This research briefing reports on the key findings of a computer-assisted text analysis of records from The Press newspaper related to the Earthquake Commission (EQC) from 2010 to 2019. The briefing has been prepared as a submission to the Public Inquiry into the Earthquake Commission. The aim of producing this research briefing is to provide the Public Inquiry with preliminary findings of a large-scale overview of media coverage on EQC and to identify and quantify key features and trends in public discourse about EQC over time. This research, which aggregates many stories and voices over time, offers a unique lens to view how EQC has been collectively represented, understood and experienced by the people of Canterbury.
Christchurch and Canterbury suffered significant housing losses due to the earthquakes. Estimates from the Earthquake Commission (EQC) (2011) suggest that over 150,000 homes (around three quarters of Christchurch housing stock) sustained damage from the earthquakes. Some areas of Christchurch have been declared not suitable for rebuilding, affecting more than 7,500 residential properties.
The Canterbury Earthquake Sequence (CES), induced extensive damage in residential buildings and led to over NZ$40 billion in total economic losses. Due to the unique insurance setting in New Zealand, up to 80% of the financial losses were insured. Over the CES, the Earthquake Commission (EQC) received more than 412,000 insurance claims for residential buildings. The 4 September 2010 earthquake is the event for which most of the claims have been lodged with more than 138,000 residential claims for this event only. This research project uses EQC claim database to develop a seismic loss prediction model for residential buildings in Christchurch. It uses machine learning to create a procedure capable of highlighting critical features that affected the most buildings loss. A future study of those features enables the generation of insights that can be used by various stakeholders, for example, to better understand the influence of a structural system on the building loss or to select appropriate risk mitigation measures. Previous to the training of the machine learning model, the claim dataset was supplemented with additional data sourced from private and open access databases giving complementary information related to the building characteristics, seismic demand, liquefaction occurrence and soil conditions. This poster presents results of a machine learning model trained on a merged dataset using residential claims from the 4 September 2010.
In this thesis, focus is given to develop methodologies for rapidly estimating specific components of loss and downtime functions. The thesis proposes methodologies for deriving loss functions by (i) considering individual component performance; (ii) grouping them as per their performance characteristics; and (iii) applying them to similar building usage categories. The degree of variation in building stock and understanding their characteristics are important factors to be considered in the loss estimation methodology and the field surveys carried out to collect data add value to the study. To facilitate developing ‘downtime’ functions, this study investigates two key components of downtime: (i) time delay from post-event damage assessment of properties; and (ii) time delay in settling the insurance claims lodged. In these two areas, this research enables understanding of critical factors that influence certain aspects of downtime and suggests approaches to quantify those factors. By scrutinising the residential damage insurance claims data provided by the Earthquake Commission (EQC) for the 2010- 2011 Canterbury Earthquake Sequence (CES), this work provides insights into various processes of claims settlement, the time taken to complete them and the EQC loss contributions to building stock in Christchurch city and Canterbury region. The study has shown diligence in investigating the EQC insurance claim data obtained from the CES to get new insights and build confidence in the models developed and the results generated. The first stage of this research develops contribution functions (probabilistic relationships between the expected losses for a wide range of building components and the building’s maximum response) for common types of claddings used in New Zealand buildings combining the probabilistic density functions (developed using the quantity of claddings measured from Christchurch buildings), fragility functions (obtained from the published literature) and cost functions (developed based on inputs from builders) through Monte Carlo simulations. From the developed contribution functions, glazing, masonry veneer, monolithic and precast concrete cladding systems are found to incur 50% loss at inter-storey drift levels equal to 0.027, 0.003, 0.005 and 0.011, respectively. Further, the maximum expected cladding loss for glazing, masonry veneer, monolithic, precast concrete cladding systems are