<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for the appropriate allocation of resources in healthcare, insurance, civil engineering and other industries. We show how images of houses are predictive of car accidents. We analyse 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View and annotate house features such as age, type and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, the public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.</p></abstract>

Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for the appropriate allocation of resources in healthcare, insurance, civil engineering and other industries. We show how images of houses are predictive of car accidents. We analyse 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View and annotate house features such as age, type and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, the public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.

Google Street View image predicts car accident risk

Central European Economic Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Google Street View image predicts car accident risk"}

Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for the appropriate allocation of resources in...

	Original database	After data cleansing
Number of polices	20,000	19,871

Risk exposure	11,349	11,209

MTPL PD claim count	571	570
Observed MTPL PD frequency	5,03%	5,09%

Variable	Original granularity	Inter-rater reliability		Risk model

		Fleiss’ kappa	Interpretation	Granularity after simplification	p-value
Neighbourhood type	Seven types, multi-choice	0.52	Moderate agreement	2	00.01

Building density	Scale 1–5	0.50	Moderate agreement	Not significant

Street View quality	Good/bad/missing	0.79	Substantial agreement	2	00.02

House type	Five types, single-choice	0.69	Substantial agreement	2	00.01

House age	Scale 1–3	0.51	Moderate agreement	2	00.03

House condition	Scale 1–3	0.54	Moderate agreement	2	00.04
Wealth of residents	Scale 1–10	0.32	Fair agreement	Not significant

Number of claims	Observed exposure (O)	Expected prob.P(X = k)	Expected exposure (E)	(E – O)²/E
0	10,784	96%	10,785	0,00

1	417	4%	416	0,01

2	7	0%	8	0,08
All	11,209

Google Street View image predicts car accident risk

Published Online: Apr 18, 2020

Page range: 151 - 163

DOI: https://doi.org/10.2478/ceej-2019-0011

Keywords
Generalized Linear Model, risk modelling, insurance pricing, satellite imagery, Google Street View

© 2019 Kinga Kita-Wojciechowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Summary statistics of the dataset—before and after cleansing.

Statistics for seven newly created variables—original granularity, inter-rater reliability of 4 selected annotators on the common set of 500 observations and significance in our risk model after applying necessary simplifications.

Data for calculation of X2 statistic for hypothesis verification whether claims in our dataset follow the Poisson distribution. On average λ = 3.9% and the corresponding X2 = 0.08 with 1 degree of freedom.

Google Street View image predicts car accident risk

Published Online: Apr 18, 2020

Page range: 151 - 163

DOI: https://doi.org/10.2478/ceej-2019-0011

KeywordsGeneralized Linear Model, risk modelling, insurance pricing, satellite imagery, Google Street View

© 2019 Kinga Kita-Wojciechowska et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Summary statistics of the dataset—before and after cleansing.

Statistics for seven newly created variables—original granularity, inter-rater reliability of 4 selected annotators on the common set of 500 observations and significance in our risk model after applying necessary simplifications.

Data for calculation of X2 statistic for hypothesis verification whether claims in our dataset follow the Poisson distribution. On average λ = 3.9% and the corresponding X2 = 0.08 with 1 degree of freedom.

Keywords
Generalized Linear Model, risk modelling, insurance pricing, satellite imagery, Google Street View