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: actuaries@actuaries.asn.au Website: www.actuaries.asn.au
Insights into Comprehensive Motor Insurance Rating
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
Insights into Comprehensive Motor Insurance Rating
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
tm|K:\PRACTICE AREAS\PLCA\IIA08\COMPETITOR DECONSTRUCTIONS\REPORT\COMP MOTOR RATING PAPER FINAL.DOC
i
Insights into Comprehensive Motor Insurance Rating
Part II
1
Insights into Comprehensive Motor Vehicle Insurance
Rating
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.
1
Insights into Comprehensive Motor Insurance Rating
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:
z
z
z
Sole owner and driver of the vehicle : 40 year old (y.o.) male
X
1 year of driving experience
X
No accidents or events which would have given rise to a claim in the last 5 years
Small family vehicle being either a:
X
2004 Toyota Corolla Ascent; or
X
2002 Hyundai Getz GL
Suburb garaged:
X
NSW : Castle Hill (2154)
X
VIC: Footscray (3011)
X
QLD: Paddington (4064)
X
SA: Croydon (5008)
X
WA: Floreat (6014)
(For insurers requiring an address, we randomly selected an address in the base suburb from
the GNAF1 database).
z
No finance
z
Private use
z
Default excess
z
Single policy
z
No membership based discounts.
1
Geocoded National Address File
2
Insights into Comprehensive Motor Insurance Rating
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:
z
owner(s) and driver(s) of the vehicle;
z
the vehicle to be insured; and
z
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.
3
Insights into Comprehensive Motor Insurance Rating
1.3
Structure
The paper is structured in the following way:
z
Introduction
z
Driver rating
z
Vehicle rating
z
Location rating
z
Interactions
z
Other rating factors
z
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.
4
Insights into Comprehensive Motor Insurance Rating
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:
z
Whether the insured drives another vehicle – in which case one company offers a premium
discount;
z
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:
z
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;
z
based on the youngest driver with an economic interest in the vehicle – referred to as the
youngest “owner”;
z
in some cases it is less precise and defined on the basis of the youngest “regular” driver; and
z
in one case the rated driver is taken as the youngest owner or driver of the vehicle.
5
Insights into Comprehensive Motor Insurance Rating
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
2.8
2.5
Relativity to 40yo Male
2.2
2.2
1.9
1.6
1.3
1.0
0.7
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
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:
z
higher premiums are charged for younger drivers and substantially so in some cases for
drivers aged younger than the mid 20s;
z
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;
z
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.
6
Insights into Comprehensive Motor Insurance Rating
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
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
20
22
24
26
28
30
32
34
36
38
40
Rated Driver Age
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.
Age
<= 21
22 - 24
>= 25
Table 2.1 – Excesses by Age ($) NSW
Insurers
6
7
12
17
$1,750
$1,600
$1,000
$1,050
$1,150
$1,100
$1,000
$1,050
$550
$600
$600
$500
20
$900
$900
$500
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).
7
Insights into Comprehensive Motor Insurance Rating
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" driver
7
Youngest regular
12
Youngest regular
17
Youngest owner or driver
20
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
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
84
86
88
90
Rated Driver Age
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.
8
Insights into Comprehensive Motor Insurance Rating
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
2.8
Relativity to 40yo Female
2.5
2.2
1.9
1.6
1.3
1.0
0.7
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
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
Relativity - Male Premium vs Female Premium
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
Age
Insurer 6
Insurer 7
9
Insurer 12
Insurer 17
Insurer 20
78
81
84
87
90
Insights into Comprehensive Motor Insurance Rating
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;
z
for the remainder of the insurers, male premiums are typically higher and the difference is
generally highest for younger drivers;
z
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;
z
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;
z
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;
z
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.
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
2.50
2.20
Relativity to 40yo Male
2.3
z
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
10
50% Below
75% Below
27
28
29
30
Insights into Comprehensive Motor Insurance Rating
It is apparent that much has changed in 16 years and most noticeably the following:
z
there was no differentiation between male and female drivers where this is quite
commonplace today;
z
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;
z
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);
z
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:
z
the premium charged for single younger driver to
z
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
11
Insights into Comprehensive Motor Insurance Rating
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)
Insurer 4 (Youngest Owner)
Insurer 7 (Youngest Regular)
140%
140%
140%
120%
120%
120%
100%
100%
100%
80%
80%
80%
60%
60%
60%
40%
40%
40%
20%
20%
20%
0%
0%
0%
-20%
-20%
-20%
-40%
-40%
-40%
18
19
20
21
22
23
24
25
26
27
28
18
19
20
Insurer 10 (Youngest Owner)
21
22
23
24
25
26
27
18
28
140%
120%
120%
120%
100%
100%
100%
80%
80%
80%
60%
60%
60%
40%
40%
40%
20%
20%
20%
0%
0%
0%
-20%
-20%
-20%
20
21
22
23
24
25
26
27
18
28
19
20
Insurer 13 (Youngest Once a Month or More)
21
22
23
24
25
26
27
18
28
140%
120%
120%
120%
100%
100%
100%
80%
80%
80%
60%
60%
60%
40%
40%
40%
20%
20%
20%
0%
0%
0%
-20%
-20%
-20%
20
21
22
23
24
25
26
27
28
24
25
26
27
28
20
21
22
23
24
25
26
27
28
26
27
28
-40%
-40%
19
23
Insurer 23 (Youngest > 15% Use)
140%
18
19
Insurer 14 (Most Frequent)
140%
-40%
22
-40%
-40%
-40%
21
Insurer 12 (Youngest Owner)
140%
19
20
Insurer 11 (Youngest Owner)
140%
18
19
18
19
20
21
22
23
24
25
26
27
28
18
19
20
21
22
23
24
25
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
12
Insights into Comprehensive Motor Insurance Rating
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:
z
the number of levels within the system;
z
the highest level of discount – typically varying between 50 and 70%;
z
step back rules following claims; and
z
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
13
Insights into Comprehensive Motor Insurance Rating
Table 2.3 – NCD Structure (Rating Level) – Insurer 6
Number of
Accidents
and Thefts
9
8
7
0
1*
1*
1*
1
1*
1*
1*
2
1*
1*
1
3
1*
1
2
4
2
2
3
5
3
3
4
6
4
4
5
7
5
5
6
8
6
6
7
9
7
7
R
* Rating 1 (maximum NCD) for Life
Years of Experience
6
1*
1
2
3
4
5
6
7
R
R
5
1
2
3
4
5
6
7
R
R
R
4
2
3
4
5
6
7
R
R
R
R
3
3
4
5
6
7
R
R
R
R
R
2
4
5
6
7
R
R
R
R
R
R
1
5
6
7
R
R
R
R
R
R
R
Table 2.4 – NCD Structure (Discount) – Insurer 12
Years of Experience
No. Accidents Years since
(Last 5 yrs) most recent
5+
4
3
2
1
0
n/a
65% 55% 45% 35% 25%
0
6
7
R
R
R
R
R
R
R
R
0
0%
1
0-1
1-2
55%
65%
45%
55%
35%
45%
25%
35%
0%
n/a
n/a
n/a
2
0-1
1-2
2-3
45%
55%
65%
35%
45%
55%
25%
35%
45%
0%
25%
n/a
0%
n/a
n/a
n/a
n/a
n/a
3
0-1
1-2
2-3
3-4
35%
45%
55%
65%
25%
35%
45%
55%
0%
25%
35%
n/a
0%
0%
n/a
n/a
0%
n/a
n/a
n/a
n/a
n/a
n/a
n/a
4+
All
R
R
R
R
R
R
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
14
Insights into Comprehensive Motor Insurance Rating
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
NCD
Discount
Actual
Rating Communicated Discount
-70%
-70.0%
1*
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%
R
8
R
9
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
15
Insights into Comprehensive Motor Insurance Rating
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
3.0
Relativity to Base Quote
2.5
2.0
1.5
1.0
0.5
0.0
1
2
3
4
5
6
7
8
Profile Number
Insurer 6
Insurer 7
Insurer 12
Insurer 17
Insurer 20
Table 2.6 – Profile Description – Group 1
No.
Profile Decription
1
Single male, clean history
2
Single male, 1 AF claim in 2 years
3
Single male, 2 AF claims in 2 years
4
Single male, 2 AF claims + speeding
5
Single male, 1 yr driving exp
6
Single female, clean history
7
Single female, 2 NAF claims in 2 years
8
Single female, 1 yr driving exp
In relation to the first set of profiles, our observations are the following:
z
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.
z
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.
16
Insights into Comprehensive Motor Insurance Rating
z
In Profile #4, we add some speeding offences in the driving history however this does not
affect the premium of any of the insurers.
z
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.
z
The premium differentials in Profile #6 reflect only the driver age/sex differentials to the
base case.
z
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.