found to be 368.2, 331.9, 365.0, and 136.2 NZD per square meter of floor area, respectively. In the second stage of this research, a detailed cost breakdown of typical buildings designed and built for different purposes is conducted. The contributions of structural and non- structural components to the total building cost are compared for buildings of different usages, and based on the similar ratios of non-structural performance group costs to the structural performance group cost, four-building groups are identified; (i) Structural components dominant group: outdoor sports, stadiums, parkings and long-span warehouses, (ii) non- structural drift-sensitive components dominant group: houses, single-storey suburban buildings (all usages), theatres/halls, workshops and clubhouses, (iii) non-structural acceleration- sensitive components dominant group: hospitals, research labs, museums and retail/cold stores, and (iv) apartments, hotels, offices, industrials, indoor sports, classrooms, devotionals and aquariums. By statistically analysing the cost breakdowns, performance group weighting factors are proposed for structural, and acceleration-sensitive and drift-sensitive non-structural components for all four building groups. Thus proposed building usage groupings and corresponding weighting factors facilitate rapid seismic loss estimation of any type of building given the EDPs at storey levels are known. A model for the quantification of post-earthquake inspection duration is developed in the third stage of this research. Herein, phase durations for the three assessment phases (one rapid impact and two rapid building) are computed using the number of buildings needing inspections, the number of engineers involved in inspections and a phase duration coefficient (which considers the median building inspection time, efficiency of engineer and the number of engineers involved in each assessment teams). The proposed model can be used: (i) by national/regional authorities to decide the length of the emergency period following a major earthquake, and estimate the number of engineers required to conduct a post-earthquake inspection within the desired emergency period, and (ii) to quantify the delay due to inspection for the downtime modelling framework. The final stage of this research investigates the repair costs and insurance claim settlement time for damaged residential buildings in the 2010-2011 Canterbury earthquake sequence. Based on the EQC claim settlement process, claims are categorized into three groups; (i) Small Claims: claims less than NZD15,000 which were settled through cash payment, (ii) Medium Claims: claims less than NZD100,000 which were managed through Canterbury Home Repair Programme (CHRP), and (iii) Large Claims: claims above NZD100,000 which were managed by an insurance provider. The regional loss ratio (RLR) for greater Christchurch for three events inducing shakings of approximate seismic intensities 6, 7, and 8 are found to be 0.013, 0.066, and 0.171, respectively. Furthermore, the claim duration (time between an event and the claim lodgement date), assessment duration (time between the claim lodgement day and the most recent assessment day), and repair duration (time between the most recent assessment day and the repair completion day) for the insured residential buildings in the region affected by the Canterbury earthquake sequence is found to be in the range of 0.5-4 weeks, 1.5- 5 months, and 1-3 years, respectively. The results of this phase will provide useful information to earthquake engineering researchers working on seismic risk/loss and insurance modelling.
This study analyses the Earthquake Commission’s (EQC) insurance claims database to investigate the influence of seismic intensity and property damage resulting from the Canterbury Earthquake Sequence (CES) on the repair costs and claim settlement duration for residential buildings. Firstly, the ratio of building repair cost to its replacement cost was expressed as a Building Loss Ratio (BLR), which was further extended to Regional Loss Ratio (RLR) for greater Christchurch by multiplying the average of all building loss ratios with the proportion of building stock that lodged an insurance claim. Secondly, the total time required to settle the claim and the time taken to complete each phase of the claim settlement process were obtained. Based on the database, the regional loss ratio for greater Christchurch for three events producing shakings of intensities 6, 7, and 8 on the modified Mercalli intensity scale were 0.013, 0.066, and 0.171, respectively. Furthermore, small (less than NZD15,000), medium (between NZD15,000 and NZD100,000), and large (more than NZD100,000) claims took 0.35-0.55, 1.95-2.45, and 3.35-3.85 years to settle regardless of the building’s construction period and earthquake intensities. The number of claims was also disaggregated by various building characteristics to evaluate their relative contribution to the damage and repair costs.
