Amsterdam Data Collective (ADC) is ready to support our clients to quantify the impact of climate risks on their exposures. By identifying and incorporating relevant climate attributes in your bank’s modelling framework, we can help increase its accuracy, adaptability, and resilience to climate-related risk challenges.

In recent years, a broad understanding and recognition has emerged that climate change is a driver of financial risks by means of both the transition and physical channel. However, the approaches to assessing the materiality of this correlation are limited in both depth and sophistication. This presents new climate-related challenges for risk managers.

A very clear example is real estate portfolios, which are prone to both physical and transition risks. Firstly, from the physical risk perspective: 59% of the surface in the Netherlands is susceptible to flooding [Netherlands Environmental Assessment Agency]. This impacts house prices and the ability to repay mortgage loans in case of catastrophic events. From the transition risk perspective, the energy efficiency status of a building affects its price, especially nowadays with increasing awareness that a less efficient energy label will lead to higher energy bills.

These climate-related risks can potentially result in financial losses, as it is expected that these factors are not yet appropriately incorporated into banks’ risk management frameworks. This is particularly apparent in Dutch banks because they hold relatively large mortgage portfolios with high loan to value ratios. Consequently, the credit risk on these loans is dependent on the correct valuation of the collateral value. In this case: the commercial value of the house. 

The approach: model the impact of flood risk and energy labels on house prices

Due to the quantification of climate risk still being in its nascent stage, we have conducted theoretical research using several econometrical models. Our research quantifies the impact of both the physical risk driver (flood risk) and the transition risk driver (energy efficiency) on the selling prices of Dutch houses. This is based on transaction data provided by Matrixian.

Since sales price data has been recorded over many years on a four-digit zip code level, it is possible to model the house prices for a series of points in time. This approach makes it possible to disentangle more information compared to modelling at a certain moment. With this approach, the climate variables can be separated from other time-varying attributes that influence house prices.

Furthermore, by incorporating as many different relevant factors as the data allows, such as characteristics of houses and inhabitants, and macro-economic factors, the impact of flood risk and energy efficiency on house prices can be determined.

The solution: incorporate these factors in the full risk management cycle

Our team has translated the macro level approach into a bank mortgage portfolio approach. We also further developed the framework to be able to inform the complete risk management cycle. From a risk identification perspective, ADC can develop a risk heat map revealing in which flood-prone areas the bank has high exposures. From a risk mitigation perspective, translating these insights creates more accurate collateral valuations.

Of course, this assessment is not limited to flood risk. However, more analysis has been conducted in this space since this data is more often available. The more we turn the ‘alternative’ ESG data into fundamental risk data, the more we can incorporate these insights into the risk management cycle.

Learn more about how ADC can help your organisation set up a sufficient ESG data framework and strategy

Traditional models and insights are beginning to diverge from reality by not including ESG data input. By identifying and incorporating relevant climate attributes in a bank’s modelling framework, buffers and provisions will become more accurate. This makes the bank increasingly resilient to climate events.

Including ESG insights in the entire credit cycle should keep the introduction of new climate-related risk challenges at origination within risk appetites. As a result, making these risks measurable allows climate-related risks to become actionable.

In addition, including these insights in commercial propositions enables banks to stimulate their clients to make improvements for both climate change adaptation and mitigation measures, thus contributing to our joint efforts to meet climate targets.


Would you like to know more about Amsterdam Data Collective? Do you want to discover how we can help your organisation quantify the impact of climate risk?  Get in touch with Julia van Huizen at, or check our contact page.