Figure 2.9 – Profile Relativities – Group 2
1.6
1.5
Relativity to Base Quote
1.4
1.3
1.2
1.1
1.0
0.9
0.8
9
10
11
12
13
14
15
16
Profile Number
Insurer 6
Insurer 7
Insurer 12
Insurer 17
Insurer 20
Table 2.7 – Profile Descriptions – Group 2
No.
Profile Decription
9
Couple, both clean history
10
Couple, male with 2 AF claims
11
Couple, female with 2 NAF claims
12
Couple, both have 2 claims
13
Wife's car, both clean history
14
Wife's car, male has 2 AF claims
15
Wife's car, female has 2 NAF claims
16
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.
z
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.
17
Insights into Comprehensive Motor Insurance Rating
z
The premiums for Profile #10 bear the same relationship to Profile #9 as the premiums in
Profile #3 vs. Profile #1.
z
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.
z
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.
z
In Profile #13, the rated driver switches to the younger female driver for Insurers 6 and 20.
Interestingly, the premiums move in different directions.
z
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.
z
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.
Figure 2.10 – Profile Relativities – Group 3
6.0
5.5
Relativity to Base Quote
5.0
4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
17
18
19
20
21
22
23
Insurer 17
Insurer 20
Profile Number
Insurer 6
Insurer 7
18
Insurer 12
24
25
Insights into Comprehensive Motor Insurance Rating
Table 2.8 – Profile Descriptions – Group 3
No.
Profile Decription
17
Young male, clean history
18
Young male, 2 AF claims in 2 years
19
Young male, 2 AF claims, with speeding
20
Young female, clean history
21
Young female, 2 AF claims in 2 years
22
Two teens, both have clean history
23
Two teens, older teen has 2 AF
24
Two teens, younger teen has 2 AF
25
Two teens, both teens have 2 AF
The observations made regarding these driver profiles are as follows:
z
Insurer 17 will not offer an on-line quote on the young driver profiles we have surveyed
involving two or more at-fault claims.
z
Profile #17 and #20 show the impact of the age/sex relativity for young drivers compared to
the base profile.
z
Profile #18 illustrates the different operation of the NCD structure for Insurer 6 which offers
a 0% NCD in this scenario.
z
Profile #19 delivers the same premium relativities as Profile #18 illustrating that speeding
fines have no impact on the premium even for younger drivers.
z
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.
z
In Profile #22 we see the impact of the process for the assessment of the rated driver.
z
Profile #23 through Profile #25 show the impact on the premium relativities of at-fault
accidents for the younger drivers.
X
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.
X
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.
19
Insights into Comprehensive Motor Insurance Rating
The final set of profiles is a group of family vehicle based scenarios.
Figure 2.11 – Profile Relativities – Group 4
7.0
Relativity to Base Quote
6.0
5.0
4.0
3.0
2.0
1.0
0.0
26
27
28
29
30
31
32
33
Profile Number
Insurer 6
Insurer 7
Insurer 12
Insurer 17
Insurer 20
Table 2.9 – Profile Description – Group 4
No.
Profile Decription
26
Adult with occasional teen, both clean
27
Adult with occ. teen, adult has 2 AF claims
28
Adult with occ. teen, teen has 2 AF claims
29
Adult with occ. teen, both have 2 AF claims
30
Adult with 2 occ teens, both teens clean
31
Adult with 2 occ teens, older teen has 2 AF
32
Adult with 2 occ teens, younger teen has 2 AF
33
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:
z
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.
z
For Insurer 17, we are unable to obtain an on-line quote for any profile involving two atfault accidents for the younger driver despite the fact that the younger drivers use the
vehicle a small percentage of the time in these scenarios.
z
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.)
z
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.)
20
Insights into Comprehensive Motor Insurance Rating
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.
21
Insights into Comprehensive Motor Insurance Rating
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)
22
Insights into Comprehensive Motor Insurance Rating
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:
z
Rating question – if the premium changes based on the response;
z
Underwriting question – if the response to the question can lead to a refusal or referral; or
z
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 Collection
Vehicle
14
12
#
Sum Insured
9
Accessories
10
8
Modifications
6
11
Vehicle Condition
11
Value Type (Selection of AV/MV)
7
Security
5
Colour
1
Odometer Reading
1*
3
Expected Mileage
1
Commuting Use
1
Experience with similar vehicles
1
Australian 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 drilldown 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
23
Insights into Comprehensive Motor Insurance Rating
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 Collection
Purchase Date
2
5
Purchase Price
1
6
Uninsured
1
2
Accidents while Uninsured
1
Duration Owned
1
Location of Purchase
1
Privately 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.
24
Insights into Comprehensive Motor Insurance Rating
Figure 3.1: Histogram of Vehicle Relativities (NSW)
16%
14%
Proportion of Vehicles
12%
10%
8%
6%
4%
2%
0.0-0.45
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+
0%
Relativity Against Median Premium
Insurer 6
Insurer 7
Insurer 12
Insurer 17
Insurer 20
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.
25
Insights into Comprehensive Motor Insurance Rating
Figure 3.2 – Histogram of Vehicle Relativities – Tail (NSW)
5%
Proportion of Vehicles
4%
3%
2%
1%
6.75-7.0
6.5-6.75
6.25-6.5
6.0-6.25
5.75-6.0
5.5-5.75
5.25-5.5
5.0-5.25
4.25-5.0
4.0-4.25
3.75-4.0
3.5-3.75
3.25-3.5
3.0-3.25
2.75-3.0
2.5-2.75
2.25-2.5
2.0-2.25
1.75-2.0
1.5-1.75
0%
Relativity Against Median Premium
Insurer 6
Insurer 7
Insurer 12
Insurer 17
Insurer 20
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.
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)
4.0
3.5
3.0
Relativities
3.4
2.5
2.0
1.5
1.0
0.5
Insurer
Insurer 6
Insurer 7
Insurer 12
Insurer 17
Insurer 20
05 Corolla
08 Commodore S
04 Insight
03 Getz
02 SLK200
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Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
Figure 3.4 – Range of Relativities for NSW Insurers – Medium Family
6.0
5.0
Relativities
4.0
3.0
2.0
1.0
07 550i
05 C220 CDI
04 PASSAT W8
07 320Ci
08 MAZDA6 MPS
07 PASSAT 2.0T
01 318ti
00 C180
02 626 ECLIPSE
01 ACCORD V6-L
01 626 LIMITED
03 VECTRA CDXi
07 EPICA CDXi
05 ACCORD VTi
97 VENTO
07 CAMRY GRANDE
95 TELSTAR
07 SONATA ELITE
00 VECTRA GL
99 ACCORD VTi-L
98 MONDEO GLX
97 MAGNA SE
00 CAMRY CONQUEST
97 MONDEO LX
99 SONATA GLE
99 MAGNA
97 CAMRY CSi
95 MAGNA GLX
95 SONATA GLE
0.0
Vehicles
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
6.0
5.0
Relativities
4.0
3.0
2.0
1.0
99 NSX
07 CL65 AMG
03 MUSTANG
98 3000GT
95 RX7 SP
96 3000GT
95 3000GT
05 330Ci
02 SLK200
05 CLK320
97 Z3
99 PRELUDE VTi-R
98 PROBE
95 CALIBRA A
07 EOS
95 MR2GT
95 MX-5
95 CALIBRA
02 TIBURON V6
98 GOLF
99 GOLF
01 COUGAR
96 INTEGRA GSi
99 CELICAZR
97 EUNOS 30X
96 PASEO
99 COUPE FX
96 S COUPE LS
00 BARINA CABRIO
0.0
Vehicles
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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Insights into Comprehensive Motor Insurance Rating
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.
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
2.0
1.8
1.6
Relativity
3.5
1.4
1.2
1.0
0.8
0.6
Insurer
Insurer 1
Insurer 10
Insurer 23
Insurer 4
Insurer 11
Insurer 26
Insurer 5
Insurer 12
03 G. Vitara
Insurer 6
Insurer 17
03 Outback
Insurer 7
Insurer 20
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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Insights into Comprehensive Motor Insurance Rating
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
2.0
1.8
Relativity
1.6
1.4
1.2
1.0
0.8
0.6
1
4
5
6
7
10
11
12
17
20
23
Insurers
12.5-13.5k
13.5-14.5k
14.5-15.5k
15.5-16.5k
16.5-17.5k
Figure 3.8 below shows the relationship between the vehicle relativities and the year of
manufacture.
