Google Street View image predicts car accident risk

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Summary statistics of the dataset—before and after cleansing_

	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%

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_

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

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_

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