Insights into Comprehensive Motor Insurance...

Insights into Comprehensive Motor Insurance Rating

Prepared by Nelson Henwood and Bo Wang

Presented to the Institute of Actuaries of Australia 2009 Biennial Convention, 19-22 April 2009

Sydney

This paper has been prepared for the Institute of Actuaries of Australia’s (Institute) 2009 Biennial Convention The Institute Council wishes it to be understood that opinions put forward herein are not necessarily those of the Institute and

the Council is not responsible for those opinions.

© Finity Consulting Pty Limited

The Institute will ensure that all reproductions of the paper acknowledge the Author/s as the author/s, and include the above copyright statement:

The Institute of Actuaries of Australia Level 7 Challis House 4 Martin Place

Sydney NSW Australia 2000 Telephone: +61 2 9233 3466 Facsimile: +61 2 9233 3446

Email: [email protected] Website: www.actuaries.asn.au

mailto:[email protected]

http://www.actuaries.asn.au/

tm|K:\PRACTICE AREAS\PLCA\IIA08\COMPETITOR DECONSTRUCTIONS\REPORT\COMP MOTOR RATING PAPER FINAL.DOC


Part I Abstract and Keywords i

Part II Insights into Comprehensive Motor Vehicle Insurance Rating 1

1 Introduction 1

2 Driver Rating 5

3 Vehicle Rating 22

4 Location Rating 35

5 Interactions 44

6 Other Rating Factors 52

7 A Distribution of Competitive Position 53

8 Conclusions 57

9 References 60

Part III Appendices 61

A Driver Rating 61

B Vehicle Rating 85

C Location Rating 92

D Interactions 94


tm|K:\PRACTICE AREAS\PLCA\IIA08\COMPETITOR DECONSTRUCTIONS\REPORT\COMP MOTOR RATING PAPER FINAL.DOC i

Part I Abstract and Keywords

This paper presents the results of detailed survey of competitor premium data for Comprehensive Motor insurance in Australia conducted in 2008. Our survey covered fourteen brands and five States. The survey was conducted on-line and involved around 40,000 quotes. In the paper, we explore the approaches to rating and examine the shape of the primary rating factors as well as the interrelationships between them. The primary rating factors forming the focus of the paper are rating for driver, vehicle and location. We make comparisons of our findings to those of a similar survey conducted in 1992 (Brigstock and Yee, 1992) and observe major changes in the granularity and complexity of rating which has developed in the intervening period. Whilst some similarities are evident, we uncover diversity in both the approach and the shape of the factors across the competitors. The diversity we uncover is a key discussion point in the paper as well as consideration of the implications for the assessment of competitive position. We conclude with some discussion about the role of competitor premium data in pricing analysis and decision making. Our view is that competitor premium data must play a role in an effective pricing analysis. Keywords: pricing, competitor analysis, rating, Comprehensive Motor, NCD, driver rating, vehicle rating, location rating


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Part II Insights into Comprehensive Motor Vehicle Insurance Rating

1 Introduction

The rating of Comprehensive Motor insurance is an interesting subject. The Australian market is highly competitive and pricing is one of the key ways in which insurers seek to create competitive advantage. This paper examines the rating practices for Comprehensive Motor insurance adopted by insurers in Australia. The pricing of this product has evolved very rapidly in recent years. This has been driven by more sophisticated analyses of the cost of the business and also more recently the adoption of rigorous analysis frameworks to incorporate the cost, demand and the competitive environment into the pricing analysis. Improvements in the capability of pricing delivery systems have provided much needed further impetus in making this a reality. In 2008, we conducted a survey of Comprehensive Motor insurance premiums across a large number of insurance brands. Our survey covered fourteen brands and five States. This survey and additional regular monitoring of the market which we conduct forms the basis for the results presented in this paper. In the paper, we focus on the three primary rating factors for Comprehensive Motor insurance – driver, vehicle and location. We analyse the approach taken by each insurer to assess the risk and compare the shape of the rating across the market players. A primary theme that comes through during the paper is the diversity of the rating parameters that we observe across the insurers. There are important implications of this for assessing competitive position which clearly becomes a process of estimating a distribution of differences in premium across individual rating cells. We have examined the effect of interactions in the rating structures. This analysis has also focussed on driver age, vehicle and location. Our findings reveal the presence of these effects to be common and a material part of the rating structure for a number of players. This increases the complexity of the rating many-fold and also has important implications for the assessment of competitive position. In the conclusion to the paper we summarise our findings and consider the importance of competitor data in pricing analysis and decision making.

1.1 Background to the Survey

In 2008, we conducted an internet based survey of Comprehensive Motor insurance premiums in all mainland States of Australia. In each State, our survey included the top three or four underwriters by market share. The major national brands were included for each State in our survey. We also included one smaller insurer which writes on a national basis. So, for each State we have surveyed four to six brands. We estimate that the coverage by market share would be around 60% to 90% depending on the State.


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We have not disclosed the brands when presenting our results. Instead, we have devised a numbering system whereby each State and brand combination is allocated a unique number. The numbers were allocated randomly. As the survey is internet based, the findings relate only to premiums offered on the websites of the various insurers. We are aware of some pricing differentials between call-centre and internet business; but based on our understanding of the nature of these differences, we do not expect the results to be materially affected. It is important to also note that the premiums are new business premiums only. Any differentials in the rating structure which apply to renewal pricing would not be captured in our results (e.g., loyalty discounts). Our analysis relates primarily to the key rating dimensions: driver, vehicle, and location, as well as the interactions between these factors. To examine the effects of each of these factors we have varied the level of the rating factors in sequence whilst keeping other factors constant and examined the premium relativities. To examine interaction effects we varied each of these key rating dimensions simultaneously. Premium relativities are calculated relative to a base risk which included the following characteristics:

Sole owner and driver of the vehicle : 40 year old (y.o.) male

1 year of driving experience

No accidents or events which would have given rise to a claim in the last 5 years

Small family vehicle being either a:

2004 Toyota Corolla Ascent; or

2002 Hyundai Getz GL

Suburb garaged:

NSW : Castle Hill (2154)

VIC: Footscray (3011)

QLD: Paddington (4064)

SA: Croydon (5008)

WA: Floreat (6014)

(For insurers requiring an address, we randomly selected an address in the base suburb from the GNAF1 database).

No finance

Private use

Default excess

Single policy

No membership based discounts.

1 Geocoded National Address File


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The survey includes market value and agreed value products. Where an insurer offers both products we have selected the default option. In total, the survey consisted of over 40,000 quotes.

1.2 Background to Rating

A typical rating structure for Comprehensive Motor insurance involves a large number of rating “cells”. A cell in the structure is defined by each possible combination of all the values for each of the rating variables. There are a large number of rating variables, some of which contain many thousands of potential values (e.g., vehicles or geographic location). A traditional approach has been to define a base premium and apply a series of relativities to the base premium depending on the value of each rating variable. This is commonly known as a multiplicative rating structure. It is also quite regular to incorporate additive loadings in addition to multiplicative loadings and these typically relate to policy options or variable excess levels. More recently, some insurers have adopted a component based approach to rating where separate calculation of peril based costs and other loadings (including the profit loading) are combined to form a total premium. With Comprehensive Motor rating, the application of the No Claim Discount (NCD) to the calculated premium is typically the final step in the rating process. The primary rating factors for Comprehensive Motor insurance are risk factors relating to the:

owner(s) and driver(s) of the vehicle;

the vehicle to be insured; and

the location that the vehicle is garaged (or where it is typically kept overnight).

The focus of our paper is on these primary factors and the relationships between them. Additional common rating factors include vehicle use and vehicle financing. An array of discounts may also be applied to the premium including those in relation to the duration and breadth of the customer relationship (including across family members), organisation membership (typically motor clubs) and seniors card holders (in addition to or in place of older age owner/driver relativities). The diversity of approaches to rating leads to a large spread of premiums for any individual risk despite this being a highly competitive market. The differences are a key finding of the paper and have important implications for an assessment of competitive position.


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1.3 Structure

The paper is structured in the following way:

Introduction

Driver rating

Vehicle rating

Location rating

Interactions

Other rating factors

Conclusions.

In the main body of the paper, we contrast the premium relativities across the primary rating factors concentrating on the NSW market; findings for other States are summarised in the appendices.


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2 Driver Rating

Driver rating is a critical component of Comprehensive Motor premium rating. Across the companies we surveyed, each insurer’s rating structure includes premium differentials by the age of the driver and all but one company includes differentials by driver gender. The driving record and accident history are also taken into account by each surveyed insurer either by explicit collection of a current NCD entitlement or by determining an NCD entitlement through the collection of driving experience (or years of insurance) and accident history. The details of additional drivers of the vehicle may also impact the premium (see below discussion on The Rated Driver and Additional Driver Loadings). The rating treatment of multiple drivers varies significantly across the companies surveyed. Around half the companies surveyed collect information on traffic offences and suspensions. In each case, the responses to these questions do not affect the premium directly but typically form part of the underwriting rules. Information captured on previous convictions or previous insurance declines is also used in the same way for the companies which collect this information. Some of the less common driver rating type factors we encountered in our survey were:

Whether the insured drives another vehicle – in which case one company offers a premium discount;

Residential and employment status – these are both asked and rated on for one insurer.

In this section of the paper we examine some of the details of the driver rating component of the Comprehensive Motor premium across the insurers surveyed.

2.1 The Rated Driver

Where there is a single owner/driver of a vehicle, the matter of determining the rated driver is trivial. In this case, the first of a number of steps in relation to determining the contribution of driver rating to the premium will be application of the age/sex relativity as outlined in the section below. Where there is more than one driver of a vehicle, the approach taken to driver rating varies between insurers. Typically, one of the drivers is selected as the rated driver and the age and gender of this driver are used to calculate an initial basis for the driver rating. This, however, may then be adjusted depending on the details of additional drivers. This adjustment is discussed further in section below on Additional Driver Loadings. Some examples of the rules used to select the rated driver across different insurers are as follows:

based on proportion of use. In many cases, the most frequent driver of the vehicle is the rated driver. In other cases, the youngest driver exceeding a certain proportion of use is used;

based on the youngest driver with an economic interest in the vehicle – referred to as the youngest “owner”;

in some cases it is less precise and defined on the basis of the youngest “regular” driver; and

in one case the rated driver is taken as the youngest owner or driver of the vehicle.


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2.2 Age and Sex Relativities

In this section we examine the age relativities applying to the rated driver. The relativities below are those applying in respect of standard vehicle (in this case a 2002 Hyundai Getz) and for a particular suburb in each state. It is relatively common for insurers to vary the shape of these age/sex curves depending on the type of vehicle and the suburb/location garaged. These features are discussed further in the Section 5. In this section we examine in detail the results of our survey for NSW; the results for the other states are contained in Appendix A. The typical shape of the age curve is somewhere between a mirror image of the letter J and a letter U. This indicates loadings at younger and older ages relative to the most favourably rated driver age. From the data we have gathered we have compared the premium for a driver of each age, 18 through to 90, to a baseline 40 year old (y.o.) driver. For male drivers, the curves are shown below in Figure 2.1.

Figure 2.1 – NSW Insurer Age Relativities – Males

0.7

1.0

1.3

1.6

1.9

2.2

2.5

2.8

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90

Rated Driver Age

Rel

ativ

ity to

40y

o M

ale

Insurer 6 Insurer 7 Insurer 12 Insurer 17 Insurer 20 The basic shape of the curves implies that, relative to a 40 year old driver:

higher premiums are charged for younger drivers and substantially so in some cases for drivers aged younger than the mid 20s;

premiums generally decrease for drivers aged older than 40 up to a point. The low point of the curves is reasonably consistent amongst insurers being around the early 60’s. From there, premiums tend to increase, substantially for some insurers but with a much flatter slope for others;

typically the insurers that have high loadings for younger drivers tend to have high loadings for older drivers. This may indicate a distinct preference for certain market segments.


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The age curve for Insurer 7 appears to exhibit the shape of a “saw-tooth”. At reasonably regular intervals, there is a spike in the age loading. This pattern is evident at ages leading up to around 40 y.o. and then again from ages around the mid 50’s. This may be the result of a controlled experiment being undertaken by the insurer. The sales experience for driver ages containing the spikes may be compared to that for neighbouring ages in an attempt to understand the price sensitivity of the market at various ages. Figure 2.2 below shows the same curve but is focussed on the driver age loadings for under 40’s. The figure indicates a large differential in the pattern of loadings between the insurers from around the age of 31 and under. Insurers 6 and 12 have much flatter age curves than Insurers 7, 17 and 20. These differences are large for driver ages 21 and under. It is interesting to examine whether this can be understood relative to the excess structures and method of selecting the rated driver.

Figure 2.2 – NSW Insurer Age Relativities – 40 y.o. and Under - Males

0.7

1.0

1.3

1.6

1.9

2.2

2.5

2.8

18 20 22 24 26 28 30 32 34 36 38 40

Rated Driver Age

Rel

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40y

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ale

Insurer 6 Insurer 7 Insurer 12 Insurer 17 Insurer 20 Table 2.1 below shows the excesses which apply to various driver ages. Each insurer applies a higher excess to drivers aged under 25. Insurer 6 and Insurer 7 also apply a higher excess to drivers aged under 21.

Table 2.1 – Excesses by Age ($) NSW

6 7 12 17 20<= 21 $1,750 $1,600 $1,000 $1,050 $900

22 - 24 $1,150 $1,100 $1,000 $1,050 $900>= 25 $550 $600 $600 $500 $500

InsurersAge

Referring back to Figure 2.2, it is not apparent that the companies with the lower age loadings for the youngest drivers have the highest excesses. Insurer 7 applies relatively high age loadings for drivers aged 21 and under (despite the high excesses); while Insurer 12 applies relatively low age loadings for these drivers (despite the relatively low excesses).


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It is also noticeable that whilst the general shape reflects increasing loadings as the driver age gets younger, there is some variation and a number of cases where the age loading is lower at a neighbouring younger age. In some cases, this may be explained by the shape of the excesses as outlined above, e.g., Insurer 20 has a lower loading for a 24 y.o. driver than for a 25 y.o. driver; however, the excess is $400 higher. Others are more difficult to explain, e.g., Insurer 12 has a higher loading for a 23 y.o. driver than for a 22 y.o. driver yet the same excess applies to both ages. Table 2.2 below shows the basis on which the rated driver is selected for the NSW insurers. It might be expected that a “weaker” definition (i.e. tending to select the older drivers where there are multiple drivers of the vehicle) may give rise to a flatter set of loadings but there is no apparent relationship between this basis and the shape of the age loadings for younger drivers.

Table 2.2 – Rated Driver Basis – NSW Insurer Rated Driver Basis

6 "Main" driver7 Youngest regular

12 Youngest regular17 Youngest owner or driver20 Youngest owner

At older ages there is also a considerable degree of variation in loadings amongst insurers. Figure 2.3 focuses on the age relativities applying above age 50.

Figure 2.3 – NSW Insurer Age Relativities – 50 y.o. and Over - Males

0.7

1.0

1.3

1.6

1.9

2.2

2.5

2.8

50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90

Rated Driver Age

Rel

ativ

ity to

40y

o M

ale

Insurer 6 Insurer 7 Insurer 12 Insurer 17 Insurer 20 Insurer 6 and Insurer 12 have the lowest loadings for drivers aged 21 and under also have the lowest loadings for drivers aged 79 and over. In fact, the relativities for Insurer 6 remain below that of a 40 y.o. driver for all ages up to 90. For the other insurers, rates become higher than that for a 40 y.o. driver for drivers above a certain age. This age varies from 66 to 75. For three of the insurers surveyed, the rates are substantially higher for drivers over 80.


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Insurers typically apply a different age curve for female drivers and this is shown for NSW in Figure 2.4.