The 2010 and 2011 earthquakes in the region of Canterbury, New Zealand caused widespread damage and the deaths of 185 people. Suburbs on the eastern side of Christchurch and in the satellite town of Kaiapoi, 20 kilometres north of Christchurch, were badly damaged by liquefaction. The Canterbury Earthquake Recovery Authority (CERA), a government organisation set up in the wake of the earthquakes, began to systematically zone all residential land in 2011. Based on the possibility for land remediation, 7860 houses in Christchurch and Kaiapoi were zoned red. Those who were in this zone were compensated and had to buy or build elsewhere. The other zone examined within this research – that of TC3 – lies within the green zone. Residents, in this zone, were able to stay in their houses but land was moderately damaged and required site-specific geotechnical investigations. This research sought to understand how residents’ senses of home were impacted by a disaster and the response efforts. Focusing on the TC3 and red zone of the eastern suburbs and the satellite town of Kaiapoi, this study interviewed 29 residents within these zones. The concept of home was explored with the respondents at three scales: home as a household; home as a community; and home as a city. There was a large amount of resistance to the zoning process and the handling of claims by insurance companies and the Earthquake Commission (EQC) after the earthquakes. Lack of transparency and communication, as well as extremely slow timelines were all documented as failings of these agencies. This research seeks to understand how participant’s sense of home changed on an individual level and how it was impacted by outside agencies. Homemaking techniques were also focused on showing that a changed sense of home will impact on how a person interacts with a space.
In September 2010 and February 2011, the Canterbury region experienced devastating earthquakes with an estimated economic cost of over NZ$40 billion (Parker and Steenkamp, 2012; Timar et al., 2014; Potter et al., 2015). The insurance market played an important role in rebuilding the Canterbury region after the earthquakes. Homeowners, insurance and reinsurance markets and New Zealand government agencies faced a difficult task to manage the rebuild process. From an empirical and theoretic research viewpoint, the Christchurch disaster calls for an assessment of how the insurance market deals with such disasters in the future. Previous studies have investigated market responses to losses in global catastrophes by focusing on the insurance supply-side. This study investigates both demand-side and supply-side insurance market responses to the Christchurch earthquakes. Despite the fact that New Zealand is prone to seismic activities, there are scant previous studies in the area of earthquake insurance. This study does offer a unique opportunity to examine and document the New Zealand insurance market response to catastrophe risk, providing results critical for understanding market responses after major loss events in general. A review of previous studies shows higher premiums suppress demand, but how higher premiums and a higher probability of risk affect demand is still largely unknown. According to previous studies, the supply of disaster coverage is curtailed unless the market is subsidised, however, there is still unsettled discussion on why demand decreases with time from the previous disaster even when the supply of coverage is subsidised by the government. Natural disaster risks pose a set of challenges for insurance market players because of substantial ambiguity associated with the probability of such events occurring and high spatial correlation of catastrophe losses. Private insurance market inefficiencies due to high premiums and spatially concentrated risks calls for government intervention in the provision of natural disaster insurance to avert situations of noninsurance and underinsurance. Political economy considerations make it more likely for government support to be called for if many people are uninsured than if few people are uninsured. However, emergency assistance for property owners after catastrophe events can encourage most property owners to not buy insurance against natural disaster and develop adverse selection behaviour, generating larger future risks for homeowners and governments. On the demand-side, this study has developed an intertemporal model to examine how demand for insurance changes post-catastrophe, and how to model it theoretically. In this intertemporal model, insurance can be sought in two sequential periods of time, and at the second period, it is known whether or not a loss event happened in period one. The results show that period one demand for insurance increases relative to the standard single period model when the second period is taken into consideration, period two insurance demand is higher post-loss, higher than both the period one demand and the period two demand without a period one loss. To investigate policyholders experience from the demand-side perspective, a total of 1600 survey questionnaires were administered, and responses from 254 participants received representing a 16 percent response rate. Survey data was gathered from four institutions in Canterbury and is probably not representative of the