30
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Insights into Comprehensive Motor Insurance Rating
Figure 3.8 Range of CSUV Relativities by Year of Manufacture
2.0
1.8
Relativity
1.6
1.4
1.2
1.0
0.8
0.6
1
4
5
6
7
10
11
12
17
23
20
26
Insurers
2000
2001
2002
2003
2004
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
2.0
1.8
Relativity
1.6
1.4
1.2
1.0
0.8
0.6
1
4
5
6
7
10
11
17
12
20
23
Insurers
2.0L
2.4L
2.5L
31
2.7L
3.0L
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Insights into Comprehensive Motor Insurance Rating
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:
X
Some form of broad categorisation of vehicles
X
The year of manufacture of the vehicle
X
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:
z
number of cylinders;
z
vehicle value;
z
power to weight ratio;
z
engine size;
z
year of manufacture;
z
body segment which is the vehicle family as defined by Glasses’ Guide;
z
country of manufacture; and
z
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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Insights into Comprehensive Motor Insurance Rating
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
Factor
Number of Cyclinders
Value
Power to Weight
Engine Size
Year of Manufacture
Segment
Country of M'fture
Body Style
2
R
5 (M)
0.97
0.41
0.77
0.50
1.00
0.81
0.69
66%
6 (M)
1.00
0.40
0.55
0.36
0.61
0.63
0.09
62%
7 (M)
1.00
0.19
0.19
0.13
0.15
65%
12 (A)
0.26
0.66
1.00
0.15
0.20
0.79
0.26
0.24
63%
Insurer
13 (A) 14 (M) 17 (M) 20 (M) 23 (A)
0.56
0.29
1.00
0.78
1.00
1.00
1.00
1.00
0.41
0.10
0.24
0.39
0.14
0.31
0.27
0.18
0.30
0.17
0.30
0.68
0.63
0.213 0.44
73%
53%
47%
67%
69%
26 (A)
1.00
0.48
0.14
0.28
0.24
0.23
69%
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
4
variation explained by variables
total variation of the premiums
The insurers presented here are a selection across Australia and each insurer represents an individual
“brand”.
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Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
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:
z
“in New South Wales, up to 10 rating areas are used by some companies and all companies
use some degree of area rating
z
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
z
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 Collection
Postcode
14
Suburb
13
1
Garaging
2
3
Address
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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Insights into Comprehensive Motor Insurance Rating
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
30%
Weighted Proportion of Suburbs
25%
20%
15%
10%
5%
2.0+
1.9-2.0
1.8-1.9
1.7-1.8
1.6-1.7
1.5-1.6
1.4-1.5
1.3-1.4
1.2-1.3
1.1-1.2
1.0-1.1
0.9-1.0
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
0%
0.0-0.4
4.3
Relativity Against Weighted Median Premium
Insurer 6
Insurer 7
Insurer 12
Insurer 17
Insurer 20
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
36
Insights into Comprehensive Motor Insurance Rating
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
Relativity Against Weighted Median Premium
2.5
2
1.5
1
0.5
0
Insurer 6
4.4
Insurer 7
Insurer 12
Insurer 17
Insurer 20
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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Insights into Comprehensive Motor Insurance Rating
Figure 4.4 – Insurer 6 Sydney Metro Relativity Map
Figure 4.5 – Insurer 12 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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Insights into Comprehensive Motor Insurance Rating
Figure 4.6 – Insurer 17 Sydney Metro Relativity Map
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.
Figure 4.7 – Insurer 20 Sydney Metro Relativity Map
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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Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
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:
z
driver age
z
location
z
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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Insights into Comprehensive Motor Insurance Rating
Table 5.1 – Vehicles used for Interaction Quotes
NVIC
0RX
3XI
4UI
5Y902A
8NK
ASR02A
DA008A
DFM04B
DP606A
DXU05A
G9P08A
GBX07A
GHZ07A
GSO07A
GTC08A
H0D07G
H3707G
H6T08A
O86
ZOH
Series
Make
HONDA
MITSUBISHI
FORD
MAZDA
MAZDA
BMW
FORD
HYUNDAI
HOLDEN
MITSUBISHI
FORD
MITSUBISHI
HOLDEN
MITSUBISHI
MITSUBISHI
MITSUBISHI
HYUNDAI
VOLKSWAGEN
HYUNDAI
MAZDA
Model
INTEGRA
PAJERO
PROBE
B2600
121
7
EXPLORER
TERRACAN
CRUZE
EXPRESS
TRANSIT
OUTLANDER
RODEO
LANCER
COLT
GRANDIS
SANTA FE
GOLF
SONATA
B2600
YOM
1996
1997
1998
2002
2000
2002
2008
2004
2006
2005
2008
2007
2007
2007
2008
2007
2007
2008
1995
1996
Engine Size
1.8L
3.5L
2.5L
2.6L
1.5L
4.4L
4.0L
3.5L
1.5L
2.0L
2.2L
2.4L
3.0L
2.4L
1.5L
2.4L
2.2L
2.0L
2.0L
2.6L
Body Style
2D COUPE
2D HARDTOP
2D COUPE
P/UP
5D HATCHBACK
4D SEDAN
4D WAGON
4D WAGON
4D WAGON
WINDOW VAN
VAN
4D WAGON
SPACE C/CHAS
4D SEDAN
5D HATCHBACK
4D WAGON
4D WAGON
5D HATCHBACK
4D SEDAN
DUAL CAB P/UP
Pwr to Wgt
93
78
92
65
68
107
76
71
74
66
48
80
73
91
72
73
59
83
81
55
Segment
SPORTS
SUV MEDIUM
SPORTS
PICK UP OR CAB CHASSIS 4X2
LIGHT
UPPER LARGE
SUV LARGE
SUV MEDIUM
SUV COMPACT
VAN
VAN
SUV COMPACT
PICK UP OR CAB CHASSIS 4X4
SMALL
LIGHT
PEOPLE MOVER
SUV COMPACT
SMALL
MEDIUM
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
Relativity to 40yo Driver
3.1
2.6
2.1
1.6
1.1
0.6
18
25
40
65
80
DENISTONE WEST
18
25
40
65
80
18
EUREKA
25
40
65
POTTS POINT
80
18
25
40
65
80
WORONORA HEIGHTS
Suburb
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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Insights into Comprehensive Motor Insurance Rating
Figure 5.2 – Age/Location/Vehicle Interactions – Insurer 6
2.4
Relativity to 40yo Driver
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
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
BELMORE
DENISTONE WEST
EUREKA
POTTS POINT
WORONORA
HEIGHTS
Suburb
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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Insights into Comprehensive Motor Insurance Rating
Figure 5.3 – Age/Location/Vehicle Interactions – Insurer 20
2.6
Relativity to 40yo Driver
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
18
25
40
65
BELMORE
80
18
25
40
65
80
18
DENISTONE WEST
25
40
65
POTTS POINT
80
18
25
40
65
80
WORONORA HEIGHTS
Suburb
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:
z
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.
z
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.
z
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.
z
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:
z
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.
z
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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Insights into Comprehensive Motor Insurance Rating
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 ( ).
Figure 5.4 – Age/Location/Vehicle Interactions – Insurer 12
Relativity to 40yo Driver
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
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
BELMORE
DENISTONE WEST
EUREKA
POTTS POINT
WORONORA
HEIGHTS
Suburb
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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Insights into Comprehensive Motor Insurance Rating
Figure 5.5 – Age/Location/Vehicle Interactions based at Age 65 – Insurer 12
2.2
Relativity to 40yo Driver
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
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
BELMORE
DENISTONE WEST
EUREKA
POTTS POINT
WORONORA
HEIGHTS
Suburb
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-suburbvehicle interactions for Insurer 7 are shown below in Figure 5.6.
49
Insights into Comprehensive Motor Insurance Rating
Figure 5.6 – Age/Location/Vehicle Interactions – Insurer 7
Relativity to 40yo Driver
3.1
2.6
2.1
1.6
1.1
0.6
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
BELMORE
DENISTONE WEST
EUREKA
POTTS POINT
WORONORA
HEIGHTS
Suburb
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
50
Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
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 Collection
Vehicle Financing
14
Vehicle Usage
14
Membership
5
1
Multiple Policies
10
2
Family Discounts
1
1
Seniors Card
4
4
Voluntary 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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Insights into Comprehensive Motor Insurance Rating
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:
z
the number of individual vehicle relativities to be estimated and the difficulty of estimating
a competitor’s vehicle relativity based on the vehicle characteristics;
z
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;
z
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:
z
what do I sample from?