Figure 2.4 – NSW Insurer Age Relativities - Females

0.7

1.0

1.3

1.6

1.9

2.2

2.5

2.8

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90

Rated Driver Age

Rel

ativ

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40y

o Fe

mal

e

Insurer 6 Insurer 7 Insurer 12 Insurer 17 Insurer 20 The basic shape of the curves is similar to that for males, however, it does not tend to be as steep at the younger end of the age spectrum. It is also interesting to compare the male driver premiums at each individual age to those for the female drivers. This is shown below in Figure 2.5.

Figure 2.5 – Male vs. Female Driver Premiums - NSW

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 66 69 72 75 78 81 84 87 90

Age

Rel

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Mal

e Pr

emiu

m v

s Fe

mal

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emiu

m

Insurer 6 Insurer 7 Insurer 12 Insurer 17 Insurer 20


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There are a number of noticeable features and the differentials do vary quite substantially between insurers:

Insurer 7 does not differentiate male and female driver premiums;

for the remainder of the insurers, male premiums are typically higher and the difference is generally highest for younger drivers;

for Insurer 17, the differential tends to decrease with age and is quite small from age 40. Interestingly, the premiums are lower for males from around age 50 to age 70 by varying amounts and at individual ages and intervals throughout this age group the premiums are equal;

Insurer 12 has a relatively constant differential of around 5% up to age 64. The differential decreases to around 1% at age 68 and above;

Insurer 6 charges higher premiums for males under the age of 30 at around at constant 4% differential. From this age the premiums are mostly equal however there are also some “spikes” in the premium differential with large differences between male and female premiums at individual ages;

Insurer 20 charges higher rates for males aged 43 and under and age 79 and over. In between these ages there are intervals where the female premium is higher than the male premium (age 43 to 49 and 70 to 79). For ages 50 to 60, the male premiums are higher by a varying amount.

2.3 Comparison to 16 Years Prior

In a 1992 paper by Brigstock and Yee, the profile of age loadings by state which applied at that time was shown. The profile for NSW is reproduced below (expressing the loading for each age as a relativity).

Figure 2.6 – NSW Age Loadings 1992

0.70

1.00

1.30

1.60

1.90

2.20

2.50

17 18 19 20 21 22 23 24 25 26 27 28 29 30

Rated Driver Age

Rel

ativ

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40y

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25% Below 50% Below 75% Below


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It is apparent that much has changed in 16 years and most noticeably the following:

there was no differentiation between male and female drivers where this is quite commonplace today;

there was no variation in premiums from age 30 until “older ages”. It was commented in the paper that, in relation to older aged drivers “some companies offer lower premiums for this sub-group (with a discount of 5%-10% being typical)”. Today, there is a great deal of shape from age 30 and upwards and this has been commented on above. It is also apparent from this quote from the Brigstock and Yee paper that there were not loadings for very old drivers previously whereas now this is quite prevalent;

the driver age relativities today are much more graduated – it is apparent that in 1992, there was much greater banding of ages (even below 30 years where there was variation in the premiums);

in 1992 only 50% of insurers had higher rates for drivers aged between 26 and 30. All insurers differentiate these ages today.

In Appendix A.2 we show a comparison of the relativities from our 2008 survey for drivers aged under 30 to the findings from the Brigstock and Yee (1992) paper for each state we have surveyed.

2.4 Additional Driver Loadings

As discussed above, where there are multiple drivers of a vehicle, typically one of the drivers is selected as the rated driver and the age/sex relativity applied would relate to this driver. In some cases, where there are drivers in addition to the rated driver an additional loading is applied. The structure of these loadings is discussed in this section. Additional driver loadings typically apply where the rated driver is aged over 28 years and the additional drivers are under 28 years. We tested scenarios where the rated driver is a 40 year old male and we have explored the impact of additional younger drivers. We found it interesting to compare:

the premium charged for single younger driver to

that applying for a 40 year old driver with an additional listed younger driver

effectively comparing the impact on premium of not selecting the younger driver of the vehicle to be the rated driver. In Figure 2.7 overleaf, we show this comparison for younger drivers aged 28 years and under in the form of a loading to the adult driver. The comparison is not shown for all insurers surveyed. As discussed above, the rules used to select the rated driver varies across insurers; and the comparison is not valid for some insurers, e.g., where the rated driver is the youngest owner or driver of the vehicle. These insurers are not included in the graphs. Included in the graph for each insurer is the basis on which the rated driver is selected. It would be reasonable to expect a lower premium (discount) on the premium comparison we have made as the younger driver contributes to but does not completely define the risk profile. As can be seen


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though, there is a great deal of variation across insurers when comparing the premium charged for a young driver with the premium charged if that young driver is listed along with the rated driver. The premium differential when compared to a single young rated driver can vary from a discount of almost 40% to a loading of almost 120%.

Figure 2.7 – Additional Driver Loadings Insurer 1 (Youngest Owner)

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

18 19 20 21 22 23 24 25 26 27 28

Insurer 4 (Youngest Owner)

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

18 19 20 21 22 23 24 25 26 27 28

Insurer 7 (Youngest Regular)

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

18 19 20 21 22 23 24 25 26 27 28


-40%

-20%

0%

20%

40%

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120%

140%

18 19 20 21 22 23 24 25 26 27 28


-40%

-20%

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140%

18 19 20 21 22 23 24 25 26 27 28


-40%

-20%

0%

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18 19 20 21 22 23 24 25 26 27 28

Insurer 13 (Youngest Once a Month or More)

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

18 19 20 21 22 23 24 25 26 27 28

Insurer 14 (Most Frequent)

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

18 19 20 21 22 23 24 25 26 27 28

Insurer 23 (Youngest > 15% Use)

-40%

-20%

0%

20%

40%

60%

80%

100%

120%

140%

18 19 20 21 22 23 24 25 26 27 28

Nb: Blue and pink bars refer to the loading applied for male and female younger drivers, respectively

2.5 Driving Experience and Accident History

Comprehensive Motor insurance premiums also include an experience rating component. This is usually a function of the driver’s (or drivers’) length of driving experience and accident history. Typically the experience rating is incorporated via a No Claims Discount (NCD) system. NCD systems work by assigning a discount to a base premium for each claim free year of the insured, up to a maximum. When an at-fault claim occurs, the insured drops down the discount scale; the insured then progress from that level subsequent to further claim free years. Traditionally only at-fault collision and theft claims have counted; however, this practice has


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varied between insurers and over time. The various levels of discount within an NCD structure are sometimes also known as “Rating” levels. These terms tend to be used somewhat interchangeably and insurers have typically published the discount associated with each rating level in its structure. Traditionally an insured entered the NCD system at the base level however this has also varied over time and between insurers. Some insurers’ NCD systems have incorporated “malus” (or penalty) levels for poor claims experience where a premium in excess of the base is charged. When a customer switched insurers, it was usual practice for the new insurer to offer an NCD consistent with that offered by the previous insurer. Indeed, the new insurer would typically require evidence of the NCD offered by the previous insurer in order to agree to offer that discount. Other typical variations in NCD systems include:

the number of levels within the system;

the highest level of discount – typically varying between 50 and 70%;

step back rules following claims; and

NCD protection which is either given away under certain circumstances or offered for a premium in others.

Variations in the systems have been used as key marketing tools for insurers. However, on balance, NCD systems have historically been reasonably uniform and transparent. Over the last several years, there have been some major changes to the method of driver experience rating. The NCD system typically still forms a key part of the process; however, these systems are now not the only mechanism in operation to reflect the claims/accidents experience and as a result experience rating has become much more opaque. One major change that we have observed is that several insurers now do not ask for a current NCD entitlement when proving a quote but calculate an NCD entitlement based on the number of years of experience and claims or accident history. One implication of this change is that an insurance history is not required to be eligible for an NCD as driving experience and accident history whilst not insured also counts in this type of systems. Another implication of this change is that the insurer implicitly superimposes its own rules for placement within in the system based on the experience rather than accepting the offered NCD from the customer’s previous insurer. Two insurers which now work this way in our sample for NSW are Insurer 6 and Insurer 12. The tables below illustrate the NCD offered at quote time for these insurers depending on the driving experience and accident history of the proposer. Note that an entry of “R” in the table indicated the triggering of a refusal or referral underwriting rule


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Table 2.3 – NCD Structure (Rating Level) – Insurer 6 Number of Accidentsand Thefts 9 8 7 6 5 4 3 2 1 0

0 1* 1* 1* 1* 1 2 3 4 5 61 1* 1* 1* 1 2 3 4 5 6 72 1* 1* 1 2 3 4 5 6 7 R3 1* 1 2 3 4 5 6 7 R R4 2 2 3 4 5 6 7 R R R5 3 3 4 5 6 7 R R R R6 4 4 5 6 7 R R R R R7 5 5 6 7 R R R R R R8 6 6 7 R R R R R R R9 7 7 R R R R R R R R

* Rating 1 (maximum NCD) for Life

Years of Experience

Table 2.4 – NCD Structure (Discount) – Insurer 12 No. Accidents Years since

(Last 5 yrs) most recent 5+ 4 3 2 1 00 n/a 65% 55% 45% 35% 25% 0%

1 0-1 55% 45% 35% 25% 0% n/a1-2 65% 55% 45% 35% n/a n/a

2 0-1 45% 35% 25% 0% 0% n/a1-2 55% 45% 35% 25% n/a n/a2-3 65% 55% 45% n/a n/a n/a

3 0-1 35% 25% 0% 0% 0% n/a1-2 45% 35% 25% 0% n/a n/a2-3 55% 45% 35% n/a n/a n/a3-4 65% 55% n/a n/a n/a n/a

4+ All R R R R R R

Years of Experience

From the above tables, we can infer the step-back rules that apply within the systems. In both the structures, there is a single step back through the system for each at-fault and theft claim. Historically, it was more common to have two steps back however the single step is now fairly commonplace. Whilst the insurers discussed above determine an NCD based on the experience at quote time, practice does vary across insurers and a number continue to operate under the traditional basis of honouring the NCD offered by a previous insurer when customers switch. In our survey, in NSW, Insurers 7, 17 and 20 continue to operate in this way. It is also currently quite commonplace that insurers offer “Rating 1 for Life” or “Protected Maximum NCD”. This is typically given to customers who have been on the maximum NCD for more than one year. Some insurers only offer this to over 25 year old drivers. These changes outlined above (step-back rule and protected NCD) have typically resulted in an increasing proportion of the book on maximum NCD and therefore in less “separation” (at least superficially) between the premium charged for drivers of varying quality. Another recent feature of driver experience rating however acts to ameliorate this loss of separation. Some of the rating structures we have observed in our survey now incorporate


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premium adjustments based on the driving experience which operate outside of and in addition to the NCD system. The basic form of these adjustments is a premium loading dependent upon the number, timing, type and combination of incidents in a driver’s history which could have / or did give rise to a claim. The additional premium loadings applied are not made explicit to the insured as has traditionally been the case with the NCD system. Under such a driver experience rating mechanism it would be possible for a driver with a protected NCD to experience an increase in premium following a claim whilst being offered the same NCD at renewal. Insurer 12 operates this system in NSW. The system operated by Insurer 6 also contains an interesting curiosity. The discount communicated to the customer by the insurer does not appear to align to the actual discount from the base premium ie to the premium offered to a customer on a 0% NCD. The NCD system also contains additional levels which attract a loading which varies by State. The amount of the loading is not communicated to the customer. (Note that in Table 2.5 below, a negative discount indicates a lower premium and vice versa).

Table 2.5 – Discount to Base Rate vs. Discount Communicated – Insurer 6

1* -70% -70.0%1 -70% -70.0%2 -50% -62.5%3 -40% -55.0%4 -30% -47.5%5 -20% -40.0%6 0% 0.0%7 0% 20.0%8 - R9 - R

Actual Discount

NCD Rating

Discount Communicated

Insurer 17 and Insurer 7 also incorporate loadings to premiums outside of the NCD structure into their rating systems. Insurer 17 does this through a “Safe Driver Discount” which applies where a driver transferring a maximum NCD from another insurer does not have any at-fault claims in the last 5 years. Insurer 7 has a similar feature in its structure whereby a loading of 25% applies to the premium at any NCD level brought across from another insurer where the customer has had any claims (regardless of fault) in the last 5 years. Insurer 7 also does not publish the discount level associated with each rating level within its NCD system. In NSW, insurer 20 operates a traditional NCD system and does not appear to incorporate any additional loadings for driver experience outside of that system (at least in relation to new business).

2.6 Some Driver Profiles

Across the range of elements which come together to form the driver rating component of Comprehensive Motor insurance, our survey and the results presented show that there is a range of approaches taken in the market. In order to illustrate the differences in premium which can result from these differences, we have constructed a number of driver profiles and obtained prices for each profile from the NSW insurers forming part of our survey. The relativities of the premium for each profile against the base profile (Profile 1 – Single male driver, clean driving history) are presented below. The profiles incorporate elements such as multiple drivers and experience


16

scenarios and we have segmented the profiles on this basis. The range of profiles is intended to cover quite common risk scenarios. A full description of each of the scenarios is given in Appendix A.3. For Insurers 7, 17 and 20 which award an NCD on the basis of that offered by a previous insurer, we have assumed that the NCD of the insurer with the greatest market share will apply. The first group of profiles presented in Figure 2.8 below contains single driver scenarios with variations in the driving experience. A listing and description of the profiles is given below the table.

Figure 2.8 – Profile Relativities – Group 1

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Table 2.6 – Profile Description – Group 1

No. Profile Decription1 Single male, clean history2 Single male, 1 AF claim in 2 years3 Single male, 2 AF claims in 2 years4 Single male, 2 AF claims + speeding5 Single male, 1 yr driving exp6 Single female, clean history7 Single female, 2 NAF claims in 2 years8 Single female, 1 yr driving exp

In relation to the first set of profiles, our observations are the following:

The premium differentials in Profile #2 for Insurer 12 and Insurer 7 are driven by the prior claims adjustment as the single claim in the profile, being 1-2 years ago does not affect the NCD entitlement.

Profile #3 includes an additional accident which affects the NCD and prior claims adjustments. The NCD for Insurer 6 is unaffected by the claims scenario in this profile and the premium becomes relatively much more competitive.


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In Profile #4, we add some speeding offences in the driving history however this does not affect the premium of any of the insurers.

In Profile #5, the premium relativities reflect the impact of a lower NCD level with varying effect across the insurers depending on the adopted structure.

The premium differentials in Profile #6 reflect only the driver age/sex differentials to the base case.

Profile #7 and Profile #8 illustrate the same differentials as Profile #2 vs. Profile #1 and Profile #5 vs. Profile #3 respectively.

The second set of profiles involves scenarios with multiple adult drivers with varying driving histories.


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Table 2.7 – Profile Descriptions – Group 2

No. Profile Decription9 Couple, both clean history10 Couple, male with 2 AF claims11 Couple, female with 2 NAF claims12 Couple, both have 2 claims13 Wife's car, both clean history14 Wife's car, male has 2 AF claims15 Wife's car, female has 2 NAF claims16 Wife's car, both have 2 claims

Note that the scale on the y-axis of the graph for this set of profiles is much more compressed than for the first group.

For Profile #9, only Insurer 12 has a different premium to Profile #1. This is brought about by adopting the younger female owner/driver as the rated driver coupled with a lower risk rating for this driver. Other insurers adopt the male driver as the rated driver for the vehicle.


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The premiums for Profile #10 bear the same relationship to Profile #9 as the premiums in Profile #3 vs. Profile #1.

Profile #11, incorporating not-at-fault claims for the female owner/driver, illustrates the impact of these claims (which do not affect the NCD) on the premiums of Insurer 12 and Insurer 7.

Profile #12 incorporates at-fault and not-at-fault claims. The generosity of the experience rating and main driver rating approach of Insurer 6 is highlighted.

