Motor Postcode Zoning Working Party

Duncan Anderson Roger MasseySaideh Charlton Chris SpillerDave Coughlan (Chair) Kim PapeMark Harrison James TanserStephen Jones Richhard Verrall

Aims

• Understand market

• Data availability

• Possible approach

Importance of zoning

• Review of postcodes

• 3rd biggest factor

• Needed to limit scope

• Theft only 10 - 15%

Market Analysis

Investigating market rates

• EL Systems Præmium• "Average" risk considered

for every UK post code district• 18 comprehensive quotes

obtained from 15 insurers• Some possible interactions also examined• Limited

– districts only– no direct writers– off screen discounts

Post code categorisation

InsurerNumber ofcategories

123456789

101112131415161718

1011121415161617171819191920202122

125

Post code categorisation

InsurerNumber ofcategories

Highest premium /lowest premium

123456789

101112131415161718

1011121415161617171819191920202122

125

2.121.972.293.302.202.182.192.672.122.162.701.932.313.172.173.132.762.26

CompanyInteraction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Age / Sex Age / vehicle group

Area / vehicle group

Area / garaged Age / occupation

Use / mileage Occupation / use Age / marital status / sex

Interactions

Age Factor

17 2.52

18 2.05

19 1.97

20 1.85

21-23 1.75

24-26 1.54

27-30 1.42

31-35 1.20

36-40 1.00

41-45 0.93

46-50 0.84

51-60 0.76

60+ 0.78

Group 1 2 3 4 5 6 7 8 9 10 11 12 13

Factor 0.54 0.65 0.73 0.85 0.92 0.96 1.00 1.08 1.19 1.26 1.36 1.43 1.56

1.00

1.00

1.17

1.00

Group > 1 2 3 4 5 6 7 8 9 10 11 12 13Age v

17 1.36 1.64 1.79 2.09 2.27 2.42 2.56 2.65 3.27 3.71 4.08 4.36 4.8418 1.12 1.31 1.47 1.76 1.84 2.00 2.11 2.19 2.43 2.97 3.29 3.55 3.9019 1.08 1.30 1.46 1.63 1.82 1.91 2.02 2.11 2.53 2.88 3.30 3.35 3.6320 0.98 1.18 1.36 1.54 1.68 1.79 1.83 1.97 2.19 2.66 3.02 3.20 3.38

21-23 0.96 1.13 1.24 1.51 1.65 1.64 1.80 1.85 2.04 2.26 2.55 2.53 2.8924-26 0.82 0.99 1.10 1.31 1.43 1.52 1.51 1.64 1.81 1.93 2.13 2.22 2.4727-30 0.78 0.90 1.07 1.19 1.32 1.39 1.41 1.51 1.65 1.77 1.91 2.01 2.2431-35 0.63 0.78 0.86 0.99 1.09 1.17 1.22 1.32 1.42 1.54 1.66 1.71 1.8836-40 0.55 0.64 0.71 0.85 0.91 0.93 0.99 1.07 1.18 1.29 1.40 1.41 1.5341-45 0.51 0.61 0.66 0.79 0.88 0.88 0.94 0.99 1.09 1.15 1.29 1.31 1.4246-50 0.46 0.55 0.61 0.70 0.76 0.81 0.84 0.92 1.02 1.07 1.12 1.18 1.3151-60 0.40 0.49 0.56 0.64 0.68 0.71 0.78 0.82 0.90 0.99 1.02 1.12 1.20

60+ 0.43 0.52 0.55 0.67 0.72 0.73 0.78 0.83 0.93 0.98 1.04 1.11 1.25

CompanyInteraction 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Age / Sex Age / vehicle group

Area / vehicle group

Area / garaged Age / occupation

Use / mileage Occupation / use Age / marital status / sex

Interactions

Age Factor

17 2.52

18 2.05

19 1.97

20 1.85

21-23 1.75

24-26 1.54

27-30 1.42

31-35 1.20

36-40 1.00

41-45 0.93

46-50 0.84

51-60 0.76

60+ 0.78

Group 1 2 3 4 5 6 7 8 9 10 11 12 13

Factor 0.54 0.65 0.73 0.85 0.92 0.96 1.00 1.08 1.19 1.26 1.36 1.43 1.56

1.00

1.00

1.17

1.00

Group > 1 2 3 4 5 6 7 8 9 10 11 12 13Age v

17 1.36 1.64 1.79 2.09 2.27 2.42 2.56 2.65 3.27 3.71 4.08 4.36 4.8418 1.12 1.31 1.47 1.76 1.84 2.00 2.11 2.19 2.43 2.97 3.29 3.55 3.9019 1.08 1.30 1.46 1.63 1.82 1.91 2.02 2.11 2.53 2.88 3.30 3.35 3.6320 0.98 1.18 1.36 1.54 1.68 1.79 1.83 1.97 2.19 2.66 3.02 3.20 3.38