z
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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Insights into Comprehensive Motor Insurance Rating
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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Insights into Comprehensive Motor Insurance Rating
Figure 7.1 – Distribution of Competitive Position – Insurer 6 vs Insurer 12
25%
Proportion of Quotes
20%
15%
10%
5%
0%
< -30
-30 to -20 -20 to -10
-10 to -5
-5 to 0
0 to 5
5 to 10
10 to 20
> 30
Percentage Difference
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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Insights into Comprehensive Motor Insurance Rating
Figure 7.2 – Comparison of Differences in Premiums between Pairs of Insurers
Difference in Premiums, Insurer 12 vs. Insurer 17
0.25
0.25
0.2
0.2
Proportion of Quotes
Proportion of Quotes
Difference in Premiums, Insurer 6 vs. Insurer 12
0.15
0.1
0.05
0.15
0.1
0.05
0
< -30
-30 to -20 -20 to -10 -10 to -5
-5 to 0
0 to 5
5 to 10
10 to 20
> 30
0
Percentage Difference
< -30
-30 to -20 -20 to -10 -10 to -5
-5 to 0
0 to 5
5 to 10
10 to 20
> 30
10 to 20
> 30
10 to 20
> 30
Percentage Difference
Difference in Premiums, Insurer 12 vs. Insurer 20
0.25
0.25
0.2
0.2
Proportion of Quotes
Proportion of Quotes
Difference in Premiums, Insurer 6 vs. Insurer 17
0.15
0.1
0.05
0.15
0.1
0.05
0
0
< -30
< -30
-30 to -20 -20 to -10 -10 to -5
-5 to 0
0 to 5
5 to 10
10 to 20
> 30
-30 to -20 -20 to -10 -10 to -5
-5 to 0
0 to 5
5 to 10
Percentage Difference
Percentage Difference
Difference in Premiums, Insurer 17 vs. Insurer 20
0.25
0.25
0.2
0.2
Proportion of Quotes
Proportion of Quotes
Difference in Premiums, Insurer 6 vs. Insurer 20
0.15
0.1
0.05
0.15
0.1
0.05
0
< -30
-30 to -20 -20 to -10 -10 to -5
-5 to 0
0 to 5
5 to 10
10 to 20
> 30
0
< -30
Percentage Difference
-30 to -20 -20 to -10 -10 to -5
-5 to 0
0 to 5
5 to 10
Percentage Difference
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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Insights into Comprehensive Motor Insurance Rating
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:
z
major differences in the approach for and rating implications of multiple drivers;
z
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:
z
unique features of individual vehicles;
z
selection effects relating to the drivers of such vehicles; and
z
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
57
Insights into Comprehensive Motor Insurance Rating
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.
58
Insights into Comprehensive Motor Insurance Rating
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 tradeoff. 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.
59
Insights into Comprehensive Motor Insurance Rating
9
References
Brigstock, C. & R. Yee, 1992, “Rating Practices in Motor Insurance”, Proceedings of the Eighth
General Insurance Seminar, Hyatt Regency Coolum, Queensland
60
Insights into Comprehensive Motor Insurance Rating
Part III
Appendices
A
Driver Rating
A.1
Driver Age Relativities – Other States
61
Insights into Comprehensive Motor Insurance Rating
Figure A.1 – VIC Insurer Age Relativities - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
Rated Driver Age
Insurer 1
62
Insurer 2
Insurer 8
Insurer 22
Insurer 24
78
81
84
87
90
Insights into Comprehensive Motor Insurance Rating
Figure A.2 – VIC Insurer Age Relativities – 40 yo and Under - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
20
22
24
26
28
30
32
34
36
38
40
Rated Driver Age
Insurer 1
Insurer 2
Insurer 8
Insurer 22
Insurer 24
Figure A.3 –VIC Insurer Age Relativities – 50yo and Over - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
Rated Driver Age
Insurer 1
Insurer 2
63
Insurer 8
Insurer 22
Insurer 24
84
86
88
90
Insights into Comprehensive Motor Insurance Rating
Figure A.4 –VIC Insurer Age Relativities – Females
2.8
Relativity to 40yo Female
2.5
2.2
1.9
1.6
1.3
1.0
0.7
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
78
81
84
87
90
Rated Driver Age
Insurer 1
Insurer 2
Insurer 8
Insurer 22
Insurer 24
Figure A.5 – Male vs Female Driver Premiums – VIC
Relativity - Male Premium vs Female Premium
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
Age
Insurer 1
Insurer 2
64
Insurer 8
Insurer 22
Insurer 24
Insights into Comprehensive Motor Insurance Rating
Figure A.6 – QLD Insurer Age Relativities – Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
78
81
Rated Driver Age
Insurer 11
65
Insurer 14
Insurer 15
Insurer 16
Insurer 21
Insurer 26
84
87
90
Insights into Comprehensive Motor Insurance Rating
Figure A.7 – QLD Insurer Age Relativities – 40yo and Under - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
20
22
24
26
28
30
32
34
36
38
40
Rated Driver Age
Insurer 11
Insurer 14
Insurer 15
Insurer 16
Insurer 21
Insurer 26
Figure A.8 – QLD Insurer Age Relativities – 50yo and Over - Males
2.5
Relativity to 40yo Male
2.2
1.9
1.6
1.3
1.0
0.7
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
84
Rated Driver Age
Insurer 11
Insurer 14
Insurer 15
66
Insurer 16
Insurer 21
Insurer 26
86
88
90
Insights into Comprehensive Motor Insurance Rating
Figure A.9 – QLD Insurer Age Relativities – Females
2.8
Relativity to 40yo Female
2.5
2.2
1.9
1.6
1.3
1.0
0.7
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
84
87
90
Rated Driver Age
Insurer 11
Insurer 14
Insurer 15
Insurer 16
Insurer 21
Insurer 26
Figure A.10 – Male vs Female Driver Premiums – QLD
Relativity - Male Premium vs Female Premium
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
78
81
Age
Insurer 11
Insurer 14
Insurer 15
67
Insurer 16
Insurer 21
Insurer 26
Insights into Comprehensive Motor Insurance Rating
Figure A.11 – SA Insurer Age Relativities – Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
Rated Driver Age
Insurer 5
68
Insurer 10
Insurer 27
Insurer 28
Insurer 29
75
78
81
84
87
90
Insights into Comprehensive Motor Insurance Rating
Figure A.12 – SA Insurer Age Relativities – 40yo and Under - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
20
22
24
26
28
30
32
34
36
38
40
Rated Driver Age
Insurer 5
Insurer 10
Insurer 27
Insurer 28
Insurer 29
Figure A.13 – SA Insurer Age Relativities – 50yo and Over - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
Rated Driver Age
Insurer 5
Insurer 10
69
Insurer 27
Insurer 28
Insurer 29
84
86
88
90
Insights into Comprehensive Motor Insurance Rating
Figure A.14 – SA Insurer Age Relativities – Females
2.8
Relativity to 40yo Female
2.5
2.2
1.9
1.6
1.3
1.0
0.7
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
78
81
84
87
90
Rated Driver Age
Insurer 5
Insurer 10
Insurer 27
Insurer 28
Insurer 29
Figure A.15 – Male vs Female Driver Premiums – SA