In Profile #13, the rated driver switches to the younger female driver for Insurers 6 and 20. Interestingly, the premiums move in different directions.

The relationship of the premium relativities from Profile #14 and Profile #15 vs. Profile #13 are the same as the relationship between Profile #10 and Profile #11 vs. Profile #9. This illustrates that the ownership and percentage use of the vehicle does not affect the impact of the driving history on the premium in these scenarios.

The premium relativities in Profile #16 are the same as those in Profile #14. For Insurer 6 this occurs due to the main driver rating approach. For other insurers, this shows that the accidents are not added across the drivers to determine the NCD for the policy.

The third set of profiles involves younger drivers: both single drivers and in combination with a range of driving histories.


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Table 2.8 – Profile Descriptions – Group 3 No. Profile Decription17 Young male, clean history18 Young male, 2 AF claims in 2 years19 Young male, 2 AF claims, with speeding20 Young female, clean history21 Young female, 2 AF claims in 2 years22 Two teens, both have clean history23 Two teens, older teen has 2 AF24 Two teens, younger teen has 2 AF25 Two teens, both teens have 2 AF

The observations made regarding these driver profiles are as follows:

Insurer 17 will not offer an on-line quote on the young driver profiles we have surveyed involving two or more at-fault claims.

Profile #17 and #20 show the impact of the age/sex relativity for young drivers compared to the base profile.

Profile #18 illustrates the different operation of the NCD structure for Insurer 6 which offers a 0% NCD in this scenario.

Profile #19 delivers the same premium relativities as Profile #18 illustrating that speeding fines have no impact on the premium even for younger drivers.

The impact of one less year of driving experience on the NCD is seen in comparing the change in premium relativities from Profile #20 to Profile #21 vs. the change from Profile #17 to Profile #18.

In Profile #22 we see the impact of the process for the assessment of the rated driver.

Profile #23 through Profile #25 show the impact on the premium relativities of at-fault accidents for the younger drivers.

Where the claims occur on the younger driver in Profile #24, the policy attracts a lower NCD than for Profile #23 where the claims occur on the elder driver. Note that for Insurer 6, the premium relativity is unaffected by the accidents in Profile #24 as the policy is main driver rated.

Profile #25 illustrates that accidents are not added across the drivers to determine the policy NCD as we saw in Profile #16 for older drivers.


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The final set of profiles is a group of family vehicle based scenarios.


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Table 2.9 – Profile Description – Group 4

No. Profile Decription26 Adult with occasional teen, both clean27 Adult with occ. teen, adult has 2 AF claims28 Adult with occ. teen, teen has 2 AF claims29 Adult with occ. teen, both have 2 AF claims30 Adult with 2 occ teens, both teens clean31 Adult with 2 occ teens, older teen has 2 AF32 Adult with 2 occ teens, younger teen has 2 AF33 Adult with 2 occ teens, both teens has 2 AF

Our observations on the premium relativities for this final group of driver profiles are as follows:

The premium relativities for Insurer 6 are not affected under any of these scenarios due to the main driver rating approach and the NCD remaining unaffected in the scenarios with claims for the adult driver.

For Insurer 17, we are unable to obtain an on-line quote for any profile involving two at-fault accidents for the younger driver despite the fact that the younger drivers use the vehicle a small percentage of the time in these scenarios.

The premium relativities are the same for Profile #26 and Profile #30 indicating that, in this scenario, the additional younger driver does not affect the premium. In fact the 20 y.o. male and 19 y.o. female drivers are assessed as equivalent risk and the NCD of the older driver applies. (These comments do not apply to Insurer 6.)

The profiles involving claims across the drivers illustrate equivalent operation of the NCD in these scenarios as for the profiles involving claims in Group 3. This shows that the percentage use of the vehicle does not affect the NCB assessment. (These comments do not apply to Insurer 17.)


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2.7 Conclusions

In this section, we have explored a number of key elements of driver rating and compared the relativities within the rating structures for a number of insurers. We have observed a range of approaches to selecting the rated driver where there are multiple drivers of the vehicle. There are some common elements to the shape of the driver age relativities across insurers however there are also significant variations – primarily at either end of the age spectrum. The differences in the shape for younger drivers are not easily explained with reference to the rules for the selection of the rated driver or patterns of excesses which apply. There are also significant differences in the variations in premium by sex of driver for different ages. We contrasted the shape of the driver age curves to those in existence 16 years prior and can see that significant change has taken place during this time. Where there are multiple drivers of a vehicle, we have observed large differences in the approach to determine the premiums across the insurers. Experience rating has become more complex and opaque. A number of insurers now determine an NCD based on driving history of the proposed risk. A number of structures now contain adjustments to premiums based on the driving history which operate outside the NCD system. Finally, we brought all of these elements together and found large differences in the premium relativities against a basic driver profile across the insurers. These differences present a challenge for an insurer seeking to understand its competitive position across driver profiles as the market positioning can be very different depending on the characteristics of the driver(s) of the vehicle.


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3 Vehicle Rating

3.1 Overview

Vehicle rating is one of the more complex components of Comprehensive Motor pricing. The segmentation inherent in rating structures has increased markedly over the last two decades. One contributor to the complexity is the scale of the task. There are currently around sixty thousand individual vehicles in the Glass’s Vehicles Guide. That is not to say that there are sixty thousand individual levels of risk but the challenge is to classify the vehicles into homogenous groups of similar levels of risk. The challenge is obviously more difficult for an insurer with a smaller portfolio. The smaller insurers will quickly run out of exposure when looking to segment the portfolio and examine the experience. A possible approach to classifying the vehicles may be based on segmenting the portfolio according to combinations of the vehicle characteristics, e.g., Power to Weight ratio, Vehicle Family, etc., which are available in the vehicle books (e.g. Redbook and Glass’s Guide). Indeed, for a smaller insurer, this may be the only feasible way to examine its own experience in a credible manner. The approach may involve incorporating these factors, independently and in combination, into a statistical model of claims cost in order to determine groups of vehicles with a similar risk profile. A second layer of complexity that vehicle rating brings is the characteristics of the policyholder that his or her vehicle selection infers. This can manifest through the experience across vehicle badging but could also include characteristics such as vehicle colour (currently not a rating variable as far as we can tell). A classic example is that of the Ford Laser and Mazda 323 in the mid to late 1990’s. They are essentially the same car from the same manufacturing plant but with different badging and a slightly differently tuned engine. Comparatively, the Ford Laser has more torque and slightly less power2; and would possibly attract a younger consumer group (or indeed younger at heart). After normalising for age, gender and location, the claims experience of the Laser is typically found to be significantly worse than the 323. Out of interest for this paper we examined the premiums of three major insurers for these vehicles. The Ford Laser attracted a higher premium for all three insurers when other factors were held constant. Two of the three insurers we looked at offered a market value product and the Laser actually has a lower market value. For the third insurer, with an agreed value policy the same sum insured was input for both the Laser and the 323 to produce the result. In this section of the paper, we will first examine the range of factors used to price and underwrite the vehicle component of the premium. We then examine the range of vehicle risk relativities applied by the various insurers we have surveyed. A key observation is the range of risk loadings that apply across the insurers for the same vehicle. Finally we constructed a simple decision tree model to examine the “drivers” of the vehicle risk relativity component in the rating structures we surveyed. It is important to note that in this analysis that we have examined vehicle risk rating as the combination of the vehicle loading, vehicle value and vehicle age together and considered these various aspects as a single problem. This permits a valid comparison between insurers offering

2 Source: Redbook, 1998 Ford Laser Sedan GLXi 1.8L (90kW and 160Nm), 1998 Mazda 323 Protégé 1.8L (92kW and 123Nm)


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market and agreed value products. Also note that we have excluded the impact of after-market accessories or modifications to the vehicle. This section considers vehicle risk rating (as described above) independently of other aspects of the rating structure. An examination of interaction effects is left to a later section.

3.2 Vehicle Rating and Underwriting Questions

Table 3.1 below lists the subject of the questions asked as a part of the quoting process from the surveyed websites. (In this table a nationally offered brand counts as one unit whereas with our insurer numbering system counts individual brand/state combinations). The questions have been classified depending how the responses to the questions are used:

Rating question – if the premium changes based on the response;

Underwriting question – if the response to the question can lead to a refusal or referral; or

Data collection questions – if there are no noticeable impacts from the answer provided.

The table includes questions from the “buy phase” of each website where more detail may be required than to obtain an initial quote.

Table 3.1 – Vehicle Rating and Underwriting Questions Question Rating Underwriting Data CollectionVehicle 14 12Sum Insured# 9Accessories 10 8Modifications 6 11Vehicle Condition 11Value Type (Selection of AV/MV) 7Security 5Colour 1Odometer Reading 1* 3Expected Mileage 1Commuting Use 1Experience with similar vehicles 1Australian Compliant 2# A number of insurers incorporate global or vehicle specific underwriting rules but these have not been fully explored and reflected in this table* Market Value is adjusted with odometer reading

During the quoting process, each website makes use of a make-model-variant filtering or a drill-down approach to guide the customer to identify the correct vehicle. In some cases, the vehicle details or sum insured may trigger underwriting rules which result in a referral or a refusal to quote. The sum insured is usually required for agreed value policies where offered and vehicle specific values outside a certain range may trigger underwriting rules. Underwriting rules may also be triggered above a fixed vehicle value. Some agreed value insurers do not ask for the sum insured and specify this for the customer. Half the insurers we surveyed offer a choice of agreed or market value. Accessories and modifications may be used as both a rating factor and/or to trigger underwriting rules. Vehicle condition is an underwriting criterion that most insurers use, with anything less than “Free from existing damage” triggering a referral. Other underwriting questions used include whether the car is compliant with Australian (or other relevant) standards and regulation and the


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level of experience the driver has had with performance vehicles when the customer is attempting to insure a performance vehicle. A number of insurers provide discounts on security features of the car, such as alarms or Microdot/Datadot vehicle identification security. Some insurers require specific security features on high value vehicles. The commuting use and expected mileage is used as a rating factor by one of the insurers we surveyed. One insurer uses the odometer reading to adjust the market value of the vehicle. A number of insurers appear to be collecting data on the odometer reading and may be preparing to incorporate distance travelled into the rating structure. There are also questions in relation to vehicle ownership asked by a number of the surveyed insurers. The subject of these questions and classification of how the response is used is shown in Table 3.2 below. None of these questions impacts rating as far as we can tell.

Table 3.2 –Vehicle Ownership Rating and Underwriting Questions Question Rating Underwriting Data CollectionPurchase Date 2 5Purchase Price 1 6Uninsured 1 2Accidents while Uninsured 1Duration Owned 1Location of Purchase 1Privately Imported 1

3.3 Distribution of Relativities

We undertook a survey of the range of vehicle relativities adopted by the various insurers in the industry. This part of our survey consisted of 300 vehicles from a range of year of manufacture, make, market segment (vehicle family) and trim levels. Further details of the vehicle selection process can be found in Appendix B.1. The results of our findings are presented here and discussed for NSW only. Findings for the other states can be found in Appendix B. In Figure 3.1, we plot histograms of the range of vehicle risk relativities that are adopted by the surveyed insurers in NSW. The relativities are calculated relative to the median premium of each insurer across the selected vehicles. Base level characteristics were adopted for other rating factors. These included the following characteristics: a 40 year old single male driver living in a metropolitan suburb with one year of driving experience and no claims or incidents which could have given rise to a claim.


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Figure 3.1: Histogram of Vehicle Relativities (NSW)

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Figure 3.1 gives a broad overview of the scale of vehicle risk relativities based on the vehicles sampled. A constraint on portraying a comparable range is that some of the insurers sampled will not offer premium quotations on all of the vehicles in our survey. For example, one of the insurers does not provide quotes for light commercial or high performance vehicles. Other insurers will not provide a quote above an overall sum insured limit. Whilst acknowledging this potential bias in Figure 3.1 we do not think the view is compromised as we have been able to obtain a quote for the vast majority of vehicles in our survey for each insurer. It is apparent on inspecting Figure 3.1 that there is a degree of similarity between the shapes of the distributions of vehicle relativities across the insurers in NSW. The distribution for each of the insurers is right skewed as we would expect. We note that the distributions of relativities for Insurers 17 and 20 have a lower variance (hence a higher “peak”) and more of the relativities are concentrated in the band of relativities between 15% below and 5% above the median. These insurers have a relatively short tail of relativities above 200% of the median premium (around 3% and 1% respectively). The relativities of Insurers 6 and 12 have greater skewness and a larger portion of the premiums in the tail (around 8% and 6% respectively). This indicates a greater level of segmentation in vehicle rating for these insurers. Insurer 7 has a relatively large proportion of the relativities more evenly spread between 0.65 and 1.2 – thus the “peak” of the distribution is comparable to Insurers 6 and 12; however, there is a relatively shorter tail. Figure 3.2 plotted below focuses on the tail of the distribution. Whilst Insurer 6 and 12 both display a relatively large proportion of vehicle relativities over 2.0, Insurer 6 places a large portion of these at around 2.5 whereas the tail of relativities for Insurer 12 extends further.


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Figure 3.2 – Histogram of Vehicle Relativities – Tail (NSW)

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In considering the vehicle relativities of the NSW insurers in our survey, our overall conclusion is that whilst the overall degree of segmentation is comparable, Insurers 6 and 12 show the greatest and a similar degree of segmentation in vehicle relativities. Insurer 12 appears to have a longer tail than insurer 6 which indicates a slightly higher spread.

3.4 A Comparison of Some Specific Vehicles

In Figure 3.3 below, we highlight the relativities of a number of individual vehicles across the NSW insurers in our survey. Note that we have trimmed the y-axis at a relativity of 4 so the longer tail of insurer 12 is not apparent in this diagram.

Figure 3.3 Relativities of Specific Vehicles (NSW)

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05 Corolla 08 Commodore S 04 Insight 03 Getz 02 SLK200


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We have highlighted the individual relativities of 5 vehicles across the insurers in this figure. We chose these vehicles on the basis that they are relatively popular vehicles and span the relativity scale of the structure of insurer 6. It is interesting to note the differences. The 2005 Toyota Corolla is a relatively abundant private use vehicle in the small car segment. It has a relativity close to the median for each of the insurers. There is some difference in view of the 2008 Commodore S, a performance version of Australia’s favourite car, across the insurers relative to the Corolla. Three of the five insurers place the Commodore at around a 20% loading to the Corolla while the other two rate the Commodore as similar to the Corolla – Insurer 12 actually offers a slightly cheaper premium! Some large differences become apparent across the insurers with respect to the relativity for the 2004 Honda Insight. The Insight is one of the first electric-combustion hybrid vehicles; and it travels at slower speeds than comparable combustion only vehicles. Three insurers treat it similar to the Corolla possibly due to the similar characteristics. Insurers 12 and 17 incorporate a substantial loading for the vehicle relative to the Corolla. This may be due to a perceived higher repair cost for hybrid vehicles relative to a standard combustion vehicle however industry opinions seem to differ about the relative repair costs of hybrid vehicles. The lowest vehicle relativity for Insurer 6 from our surveyed vehicles was for the 2003 Hyundai Getz. The vehicle relativity is lower than the median for each of the insurers; however, there is still a 50% difference, relative to the median, between lowest and highest. The 2002 Mercedes SLK200 convertible has a relatively high loading for Insurer 6 and the relativities are quite different amongst the insurers. Three insurers apply relativities between 150% and 170% of the median. Insurer 6 applies a relativity of 270%. Insurer 17 adopts the same relativity for the sporty German convertible as the Corolla! For a broader scale comparison, we compiled a graph of the range of relativities (lowest to highest vs. the median) for each of the vehicles in our survey by vehicle family group. Figure 3.4 shows the range of relativities across the vehicles in the “Medium” family. Figure 3.5 shows the range of relativities for the “Sports” family. The graphs for the remainder of the families are shown in Appendix B. The y-axis scale was kept the same for the two graphs to assist comparison. Note that in the following graphs, we have defined the range on the basis of premiums from the insurers where we were able to obtain a quote. As some insurers will not provide an on-line quote for certain vehicles we concede that this introduces some bias in the illustration of the perceived range of risk relativities. This however does not materially affect the broad conclusions.