entire population. The results of the survey show that the change from full replacement value policy to nominated replacement value policy is a key determinant of the direction of change in the level of insurance coverage after the earthquakes. The earthquakes also highlighted the plight of those who were underinsured, prompting policyholders to update their insurance coverage to reflect the estimated cost of re-building their property. The survey has added further evidence to the existing literature, such as those who have had a recent experience with disaster loss report increased risk perception if a similar event happens in future with females reporting a higher risk perception than males. Of the demographic variables, only gender has a relationship with changes in household cover. On the supply-side, this study has built a risk-based pricing model suitable to generate a competitive premium rate for natural disaster insurance cover. Using illustrative data from the Christchurch Red-zone suburbs, the model generates competitive premium rates for catastrophe risk. When the proposed model incorporates the new RMS high-definition New Zealand Earthquake Model, for example, insurers can find the model useful to identify losses at a granular level so as to calculate the competitive premium. This study observes that the key to the success of the New Zealand dual insurance system despite the high prevalence of catastrophe losses are; firstly the EQC’s flat-rate pricing structure keeps private insurance premiums affordable and very high nationwide homeowner take-up rates of natural disaster insurance. Secondly, private insurers and the EQC have an elaborate reinsurance arrangement in place. By efficiently transferring risk to the reinsurer, the cost of writing primary insurance is considerably reduced ultimately expanding primary insurance capacity and supply of insurance coverage.
Between 2010 and 2011, Canterbury experienced a series of four large earthquake events with associated aftershocks which caused widespread damage to residential and commercial infrastructure. Fine grained and uncompacted alluvial soils, typical to the Canterbury outwash plains, were exposed to high peak ground acceleration (PGA) during these events. This rapid increase in PGA induced cyclic strain softening and liquefaction in the saturated, near surface alluvial soils. Extensive research into understanding the response of soils in Canterbury to dynamic loading has since occurred. The Earthquake Commission (EQC), the Ministry of Business and Employment (MBIE), and the Christchurch City Council (CCC) have quantified the potential hazards associated with future seismic events. Theses bodies have tested numerous ground improvement design methods, and subsequently are at the forefront of the Canterbury recovery and rebuild process. Deep Soil Mixing (DSM) has been proven as a viable ground improvement foundation method used to enhance in situ soils by increasing stiffness and positively altering in situ soil characteristics. However, current industry practice for confirming the effectiveness of the DSM method involves specific laboratory and absolute soil test methods associated with the mixed column element itself. Currently, the response of the soil around the columns to DSM installation is poorly understood. This research aims to understand and quantify the effects of DSM columns on near surface alluvial soils between the DSM columns though the implementation of standardised empirical soil test methods. These soil strength properties and ground improvement changes have been investigated using shear wave velocity (Vs), soil behaviour and density response methods. The results of the three different empirical tests indicated a consistent improvement within the ground around the DSM columns in sandier soils. By contrast, cohesive silty soils portrayed less of a consistent response to DSM, although still recorded increases. Generally, within the tests completed 50 mm from the column edge, the soil response indicated a deterioration to DSM. This is likely to be a result of the destruction of the soil fabric as the stress and strain of DSM is applied to the un‐mixed in situ soils. The results suggest that during the installation of DSM columns, a positive ground effect occurs in a similar way to other methods of ground improvement. However, further research, including additional testing following this empirical method, laboratory testing and finite 2D and 3D modelling, would be useful to quantify, in detail, how in situ soils respond and how practitioners should consider these test results in their designs. This thesis begins to evaluate how alluvial soils tend to respond to DSM. Conducting more testing on the research site, on other sites in Christchurch, and around the world, would provide a more complete data set to confirm the results of this research and enable further evaluation. Completing this additional research could help geotechnical DSM practitioners to use standardised empirical test methods to measure and confirm ground improvement rather than using existing test methods in future DSM projects. Further, demonstrating the effectiveness of empirical test methods in a DSM context is likely to enable more cost effective and efficient testing of DSM columns in future geotechnical projects.
