The article in the url below conveys the complexities (and challenges) of measuring complex notions such as social value or social impact. In this case, it is the level of climate risk associated with a property that is advertised as part of Zillow’s home listings:
"When Zillow Group Inc. removed climate risk scores from its home listings last month, following a complaint from the real estate industry, many observers took to the press and social media to warn — correctly — that disappearing the data doesn’t get rid of the risk. In a country assailed by extreme weather, it’s crucial that people be able to learn about a home’s vulnerability to wildfire or flooding. But the information that’s increasingly available is not as straightforward as it might seem."
The article correctly understates:
"Climate risk modeling is far from perfect, a growing body of research shows, with different models often yielding different results. And it’s not always obvious to experts, let alone homebuyers, what the data represent or how they should inform decisions."
To put that bluntly in a way that concisely demonstrates the measurement issues:
"In October, a UK industry group, the Climate Financial Risk Forum, looked at how 13 different climate-risk companies each rated the same 100 properties around the world. The ratings were all over the place. Some saw a given property as highly vulnerable to flooding, for example, while others saw no risk there at all. For 20 of the properties, the study designers provided incomplete location data, to see if models could pinpoint the address. One model placed 'a well-known store in Boston' on an Atlanta road with a similar name."
As such, using these measures carries its own risk of unintended consequences:
"Risk modeling 'is a relatively new discipline and it will take time for norms to emerge and become established,' the authors of the analysis wrote. Oversimplifying climate-impact projections — or worse, getting them wrong — could lead to some people’s assets losing value for no reason, as well as distorting market pricing."
But, equally true is that removing the measures, however incomplete or flawed, does not remove the underlying risk. We just haven't developed very good ways to quantify that risk that allows a like-for-like comparison, across organizations and industries. Nevertheless, the statistic accompanying the article demonstrates the scale of the challenge and how far from ideal we are currently:
“21%: The percentage of times two flood-risk models got matching results."
Take care
David
David Chandler
Strategic Corporate Social Responsibility: Sustainable Value Creation (6e)
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The Imperfect Science of Home Climate Risk Scores
By Eric Roston
December 9, 2025
Bloomberg