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Figure 3.4 – Range of Relativities for NSW Insurers – Medium Family

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We selected Medium vehicles for the first illustration as the range of relativities appeared to be smallest across the insurers for this family. Even so there is still noticeable variation between the insurers. Differentials of 30-40% are not uncommon and the range seems to increase (unsurprisingly) for the vehicles with higher relativities.

Figure 3.5 – Range of Relativities for NSW Insurers – Sports Family

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The range of relativities for Sports vehicles is higher on average than for Medium vehicles (indeed we chose this family for comparison here as it exhibited the greatest spread across the range of


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vehicles). A range of relativities of 100% is pretty much the norm in the upper half of the risk spectrum. We conclude that the vehicle relativities assigned by individual insurers vary considerably. In the next section we take a look at the range of relativities across a group of vehicles we have found to have a similar risk profile.

3.5 A Comparison of Compact SUVs

In the previous sections, we examined the range of vehicle relativities across insurers of vehicles from a broad cross-section of characteristics. In this section we examine the range of relativities for Compact SUVs (CSUVs) which, from our research, tend to exhibit a relatively similar claims profile. For this study we surveyed premiums for 50 CSUVs from the Glass’s Guide Database with a similar value, year of manufacture and common country of manufacture. Figure 3.6 below plots the relativities of the vehicles against the median premium (for the sampled CSUVs) of each insurer, from a selection of the surveyed insurers.

Figure 3.6 Range of CSUV Relativities by Insurer

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Insurer 1 Insurer 4 Insurer 5 Insurer 6 Insurer 7Insurer 10 Insurer 11 Insurer 12 Insurer 17 Insurer 20Insurer 23 Insurer 26 03 G. Vitara 03 Outback 03 CRV

There is some commonality of the range of vehicle relativities for this family across the insurers. The relativities for most insurers generally fall within a band of 30% around the median price. However, there are some clear differences. Insurer 5 has all the sampled vehicles within a 5% band. Insurer 10 has the largest range, where the most expensive policy is almost 3 times the premium of the cheapest. In the same graph, there are also three lines plotting the relative positions of three CSUVs from each of the insurers surveyed. There is quite a wide range of treatment of these three vehicles across the structures of the surveyed insurers.


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We have also examined the vehicle relativities across the insurers’ structures against a range of the vehicle characteristics – value; year of manufacture; and engine size. The results are shown below. In Figure 3.7 we show the range of relativities by vehicle value. There appears to be a reasonable positive correlation as we would expect for most of the insurers. The correlation is the strongest for Insurer 26 and the weakest for Insurer 23. For insurer 10 it is actually negative! There is in every case a good deal of overlap with the relativities suggesting that sum insured is but one factor driving the vehicle relativities across the insurers.

Figure 3.7 Range of CSUV Relativities by Market Value

262017121176541 23100.6

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Figure 3.8 below shows the relationship between the vehicle relativities and the year of manufacture.


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Figure 3.8 Range of CSUV Relativities by Year of Manufacture

201054 176 23117 261210.6

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The figure above shows a range of results. The correlation is on average negative although the correlation is weaker than with the vehicle value per the previous graph. The vehicle relativities for Insurer 5 shows a positive correlation whilst those for Insurer 12 show a negative correlation. In Figure 3.9 below, we show the vehicle relativities for each insurer by engine size. Given the relatively homogenous nature of the vehicles, examining the vehicle relativities by the power to weight ratio gives a similar picture and it is not shown here.

Figure 3.9 Range of CSUV Relativities by Engine Size

26201712117541 231060.6

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There is again a range of relationships across the insurers. Insurer 5 shows a negative correlation whilst Insurer 26 shows a positive correlation. The analysis of the CSUVs reinforces our conclusion about the broad range of relativities adopted across insurers. In this analysis we also broke the family down further by a number of characteristics and encountered a range of relationships between the vehicles risk relativities and these underlying characteristics. This illustrates that the factors “driving” vehicles to various points in the risk spectrum differ substantially across the insurers. We explore this further in the section below.

3.6 Factors Used in Vehicle Rating

In the 1992 paper by Brigstock and Yee it is noted that

“… vehicle related premium relativity is generally a function of three rating factors: Some form of broad categorisation of vehicles

The year of manufacture of the vehicle

The value (or sum insured) of the vehicle” (Brigstock and Yee, 1992).

We believe this statement is still broadly true however, our findings indicate that vehicle categorisation in 2008 is much more granular and of course this comes as no surprise. Brigstock and Yee note that in 1992 most insurers used 4 to 10 vehicle categories. These were, they said, generally developed by the larger players and adopted by the smaller ones. Currently, a significantly larger number of categories are used for the most part (excluding vehicle age and sum insured effects) and many insurers would have the flexibility in their rating structure to incorporate different risk relativities for individual vehicles. We conducted a simple experiment to give us a sense of the factors “driving” the vehicle relativities across the rating structures of the insurers we surveyed. This involved building a model of the vehicle relativity as a function of a number of vehicle characteristics. The vehicle characteristics used as explanatory candidates were the following:

number of cylinders;

vehicle value;

power to weight ratio;

engine size;

year of manufacture;

body segment which is the vehicle family as defined by Glasses’ Guide;

country of manufacture; and

body style (e.g., sedan, coupe, etc.).

The model was built using Decision Trees. Table 3.3 below shows the relative importance of various vehicle characteristics as an explanatory variable for determining the vehicle relativities of a number of insurers. The table also shows the R2 value based on the amount of the variation in


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the relativities explained by the six candidate characteristics3. The relative importance measures are standard output from the Decision Tree diagnostics and are directly related to the impact the variables have on the R2 value.

Table 3.3 – Vehicle Factor Importance by Insurer4

5 (M) 6 (M) 7 (M) 12 (A) 13 (A) 14 (M) 17 (M) 20 (M) 23 (A) 26 (A)Number of Cyclinders 0.97 1.00 - 0.26 - - 0.56 0.29 - - Value 0.41 0.40 1.00 0.66 1.00 0.78 1.00 1.00 1.00 1.00 Power to Weight 0.77 0.55 0.19 1.00 - 1.00 - 0.41 - 0.48 Engine Size - 0.36 - 0.15 - - - - - - Year of Manufacture 0.50 - 0.19 0.20 0.10 0.24 - - 0.39 0.14 Segment 1.00 0.61 0.13 0.79 0.14 0.31 0.27 - - 0.28 Country of M'fture 0.81 0.63 0.15 0.26 0.18 - 0.30 0.17 0.30 0.24 Body Style 0.69 0.09 - 0.24 - 0.68 0.63 0.213 0.44 0.23 R2 66% 62% 65% 63% 73% 53% 47% 67% 69% 69%

Factor Insurer

Out of interest we noted which of the insurers’ quotes we obtained were for a market value cover and which were for an agreed value cover, as indicated by M or A respectively in brackets beside the insurer number. Overall, the vehicle value is the most important explanatory variables from the candidate list. The vehicle value provides the most explanatory power for 6 of the 10 insurers. The power to weight ratio is the second most important explanatory factor. It has the highest predictive power in the vehicle relativities sampled for Insurer 12 and Insurer 14. The country of manufacture and number of cylinders are the next most important variables. Outside of these, the vehicle segment (family) is the most predictive factor for Insurers 5. The engine size probably has the least overall explanatory power although we note that it would be highly correlated with the number of cylinders (as it would be with power to weight when considered in conjunction with the body segment). Interestingly, the level of R2 values across the models for each insurer is similar with the majority of models around 65-70%. (For one insurer that we did not include in the results presented for this paper, the R2 value was 22%.) Although a reasonable proportion of the variability can be explained by these simple vehicle characteristics, there is also a significant portion of variability that cannot. The residual variation could be caused by a number of factors including selection effects reflected in the individual vehicle experience as described at the beginning of this section.

3.7 Conclusions

Vehicle rating is a challenging exercise. There are a large number of unique vehicles in the Australian “car park” and the challenge is to grade the rating of these vehicles to incorporate risk whilst managing competitive positioning and sales performance. One possible approach may be to classify the vehicles into a smaller number of groups on the basis of factors driving the risk and make adjustments to individual vehicles based on the experience. Smaller insurers are at a distinct data disadvantage and must carefully monitor competitive position.

3 R2 is a measure of the explanatory power of the variables, it is defined as variation explained by variables

total variation of the premiums

4 The insurers presented here are a selection across Australia and each insurer represents an individual “brand”.


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We have examined the spread of vehicle relativities across insurers and found a similar overall shape in the vehicle relativities we sampled for the surveyed insurers. We did however find some large differences in the positioning of individual vehicles across the insurers. These differences tend to increase for vehicles assessed by the market as higher risk. We looked at what we expected to be a relatively homogenous family of vehicles (CSUVs) in some detail. Here we found larger differences in the spread of vehicle relativities than we expected as well as large differences around the positioning of individual vehicles. We further dissected this family based on factors we expected might be influencing the classification and found that the factors driving individual vehicles to different ends of the risk spectrum varied greatly among the insurers surveyed. Finally, we have examined the question of risk drivers more broadly. We have found that factors such as vehicle value, power to weight ratio, country of manufacture and number of cylinders contribute to the grading of vehicles across insurers. Even so, a large degree of the variation remains unexplained by our simple model. It is challenging to classify vehicles in a rating structure based an insurer’s own experience and it is also challenging to monitor how competitors are classifying vehicles. A model of the premium relativities using risk based factors will explain a proportion of the variation as we have seen above. The complicated nature of vehicle classification systems makes the assessment of competitive position for vehicles across a portfolio of business very challenging. This is complicated further by interactions in the rating structures and we address this issue later in the paper.


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4 Location Rating

4.1 Overview

Location rating is another of the key components of Comprehensive Motor Vehicle insurance rating. As with the other key rating drivers, the complexity of location rating has generally increased significantly since the paper by Brigstock and Yee (1992) was published seventeen years ago. The observations in that paper were that:

“in New South Wales, up to 10 rating areas are used by some companies and all companies use some degree of area rating

in Victoria and South Australia, a small number of insurers elect not to rate by area. Where it is used, the general practice is only to split the State into metropolitan and country only

in other States, particularly Tasmania, there is a significant proportion of insurers who do not rate by area garaged.”

Currently, most insurers rate by suburb and have a much large number and spread of district relativities. Some insurers have implemented rating below a suburb level. It might be perplexing at first observation that the premium varies so substantially within a given metropolitan area based on the area garaged. The claims occurring within the garaged area are only a proportion of the total claims cost (e.g., theft claims and accidents close to home). However, just as the vehicle signals something about the driver, the garaged address signals a degree of socio-demographic information which impacts the claims cost. In this section, we first examine the suburb rating practices observed in the industry. We then briefly examine the specific address rating practises in the industry.

4.2 Location Rating and Underwriting Questions

The following table tallies the questions asked as a part of the quoting process of the insurers surveyed and how the questions are used. A description of the classification of the questions is contained in Section 3.2.

Table 4.1 – Location Rating and Underwriting Questions Question Rating Underwriting Data CollectionPostcode 14Suburb 13 1Garaging 2 3Address 3 5

All but one of the insurers surveyed implement location rating below postcode level. Two insurers varied the premium quoted by the type of garaging the vehicle has (e.g., in garage, driveway, on street) while three others collect this data but do not currently differ the premium quoted based on the response. Two insurers have implemented location rating below suburb level. This is further discussed in Section 4.5.


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4.3 Suburb Rating

We surveyed premiums for a range of suburbs in Australia. Our focus was primarily on the metropolitan areas in each State however we additionally included a number of suburbs around major regional centres in each State. We calculated the relativity for each suburb surveyed against the median for the suburbs in the State. The results are presented and discussed for NSW suburbs only. This section considers location rating independently of the impact of other factors. Interaction effects are discussed in Section 5. Figure 4.1 below plots the histograms of relativities for the insurers in NSW. Both the calculation of the median and proportion in each bin of the histogram are weighted for the population in each suburb5. There are differences in the shape across the insurers.

Figure 4.1 – Histogram of Suburb Relativities - NSW

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Broadly, the scale of suburb relativities is from around 0.5 to 1.5, i.e., a ratio of around 3 from highest to lowest (noting the limitations of our sample). The histogram for Insurer 6 exhibits a clearly longer tail than the other insurers in this market indicating that, considered at the suburb level, this rating structure contains the largest spread of geographic segmentation. The histogram for Insurer 7 is bi-modal. This is driven to a large extent by a metro/non-metro split with a relatively compressed range of risk grading around these levels. Insurer 12 appears to exhibit a kind of “head and shoulders” type shape. It bears some similarity to the shape of the distribution for Insurer 6 but with a shorter tail. The distribution for Insurer 20 is the most symmetrical. This indicates a more even spread of risk grading. Insurer 17 exhibits a multi-modal shape. It is difficult to interpret at this level as this insurer incorporates a great deal of segmentation in its structure below suburb level. As there is a bit of “noise” in the histograms shown above we also produced box plots of the relativities and these are shown in Figure 4.2 below. This figure gives a reasonable sense of the spread in the relativities without illustrating all of the detail. The spread of relativities from Insurer

5 Source: ABS: 2006 Census of Population and Housing


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6 is clearly evident from this picture. The compression of the relativities in the centre of the distribution for Insurer 20 becomes more apparent with this view.

Figure 4.2 – Box plots of Suburb Relativities - NSW

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4.4 Suburb Maps

We also plotted some maps of the surveyed relativities against a common scale outlined in Figure 4.3.

Figure 4.3 – Scale of Suburb Relativities

The similarities in the rating scale of Insurer 6 and Insurer 12 become more evident when viewed in this way. There is a clear north and south of the Harbour/Parramatta River effect. The divide extends beyond the boundary of the river along Parramatta Road and then Farm Road further to the west. There is some commonality in the “hot spots” around Parramatta, Bankstown and Cabramatta. Insurer 6 has a larger number of “hot spots” which are rated in the upper spectrum of the relativities for Insurer 12 but not at as an extreme a level. The relativities for Insurer 6 are also lower in the Northern Beaches, North Shore and Northern Suburbs areas.


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Figure 4.4 – Insurer 6 Sydney Metro Relativity Map


Our survey did not cover as extensive a range of suburbs for Insurers 17 and 20. The maps for these insurers are shown in Figure 4.6 and Figure 4.7 overleaf.


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The north/south divide is again evident in the map for Insurer 17 (although the divide appears not to extend as far west as that for Insurers 6 and 12. Given the symmetrical shape of the relativities for this insurer, there are not any real “hot spots” evident either.


The north/south divide is not as pronounced for Insurer 20. It also appears that the relativities are more compressed. We do need to be careful in over-interpreting this view, however, as the insurer rates below suburb level as discussed in Section 4.5 below.


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The map for Insurer 7 is shown in Figure 4.8 below.

Figure 4.8 - Insurer 7 Sydney Metro Relativity Map

The relativities for Insurer 7 are more compressed. Insurer 7 also rates below suburb level however the differentials within suburb are quite small (see Section 4.5). There are large geographical areas with similar rates which was evident from the histogram in Section 4.3 above. In Figure 4.9 below, we plotted the differences in the relativities from top to bottom across the insurers. It is quite common to find differences of 20% to 40% in the relativities. The largest differences appear in some of the “hot spot” areas. The areas tend to be widely recognised as high risk but the quantum of the loading can vary greatly.