Relativity - Male Premium vs Female Premium
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
Age
Insurer 5
Insurer 10
70
Insurer 27
Insurer 28
Insurer 29
Insights into Comprehensive Motor Insurance Rating
Figure A.16 – WA Insurer Age Relativities – Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
78
81
Rated Driver Age
Insurer 3
71
Insurer 4
Insurer 13
Insurer 18
Insurer 19
Insurer 23
84
87
90
Insights into Comprehensive Motor Insurance Rating
Figure A.17 – WA Insurer Age Relativities – 40yo and Under - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
18
20
22
24
26
28
30
32
34
36
38
40
Rated Driver Age
Insurer 3
Insurer 4
Insurer 13
Insurer 18
Insurer 19
Insurer 23
Figure A.18 – WA Insurer Age Relativities – 50yo and Over - Males
2.8
Relativity to 40yo Male
2.5
2.2
1.9
1.6
1.3
1.0
0.7
50
52
54
56
58
60
62
64
66
68
70
72
74
76
78
80
82
84
Rated Driver Age
Insurer 3
Insurer 4
Insurer 13
72
Insurer 18
Insurer 19
Insurer 23
86
88
90
Insights into Comprehensive Motor Insurance Rating
Figure A.19 – WA Insurer Age Relativities – Females
2.8
Relativity to 40yo Female
2.5
2.2
1.9
1.6
1.3
1.0
0.7
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
84
87
90
Rated Driver Age
Insurer 3
Insurer 4
Insurer 13
Insurer 18
Insurer 19
Insurer 23
Figure A.20 – Male vs Female Driver Premiums – WA
Relativity - Male Premium vs Female Premium
0.40
0.30
0.20
0.10
0.00
-0.10
-0.20
18
21
24
27
30
33
36
39
42
45
48
51
54
57
60
63
66
69
72
75
78
Age
Insurer 3
Insurer 4
Insurer 13
73
Insurer 18
Insurer 19
Insurer 23
81
Insights into Comprehensive Motor Insurance Rating
Comparison of Driver Age Curves to Brigstock & Yee (1992)
Figure A.21 – NSW Age Loadings 1992
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
27
28
29
30
28
29
30
Rated Driver Age
25% Below
50% Below
75% Below
Figure A.22 – NSW Age Loadings 2008 – Males
2.50
2.20
Relativity to 40yo Male
A.2
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
74
50% Below
75% Below
27
Insights into Comprehensive Motor Insurance Rating
Figure A.23 – NSW Age Loadings 2008 - Females
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
75
50% Below
75% Below
27
28
29
30
Insights into Comprehensive Motor Insurance Rating
Figure A.24 – VIC Age Loadings 1992
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
27
28
29
30
28
29
30
Rated Driver Age
25% Below
50% Below
75% Below
Figure A.25 – VIC Age Loadings 2008 – Males
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
76
50% Below
75% Below
27
Insights into Comprehensive Motor Insurance Rating
Figure A.26 – VIC Age Loadings 2008 - Females
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
77
50% Below
75% Below
27
28
29
30
Insights into Comprehensive Motor Insurance Rating
Figure A.27 – QLD Age Loadings 1992
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
27
28
29
30
28
29
30
Rated Driver Age
25% Below
50% Below
75% Below
Figure A.28 – QLD Age Loadings 2008 – Males
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
78
50% Below
75% Below
27
Insights into Comprehensive Motor Insurance Rating
Figure A.29 – QLD Age Loadings 2008 - Females
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
79
50% Below
75% Below
27
28
29
30
Insights into Comprehensive Motor Insurance Rating
Figure A.30 – SA Age Loadings 1992
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
27
28
29
30
28
29
30
Rated Driver Age
25% Below
50% Below
75% Below
Figure A.31 – SA Age Loadings 2008 – Males
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
80
50% Below
75% Below
27
Insights into Comprehensive Motor Insurance Rating
Figure A.32 – SA Age Loadings 2008 - Females
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
81
50% Below
75% Below
27
28
29
30
Insights into Comprehensive Motor Insurance Rating
Figure A.33 – WA Age Loadings 1992
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
27
28
29
30
28
29
30
Rated Driver Age
25% Below
50% Below
75% Below
Figure A.34 – WA Age Loadings 2008 – Males
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
82
50% Below
75% Below
27
Insights into Comprehensive Motor Insurance Rating
Figure A.35 – WA Age Loadings 2008 - Females
2.50
Relativity to 40yo Male
2.20
1.90
1.60
1.30
1.00
0.70
17
18
19
20
21
22
23
24
25
26
Rated Driver Age
25% Below
83
50% Below
75% Below
27
28
29
30
Insights into Comprehensive Motor Insurance Rating
Profiles
A.3
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:
z
A 40 year old (y.o.) male driver with 10 years of driving experience
z
A 38 y.o. female driver with 10 years of driving experience
z
A 20 y.o. male driver with 2 years of driving experience
z
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
Group 4
Group 3
Group 2
Group 1
No Profile
1 Single male, clean history
2 Single male, 1 AF claim in 2 years
3 Single male, 2 AF claims in 2 years
4 Single male, 2 AF claims + speeding
5 Single male, 1 yr driving exp
6 Single female, clean history
7 Single female, 2 NAF claims in 2 years
8 Single female, 1 yr driving exp
9 Couple, both clean history
10 Couple, male with 2 AF claims
11 Couple, female with 2 NAF claims
12 Couple, both have 2 claims
13 Wife's car, both clean history
14 Wife's car, male has 2 AF claims
15 Wife's car, female has 2 NAF claims
16 Wife's car, both have 2 claims
17 Young male, clean history
18 Young male, 2 AF claims in 2 years
19 Young male, 2 AF claims, with speeding
20 Young female, clean history
21 Young female, 2 AF claims in 2 years
22 Two teens, both have clean history
23 Two teens, older teen has 2 AF
24 Two teens, younger teen has 2 AF
25 Two teens, both teens have 2 AF
26 Adult with occasional teen, both clean
27 Adult with occ. teen, adult has 2 AF claims
28 Adult with occ. teen, teen has 2 AF claims
29 Adult with occ. teen, both have 2 AF claims
30 Adult with 2 occ teens, both teens clean
31 Adult with 2 occ teens, older teen has 2 AF
32 Adult with 2 occ teens, younger teen has 2 AF
33 Adult with 2 occ teens, both teens has 2 AF
* Occurred 15 months before purchase of insurance
#
Speeding infringments occurred within 2 years
Driver 1 (Owner)
#
Age/Sex Exp Claims* Spd
40 M
10
40 M
10 1 AF
40 M
10 2 AF
40 M
10 2 AF
2
40 M
1
38 F
10
38 F
10 2 NAF
38 F
1
40 M
10
40 M
10 2 AF
40 M
10
40 M
10 2 AF
40 M
10
40 M
10 2 AF
40 M
10
40 M
10 2 AF
20 M
2
20 M
2 2 AF
20 M
2 2 AF
2
19 F
1
19 F
1 2 AF
20 M
2
20 M
2 2 AF
20 M
2
20 M
2 2 AF
40 M
10
40 M
10 2 AF
40 M
10
40 M
10 2 AF
40 M
10
40 M
10
40 M
10
40 M
10
84
Usage
100%
100%
100%
100%
100%
100%
100%
100%
80%
80%
80%
80%
20%
20%
20%
20%
50%
50%
50%
50%
85%
85%
85%
85%
85%
85%
85%
85%
Driver 2 (Owner)
Driver 3
Driver 4
Age/Sex Exp Claims Usage Age/Sex Exp Claims Usage Age/Sex Exp Claims Usage
38 F
38 F
38 F
38 F
38 F
38 F
38 F
38 F
19 F
19 F
19 F
19 F
10