21-23 0.96 1.13 1.24 1.51 1.65 1.64 1.80 1.85 2.04 2.26 2.55 2.53 2.8924-26 0.82 0.99 1.10 1.31 1.43 1.52 1.51 1.64 1.81 1.93 2.13 2.22 2.4727-30 0.78 0.90 1.07 1.19 1.32 1.39 1.41 1.51 1.65 1.77 1.91 2.01 2.2431-35 0.63 0.78 0.86 0.99 1.09 1.17 1.22 1.32 1.42 1.54 1.66 1.71 1.8836-40 0.55 0.64 0.71 0.85 0.91 0.93 0.99 1.07 1.18 1.29 1.40 1.41 1.5341-45 0.51 0.61 0.66 0.79 0.88 0.88 0.94 0.99 1.09 1.15 1.29 1.31 1.4246-50 0.46 0.55 0.61 0.70 0.76 0.81 0.84 0.92 1.02 1.07 1.12 1.18 1.3151-60 0.40 0.49 0.56 0.64 0.68 0.71 0.78 0.82 0.90 0.99 1.02 1.12 1.20

60+ 0.43 0.52 0.55 0.67 0.72 0.73 0.78 0.83 0.93 0.98 1.04 1.11 1.25

Low

High

1.00

1.01

1.02

1.03

1.04

1.05

1.06

Vehicle Group

Area

High

Low

Example interaction

Loadings by postcode

• For each company, "average loading" for a district defined as premium for district divided by unweighted average of premiums across all districts

• Districts ordered by average"average loading"

• Average loading plotted against district

60%

80%

100%

120%

140%

160%

180%

District

Loadings by postcode

60%

80%

100%

120%

140%

160%

180%

District

Average Minimum Maximum

Loadings by postcode

Comparison of categorisations

244

274

307

343

385

432

484

543

609

685

770

299316334352369387404422440457475495518542632

0

20

40

60

80

100

120

140

Num

ber

of d

istr

icts

Company F

Company I

244

274

307

343

385

432

484

543

609

685

770

299316334352369387404422440457475495518542632

0

20

40

60

80

100

120

140

Num

ber

of d

istr

icts

Company F

Company I

Comparison of categorisations

275

311

325

352

396

439

525

606

318340

366385

426447

467521

555590

683734

0

50

100

150

200

Num

ber

of d

istr

icts

Company A

Company G

2/8

Comparison of categorisations

318

340

366

385

426

447

467

521

555

590

683

734

299330

357372

399426

442477

507549

591

0

50

100

150

200

Num

ber

of d

istr

icts

Company G

Company N

3/8

Comparison of categorisations

265

352

374

402

423

453

503

563

638

713

293302308314319326332343353361369382393405428442458475508633

0

50

100

150

200

250

Num

ber

of d

istr

icts

Company B

Company M

4/8

Comparison of categorisations

0

397

434

478

517

563

617

687

768

182208234284292299307315328342376409439471484495513530554579

0

20

40

60

80

100

120

140

160

Num

ber

of d

istr

icts

Company D

Company K

5/8

Comparison of categorisations

275

311

325

352

396

439

525

606

244259274290307324343363385407432457484513543575609647685726770

020

40

60

80

100

120

140

160

Num

ber

of d

istr

icts

Company A

Company F

6/8

Comparison of categorisations

275

311

325

352

396

439

525

606

265341352363374387402413423441453473503528563598638675713

0

20

40

60

80

100

120

140

160

Num

ber

of d

istr

icts

Company A

Company B

7/8

Comparison of categorisations

265

352

374

402

423

453

503

563

638

713

312353373386401425443460483501525550580614651685

0

20

40

60

80

100

120

140

160

Num

ber

of d

istr

icts

Company B

Company P

8/8

Comparison of categorisations

Data, Method and Results

Information available

• Complex

• Appropriate

• Up to date

• Higher level view

Data

• Internal data

• External

• Combined/Grouped

Interesting views

• Drive around and look

• Social deprivation index

• Subjective views

Methods

• Size of exercise

• Consider residual risk

• Three main types of approach- Smoothing- Credibility- Market related

Smoothing

“Based on underlying assumption thatareas “close” each other have similar risk”

Adjacency?

Credibility

“The risk associated with a postcode is a mixture of its own experience and some other estimate of expected”

Appropriate?

Market Related

“everybody doing it, so I should too”

Realistic?

Approach

• Smoothing - “weighted distance”

- “Spatial models”

• Credibility - “Credibility Method”

• Market - “Average Market”

- “External provider”

• Other - “Modern Heuristic”

Approach

• Split data 70/30

• Standardise data

• Use residual risk to zone

squares as test statistic

Simple results

• All proved better than null model

• Little to choose between results

• Combination of “credibility and

weighted distance” is the best

Comparison of methods

Implementation

• Solid risk cost

• Improved

understanding

Implementation - issues

• Competitive position

• Existing customers

• Systems

Conclusion

• Market is not uniform in

approach

• Suggest approach as

mixture of

methods

Motor Postcode Zoning Working Party

Duncan Anderson Roger MasseySaideh Charlton Chris SpillerDave Coughlan (Chair) Kim PapeMark Harrison James TanserStephen Jones Richhard Verrall