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Figure 4.9 – Range of Relativities

4.5 Rating below Suburb Level

The ability to rate below suburb level (down to individual addresses) is quite topical at the moment given the market activity around flood rating for Householders insurance. We expect that more insurers will be looking to cover flood and this will require an ability to rate (and/or underwrite) at the address level. It would make sense to use such a capability for perils other than flood and indeed for Motor insurance as well as Householders. So we can probably expect more activity in this area in the near future. The results presented below are for the NSW suburb of Granville. We selected this suburb to explore rating below suburb level as it is a high density area spanning both sides of Parramatta Road with a good number of geographical features which may influence the rates. In Figure 4.10 and Figure 4.11 we show the relativities for 100 individual addresses in Granville for Insurer 20 and Insurer 7 respectively. The colour coding of the dots indicates the premium relativity compared to the median for the 100 addresses we surveyed. Please note that the scales on the maps are very different due to the relative granularity of the rates between the insurers.


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Figure 4.10 – Range of Surveyed Relativities – Insurer 20

The rates for Insurer 20 differ by a range of approximately 25% around the median in this suburb. There is clear differentiation across the addresses within the various streets surveyed. There are a number of instances of large differences for adjoining properties. It is unlikely that these differences are fully risk related. The differences may incorporate an element of randomness designed to test the market response to price.

Figure 4.11 – Range of Surveyed Relativities – Insurer 7


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The scale of the differences for Insurer 7 is much more compressed. There is only around a 1% difference in premium relativity from top to bottom. It is not entirely clear due to the resolution in the map but Insurer 7 rates at street level.

4.6 Conclusions

Location rating has evolved tremendously since the Brigstock and Yee paper was published in 1992. All but one of the insurers surveyed now rate below postcode level. There is a large amount of separation in the market rates emanating from this rating factor with roughly a factor of 3 for insurers separating the highest from the lowest rate in the suburbs we surveyed in NSW. There are some common elements to the spatial dimension of the relativities. The north/south divide in Sydney is a key feature of the each of the insurers we surveyed. There is also some commonality to the separation of the “hot spots” across at least a subset of the insurers. Although these areas are widely recognised as high risk, the quantum of the loading attached varies greatly and these areas feature as having the greatest difference in assessment of relativity across the insurers. We also uncovered evidence of two insurers rating below suburb level. The level of granularity within rates below suburb level for these insurers is very different. For the suburb where we extensively surveyed, we uncovered evidence of a 25% range around the median for one insurer whilst the range for the second insurer was around 1%. In relation to the insurer with the 25% differential, we suspect that the differences in premium are not fully risk related and may incorporate some “price testing” of the market. In relation to the rating of Comprehensive Motor, the insurers rating below suburb level are raising the bar for the capability required to compete effectively.


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5 Interactions

In the previous sections, we have explored the impact of a number of the factors influencing the rating of Comprehensive Motor independent of the impact of other factors (i.e., on a one-way basis). This was achieved by holding a set of “base” factors constant and varying the level of the factor of interest. It is quite common within the present day rating structures of Comprehensive Motor to incorporate relativities which vary across combinations of factors. These are typically referred to as interactions in the structure. We have not fully explored the full range of potential interactions in our survey. In this section we focus on interactions across the following factors:

driver age

location

vehicle type.

Given the number of levels of each of these factors, age-location-vehicle interactions are potentially the most complex aspect of Comprehensive Motor rating. The results of our study are presented below.

5.1 Survey Results

We have observed a range of practice across the surveyed insurers in relation to age / location / vehicle type interactions. Detailed results are presented here for male drivers in NSW. The same results for female drivers are shown in Appendix D along with results for other states. We comment on the results for female drivers selectively in this section. The basic structure of the results we present are to show the driver age curve (based on a limited number of ages) across a range of NSW suburbs and within each suburb, across a range of vehicles. (Note that for some of the insurers we obtained quotes for 5 suburbs and for others we obtained quotes for 4 suburbs). In presenting these results, we calculated the relativity of the premium at each surveyed age against the premium at the base age separately for each vehicle. The age curve is shown relative to a base age of 40 years; however; we have explored how the picture varies when adopting different base levels and this is shown for one of the insurers. Table 5.1 below shows the range of vehicles we have used for exploring these interactions and the symbol adopted in the following graphs for each of these vehicles. The sample is not intended to be representative of a typical portfolio but has been designed to include a range of vehicle types and characteristics in order to test for the presence of interactions.


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Table 5.1 – Vehicles used for Interaction Quotes NVIC Series Make Model YOM Engine Size Body Style Pwr to Wgt Segment0RX HONDA INTEGRA 1996 1.8L 2D COUPE 93 SPORTS3XI MITSUBISHI PAJERO 1997 3.5L 2D HARDTOP 78 SUV MEDIUM4UI FORD PROBE 1998 2.5L 2D COUPE 92 SPORTS5Y902A MAZDA B2600 2002 2.6L P/UP 65 PICK UP OR CAB CHASSIS 4X28NK MAZDA 121 2000 1.5L 5D HATCHBACK 68 LIGHTASR02A BMW 7 2002 4.4L 4D SEDAN 107 UPPER LARGEDA008A FORD EXPLORER 2008 4.0L 4D WAGON 76 SUV LARGEDFM04B HYUNDAI TERRACAN 2004 3.5L 4D WAGON 71 SUV MEDIUMDP606A HOLDEN CRUZE 2006 1.5L 4D WAGON 74 SUV COMPACTDXU05A MITSUBISHI EXPRESS 2005 2.0L WINDOW VAN 66 VANG9P08A FORD TRANSIT 2008 2.2L VAN 48 VANGBX07A MITSUBISHI OUTLANDER 2007 2.4L 4D WAGON 80 SUV COMPACTGHZ07A HOLDEN RODEO 2007 3.0L SPACE C/CHAS 73 PICK UP OR CAB CHASSIS 4X4GSO07A MITSUBISHI LANCER 2007 2.4L 4D SEDAN 91 SMALLGTC08A MITSUBISHI COLT 2008 1.5L 5D HATCHBACK 72 LIGHTH0D07G MITSUBISHI GRANDIS 2007 2.4L 4D WAGON 73 PEOPLE MOVERH3707G HYUNDAI SANTA FE 2007 2.2L 4D WAGON 59 SUV COMPACTH6T08A VOLKSWAGEN GOLF 2008 2.0L 5D HATCHBACK 83 SMALLO86 HYUNDAI SONATA 1995 2.0L 4D SEDAN 81 MEDIUMZOH MAZDA B2600 1996 2.6L DUAL CAB P/UP 55 PICK UP OR CAB CHASSIS 4X4

The results of our survey for Insurer 17 are shown in Figure 5.1 below.

Figure 5.1 – Age/Location/Vehicle Interactions – Insurer 17

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In the rating structure of Insurer 17, the driver age curves have the same shape across the suburbs. The age loadings for particular vehicles are common at the different age points we have surveyed apart from at age 18. This insurer appears to vary the age loading for very young drivers based on the vehicle. The range across the vehicles we have surveyed indicates a relativity of between 2.6 and 2.87 depending on the vehicle. For this insurer, the lowest age loading for 18 y.o. drivers applies for the Hyundai Sonata ( , Medium family) and Volkswagen Golf ( , Small). The highest loading applies for the Ford Probe ( ) and Honda Integra ( ) (both Sports family). The results presented in Appendix D show that the results are the same for female drivers. The same curves are shown for Insurer 6 below.


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This insurer adopts a different approach to age-location-vehicle interactions to that adopted by Insurer 17. Firstly, the age curve is different across each suburb we have surveyed. Across the suburbs we have surveyed, the basic age loading for an 18 y.o. driver varies from around 1.165 in Belmore to around 1.575 in Eureka. The relativity for an 80 y.o. driver to a 40 y.o. driver varies from 0.85 in Belmore to 0.96 in Woronora Heights. Across the vehicles surveyed the age relativities are broadly uniform; however, the insurer appears to selectively vary the loadings for certain vehicles across certain driver ages in certain suburbs. The BMW 7 ( ) attracts a much higher loading for 18 y.o. drivers varying from 1.845 in Belmore to 2.235 in Potts Point. The relativity in the age loading for the BMW 7 relative to the core group of vehicles is around 1.585 and this applies in all suburbs surveyed except Eureka where the relativity is around 1.35. The loading for a 25 y.o. driver of this vehicle is also higher than for other vehicles, however, the adjustment is much smaller. From the vehicles we have surveyed, the only other vehicle attracting an age loading varying from the standard for the insurer is the Ford Transit ( ) and this variation is non-uniform across the ages for the suburbs surveyed.


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Figure 5.3 above shows the results of the survey for Insurer 20. There were a number of vehicles on which we could not obtain a quote for Insurer 20 including the BMW 7 ( ), Ford Probe ( ) and Mazda B2600 ( , , both years of manufacture attempted). We were also unable to obtain a quote for some vehicles in selected suburbs, e.g., the Mitsubishi Pajero in Belmore and Potts Point. Based on the vehicles for which we were able to obtain a quote, the age relativities across suburbs for Insurer 20 are broadly similar however there are some minor variations in the shape. There is also some minor variation in the age relativities across the vehicles within the suburbs. The variations appear to be quite similar across the suburbs at individual ages but vary across the age spectrum. For example, at age 18 where the variation in the age loading across the vehicles is largest:

The Ford Transit ( ) has the highest loading in each suburb surveyed varying from around 2.3 in Woronora Heights to around 2.46 in Belmore.

The Hyundai Fe ( ) also has a higher than average loading for this age for each suburb surveyed varying from around 2.3 in Woronora Heights to around 2.39 in Belmore.

The Hyundai Sonata ( ) attracts the lowest age loading in each suburb varying from around 2.01 in Woronora Heights to around 2.21 in Belmore.

The Mitsubishi Colt ( ) is also lower than average in each suburb varying from around 2.12 in Woronora Heights to around 2.25 in Belmore.

At the other end of the age spectrum for 80 y.o. drivers:

The Ford Explorer ( ) consistently attracts a lower age loading for each suburb varying from around 1.03 in Potts Point to around 1.14 in Denistone West.

The Ford Transit ( ) attracts the highest age loading across the surveyed vehicles in Denistone West and Woronora Heights (1.23 and 1.19 respectively). In the other two


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suburbs surveyed for Insurer 20, the loading for the Transit is higher than average but is not the highest for the suburb (the loading ranges 1.13 to 1.15).

As the quantum of the interaction effects apparent for this insurer were quite small, we examined whether the differences might be caused by a fixed element in the rating structure. We concluded that this could not be the sole cause of the interactions we observed. For this insurer there are some differences in the variations to the age loadings for females (see detail in Appendix D). The Honda Integra ( , Sports) has the highest age loading for 18 and 25 y.o. females. For males the age loading for the Honda Integra for 18 y.o. drivers is only just above average but is amongst the highest for 25 y.o. drivers. The shapes for Insurer 12 are interesting and these are shown relative to the 40 y.o driver of the vehicle in each suburb in Figure 5.4 below. Note that for Insurer 12, we were unable to obtain an on-line quote for the BMW 7 ( ) or Ford Explorer ( ).


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Insurer 12 displays age curves which vary both by suburb and vehicle. The difference in vehicle relativities at age 65 and 80 looked very similar so we rebased the age curve to age 65 for this insurer and got the picture shown below.


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Figure 5.5 – Age/Location/Vehicle Interactions based at Age 65 – Insurer 12

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When we base the age curves within each suburb to the premium applying for the vehicle at age 65 we observe a set of age relativities with quite a deal of variation by vehicle (as well as across suburbs). Indeed, Insurer 12 displays largest degree of vehicle based interaction variation of the insurers examined to this point. The variation by vehicle appears to “fan out” as the driver age gets younger. The variations in age loading by vehicle appear consistent across the suburbs (indicating that the structure may contain two, two-way interactions rather than a three-way interaction). With the amount of variation in age loadings by vehicle in this insurer’s structure, it is possible to have an age loading for a 25 y.o. driver higher than that of an 18 y.o. driver. For example, the loading for the Holden Rodeo ( ) in Belmore for the 25 y.o. is 1.31 whereas for the 18 y.o. driver of the Hyundai Sonata ( ) the loading is 1.17. For drivers aged 18, the highest loadings are incurred for the Hyundai Santa Fe ( ), Mitsubishi Outlander ( ) and Holden Rodeo ( ). Two of these vehicles are in the SUV family, The Holden Rodeo also attracts a relatively high age loading for 25 y.o. drivers. The lowest age loadings for drivers age 18 are incurred for the Hyundai Sonata ( ,Medium family) and Mitsubishi Lancer ( , Small family). The age loading for the Hyundai Terracan ( , SUV family) is one of the highest for drivers aged 25 and 40. This vehicle also attracts a relatively high age loading for 18 y.o. drivers as well. For Insurer 12, the variations in age loadings by suburb and vehicle are the same for female drivers as for males. Based on our survey, the insurer with the largest level of interactions in its rating structure is Insurer 7. Interestingly, this insurer does not vary its rates for female drivers as we saw in Section 2.2. For Insurer 7 we were unable to obtain an on-line quote for the BMW 7 ( ) and Ford Explorer ( ) (interestingly the same vehicles as for Insurer 12). We were also unable to obtain an on-line quote for the Honda Integra ( ) or Ford Probe ( ) for 18 y.o. drivers. The age-suburb-vehicle interactions for Insurer 7 are shown below in Figure 5.6.


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The basic shape of the age curve for Insurer 7 appears similar across suburbs but shows a large degree of variation for individual vehicles across suburbs and driver ages. There are a couple of striking examples of the shape of the interactions with Insurer 7. In Belmore, the highest loading for an 18 y.o. driver is incurred for a Ford Transit ( ) and Mitsubishi Express ( ) (both Vans) of around 2.79 and 2.74 respectively. The Express also has a high 18 y.o. driver loading in Denistone West (around 2.69) however, the Ford Transit attracts a relatively the lowest loading for an 18 y.o. driver (around 1.64). The age loading for an 18 y.o. driver is also relatively low for the Transit in Potts Point (around 1.63). The age loading is also amongst the lowest at ages 25, 65 and 80 in Potts Point for the Transit. The Holden Rodeo ( ) attracts the highest 18 y.o. driver loading in Eureka (around 1.73); however, it is amongst the lowest for this age in Belmore (around 1.59). The Mitsubishi Grandis ( ) attracts the highest loading for an 18 y.o. driver from our sampled vehicles in Potts Point and Woronora Heights (around 2.76 in each location). This vehicle however attracts a relatively low loading for an 18yo driver in Belmore (around 1.56). The last example we will highlight is the Volkswagen Golf ( ). For an 80 y.o. driver in Eureka, the Golf attracts the lowest age loading (around 1.08). For the same vehicle in Denistone West, the loading for an 80 y.o. driver is the highest of the vehicles surveyed (around 1.74). It is difficult to imagine that these differentials would be purely driven by established differentials in the cost.

5.2 Conclusions

From our relatively limited survey of driver age-vehicle-location interactions we have observed a great deal of variation in the approach adopted by insurers. The common presence, magnitude and range of approaches relating to such interactions greatly complicate the task of understanding competitive position. There are also obvious implications for depth of analysis required to understand one’s own claims cost relativities and sales performance. Another key implication stemming from these findings is the degree of flexibility required in the rating structure to achieve


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a desired positioning in the market. Without the ability to flex rates in these dimensions, an insurer will be placed at a distinct competitive disadvantage and open to anti-selection and deteriorating portfolio performance.


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6 Other Rating Factors

In this section we comment briefly on factors in addition to driver, vehicle and location rating currently being used for the pricing and underwriting of Comprehensive Motor insurance. In Table 6.1, we have compiled a list of these factors across the surveyed insurers.

