18 January 2023
Real estate investment and development decisions are reached in a world of intrinsic future climate risk and uncertainty. Risk is different from uncertainty because risk is quantifiable in a sense that an evidence-based numerical probability can be assigned to measure risk. To make sound business decisions about climate risk, it is important to measure the objective risk associated with a particular hazard – rather than obtaining a subjective and biased risk estimate. Behavioural economics and finance findings highlight that individual human beings find it exceedingly difficult to evaluate risk objectively, accurately, and precisely – because we all have our own vastly different individual beliefs, fears, biases, coping mechanisms and risk-tolerance thresholds associated with specific risks. Hence, most individual ‘back-of-the envelope’ estimates and perspectives on climate risk are highly subjective, varying along a vast spectrum of uncertainty from zero to infinity. Therefore, there is a real necessity for using the most methodologically non-biased, well-rounded, and objective modelling and estimation of climate risks faced by real estate investors.
Objective Climate Risk Measures
In line with the TCFD and CRREM guidance, objective climate risk for a physical asset is defined as the non-biased probability of an adverse climatic event occurring – multiplied by the magnitude of the hazard – accounting for the asset and location-specific characteristics. For example, the risk of flash flooding for a Central London office property will account for the probability of this flood occurring, the expected costs of an expected flood to this unique asset, and the degree of location-specific asset-level exposure. Climate risks such as flash flooding, heatwaves, or other extreme weather events are referred to as physical climate risks. Adding to this, we acknowledge that investors must fully account for asset-level and location-specific transition climate risks to gain a complete understanding of climate risk exposure for their real estate portfolios. Thus, in the Climate Resilience service line at Longevity Partners, one of our most fundamental aims is the transparently objective quantification of complex physical and transition risk – tailored specifically to the client’s unique portfolio or asset.
Objective climate risk measures are preferred because transparent, honest, accurate, and ideologically unbiased information can make the largest practical difference in managing environmental hazards to real estate assets. To measure objective risk, it is important to acknowledge the findings of Kahneman & Tversky (1979), who documented that individuals tend to produce subjective overestimates or underestimates of objective risk. In the words of the founder of behavioural economics, Herbert Simon, this conclusion follows from our ‘bounded rationality’ – our limited cognitive capacity to independently process vast amounts of information under time and opportunity cost constraints. In turn, ‘bounded rationality’ leads to the difficulty of reaching objective conclusions about the complex data at hand when relying solely on one’s mental faculties.
The potential for biased estimates that are based solely on mental assessments is especially prevalent in fast-paced corporate environments. In his best-selling novel, ‘Thinking Fast and Slow’, Kahneman (2011) described our limited rationality as the primary catalyst for our reliance on an extensive range of cognitive biases and heuristics, which constitute our so-called ‘System 1’ thinking; namely the automatic, intuitive, and often ideologically biased thinking that prevails at the individual level.
Thus, it is noteworthy that even the leading environmental economists can produce vastly different estimates of climate risk. For example, a recipient of the 2018 Nobel Prize in Economics, William Nordhaus (2017-2020), estimates a figure of $30-50 per metric tonne of CO2 , whereas Nicholas Stern, the author of the 2006 Stern Review, and Joseph Stiglitz, another Nobel Prize Winner, settle on a radically different figure of about $100 per tonne CO2 . This difference is explained largely by the subjective differences in their respective methodologies. Such subjectively framed divergence in the estimates of the true social cost of climate change merits the attention of the private sector. Specifically, sustainability consultancies must aim to minimise modelling subjectivity in order to produce the least biased estimates of true objective climate risk. This will provide a robust framework to enable wise and holistic business decisions for clients.
