact on costs or revenue is estimated and included in the model. Subjective probability distributions describe the occurrence of the future states of nature. Monte Carlo simulations of the DCF model are then used to estimate the impact of an individual feature on the risk (volatility) of the property value distribution. Findings– The results for Switzerland show that “use of thermal energy” (29.3 per cent), followed by “access to public transportation” (16.3 per cent), “day light” (9.6 per cent) and “story height” (6.3 per cent) have the highest single impact on property value risk. Practical implications– The results are used for a risk-based weighting of a sustainability rating. The rating illustrates how sustainability criteria affect the risk of specific properties and are used as a basis for real estate investment decisions. Originality/value– In this paper, an effort is made to rigorously ground sustainability ratings in financial theory.

Journal

Journal of European Real Estate ResearchEmerald Publishing

Published: May 5, 2015

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