Table 6.1 – Other Questions Asked in the Quote Process Question Rating Underwriting Data CollectionVehicle Financing 14Vehicle Usage 14Membership 5 1Multiple Policies 10 2Family Discounts 1 1Seniors Card 4 4Voluntary Excess 10

All insurers surveyed used vehicle financing as a rating factor. Typically a different loading is applied depending on the type of finance on the vehicle. Brigstock & Yee (1992) noted that, at that time, over 30% of the insurers did not use financial encumbrance as a rating factor. All the insurers surveyed also use vehicle usage as a rating factor. The typical options are usually Private and Business, and other slight variations. In 1992, one-quarter of the insurers surveyed rated on vehicle usage. Our survey indicates that all bar one of all the insurers with affiliated motoring clubs offer a discount if the policyholder has a membership with the club. Numerous insurers, 10 out of the 14 surveyed, award discounts for customers with multiple policy holdings with the insurer. One of the insurers has extended this to multiple policies in the family. In this case, a discount is offered if parents of grandparents hold policies with the insurer. Another interesting factor used by a number of insurers is whether the customer has a Senior’s Card. Seniors cards are typically offered to people over 60 years of age. Four insurers provided a discount while an additional four ask the question but do not provide a discount. This is interesting as we noted in Section 2 that insurers are now applying a loading to older drivers and this discount would serve to offset that to some extent for a number of insurers. Lastly, ten of the insurers surveyed also offer flexible excess levels. Interestingly, one of the insurers surveyed appears to selectively vary the excess from the basic level as the default option when obtaining a quote.


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7 A Distribution of Competitive Position

We have observed in the preceding sections in the paper a large amount of difference in the rating parameters adopted by insurers in the market. In this section we bring together rating elements and build a picture of the implications for measuring competitive position.

7.1 Assessing Competitive Position

A typical question that may be asked of a Product Manager or Pricing Actuary may be – how are our rates positioned in the market? Indeed, the market rates may be a critical input into the rate setting process (and we think they should be). The question will need to be answered with some information about the distribution of competitive position. There are a number of challenges and a range of possible approaches to estimate the distribution. One possible approach is to attempt to “deconstruct” a competitor’s rate set based on a structured analysis of its rates to estimate the parameters of the rating algorithm. This understanding could then be used to estimate the competitor’s premium, merging this onto an in-force file or quote file. It would then be possible to compare premiums and, hence, estimate the distribution of the difference across the file. The analysis presented in this paper however shows that there a number of difficulties with this approach including:

the number of individual vehicle relativities to be estimated and the difficulty of estimating a competitor’s vehicle relativity based on the vehicle characteristics;

a number of insurers are rating below suburb level which again implies a very large volume of rating parameters. Some insurers may not yet record the risk address in a structured form making it difficult or impossible to form a view on the price that competitors may be charging for an individual risk;

the common presence and materiality of interactions in the structures of the main players means that the average rating structure is very complex.

If competitive positioning is to be used as a direct input into the rate setting process then attempting to address the problem of competitive positioning will likely require an attempt at deconstruction to some level. Another possible approach is to estimate the distribution via sampling. An advantage of this approach is that a deconstruction of the competitors’ rating algorithm is not required. The key questions that arise are:

what do I sample from?

how many quotes do I need?

Sampling combinations of policyholder characteristics from an in-force file would probably result in a biased view of the market. After all, the in-force file is business that the insurer has won and therefore, all other things being equal, this probably means that the insurer is relatively more competitive on this business. If the insurer records quote data then perhaps this could be used for sampling to remove some of this pricing bias. However, the profile of quotes will also contain some bias and will be influenced by the insurer’s brand profile and positioning and other marketing activities. (Note that these elements of bias would also be relevant for the deconstruct


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and merge approach described above). There are other challenges in using a quote profile of course and these include the presence of competitor monitoring quotes in the quote data and the impact of the sales process. Statistical theory can give us some insight as to the number of quotes that may be needed depending on the spread of the differences and the degree of confidence required. In practice, we have found that a working approximation of the middle percentiles of the distribution can be obtained with several hundred quotes. It may also be possible to design a synthetic dataset which is representative of the market, or at least, the segments of the market in which the insurer wishes to compete. The challenge is to appropriately represent the combinations of rating factors as these come together to form an insurance profile i.e., to ensure an appropriate weight. Summarised RTA data may be of some help however the fact that the data is summarised and the lack of granularity in the data combined with the fact that the data represents the profile of registered owners (as opposed to driver(s) of the vehicle) leaves one needing to make a number of assumptions to build the dataset. It may be necessary to apply some explicit weighting factors in constructing the distribution to ensure that each segment is represented appropriately. Other challenges to overcome in addressing the problem in include issues of product and coverage comparability and differences in the rating factors (both the presence and individual levels) across insurers. Depending on the context of the business problem in which the question is being addressed, any or a combination of these approaches may be appropriate. Our objective for this section of the paper is to give a broad appreciation for the shape of the distribution of competitive position which came about in practice. As such, we have based the distributions on a synthetic dataset of around 500 quotes which is broadly representative of the market. Results are presented for a subset of the competitors in our survey for NSW only.

7.2 Survey Distributions

In Figure 7.1 below, we show the results of our survey for NSW comparing the percentage difference in premiums charged for Insurer 6 vs. Insurer 12.


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Figure 7.1 – Distribution of Competitive Position – Insurer 6 vs Insurer 12

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It can be seen that the distribution is quite wide. The average premium difference across our sample is around -5% (i.e., Insurer 6 is 5% cheaper than Insurer 12, overall), however, the range of outcomes is rather large. We can see that in around 12% of cases, the premium for Insurer 6 will be more than 30% cheaper than Insurer 12 and conversely at the other end of the distribution, approximately 10% of the time, Insurer 6 will be 30% more expensive than Insurer 12. In Figure 6.2 overleaf, we show this comparison across each pair of insurers from 4 of our sampled 5 insurers in NSW.


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Figure 7.2 – Comparison of Differences in Premiums between Pairs of Insurers Difference in Premiums, Insurer 6 vs. Insurer 12

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In every case, there is a wide distribution of outcomes of competitive position. Without appropriate regard to the design of the quotes or sample size, it would be difficult to address the question of competitive positioning with any degree of accuracy.

7.3 Conclusions

When we combine the differences in rating parameters across insurers as they come together to form a premium, we observe large differences in the shape of rates across individual insurance profiles. This implies that an estimate of competitive position for a portfolio must necessarily require estimation of a distribution. We have discussed a number of possible approaches to estimating the distribution and highlighted some of the challenges with each approach. In practice, the approach taken will need to be appropriate in the context of the business problem being addressed. We have illustrated the distributions of competitive position for a number of pairs of insurers in our survey. The distributions we tested are quite wide.


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8 Conclusions

The rating of Comprehensive Motor has evolved rapidly. Throughout this paper we have contrasted the findings from a survey of market premiums conducted in 2008 to the findings from a 1992 survey conducted by Brigstock and Yee. There are major differences in the granularity and complexity of the typical rating structure in operation in 2008 than was the case 16 years prior.

8.1 Summary of Findings

We examined in some detail the current market practices around the rating of the principal factors of driver, vehicle and location as well as interaction relationships between these factors. Here we reflect on some of the key take outs from this analysis. Whilst we have observed some common elements to the shape of driver age curves across the market participants it is important to understand the differences. The differences are typically at both extremes of the age spectrum, i.e., younger and aged drivers. The differences for younger drivers are not easily explainable with reference to the excess structures or rules regarding selection of the rated driver. Other key observations from the driver rating analysis include:

major differences in the approach for and rating implications of multiple drivers;

increased complexity of the approach to rating for driving experience. This has been accompanied by a decrease in transparency for consumers.

Development of appropriate vehicle rating is one of the key challenges in the rating of Comprehensive Motor. The sheer number of unique vehicles in the market makes this a difficult exercise. A possible approach to this problem would be to quantify the impact of the characteristics of the vehicle (independently and in combination) on the claims cost. We have seen through building a simple model of the relativities of a number of the companies surveyed that a small number of factors can give a reasonable prediction of the premium relativities. The “drivers” do appear to be different across the insurers however and a large proportion of the variation remains unexplained. This signals that the vehicle relativities are impacted by a number of factors outside of the simple model we constructed such as:

unique features of individual vehicles;

selection effects relating to the drivers of such vehicles; and

market positioning and underwriting judgement.

We examined the rating of location and observed that all but one of the insurers surveyed rates below postcode level. It is apparent that the granularity of rating has increased greatly since the Brigstock and Yee paper. We observed that the spread of high to low risk relativities across the suburbs we surveyed in NSW involved a factor of approximately 3. There is variation in the spread of relativities across the insurers. We undertook some mapping of the suburb relativities and observed a reasonable level of commonality across the insurers in the risk assessment of Sydney metropolitan suburbs. The greatest difference in relativities between the insurers occurs in the “hot spot” areas. The relativities tend to be at the upper end of the spectrum for each of the insurers in these areas however the assessment of the quantum of the premium relativity drives the difference. We also observed that two insurers rate below suburb level – one at individual address


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level and one at street level. The insurer rating at individual address level incorporates large differences in location relativity for properties in close proximity. It is unlikely that these differences are purely driven by risk assessment. We examined the interactions between the primary rating factors and found that interactions are commonly used and can have a material effect on the premium. The approach however varies greatly across the surveyed insurers. The interactions for one insurer in particular appear to be almost random in nature. The diversity in the rating parameters across the insurers come together to bring about large differences in relative competitiveness across the rating cells. An assessment of competitive position must therefore involve an estimate of a distribution of premium differentials. Our survey indicated that in comparing the premiums between several combinations of two insurers, the distribution of the percentage differences in premium is quite wide. We also briefly commented on the other rating factors used for Comprehensive Motor. The market for Comprehensive Motor insurance is highly competitive. The granularity and complexity of rating has evolved rapidly. It is important for market participants to monitor market pricing regularly and form a view about price positioning. This is however not a simple exercise. The key challenge in making the assessment is to effectively deal with the underlying complexity and heterogeneity of the rating.

8.2 The Role of Competitor Data in Pricing Analysis and Decision Making

Competitor premium data must play an important role in the development of the pricing strategy for an insurer. The composition of an insurer’s portfolio and hence its profitability will be strongly influenced by the positioning of its rates in the market. A smaller insurer might have a relatively greater need to incorporate competitor premium data into its pricing analysis. A smaller insurer operating in the personal lines market suffers a competitive disadvantage compared to its larger rivals in that it does not have the same volume of data to analyse to uncover the drivers of claims cost. Of course, the business model of the small insurer may present other, competitive advantages; however, to avoid anti-selection, the smaller insurer must closely monitor the alignment of its rates to the market. Whilst the premiums of its larger rivals may not bear a direct relationship to their assessment of the cost, we would expect a reasonable relationship to be present. Therefore through understanding these relativities in the larger insurers’ structure, the smaller insurer can negate some of this disadvantage. The need to understand market pricing though extends beyond a requirement for smaller insurers. For any market participant, it would seem to be just good common business sense to perform market comparisons on its rates. Many companies recognise this and have dedicated resources in place to collect and monitor competitor premium data. A sensible place to start might be to examine whether a proposed rate set would “leave money on the table” (or at least to examine the extent to which this may be happening). Going further, a company armed with a thorough understanding of its claims and other costs would wish to determine whether its rates are more competitive in profitable segments of the portfolio and that the rates are positioned to avoid writing loss making business.


59

A key question that arises in all of this is who or what does an insurer need to measure alignment of its rates to? Is it an individual insurer, does this vary by segment, is it some combination of a number of insurers? These questions could be the subject of a paper in their own right. The availability of competitor premium data also enriches the analysis of sales data such as quote conversion, cancellation or retention statistics. By using competitor data as an explanatory factor in the analysis, it becomes possible to separate the drivers of the subject of the analysis into price related vs. “general inertia” related. Indeed, market price positioning would be the key price driver in an analysis of quote conversion data. “Price Optimisation” is attracting increasing attention in actuarial literature as a process to determine go-to-market rates. Price optimisation involves explicit use of an understanding of price elasticity in addition to claims costs for setting prices in order to optimise the price-volume trade-off. This is done within a rigorous analysis making framework explicitly incorporating portfolio objectives and constraints. The market positioning of an insurer’s rates will have an important impact on the sales response (although this may be a secondary impact for renewal business). Therefore, competitor premium data has an important role to play in price optimisation analysis. As we have seen throughout this paper, it is challenging to fully understand the way in which a competitor is pricing its business. In our view however, an attempt to formulate pricing without incorporating competitor premium data in some way must result in a sub-optimal set of pricing decisions.


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9 References

Brigstock, C. & R. Yee, 1992, “Rating Practices in Motor Insurance”, Proceedings of the Eighth General Insurance Seminar, Hyatt Regency Coolum, Queensland


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Part III Appendices

A Driver Rating

A.1 Driver Age Relativities – Other States


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Figure A.1 – VIC Insurer Age Relativities - Males

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Figure A.2 – VIC Insurer Age Relativities – 40 yo and Under - Males

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Figure A.3 –VIC Insurer Age Relativities – 50yo and Over - Males

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Figure A.4 –VIC Insurer Age Relativities – Females

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Figure A.5 – Male vs Female Driver Premiums – VIC

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Figure A.6 – QLD Insurer Age Relativities – Males

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Figure A.7 – QLD Insurer Age Relativities – 40yo and Under - Males

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Figure A.8 – QLD Insurer Age Relativities – 50yo and Over - Males

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Figure A.9 – QLD Insurer Age Relativities – Females

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Figure A.10 – Male vs Female Driver Premiums – QLD

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Figure A.11 – SA Insurer Age Relativities – Males

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Figure A.12 – SA Insurer Age Relativities – 40yo and Under - Males

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Figure A.13 – SA Insurer Age Relativities – 50yo and Over - Males

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Figure A.14 – SA Insurer Age Relativities – Females

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Figure A.15 – Male vs Female Driver Premiums – SA

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Figure A.16 – WA Insurer Age Relativities – Males

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Figure A.17 – WA Insurer Age Relativities – 40yo and Under - Males

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Figure A.18 – WA Insurer Age Relativities – 50yo and Over - Males

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Figure A.19 – WA Insurer Age Relativities – Females

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Figure A.20 – Male vs Female Driver Premiums – WA

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A.2 Comparison of Driver Age Curves to Brigstock & Yee (1992)

Figure A.21 – NSW Age Loadings 1992

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Figure A.22 – NSW Age Loadings 2008 – Males

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Figure A.23 – NSW Age Loadings 2008 - Females

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Figure A.24 – VIC Age Loadings 1992

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Figure A.25 – VIC Age Loadings 2008 – Males

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Figure A.26 – VIC Age Loadings 2008 - Females

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Figure A.27 – QLD Age Loadings 1992

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Figure A.28 – QLD Age Loadings 2008 – Males

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Figure A.29 – QLD Age Loadings 2008 - Females

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Figure A.30 – SA Age Loadings 1992

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Figure A.31 – SA Age Loadings 2008 – Males

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Figure A.32 – SA Age Loadings 2008 - Females

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Figure A.33 – WA Age Loadings 1992

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Figure A.34 – WA Age Loadings 2008 – Males

0.70

1.00

1.30

1.60

1.90

2.20

2.50

17 18 19 20 21 22 23 24 25 26 27 28 29 30

Rated Driver Age

Rel

ativ

ity to

40y

o M

ale



83

Figure A.35 – WA Age Loadings 2008 - Females

0.70

1.00

1.30

1.60

1.90

2.20

2.50

17 18 19 20 21 22 23 24 25 26 27 28 29 30

Rated Driver Age

Rel

ativ

ity to

40y

o M

ale



84

A.3 Profiles

In Section 2.6, a variety of driver based profiles were used to examine the premium relativities applied across the surveyed insurers in NSW. The range of profiles are intended to represent common risk scenarios. The profiles have been created different combinations of four drivers:

A 40 year old (y.o.) male driver with 10 years of driving experience

A 38 y.o. female driver with 10 years of driving experience

A 20 y.o. male driver with 2 years of driving experience

An 18 y.o. female driver with 1 year of driving experience.

