Use Case

Current physical risk metrics are stuck in the past. They operate under a potentially catastrophic illusion: the idea that the physical risks of a location like the Empire State Building apply uniformly to all its tenants. But is the risk faced by the restaurant on the ground floor, exposed to immediate floodwaters, really the same as the logistics company on the 2nd floor or the data center on the third? A universal assessment might be simpler, but its lack of specificity is both deceptive and potentially devastating.

Consider this: A power cut might send the data center into crisis mode as their servers risk overheating without the essential water cooling, just floors above, a café grinds to a halt as their espresso machines and cash registers go dormant, losing an entire day's revenue. This generalized risk assessment isn't just outdated, it's a disaster waiting to happen. It's forcing tenants to either be ill-prepared or over-protective, wasting resources or risking massive losses.

Crafting Nuanced, Industry-Specific Risk Profiles
One Concern revolutionizes risk assessment by introducing industry-specific downtime thresholds. Rather than evaluating every business through the same lens, we understand that each industry has its own unique vulnerabilities. Here's how it works:

1. Establishing Downtime Thresholds: For each industry, a downtime threshold is determined. This represents the maximum allowable downtime – due to physical risks like flooding or power outages – before significant financial impact is felt. For instance, a data center might have a threshold of just a few hours, as extended downtime threatens server integrity. In contrast, a retail store might have a more extended threshold, as temporary disruptions might be recoverable.

2. Default Probabilities & Exceedance Probabilities: With the thresholds set, we then analyze the likelihood of each industry exceeding its specific threshold in the event of a physical disturbance. This 'exceedance probability' operates similarly to a probability of default in credit risk models. It informs businesses of their chances of experiencing debilitating disruptions.

3. Conditional Downtime (Similar to Loss Given Default): Beyond just knowing the probability, it's crucial to gauge the potential severity. Should a disruption occur, how long might it last? Conditional downtime, akin to 'loss given default' for credit, predicts the likely duration of a disruption if the threshold is exceeded.

4. Generating Resilience Scores: This is where we transform traditional risk assessment. By multiplying the 'exceedance probability' (how likely disruptions are to cross industry norms) with the 'conditional downtime' (potential length of disruption), we generate a 'Resilience Score'. It's a dynamic score that encapsulates both the likelihood and potential severity of disruptions for a specific industry at a particular location.

Imagine a commercial hub with varied tenants:

Ground Floor - Jewelry Store: Set against the industry benchmark, this store is rated at a higher risk due to its location. With potential disruptions threatening their security systems, they stand to suffer significant losses in the event of extended power outages. Their exceedance probability of 20% against the industry benchmark underscores the urgency for enhanced protective measures.

2nd Floor - Software Firm: Despite being in the same building, their risk rating is lower. Their benchmark analysis indicates that they can tolerate minor disruptions without significant financial consequences. However, with a 10% exceedance probability, diversifying operational bases becomes a viable consideration.

With the power of Industry Benchmarking coupled with Resilience Scores, commercial real estate can now offer a dual-layered, granular risk and resilience assessment for every tenant. A quantum leap from the outdated one-size-fits-all approach.