The effect of background ‘Noise’ on objective quantification of climate risk
Our ability to objectively quantify climate risk is further compromised by another flaw in human judgment resulting from ‘Noise’. Noise refers to external circumstances and group dynamics surrounding a specific business problem, such as climate risk modelling. Excessive noise in the corporate environment affects the analyst’s or manager’s mood and makes them prone to emotional or irrational reactions when working on complex business problems. This predisposes analysts and corporate decision-makers towards subjectivity in their independent modelling estimation of climate risk. For example, in their book ‘Noise: A Flaw in Human Judgment,’ Kahneman, Sibony & Sunstein (2021) report that the median risk premiums set by underwriters independently for the same five fictive customers varied by 55% – five times as much as expected by most underwriters and their executives themselves. Moreover, the authors quote another fascinating study by Uri Simonsohn (2006) titled, ‘Clouds Make Nerds Look Good’, in which the author analysed 682 real decisions by college admissions officers to find compelling evidence that the officers awarded the academic strengths of applicants more importance on cloudy days – but favoured non-academic strengths on sunny days. Viewed together, these studies illustrate that our external circumstances, as seemingly insignificant as the weather outside a risk analyst’s window, can influence real-life business decisions. This finding applies consistently across all business environments, especially when companies aim to tackle intricate business problems, such as estimating climate risk to their real estate investments. Therefore, it is crucial that teams aiming to produce objective estimates of climate risk are conscious of the various strains of external ‘Noise’ that may interfere with the objectivity of their analysis.
Our awareness of external noise, as well as an in-depth understanding of our internal cognitive biases, calls for ‘debiasing’ and ‘noise awareness & hygiene’ practices to be implemented by teams of sustainability consultants working to produce the most objective estimates of climate risk. Indeed, research highlights that collaboration, diversity of individual team members’ backgrounds, and promotion of independent thought are the three most important characteristics that define the extent to which teams of sustainability professionals can successfully overcome subjectivity in climate risk modelling. Climate Resilience professionals working in diverse and well-rounded groups act as ‘debiasing agents’ for one another under free dialogue and collaboration – producing more objective estimates of risk than they would individually (Arlen & Tontrup, 2015).
Objective climate risk assessments – adding value for real estate investors
By ensuring assessments are made using more objective risk estimates, sustainability consultants can perform cost-benefit analyses of climate change mitigation and adaptation measures for real estate assets with far greater certainty and precision. This leads to a lower degree of uncertainty among real estate investors about informational deficiencies concerning the true expected impact of climate change on their real estate portfolios. Therefore, reliance on objective risk estimates improves the efficiency of financial resource allocation towards mitigation and adaptation measures – protecting real estate assets from climate change risk and unlocking sustainable investment value.
Put simply, well-rounded, diverse, and collaborative teams of climate risk expert consultants produce more objective and accurate climate risk estimates than individual subjectively oriented decision-makers in the real estate industry.
How can Longevity Partners provide you with an objective climate risk assessment?
Finally, to hedge against the risk of subjectivity and ‘bounded rationality’ in Longevity’s internal climate risk models, we leverage our strong working partnerships with the industry-leading data providers – aligning our modelling processes with world-leading practices pioneered by the likes of GRESB, CRREM, TCFD, UNPRI and UNEP FI. We leave no room for bias in our climate risk modelling by processing data that is provided exclusively by the most reputable international organisations in the field, that are peer reviewed, or that are sourced directly from our clients. Further still, we process this high-quality data in well-rounded and diverse teams, composed of analysts, engineers, environmental science specialists, and economists. In addition, we conduct multiple rounds of actively implemented quality assurance checks on our models before our climate risk estimates ever land on our client’s desk. Over the years that Longevity Partners has been delivering Climate Resilience to our clients, our processes and models have been refined to the highest standards of quality, objectivity, and methodological robustness.
Our Climate Resilience experts are uniquely focused and trained on leveraging rational objectivity for the purpose of building highly robust climate risk. By continuously striving to deliver the most objective contextually tailored estimates of climate risk in a diverse and collaborative working environment, our team is able to consistently add climate-resilient, sustainable value to our clients’ real estate investment portfolios.
 Kahneman, D. and Tversky, A. (1979) Prospect Theory: An Analysis of Decision under Risk. Econometrica: Journal of the Econometric Society, 47, 263-291. http://dx.doi.org/10.2307/1914185
 Simon, H. A. (1990). Bounded rationality. In Utility and probability (pp. 15-18). Palgrave Macmillan, London.
 Kahneman, D. (2011). Thinking, fast and slow. Macmillan.
 Nordhaus, W. D. (2017). Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 114(7), 1518-1523.
 Stern, N. (2006). Stern Review: The economics of climate change.
 Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. Little, Brown.
 Simonsohn, U. (2007). Clouds make nerds look good: Field evidence of the impact of incidental factors on decision making. Journal of Behavioral Decision Making, 20(2), 143-152.
 Arlen, J., & Tontrup, S. (2015). Strategic bias shifting: herding as a behaviorally rational response to regret aversion. Journal of Legal Analysis, 7(2), 517-560.