Various claim histories have been applied to the combination of these drivers. Detailed information regarding each of the profiles is shown in the table below. Base risk characteristics were adopted for the other rating factors.

Table A.1 – Profile Details Age/Sex Exp Claims* Spd# Usage Age/Sex Exp Claims Usage Age/Sex Exp Claims Usage Age/Sex Exp Claims Usage

1 Single male, clean history 40 M 10 100%2 Single male, 1 AF claim in 2 years 40 M 10 1 AF 100%3 Single male, 2 AF claims in 2 years 40 M 10 2 AF 100%4 Single male, 2 AF claims + speeding 40 M 10 2 AF 2 100%5 Single male, 1 yr driving exp 40 M 1 100%6 Single female, clean history 38 F 10 100%7 Single female, 2 NAF claims in 2 years 38 F 10 2 NAF 100%8 Single female, 1 yr driving exp 38 F 1 100%9 Couple, both clean history 40 M 10 80% 38 F 10 20%

10 Couple, male with 2 AF claims 40 M 10 2 AF 80% 38 F 10 20%11 Couple, female with 2 NAF claims 40 M 10 80% 38 F 10 2 NAF 20%12 Couple, both have 2 claims 40 M 10 2 AF 80% 38 F 10 2 NAF 20%13 Wife's car, both clean history 40 M 10 20% 38 F 10 80%14 Wife's car, male has 2 AF claims 40 M 10 2 AF 20% 38 F 10 80%15 Wife's car, female has 2 NAF claims 40 M 10 20% 38 F 10 2 NAF 80%16 Wife's car, both have 2 claims 40 M 10 2 AF 20% 38 F 10 2 NAF 80%17 Young male, clean history 20 M 218 Young male, 2 AF claims in 2 years 20 M 2 2 AF19 Young male, 2 AF claims, with speeding 20 M 2 2 AF 220 Young female, clean history 19 F 121 Young female, 2 AF claims in 2 years 19 F 1 2 AF22 Two teens, both have clean history 20 M 2 50% 19 F 1 50%23 Two teens, older teen has 2 AF 20 M 2 2 AF 50% 19 F 1 50%24 Two teens, younger teen has 2 AF 20 M 2 50% 19 F 1 2 AF 50%25 Two teens, both teens have 2 AF 20 M 2 2 AF 50% 19 F 1 2 AF 50%26 Adult with occasional teen, both clean 40 M 10 85% 20 M 2 15%27 Adult with occ. teen, adult has 2 AF claims 40 M 10 2 AF 85% 20 M 2 15%28 Adult with occ. teen, teen has 2 AF claims 40 M 10 85% 20 M 2 2 AF 15%29 Adult with occ. teen, both have 2 AF claims 40 M 10 2 AF 85% 20 M 2 2 AF 15%30 Adult with 2 occ teens, both teens clean 40 M 10 85% 20 M 2 10% 19 F 1 5%31 Adult with 2 occ teens, older teen has 2 AF 40 M 10 85% 20 M 2 2 AF 10% 19 F 1 5%32 Adult with 2 occ teens, younger teen has 2 AF 40 M 10 85% 20 M 2 10% 19 F 1 2 AF 5%33 Adult with 2 occ teens, both teens has 2 AF 40 M 10 85% 20 M 2 2 AF 10% 19 F 1 2 AF 5%

* Occurred 15 months before purchase of insurance# Speeding infringments occurred within 2 years

Driver 1 (Owner) Driver 2 (Owner) Driver 3 Driver 4

Gro

up 3

Gro

up 4

No Profile

Gro

up 1

Gro

up 2


85

B Vehicle Rating

B.1 Vehicle Selection

For the vehicle component of the survey, our intention was to achieve a good spread of vehicles across the risk spectrum but weighted towards more popular manufacturers. We used individual vehicle entries from the Glass’s Guide database, selected as follows:

Each of the sixteen vehicle segments was covered, including various passenger vehicle sizes, SUVs and light commercial vehicles.

Ten popular manufacturers were specified and individual vehicles were sampled from each of these where there were entries in the database for the vehicle segment.

Within each manufacturer/segment combination, individual vehicles were selected with different “trim” levels (High, medium and low) as denoted by the “new” value of the vehicles.

No restrictions were placed on the age of the vehicle, thus the vehicles extracted were from a range of years of manufacture.

Details of the vehicles surveyed are shown in Table B.1 below.


86

Table B.1 – Surveyed Vehicles SEGMENT BMW FORD HOLDEN HONDA HYUNDAILARGE 1996 520i 1995 FALCON GLi 1995 COMMODORE VACATIONER 2006 LEGEND 2000 SONATA EXECUTIVE V6

2007 530d 2007 FALCON XR6 2008 COMMODORE SV6 2005 LEGEND 2007 SONATA SLX1999 540i PROTECTION 2001 TS50 2007 CALAIS V INTERNATION 1996 LEGEND 2007 GRANDEUR LIMITED

LIGHT 2000 FESTIVA TRIO 2000 BARINA CITY 2005 JAZZ GLi 2003 GETZ XL2005 FIESTA LX 2006 BARINA 2004 JAZZ VTi 2007 ACCENT SLX2007 FIESTA XR4 2007 TIGRA 2004 INSIGHT HYBRID 2003 GETZ FX

LIGHT BUS 2000 TRANSIT 12 SEAT2005 TRANSIT 12 SEAT2008 TRANSIT 12 SEAT

MEDIUM 2001 318ti CONTOUR 1997 MONDEO LX 2000 VECTRA GL 2005 ACCORD VTi 1999 SONATA GLE2007 320Ci 1998 MONDEO GLX 2007 EPICA CDXi 1999 ACCORD VTi-L 1995 SONATA GLE2007 550i 1995 TELSTAR TX5 GHIA (4W 2003 VECTRA CDXi 2001 ACCORD V6-L 2007 SONATA ELITE

PEOPLE MOVER 2005 ZAFIRA 2004 ODYSSEY 2004 TRAJET V6 2.72006 ZAFIRA 2006 ODYSSEY LUXURY 2002 TRAJET GL WORLD CUP2003 ZAFIRA EQUIPE 2004 ODYSSEY V6L (6 SEAT) 2002 TRAJET GLS WORLD CUP

PICK UP OR CAB CHASSIS 4X2 1997 COURIER 1998 RODEO DX2006 COURIER GL 1996 COMMODORE S2007 F350 XLT 2006 CREWMAN SS THUNDER

PICK UP OR CAB CHASSIS 4X4 1997 COURIER XL (4x4) 1998 RODEO DX (4x4)2007 RANGER XL (4x4) 2007 RODEO LX (4x4)2007 F350XL (4x4) 2006 CREWMAN CROSS 8

SMALL 2005 116i 2000 LASER LXi 2005 VIVA 1997 CIVIC CXi 2005 ELANTRA 2.0 HVT2004 316ti 1999 LASER GLXi 2008 ASTRA CD 2006 CIVIC Vi 2007 ELANTRA SX2008 130i SPORT 2005 FOCUS ST170 2004 ASTRA SRi TURBO 2007 CIVIC TYPE R 2007 ELANTRA ELITE S

SPORTS 1997 Z3 2001 COUGAR 2000 BARINA CABRIO 1996 INTEGRA GSi 1996 SCOUPE LS2005 330Ci SMG 1998 PROBE 1995 CALIBRA 1999 PRELUDE VTi-R 1999 COUPE FX 2007 M6 2003 MUSTANG COBRA 1995 CALIBRA 2005 NSX 2002 TIBURON V6

SUV COMPACT 2006 ESCAPE XLS 2004 CRUZE 1999 HRV (4x4) 2007 TUCSON CITY SX2004 ESCAPE XLS 2006 CRUZE 2007 CRV (4x4) 2007 SANTA FE SX (4x4)2003 ESCAPE LIMITED 2004 CRUZE 2007 CRV (4x4) LUXURY 2007 SANTA FE ELITE CRDi

SUV LARGE 2000 EXPLORER XL (4x4) 2001 SUBURBAN 1500 (4x4)2008 EXPLORER XLT (4x4) 1999 SUBURBAN 2500 (4x4)2001 EXPLORER LIMITED (4x 1999 SUBURBAN 2500 LT (4x

SUV LUXURY 2007 X32.0d 2003 MDX 2007 X53.0i 2007 MDX 2004 X54.8is

SUV MEDIUM 1996 RAIDER BREAKAWAY (4x 2000 FRONTERA SPORT (4x4) 2004 TERRACAN 2007 TERRITORY TX (4x4) 2007 CAPTIVA LX 2007 TERRACAN HIGHLANDER2007 TERRITORY GHIA TURBO 1995 JACKAROO MONTEREY (4 2004 TERRACAN HIGHLANDER

TRUCK 2.5-3.5 GVM 1998 ECONOVAN MAXI2005 TRANSIT 2008 TRANSIT EXTENDED FRA

UPPER LARGE 2000 735i 1995 FAIRLANE GHIA 1995 STATESMAN V62002 740iL HIGH-LINE 2007 FAIRLANE GHIA 2006 STATESMAN INTERNATIO1999 L7 2001 TL50 2006 CAPRICE

VAN 2000 ECONOVAN 2000 COMBO 2008 TRANSIT LOW (SWB) 2007 COMBO 2006 TRANSIT JUMBO LWB HI 1999 COMBO

SEGMENT MAZDA MERCEDES-BENZ MITSUBISHI TOYOTA VOLKSWAGENLARGE 1996 626 SDX V6 2007 R280 CDI 2000 MAGNA EXECUTIVE 1995 LEXCEN CSi

1997 626 LUXURY V6 1998 E320 CLASSIC 2008 380 VR-X 2004 CAMRY ATEVA1996 EUNOS 800M 2007 CLS63 AMG 2005 VERADA Xi 2007 TRD AURION 3500SL

LIGHT 1997 121 1998 MIRAGE 1997 STARLET LIFE 2005 POLO CLUB2000 121 METRO SHADES 2008 COLT VR-X 2006 YARIS YRS 2007 POLO MATCH2003 MAZDA2 GENKI 2007 COLT TURBO 2007 YARIS YRX 2007 POLO GTi

LIGHT BUS 1995 HIACE COMMUTER2005 HIACE COMMUTER2007 HIACE COMMUTER

MEDIUM 2001 626 LIMITED 2000 C180 CLASSIC 1995 MAGNA GLX 1997 CAMRY CSi 1997 VENTO GL CLASSIC2002 626 ECLIPSE 2005 C220 CDI CLASSIC 1999 MAGNA EXECUTIVE 2000 CAMRY CONQUEST 2007 PASSAT 2.0T FSI2008 MAZDA6 MPS (LEATHER) 2007 CL65 AMG 1997 MAGNA SE 2007 CAMRY GRANDE 2004 PASSAT W8

PEOPLE MOVER 2000 MPV 2005 VITO 115 CDI 1996 NIMBUS 2000 SPACIA VALUE PACK 2006 CADDYLIFE1996 MPV 2006 VIANO 3.5 TREND 2007 GRANDIS LS 2003 AVENSIS VERSO ULTIMA 2003 CARAVELLE TDi2004 MPV 2007 VIANO 3.5 AMBIENTE 1995 STARWAGON GLS 2004 TARAGO ULTIMA 2006 MULTIVAN EXECUTIVE 4

PICK UP OR CAB CHASSIS 4X2 2000 B2600 BRAVO DX 2000 TRITON GL 2000 HILUX WORKMATE2002 B2600 BRAVO DX CAB P 2006 TRITON GLX 2007 HILUX WORKMATE2007 BT50 B3000 DX 2007 TRITON GLX 2007 HILUX SR5

PICK UP OR CAB CHASSIS 4X4 1998 B2600 BRAVO (4x4) 2000 TRITON GLX (4x4) 2000 HILUX (4x4)1996 B2600 BRAVO PLUS (4x 2005 TRITON GLX (4x4) 1996 HILUX (4x4)2007 BT50 B3000 SDX (4x4) 2007 TRITON GLS (4x4) 2007 LANDCRUISER GXL (4x4

SMALL 1996 323 2005 A150 CLASSIC 2000 LANCER GLi 2000 COROLLA CSi 1996 GOLF CL2008 MAZDA3 MAXX SPORT 2007 A170 ELEGANCE 2007 LANCER LS 2005 COROLLA CONQUEST 2008 GOLF 2.0 FSI COMFORT2008 MAZDA3 MPS SPORTS PA 2007 B200 TURBO 2001 LANCER RALLIART EVOL 2007 PRIUS I-TECH HYBRID 2008 GOLF R32

SPORTS 1997 EUNOS 30X SPORT 2002 SLK200 KOMPRESSOR 1996 3000GT 1996 PASEO 1998 GOLFCL1995 MX-5 2005 CLK320 AVANTGARDE 1998 3000GT 1999 CELICA ZR 2007 EOS 2.0T FSI1995 RX7 SP 2007 CL65 AMG 1995 3000GT 1995 MR2 GT 1999 GOLF GL

SUV COMPACT 2001 TRIBUTE LIMITED 2000 PAJERO iO (4x4) 2000 RAV4 (4x4)2007 CX-7 CLASSIC 2007 OUTLANDER LS 1995 RAV4 (4x4)2007 CX-7 LUXURY 2007 OUTLANDER VR-X LUXUR 2007 RAV4 ZR6

SUV LARGE 1995 LANDCRUISER LWB (4x41996 LANDCRUISER GXL (4x41996 LANDCRUISER SAHARA (

SUV LUXURY 2000 ML270 CDI (4x4) 2006 TOUAREG R5 TDi2005 ML350 SPECIAL EDITIO 2007 TOUAREG V6 TDI2007 ML63 AMG (4x4) 2004 TOUAREG V10 TDi

SUV MEDIUM 2007 CX-9 CLASSIC 1996 PAJERO GL SWB (4x4) 1996 LANDCRUISER PRADO RV2007 CX-9 LUXURY 1997 PAJERO GLS SWB (4x4) 2003 LANDCRUISER PRADO GX

1995 PAJERO EXCEED GLS LW 2007 LANDCRUISER PRADO GRTRUCK 2.5-3.5 GVM 2000 E2000 1995 DYNA 100 1995 TRANSPORTER

2003 E2000 (LWB) 2005 DYNA 100 2007 TRANSPORTER (LWB)2003 E2500 2005 DYNA 150 2007 TRANSPORTER (LWB)

UPPER LARGE 1995 S280 1999 S420 2007 S65L AMG

VAN 2000 E2000 (SWB) 2000 MB100D 2000 EXPRESS SWB 1997 TOWNACE 2005 CADDY 1.61996 E2000 DELUXE (LWB) 2004 VITO 112 CDI 2005 EXPRESS SWB 2007 HIACE LWB 2007 TRANSPORTER (LWB)2003 E2500 LWB 2008 VITO 120 CDI EXTRA L 1999 EXPRESS ECI (4x4) 2007 HIACE SLWB 2007 TRANSPORTER CREWVAN