10
10
10
10
10
10
10
2 NAF
2 NAF
2 NAF
2 NAF
1
1
1 2 AF
1 2 AF
20%
20%
20%
20%
80%
80%
80%
80%
50%
50%
50%
50%
20 M
20 M
20 M
20 M
20 M
20 M
20 M
20 M
2
2
2
2
2
2
2
2
2 AF
2 AF
2 AF
2 AF
15%
15%
15%
15%
10%
10%
10%
10%
19 F
19 F
19 F
19 F
1
1
1 2 AF
1 2 AF
5%
5%
5%
5%
Insights into Comprehensive Motor Insurance Rating
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:
z
Each of the sixteen vehicle segments was covered, including various passenger vehicle
sizes, SUVs and light commercial vehicles.
z
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.
z
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.
z
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.
85
Insights into Comprehensive Motor Insurance Rating
Table B.1 – Surveyed Vehicles
SEGMENT
LARGE
BMW
1996 520i
2007 530d
1999 540i PROTECTION
LIGHT
LIGHT BUS
MEDIUM
2001 318ti CONTOUR
2007 320Ci
2007 550i
FORD
1995 FALCON GLi
2007 FALCON XR6
2001 TS50
2000 FESTIVA TRIO
2005 FIESTA LX
2007 FIESTA XR4
2000 TRANSIT 12 SEAT
2005 TRANSIT 12 SEAT
2008 TRANSIT 12 SEAT
1997 MONDEO LX
1998 MONDEO GLX
1995 TELSTAR TX5 GHIA (4W
PEOPLE MOVER
PICK UP OR CAB CHASSIS 4X2
PICK UP OR CAB CHASSIS 4X4
SMALL
SPORTS
2005 116i
2004 316ti
2008 130i SPORT
1997 Z3
2005 330Ci SMG
2007 M6
SUV COMPACT
SUV LARGE
SUV LUXURY
TRUCK 2.5-3.5 GVM
2000 735i
2002 740iL HIGH-LINE
1999 L7
VAN
SEGMENT
LARGE
LIGHT
HONDA
2006 LEGEND
2005 LEGEND
1996 LEGEND
2005 JAZZ GLi
2004 JAZZ VTi
2004 INSIGHT HYBRID
HYUNDAI
2000 SONATA EXECUTIVE V6
2007 SONATA SLX
2007 GRANDEUR LIMITED
2003 GETZ XL
2007 ACCENT SLX
2003 GETZ FX
2000 VECTRA GL
2007 EPICA CDXi
2003 VECTRA CDXi
2005 ZAFIRA
2006 ZAFIRA
2003 ZAFIRA EQUIPE
1998 RODEO DX
1996 COMMODORE S
2006 CREWMAN SS THUNDER
1998 RODEO DX (4x4)
2007 RODEO LX (4x4)
2006 CREWMAN CROSS 8
2005 VIVA
2008 ASTRA CD
2004 ASTRA SRi TURBO
2000 BARINA CABRIO
1995 CALIBRA
1995 CALIBRA
2004 CRUZE
2006 CRUZE
2004 CRUZE
2001 SUBURBAN 1500 (4x4)
1999 SUBURBAN 2500 (4x4)
1999 SUBURBAN 2500 LT (4x
2005 ACCORD VTi
1999 ACCORD VTi-L
2001 ACCORD V6-L
2004 ODYSSEY
2006 ODYSSEY LUXURY
2004 ODYSSEY V6L (6 SEAT)
1999 SONATA GLE
1995 SONATA GLE
2007 SONATA ELITE
2004 TRAJET V6 2.7
2002 TRAJET GL WORLD CUP
2002 TRAJET GLS WORLD CUP
1997 CIVIC CXi
2006 CIVIC Vi
2007 CIVIC TYPE R
1996 INTEGRA GSi
1999 PRELUDE VTi-R
2005 NSX
1999 HRV (4x4)
2007 CRV (4x4)
2007 CRV (4x4) LUXURY
2005 ELANTRA 2.0 HVT
2007 ELANTRA SX
2007 ELANTRA ELITE S
1996 SCOUPE LS
1999 COUPE FX
2002 TIBURON V6
2007 TUCSON CITY SX
2007 SANTA FE SX (4x4)
2007 SANTA FE ELITE CRDi
2007 X32.0d
2007 X53.0i
2004 X54.8is
SUV MEDIUM
UPPER LARGE
1997 COURIER
2006 COURIER GL
2007 F350 XLT
1997 COURIER XL (4x4)
2007 RANGER XL (4x4)
2007 F350XL (4x4)
2000 LASER LXi
1999 LASER GLXi
2005 FOCUS ST170
2001 COUGAR
1998 PROBE
2003 MUSTANG COBRA
2006 ESCAPE XLS
2004 ESCAPE XLS
2003 ESCAPE LIMITED
2000 EXPLORER XL (4x4)
2008 EXPLORER XLT (4x4)
2001 EXPLORER LIMITED (4x
HOLDEN
1995 COMMODORE VACATIONER
2008 COMMODORE SV6
2007 CALAIS V INTERNATION
2000 BARINA CITY
2006 BARINA
2007 TIGRA
MAZDA
1996 626 SDX V6
1997 626 LUXURY V6
1996 EUNOS 800M
1997 121
2000 121 METRO SHADES
2003 MAZDA2 GENKI
2003 MDX
2007 MDX
1996 RAIDER BREAKAWAY (4x
2007 TERRITORY TX (4x4)
2007 TERRITORY GHIA TURBO
1998 ECONOVAN MAXI
2005 TRANSIT
2008 TRANSIT EXTENDED FRA
1995 FAIRLANE GHIA
2007 FAIRLANE GHIA
2001 TL50
2000 ECONOVAN
2008 TRANSIT LOW (SWB)
2006 TRANSIT JUMBO LWB HI
MERCEDES-BENZ
2007 R280 CDI
1998 E320 CLASSIC
2007 CLS63 AMG
2000 FRONTERA SPORT (4x4)
2007 CAPTIVA LX
1995 JACKAROO MONTEREY (4
1995 STATESMAN V6
2006 STATESMAN INTERNATIO
2006 CAPRICE
2000 COMBO
2007 COMBO
1999 COMBO
MITSUBISHI
2000 MAGNA EXECUTIVE
2008 380 VR-X
2005 VERADA Xi
1998 MIRAGE
2008 COLT VR-X
2007 COLT TURBO
LIGHT BUS
MEDIUM
PEOPLE MOVER
PICK UP OR CAB CHASSIS 4X2
PICK UP OR CAB CHASSIS 4X4
SMALL
SPORTS
SUV COMPACT
2001 626 LIMITED
2002 626 ECLIPSE
2008 MAZDA6 MPS (LEATHER)
2000 MPV
1996 MPV
2004 MPV
2000 B2600 BRAVO DX
2002 B2600 BRAVO DX CAB P
2007 BT50 B3000 DX
1998 B2600 BRAVO (4x4)
1996 B2600 BRAVO PLUS (4x
2007 BT50 B3000 SDX (4x4)
1996 323
2008 MAZDA3 MAXX SPORT
2008 MAZDA3 MPS SPORTS PA
1997 EUNOS 30X SPORT
1995 MX-5
1995 RX7 SP
2001 TRIBUTE LIMITED
2007 CX-7 CLASSIC
2007 CX-7 LUXURY
2000 C180 CLASSIC
2005 C220 CDI CLASSIC
2007 CL65 AMG
2005 VITO 115 CDI
2006 VIANO 3.5 TREND
2007 VIANO 3.5 AMBIENTE
2005 A150 CLASSIC
2007 A170 ELEGANCE
2007 B200 TURBO
2002 SLK200 KOMPRESSOR
2005 CLK320 AVANTGARDE
2007 CL65 AMG
1995 MAGNA GLX
1999 MAGNA EXECUTIVE
1997 MAGNA SE
1996 NIMBUS
2007 GRANDIS LS
1995 STARWAGON GLS
2000 TRITON GL
2006 TRITON GLX
2007 TRITON GLX
2000 TRITON GLX (4x4)
2005 TRITON GLX (4x4)
2007 TRITON GLS (4x4)
2000 LANCER GLi
2007 LANCER LS
2001 LANCER RALLIART EVOL
1996 3000GT
1998 3000GT
1995 3000GT
2000 PAJERO iO (4x4)
2007 OUTLANDER LS
2007 OUTLANDER VR-X LUXUR
SUV LARGE
SUV LUXURY
TOYOTA
1995 LEXCEN CSi
2004 CAMRY ATEVA
2007 TRD AURION 3500SL
1997 STARLET LIFE
2006 YARIS YRS
2007 YARIS YRX
1995 HIACE COMMUTER
2005 HIACE COMMUTER
2007 HIACE COMMUTER
1997 CAMRY CSi
2000 CAMRY CONQUEST
2007 CAMRY GRANDE
2000 SPACIA VALUE PACK
2003 AVENSIS VERSO ULTIMA
2004 TARAGO ULTIMA
2000 HILUX WORKMATE
2007 HILUX WORKMATE
2007 HILUX SR5
2000 HILUX (4x4)
1996 HILUX (4x4)
2007 LANDCRUISER GXL (4x4
2000 COROLLA CSi
2005 COROLLA CONQUEST
2007 PRIUS I-TECH HYBRID
1996 PASEO
1999 CELICA ZR
1995 MR2 GT
2000 RAV4 (4x4)
1995 RAV4 (4x4)
2007 RAV4 ZR6
1995 LANDCRUISER LWB (4x4
1996 LANDCRUISER GXL (4x4
1996 LANDCRUISER SAHARA (
2000 ML270 CDI (4x4)
2005 ML350 SPECIAL EDITIO
2007 ML63 AMG (4x4)
SUV MEDIUM
2007 CX-9 CLASSIC
2007 CX-9 LUXURY
TRUCK 2.5-3.5 GVM
2000 E2000
2003 E2000 (LWB)
2003 E2500
UPPER LARGE
VAN
2004 TERRACAN
2007 TERRACAN HIGHLANDER
2004 TERRACAN HIGHLANDER
2000 E2000 (SWB)
1996 E2000 DELUXE (LWB)
2003 E2500 LWB
86
2005 POLO CLUB
2007 POLO MATCH
2007 POLO GTi
1997 VENTO GL CLASSIC
2007 PASSAT 2.0T FSI
2004 PASSAT W8
2006 CADDYLIFE
2003 CARAVELLE TDi
2006 MULTIVAN EXECUTIVE 4
1996 GOLF CL
2008 GOLF 2.0 FSI COMFORT
2008 GOLF R32
1998 GOLFCL
2007 EOS 2.0T FSI
1999 GOLF GL
2006 TOUAREG R5 TDi
2007 TOUAREG V6 TDI
2004 TOUAREG V10 TDi
1996 PAJERO GL SWB (4x4)
1997 PAJERO GLS SWB (4x4)
1995 PAJERO EXCEED GLS LW
1995 S280
1999 S420
2007 S65L AMG
2000 MB100D
2004 VITO 112 CDI
2008 VITO 120 CDI EXTRA L
VOLKSWAGEN
2000 EXPRESS SWB
2005 EXPRESS SWB
1999 EXPRESS ECI (4x4)
1996 LANDCRUISER PRADO RV
2003 LANDCRUISER PRADO GX
2007 LANDCRUISER PRADO GR
1995 DYNA 100
2005 DYNA 100
2005 DYNA 150
1995 TRANSPORTER
2007 TRANSPORTER (LWB)
2007 TRANSPORTER (LWB)
1997 TOWNACE
2007 HIACE LWB
2007 HIACE SLWB
2005 CADDY 1.6
2007 TRANSPORTER (LWB)
2007 TRANSPORTER CREWVAN
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+
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+
