What We Know about Demand Surge: Brief Summary

Olsen, Anna H.

doi:10.1061/(asce)nh.1527-6996.0000028

Cited by 66 publications

(47 citation statements)

References 6 publications

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“…Clean-up costs can also be influenced by market forces, but the relationship is not straight-forward. Demand surge has been observed following large scale disasters where costs rapidly inflate by 10-50% comparatively with predisaster costs (Döhrmann et al 2013;Hallegatte and Ghil 2008;Olsen and Porter, 2011). However, costs can also decrease due to increased competition from a large number of contractors descending on an affected area (Brown et al 2016).…”

Section: Uncertainties Relating To Clean-up Costmentioning

confidence: 99%

A model to assess tephra clean-up requirements in urban environments

et al. 2017

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Tephra falls can cause a range of impacts to communities by disrupting, contaminating and damaging buildings and infrastructure systems, as well as posing a potential health hazard. Coordinated clean-up operations minimise the impacts of tephra on social and economic activities. However, global experience suggests clean-up operations are one of the most challenging aspects of responding to and recovering from tephra falls in urban environments. Here, we present a method for modelling coordinated municipal-led (town/district level authorities) tephra clean-up operations to support pre-event response and recovery planning. The model estimates the volume of tephra to be removed, clean-up duration, and direct costs. The underpinning component of the model is a scalable clean-up response framework, which identifies and progressively includes more urban surfaces (e.g., roofs, and roads) requiring clean-up with increasing tephra thickness. To demonstrate model applicability, we present four clean-up scenarios for the city of Auckland, New Zealand: 1 mm and 10 mm distal tephra fall across the city, along with two local 'wet' eruption scenarios (low and high volume tephra deposition) from within the Auckland Volcanic Field. Depending on the modelled scenario, outputs suggest that coordinated clean-up operations in Auckland could require the removal of tens of thousands to millions of cubic metres of tephra. The cost of these operations are estimated to be NZ$0.6-1.1 million (US$0.4-0.7 million) for the 1 mm distal tephra scenario and NZ$13.4-25.6 million (US$9-17 million) for the 10 mm distal tephra scenario. Estimated clean-up costs of local eruptions range from tens of millions to hundreds of millions of dollars. All eruption scenarios indicate clean-up operations lasting weeks to months, but clean-up in some areas impacted by local eruptions could last for years. The model outputs are consistent with documented historic tephra clean-up operations. Although we use Auckland as a proof-of-concept example, the method may be adapted for any city exposed to a tephra hazard.

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Section: Uncertainties Relating To Clean-up Costmentioning

confidence: 99%

A model to assess tephra clean-up requirements in urban environments

et al. 2017

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“…Lifeline services may witness price increases, or, where subject to price regulation, may require rationing. Other key goods affecting the pace of recovery may be influenced, especially construction, in terms of a demand surge phenomenon …”

Section: Analytical Frameworkmentioning

confidence: 99%

Improving Catastrophe Modeling for Business Interruption Insurance Needs

Rose

Huyck

2016

Risk Analysis

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While catastrophe (CAT) modeling of property damage is well developed, modeling of business interruption (BI) lags far behind. One reason is the crude nature of functional relationships in CAT models that translate property damage into BI. Another is that estimating BI losses is more complicated because it depends greatly on public and private decisions during recovery with respect to resilience tactics that dampen losses by using remaining resources more efficiently to maintain business function and to recover more quickly. This article proposes a framework for improving hazard loss estimation for BI insurance needs. Improved data collection that allows for analysis at the level of individual facilities within a company can improve matching the facilities with the effectiveness of individual forms of resilience, such as accessing inventories, relocating operations, and accelerating repair, and can therefore improve estimation accuracy. We then illustrate the difference this can make in a case study example of losses from a hurricane.

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“…Separate from uncertainty around the estimates of storm frequency, storm intensity, and structural vulnerability, demand surge can increase loss costs once a windstorm occurs. Demand surge is defined by the Actuarial Standards Board as “a sudden and usually temporary increase in the cost of materials, services, and labor due to the increased demand for them following a catastrophe.” However, Olsen and Porter () note that there is “no consensus on what specific material and labor costs contribute to demand surge.” The most common way of quantifying demand surge is through catastrophe models that increase the calculated first‐dollar losses by some demand surge multiplier. Olsen and Porter () note that the multiplier is typically between 1.0 and 1.6, which is a significant range.…”

Section: Factors Leading To Florida's Recent Property Insurance Markementioning

confidence: 99%

“…Demand surge is defined by the Actuarial Standards Board as "a sudden and usually temporary increase in the cost of materials, services, and labor due to the increased demand for them following a catastrophe." However, Olsen and Porter (2011) note that there is "no consensus on what specific material and labor costs contribute to demand surge." The most common way of quantifying demand surge is through catastrophe models that increase the calculated first-dollar losses by some demand surge multiplier.…”

Section: Demand Surgementioning

confidence: 99%

Market Implications of Public Policy Interventions: The Case of Florida's Property Insurance Market

Medders¹,

Nyce²,

Karl³

2013

Risk Manage Insurance Review

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This article asserts that the market for property insurance, particularly homeowners insurance, in the State of Florida is experiencing failures, and that a combination of market problems, externalities, and interventions unique to Florida led to these failures. The authors provide evidence of market failures in the form of undesirable market outcomes, both over time and in comparison to other coastal states. Also, they provide a narrative description of the market events, problems, and policies preceding these adverse market developments and link the narrative to the evidence. Recommendations for a return to risk‐based pricing and incentives for appropriate property mitigation are made.

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What We Know about Demand Surge: Brief Summary

Cited by 66 publications

References 6 publications

A model to assess tephra clean-up requirements in urban environments

A model to assess tephra clean-up requirements in urban environments

Improving Catastrophe Modeling for Business Interruption Insurance Needs

Market Implications of Public Policy Interventions: The Case of Florida's Property Insurance Market

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