87

B.2 Histograms of Vehicle Relativities for Other States

Figure B.1 – Histogram of Vehicle Relativities – VIC

0%

5%

10%

15%

20%

25%

30%

0.0-

0.40

0.4-

0.45

0.45

-0.5

0.5-

0.55

0.55

-0.6

0.6-

0.65

0.65

-0.7

0.7-

0.75

0.75

-0.8

0.8-

0.85

0.85

-0.9

0.9-

0.95

0.95

-1.0

1.0-

1.05

1.05

-1.1

1.1-

1.15

1.15

-1.2

1.2-

1.25

1.25

-1.3

1.3-

1.35

1.35

-1.4

1.4-

1.45

1.45

-1.5

1.5-

1.55

1.55

-1.6

1.6-

1.65

1.65

-1.7

1.7-

1.75

1.75

-1.8

1.8-

1.85

1.85

-1.9

1.9-

1.95

1.95

-2.0

2.0+

Relativity against Median Premium

Prop

ortio

n of

Veh

icle

s


Figure B.2 – Histogram of Vehicle Relativities – QLD

0%

5%

10%

15%

20%

25%

30%

0.0-

0.40

0.4-

0.45

0.45

-0.5

0.5-

0.55

0.55

-0.6

0.6-

0.65

0.65

-0.7

0.7-

0.75

0.75

-0.8

0.8-

0.85

0.85

-0.9

0.9-

0.95

0.95

-1.0

1.0-

1.05

1.05

-1.1

1.1-

1.15

1.15

-1.2

1.2-

1.25

1.25

-1.3

1.3-

1.35

1.35

-1.4

1.4-

1.45

1.45

-1.5

1.5-

1.55

1.55

-1.6

1.6-

1.65

1.65

-1.7

1.7-

1.75

1.75

-1.8

1.8-

1.85

1.85

-1.9

1.9-

1.95

1.95

-2.0

2.0+


Prop

ortio

n of

Veh

icle

s



88

Figure B.3 – Histogram of Vehicle Relativities – SA

0%

5%

10%

15%

20%

25%

30%

0.0-

0.40

0.4-

0.45

0.45

-0.5

0.5-

0.55

0.55

-0.6

0.6-

0.65

0.65

-0.7

0.7-

0.75

0.75

-0.8

0.8-

0.85

0.85

-0.9

0.9-

0.95

0.95

-1.0

1.0-

1.05

1.05

-1.1

1.1-

1.15

1.15

-1.2

1.2-

1.25

1.25

-1.3

1.3-

1.35

1.35

-1.4

1.4-

1.45

1.45

-1.5

1.5-

1.55

1.55

-1.6

1.6-

1.65

1.65

-1.7

1.7-

1.75

1.75

-1.8

1.8-

1.85

1.85

-1.9

1.9-

1.95

1.95

-2.0

2.0+


Prop

ortio

n of

Veh

icle

s


Figure B.4 – Histogram of Vehicle Relativities – WA

0%

5%

10%

15%

20%

25%

30%

0.0-

0.40

0.4-

0.45

0.45

-0.5

0.5-

0.55

0.55

-0.6

0.6-

0.65

0.65

-0.7

0.7-

0.75

0.75

-0.8

0.8-

0.85

0.85

-0.9

0.9-

0.95

0.95

-1.0

1.0-

1.05

1.05

-1.1

1.1-

1.15

1.15

-1.2

1.2-

1.25

1.25

-1.3

1.3-

1.35

1.35

-1.4

1.4-

1.45

1.45

-1.5

1.5-

1.55

1.55

-1.6

1.6-

1.65

1.65

-1.7

1.7-

1.75

1.75

-1.8

1.8-

1.85

1.85

-1.9

1.9-

1.95

1.95

-2.0

2.0+


Prop

ortio

n of

Veh

icle

s



89

B.3 Range of Relativities for Individual Surveyed Vehicles – NSW

Figure B.5 – Range of Relativities for NSW Insurers – Small and Light Families

0.0

1.0

2.0

3.0

4.0

5.0

6.000

BAR

INA

00 F

ESTI

VA97

STA

RLE

T03

GET

Z XL

06 B

ARIN

A97

121

05 F

IEST

A LX

07 A

CC

ENT

SLX

96 3

2303

GET

Z FX

96 G

OLF

CL

05 E

LAN

TRA

05 V

IVA

97 C

IVIC

CX

i05

PO

LO98

MIR

AGE

00 C

OR

OLL

AC

Si

07 E

LAN

TRA

SX06

YAR

IS Y

RS

07 P

OLO

05 J

AZZ

GLi

00 L

ASER

LXi

08 C

OLT

VR

-X00

121

99 L

ASER

GLX

i04

JAZ

Z VT

i07

YAR

IS Y

RX

08 A

STR

A C

D07

ELA

NTR

A EL

ITE

S08

GO

LF00

LAN

CER

GLi

03 M

AZD

A2 G

ENKI

05 C

OR

OLL

AC

ON

QU

EST

07 F

IEST

A XR

405

A15

008

MAZ

DA3

MAX

X05

116

i07

PO

LO G

Ti04

INSI

GH

T07

PR

IUSI

-TEC

H H

YBR

ID07

TIG

RA

07 L

AN

CER

LS

04 3

16ti

06 C

IVIC

Vi

07 A

170

04 A

STR

A SR

i07

CO

LT T

UR

BO

08 M

AZD

A3 M

PS07

B20

0 08

GO

LF R

3208

130

i07

CIV

IC T

YPE

R05

FO

CU

S01

LAN

CE

R E

VOLU

TIO

N

Vehicles

Rel

ativ

ities

Figure B.6 – Range of Relativities for NSW Insurers – Large and Upper Large Families

0.0

1.0

2.0

3.0

4.0

5.0

6.0

95 F

ALC

ON

GLi

95 C

OM

MO

DO

RE

95 L

EX

CEN

CSi

00 M

AG

NA

04 C

AM

RY

ATE

VA00

SO

NA

TA E

X95

FA

IRLA

NE

GH

IA07

SO

NA

TA S

LX05

VER

AD

A96

626

95 S

TATE

SM

AN

V6

07 F

ALC

ON

XR

608

380

97 6

26 L

UX

UR

Y07

GR

AND

EU

R L

TD96

EU

NO

S 80

0M08

CO

MM

OD

OR

E S

07 F

AIR

LAN

E G

HIA

96 L

EG

EN

D96

520

i07

TR

D A

UR

ION

07 C

ALA

IS V

98 E

320

06 S

TATE

SM

AN

06 C

APR

ICE

06 L

EG

EN

D95

S28

007

R28

0 C

DI

99 L

705

LE

GE

ND

00 7

35i

07 5

30d

99 S

420

01 T

S50

01 T

L50

02 7

40iL

07 C

LS63

AM

G99

540

i07

S65

L A

MG

Vehicles

Rel

ativ

ities


90

Figure B.7 – Range of Relativities for NSW Insurers – SUV Families

0.0

1.0

2.0

3.0

4.0

5.0

6.0

96 R

AID

ER

95 R

AV

4(4x

4)95

LA

ND

CR

UIS

ER

LW

B95

JA

CK

AR

OO

96 P

AJE

RO

GL

00 F

RO

NTE

RA

SP

OR

T07

TU

CS

ON

CIT

Y S

X96

PR

AD

O R

V00

PA

JER

O07

SA

NTA

FE

SX

99 H

RV

01 T

RIB

UTE

LIM

ITE

D00

EX

PLO

RE

R X

L04

CR

UZE

04 C

RU

ZE A

04 T

ER

RA

CA

N06

ES

CA

PE

XLS

07 T

ER

RA

CAN

HL

03 E

SC

APE

LIM

ITE

D00

RA

V4(

4x4)

95 P

AJE

RO

GLS

07 S

AN

TA F

E E

LITE

04 E

SC

APE

XLS

97 P

AJE

RO

GLS

04 T

ER

RA

CAN

HL

08 E

XP

LOR

ER

XLT

07 C

RV

07 T

ER

RIT

OR

Y T

X06

CR

UZE

07 C

AP

TIV

A L

X07

OU

TLA

ND

ER

LS

96 L

AN

DC

RU

ISE

R G

XL

01 E

XP

LOR

ER L

TD07

CX

-7 C

LAS

SIC

07 C

RV

LUX

UR

Y96

LA

ND

CR

UIS

ER03

PR

AD

O G

XL

07 O

UTL

AN

DE

R V

R-X

07 M

DX

03 M

DX

07 P

RA

DO

GR

AN

DE

07 T

ER

RIT

OR

Y G

HIA

07 C

X-9

CLA

SS

IC07

CX

-7 L

UX

UR

Y99

SU

BU

RBA

N 2

500

99 S

UB

UR

BA

N 2

500

LT07

RA

V4Z

R6

07 C

X-9

LU

XU

RY

01 S

UB

UR

BAN

150

007

X3

2.0d

06 T

OU

AR

EG

00 M

L270

CD

I07

TO

UA

RE

G07

X5

3.0i

05 M

L350

04 X

5 4.

8is

04 T

OU

ARE

G V

1007

ML6

3 A

MG

Vehicles

Rel

ativ

ities

Figure B.8 – Range of Relativities for NSW Insurers – Light Commercial Family

0.0

1.0

2.0

3.0

4.0

5.0

6.0

97 C

OU

RIE

R97

CO

UR

IER

XL

98 R

OD

EO

DX

00 T

RIT

ON

GL

98 R

OD

EO

DX

98 E

CO

NO

VAN

95 D

YN

A10

000

B26

0000

HIL

UX

98 B

2600

00 T

RIT

ON

GLX

96 B

2600

02 B

2600

96 H

ILU

X 9506

CO

UR

IER

GL

03 E

2000

(LW

B)

07 H

ILU

X00

HIL

UX

07 B

T50

00 E

2000

05 D

YN

A10

003

E25

0007

RO

DE

O L

X06

TR

ITO

N G

LX05

DY

NA

150

07 B

T50

SD

X05

TR

AN

SIT

05 T

RIT

ON

GLX

08

TR

AN

SIT

07 R

AN

GE

R X

L07

TR

ITO

N G

LX 96 08 07 0707

HIL

UX

SR

507

TR

ITO

N G

LS06

CR

EWM

AN

06 C

REW

MA

N07

F35

0 X

L07

F35

0 X

LT

Vehicles

Rel

ativ

ities


91

Figure B.9 – Range of Relativities for NSW Insurers – Van and People Mover Families

0.0

1.0

2.0

3.0

4.0

5.0

6.0

96 N

IMB

US

97 T

OW

NA

CE

99 C

OM

BO

00 C

OM

BO

03 Z

AFI

RA

EQ

UIP

E95

STA

RW

AG

ON

02 T

RAJ

ET

GL

00 E

CO

NO

VA

N07

CO

MB

O96

E20

00 D

ELU

XE

00 E

XP

RES

S00

E20

00 (S

WB

)04

TR

AJE

T V

6 2.

702

TR

AJE

T G

LS96

MP

V05

EX

PR

ESS

06 C

AD

DY

06 Z

AFI

RA

05 C

AD

DY

05 Z

AFI

RA

08 T

RA

NS

IT00

TR

AN

SIT

95 H

IAC

E C

OM

99 E

XP

RES

S06

TR

AN

SIT

JU

MB

O00

MP

V00

MB

100D

07 T

RA

NS

PO

RTE

R05

TR

AN

SIT

03 C

AR

AV

ELL

E T

Di

04 O

DY

SSE

Y08

TR

AN

SIT

06 O

DY

SSE

Y L

UXU

RY

00 S

PA

CIA

07 H

IAC

E L

WB

03 E

2500

LW

B03

AVE

NS

IS07

GR

AN

DIS

LS

07 T

RA

NS

PO

RTE

R A

WD

04 M

PV

06 M

ULT

IVA

N 4

MO

TIO

N07

HIA

CE

CO

M04

OD

YS

SEY

V6

07 H

IAC

E S

LWB

05 H

IAC

E C

OM

04 T

AR

AGO

04 V

ITO

05 V

ITO

06 V

IAN

O08

VIT

O07

VIA

NO

3.5

Vehicles

Rel

ativ

ities


92

C Location Rating

C.1 Histogram of Location Relativities – Other States

Figure C.1 - Histogram of Suburb Relativities - VIC

0%

5%

10%

15%

20%

25%

30%

35%0.

0-0.

4

0.4-

0.5

0.5-

0.6

0.6-

0.7

0.7-

0.8

0.8-

0.9

0.9-

1.0

1.0-

1.1

1.1-

1.2

1.2-

1.3

1.3-

1.4

1.4-

1.5

1.5-

1.6

1.6-

1.7

1.7-

1.8

1.8-

1.9

1.9-

2.0

2.0+


Wei

ghte

d Pr

opor

tion

of S

ubur

bs


Figure C.2 - Histogram of Suburb Relativities - QLD

0%

10%

20%

30%

40%

50%

0.0-

0.4

0.4-

0.5

0.5-

0.6

0.6-

0.7

0.7-

0.8

0.8-

0.9

0.9-

1.0

1.0-

1.1

1.1-

1.2

1.2-

1.3

1.3-

1.4

1.4-

1.5

1.5-

1.6

1.6-

1.7

1.7-

1.8

1.8-

1.9

1.9-

2.0

2.0+


Wei

ghte

d Pr

opor

tion

of S

ubur

bs



93

Figure C.3 - Histogram of Suburb Relativities - SA

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0.0-

0.4

0.4-

0.5

0.5-

0.6

0.6-

0.7

0.7-

0.8

0.8-

0.9

0.9-

1.0

1.0-

1.1

1.1-

1.2

1.2-

1.3

1.3-

1.4

1.4-

1.5

1.5-

1.6

1.6-

1.7

1.7-

1.8

1.8-

1.9

1.9-

2.0

2.0+


Wei

ghte

d Pr

opor

tion

of S

ubur

bs


Figure C.4 - Histogram of Suburb Relativities - WA

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

0.0-

0.4

0.4-

0.5

0.5-

0.6

0.6-

0.7

0.7-

0.8

0.8-

0.9

0.9-

1.0

1.0-

1.1

1.1-

1.2

1.2-

1.3

1.3-

1.4

1.4-

1.5

1.5-

1.6

1.6-

1.7

1.7-

1.8

1.8-

1.9

1.9-

2.0

2.0+


Wei

ghte

d Pr

opor

tion

of S

ubur

bs



94

D Interactions

For the vehicles contained in the following figures, please refer to the legend in Table 5.1.

D.1 NSW – Females

Figure D.1 – Insurer 6 – NSW Females

0.6

1.0

1.4

1.8

2.2

2.6

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

2.6

3.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


95


0.6

1.0

1.4

1.8

2.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

2.6

3.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


96

D.2 VIC – Males

Figure D.6 – Insurer 1 – VIC Males

0.6

1.0

1.4

1.8

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80

BLACK ROCK CROYDON HILLS GOLDEN SQUARE

Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

2.6

3.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


97


0.6

1.0

1.4

1.8

2.2

2.6

3.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

2.6

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


98

D.3 QLD – Males

Figure D.10 – Insurer 14 – QLD Males

0.8

1.2

1.6

2.0

2.4

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80

BUNDABERG NORTH MOUNT GRAVATT WACOL

Suburb

Rel

ativ

ity to

40y

o D

river


0.8

1.2

1.6

2.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


99


0.8

1.2

1.6

2.0

2.4

2.8

3.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.8

1.2

1.6

2.0

2.4

2.8

3.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


100

D.4 SA – Males

Figure D.14 – Insurer 5 – SA Males

0.8

1.2

1.6

2.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80

BLAIR ATHOL WEST CHERRYVILLE HAZELWOOD PARK

Suburb

Rel

ativ

ity to

40y

o D

river


0.8

1.2

1.6

2.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


101


0.8

1.2

1.6

2.0

2.4

2.8

3.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.8

1.2

1.6

2.0

2.4

2.8

3.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.8

1.2

1.6

2.0

2.4

2.8

3.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


102

D.5 WA – Males

Figure D.19 – Insurer 3 – WA Males

0.6

1.0

1.4

1.8

2.2

2.6

3.0

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80

DUNCRAIG MAHOGANY CREEK MIRRABOOKA

Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


103


0.6

1.0

1.4

1.8

2.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river


0.6

1.0

1.4

1.8

2.2

2.6

18 25 40 65 80 18 25 40 65 80 18 25 40 65 80


Suburb

Rel

ativ

ity to

40y

o D

river

Insights into Comprehensive Motor Insurance...

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