Proportion of Vehicles
B.2
Proportion of Vehicles
Insights into Comprehensive Motor Insurance Rating
Histograms of Vehicle Relativities for Other States
Figure B.1 – Histogram of Vehicle Relativities – VIC
30%
25%
20%
15%
10%
5%
0%
Relativity against Median Premium
Insurer 1
Insurer 11
Insurer 2
Insurer 14
87
Insurer 8
Insurer 15
Insurer 22
Insurer 16
Insurer 24
Figure B.2 – Histogram of Vehicle Relativities – QLD
30%
25%
20%
15%
10%
5%
0%
Relativity against Median Premium
Insurer 21
Insurer 26
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+
Proportion of Vehicles
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+
Proportion of Vehicles
Insights into Comprehensive Motor Insurance Rating
Figure B.3 – Histogram of Vehicle Relativities – SA
30%
25%
20%
15%
10%
5%
0%
Relativity against Median Premium
Insurer 5
Insurer 3
Insurer 10
Insurer 4
88
Insurer 27
Insurer 13
Insurer 28
Insurer 18
Insurer 29
Figure B.4 – Histogram of Vehicle Relativities – WA
30%
25%
20%
15%
10%
5%
0%
Relativity against Median Premium
Insurer 19
Insurer 23
95 FALCON GLi
0.0
89
Vehicles
07 S65L AMG
99 540i
07 CLS63 AMG
02 740iL
01 TL50
01 TS50
99 S420
07 530d
00 735i
05 LEGEND
99 L7
07 R280 CDI
95 S280
06 LEGEND
06 CAPRICE
06 STATESMAN
98 E320
07 CALAIS V
07 TRD AURION
96 520i
96 LEGEND
07 FAIRLANE GHIA
08 COMMODORE S
96 EUNOS 800M
07 GRANDEUR LTD
97 626 LUXURY
08 380
07 FALCON XR6
95 STATESMAN V6
96 626
05 VERADA
07 SONATA SLX
95 FAIRLANE GHIA
00 SONATA EX
04 CAMRY ATEVA
00 MAGNA
95 LEXCEN CSi
00 BARINA
00 FESTIVA
97 STARLET
03 GETZ XL
06 BARINA
97 121
05 FIESTA LX
07 ACCENT SLX
96 323
03 GETZ FX
96 GOLFCL
05 ELANTRA
05 VIVA
97 CIVIC CXi
05 POLO
98 MIRAGE
00 COROLLACSi
07 ELANTRA SX
06 YARIS YRS
07 POLO
05 JAZZ GLi
00 LASER LXi
08 COLT VR-X
00 121
99 LASER GLXi
04 JAZZ VTi
07 YARIS YRX
08 ASTRA CD
07 ELANTRA ELITE S
08 GOLF
00 LANCER GLi
03 MAZDA2 GENKI
05 COROLLACONQUEST
07 FIESTA XR4
05 A150
08 MAZDA3 MAXX
05 116i
07 POLO GTi
04 INSIGHT
07 PRIUSI-TECH HYBRID
07 TIGRA
07 LANCER LS
04 316ti
06 CIVIC Vi
07 A170
04 ASTRA SRi
07 COLT TURBO
08 MAZDA3 MPS
07 B200
08 GOLF R32
08 130i
07 CIVIC TYPE R
05 FOCUS
01 LANCER EVOLUTION
0.0
95 COMMODORE
Relativities
B.3
Relativities
Insights into Comprehensive Motor Insurance Rating
Range of Relativities for Individual Surveyed Vehicles – NSW
Figure B.5 – Range of Relativities for NSW Insurers – Small and Light Families
6.0
5.0
4.0
3.0
2.0
1.0
Vehicles
Figure B.6 – Range of Relativities for NSW Insurers – Large and Upper Large Families
6.0
5.0
4.0
3.0
2.0
1.0
96 RAIDER
95 RAV4(4x4)
95 LANDCRUISER LWB
95 JACKAROO
96 PAJERO GL
00 FRONTERA SPORT
07 TUCSON CITY SX
96 PRADO RV
00 PAJERO
07 SANTA FE SX
99 HRV
01 TRIBUTE LIMITED
00 EXPLORER XL
04 CRUZE
04 CRUZE A
04 TERRACAN
06 ESCAPEXLS
07 TERRACAN HL
03 ESCAPE LIMITED
00 RAV4(4x4)
95 PAJERO GLS
07 SANTA FE ELITE
04 ESCAPE XLS
97 PAJERO GLS
04 TERRACAN HL
08 EXPLORER XLT
07 CRV
07 TERRITORY TX
06 CRUZE
07 CAPTIVA LX
07 OUTLANDER LS
96 LANDCRUISER GXL
01 EXPLORER LTD
07 CX-7 CLASSIC
07 CRV LUXURY
96 LANDCRUISER
03 PRADO GXL
07 OUTLANDER VR-X
07 MDX
03 MDX
07 PRADO GRANDE
07 TERRITORY GHIA
07 CX-9 CLASSIC
07 CX-7 LUXURY
99 SUBURBAN 2500
99 SUBURBAN 2500 LT
07 RAV4ZR6
07 CX-9 LUXURY
01 SUBURBAN 1500
07 X3 2.0d
06 TOUAREG
00 ML270 CDI
07 TOUAREG
07 X5 3.0i
05 ML350
04 X5 4.8is
04 TOUAREG V10
07 ML63 AMG
0.0
97 COURIER
97 COURIER XL
98 RODEO DX
00 TRITONGL
98 RODEO DX
98 ECONOVAN
95 DYNA100
00 B2600
00 HILUX
98 B2600
00 TRITON GLX
96 B2600
02 B2600
96 HILUX
95
06 COURIER GL
03 E2000 (LWB)
07 HILUX
00 HILUX
07 BT50
00 E2000
05 DYNA100
03 E2500
07 RODEO LX
06 TRITON GLX
05 DYNA150
07 BT50 SDX
05 TRANSIT
05 TRITON GLX
08 TRANSIT
07 RANGER XL
07 TRITON GLX
96
08
07
07
07 HILUX SR5
07 TRITON GLS
06 CREWMAN
06 CREWMAN
07 F350 XL
07 F350 XLT
Relativities
Relativities
Insights into Comprehensive Motor Insurance Rating
Figure B.7 – Range of Relativities for NSW Insurers – SUV Families
6.0
5.0
4.0
3.0
2.0
1.0
Vehicles
Figure B.8 – Range of Relativities for NSW Insurers – Light Commercial Family
6.0
5.0
4.0
3.0
2.0
1.0
0.0
Vehicles
90
0.0
96 NIMBUS
97 TOWNACE
99 COMBO
00 COMBO
03 ZAFIRA EQUIPE
95 STARWAGON
02 TRAJET GL
00 ECONOVAN
07 COMBO
96 E2000 DELUXE
00 EXPRESS
00 E2000 (SWB)
04 TRAJET V6 2.7
02 TRAJET GLS
96 MPV
05 EXPRESS
06 CADDY
06 ZAFIRA
05 CADDY
05 ZAFIRA
08 TRANSIT
00 TRANSIT
95 HIACE COM
99 EXPRESS
06 TRANSIT JUMBO
00 MPV
00 MB100D
07 TRANSPORTER
05 TRANSIT
03 CARAVELLE TDi
04 ODYSSEY
08 TRANSIT
06 ODYSSEY LUXURY
00 SPACIA
07 HIACE LWB
03 E2500 LWB
03 AVENSIS
07 GRANDIS LS
07 TRANSPORTER AWD
04 MPV
06 MULTIVAN 4MOTION
07 HIACE COM
04 ODYSSEY V6
07 HIACE SLWB
05 HIACE COM
04 TARAGO
04 VITO
05 VITO
06 VIANO
08 VITO
07 VIANO 3.5
Relativities
Insights into Comprehensive Motor Insurance Rating
Figure B.9 – Range of Relativities for NSW Insurers – Van and People Mover Families
6.0
5.0
4.0
3.0
2.0
1.0
Vehicles
91
Insights into Comprehensive Motor Insurance Rating
C
Location Rating
C.1
Histogram of Location Relativities – Other States
Figure C.1 - Histogram of Suburb Relativities - VIC
35%
Weighted Proportion of Suburbs
30%
25%
20%
15%
10%
5%
2.0+
1.9-2.0
1.8-1.9
1.7-1.8
1.6-1.7
1.5-1.6
1.4-1.5
1.3-1.4
1.2-1.3
1.1-1.2
1.0-1.1
0.9-1.0
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
0.0-0.4
0%
Relativity Against Weighted Median Premium
Insurer 1
Insurer 2
Insurer 8
Insurer 22
Insurer 24
Figure C.2 - Histogram of Suburb Relativities - QLD
40%
30%
20%
10%
Relativity Against Weighted Median Premium
Insurer 11
Insurer 14
Insurer 15
92
Insurer 16
Insurer 21
Insurer 26
2.0+
1.9-2.0
1.8-1.9
1.7-1.8
1.6-1.7
1.5-1.6
1.4-1.5
1.3-1.4
1.2-1.3
1.1-1.2
1.0-1.1
0.9-1.0
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
0%
0.0-0.4
Weighted Proportion of Suburbs
50%
Insights into Comprehensive Motor Insurance Rating
Figure C.3 - Histogram of Suburb Relativities - SA
Weighted Proportion of Suburbs
90%
80%
70%
60%
50%
40%
30%
20%
10%
2.0+
1.9-2.0
1.8-1.9
1.7-1.8
1.6-1.7
1.5-1.6
1.4-1.5
1.3-1.4
1.2-1.3
1.1-1.2
1.0-1.1
0.9-1.0
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
0.0-0.4
0%
Relativity Against Weighted Median Premium
Insurer 5
Insurer 10
Insurer 27
Insurer 28
Insurer 29
Figure C.4 - Histogram of Suburb Relativities - WA
80%
70%
60%
50%
40%
30%
20%
10%
Relativity Against Weighted Median Premium
Insurer 3
Insurer 4
Insurer 13
93
Insurer 18
Insurer 19
Insurer 23
2.0+
1.9-2.0
1.8-1.9
1.7-1.8
1.6-1.7
1.5-1.6
1.4-1.5
1.3-1.4
1.2-1.3
1.1-1.2
1.0-1.1
0.9-1.0
0.8-0.9
0.7-0.8
0.6-0.7
0.5-0.6
0.4-0.5
0%
0.0-0.4
Weighted Proportion of Suburbs
90%
Insights into Comprehensive Motor Insurance Rating
D
Interactions
For the vehicles contained in the following figures, please refer to the legend in Table 5.1.
NSW – Females
Figure D.1 – Insurer 6 – NSW Females
Relativity to 40yo Driver
2.6
2.2
1.8
1.4
1.0
0.6
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
BELMORE
DENISTONE WEST
EUREKA
POTTS POINT
WORONORA
HEIGHTS
Suburb
Figure D.2 – Insurer 7 – NSW Females
3.0
2.6
Relativity to 40yo Driver
D.1
2.2
1.8
1.4
1.0
0.6
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
BELMORE
DENISTONE WEST
EUREKA
Suburb
94
POTTS POINT
WORONORA
HEIGHTS
Insights into Comprehensive Motor Insurance Rating
Figure D.3 – Insurer 12 – NSW Females
Relativity to 40yo Driver
2.2
1.8
1.4
1.0
0.6
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
BELMORE
DENISTONE WEST
EUREKA
POTTS POINT
WORONORA
HEIGHTS
Suburb
Figure D.4 – Insurer 17 – NSW Females
3.0
Relativity to 40yo Driver
2.6
2.2
1.8
1.4
1.0
0.6
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
BELMORE
DENISTONE WEST
EUREKA
POTTS POINT
WORONORA
HEIGHTS
Suburb
Figure D.5 – Insurer 20 – NSW Females
Relativity to 40yo Driver
2.2
1.8
1.4
1.0
0.6
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
BELMORE
DENISTONE WEST
EUREKA
Suburb
95
POTTS POINT
WORONORA
HEIGHTS
Insights into Comprehensive Motor Insurance Rating
VIC – Males
Figure D.6 – Insurer 1 – VIC Males
Relativity to 40yo Driver
1.8
1.4
1.0
0.6
18
25
40
65
80
18
25
BLACK ROCK
40
65
80
18
CROYDON HILLS
25
40
65
80
GOLDEN SQUARE
Suburb
Figure D.7 – Insurer 2 – VIC Males
3.0
2.6
Relativity to 40yo Driver
D.2
2.2
1.8
1.4
1.0
0.6
18
25
40
65
80
18
25
BLACK ROCK
40
65
CROYDON HILLS
Suburb
96
80
18
25
40
65
GOLDEN SQUARE
80
Insights into Comprehensive Motor Insurance Rating
Figure D.8 – Insurer 8 – VIC Males
3.0
Relativity to 40yo Driver
2.6
2.2
1.8
1.4
1.0
0.6
18
25
40
65
80
18
25
BLACK ROCK
40
65
80
18
CROYDON HILLS
25
40
65
80
GOLDEN SQUARE
Suburb
Figure D.9 – Insurer 22 – VIC Males
2.6
Relativity to 40yo Driver
2.2
1.8
1.4
1.0
0.6
18
25
40
65
80
18
25
BLACK ROCK
40
65
CROYDON HILLS
Suburb
97
80
18
25
40
65
GOLDEN SQUARE
80
Insights into Comprehensive Motor Insurance Rating
QLD – Males
Figure D.10 – Insurer 14 – QLD Males
Relativity to 40yo Driver
2.4
2.0
1.6
1.2
0.8
18
25
40
65
80
18
25
BUNDABERG NORTH
40
65
80
18
25
MOUNT GRAVATT
40
65
80
65
80
WACOL
Suburb
Figure D.11 – Insurer 15 – QLD Males
2.0
Relativity to 40yo Driver
D.3
1.6
1.2
0.8
18
25
40
65
80
18
25
BUNDABERG NORTH
40
65
MOUNT GRAVATT
Suburb
98
80
18
25
40
WACOL
Insights into Comprehensive Motor Insurance Rating
Figure D.12 – Insurer 16 – QLD Males
3.2
Relativity to 40yo Driver
2.8
2.4
2.0
1.6
1.2
0.8
18
25
40
65
80
18
25
BUNDABERG NORTH
40
65
80
18
25
MOUNT GRAVATT
40
65
80
65
80
WACOL
Suburb
Figure D.13 – Insurer 21 – QLD Males
3.2
Relativity to 40yo Driver
2.8
2.4
2.0
1.6
1.2
0.8
18
25
40
65
80
18
25
BUNDABERG NORTH
40
65
MOUNT GRAVATT
Suburb
99
80
18
25
40
WACOL
Insights into Comprehensive Motor Insurance Rating
SA – Males
Figure D.14 – Insurer 5 – SA Males
Relativity to 40yo Driver
2.0
1.6
1.2
0.8
18
25
40
65
80
18
25
BLAIR ATHOL WEST
40
65
80
18
CHERRYVILLE
25
40
65
80
HAZELWOOD PARK
Suburb
Figure D.15 – Insurer 10 – SA Males
2.0
Relativity to 40yo Driver
D.4
1.6
1.2
0.8
18
25
40
65
80
18
25
BLAIR ATHOL WEST
40
65
CHERRYVILLE
Suburb
100
80
18
25
40
65
HAZELWOOD PARK
80
Insights into Comprehensive Motor Insurance Rating
Figure D.16 – Insurer 27 – SA Males
3.2
Relativity to 40yo Driver
2.8
2.4
2.0
1.6
1.2
0.8
18
25
40
65
80
18
25
BLAIR ATHOL WEST
40
65
80
18
CHERRYVILLE
25
40
65
80
HAZELWOOD PARK
Suburb
Figure D.17 – Insurer 28 – SA Males
3.2
Relativity to 40yo Driver
2.8
2.4
2.0
1.6
1.2
0.8
18
25
40
65
80
18
25
BLAIR ATHOL WEST
40
65
80
18
CHERRYVILLE
25
40
65
80
HAZELWOOD PARK
Suburb
Figure D.18 – Insurer 29 – SA Males
3.2
Relativity to 40yo Driver
2.8
2.4
2.0
1.6
1.2
0.8
18
25
40
65
80
18
25
BLAIR ATHOL WEST
40
65
CHERRYVILLE
Suburb
101
80
18
25
40
65
HAZELWOOD PARK
80
Insights into Comprehensive Motor Insurance Rating
WA – Males
Figure D.19 – Insurer 3 – WA Males
3.0
Relativity to 40yo Driver
2.6
2.2
1.8
1.4
1.0
0.6
18
25
40
65
80
18
DUNCRAIG
25
40
65
80
18
MAHOGANY CREEK
25
40
65
80
MIRRABOOKA
Suburb
Figure D.20 – Insurer 4 – WA Males
2.2
Relativity to 40yo Driver
D.5
1.8
1.4
1.0
0.6
18
25
40
DUNCRAIG
65
80
18
25
40
65
MAHOGANY CREEK
Suburb
102
80
18
25
40
65
MIRRABOOKA
80
Insights into Comprehensive Motor Insurance Rating
Figure D.21 – Insurer 13 – WA Males
Relativity to 40yo Driver
2.2
1.8
1.4
1.0
0.6
18
25
40
65
80
18
DUNCRAIG
25
40
65
80
18
MAHOGANY CREEK
25
40
65
80
MIRRABOOKA
Suburb
Figure D.22 – Insurer 19 – WA Males
Relativity to 40yo Driver
2.2
1.8
1.4
1.0
0.6
18
25
40
65
80
18
DUNCRAIG
25
40
65
80
18
MAHOGANY CREEK
25
40
65
80
MIRRABOOKA
Suburb
Figure D.23 – Insurer 23 – WA Males
2.6
Relativity to 40yo Driver
2.2
1.8
1.4
1.0
0.6
18
25
40
DUNCRAIG
65
80
18
25
40
65
MAHOGANY CREEK
Suburb
103
80
18
25
40
65
MIRRABOOKA
80