Key Takeaways
- National motor vehicle theft cost $8.9 billion in 2022, up 24% from 2021.
- Average economic loss per stolen vehicle $10,856 in insurance payouts 2023.
- Chop shops dismantle 30% of stolen vehicles for $4B parts market annually.
- In 2022, the United States reported 1,037,354 motor vehicle thefts, a 10% increase from 2021, representing a rate of 308.2 per 100,000 inhabitants.
- The national motor vehicle theft rate in the US dropped 83% from 737.5 per 100,000 in 1991 to 127.9 per 100,000 in 2021 before rising.
- From 2019 to 2022, motor vehicle thefts in the US increased by 26%, from 748,841 to 1,037,354 incidents.
- In 2022, 42% of motor vehicle theft offenders nationally were aged 25-34.
- Juveniles under 18 accounted for 12.5% of motor vehicle theft arrests in 2021.
- Males comprised 82.3% of persons arrested for motor vehicle theft in 2022.
- California reported 189,945 motor vehicle thefts in 2023, highest in US, rate 477 per 100k.
- Texas had 179,363 vehicle thefts in 2022, second highest, rate 248 per 100k.
- Florida recorded 44,511 thefts in 2022, rate 206 per 100k, concentrated in Miami-Dade.
- Chevrolet Silverado most stolen vehicle nationally 2023, 31,315 thefts.
- Honda Accord second most stolen, 28,745 incidents in 2023.
- Ford F-150 third, 27,684 thefts in 2023 US-wide.
In 2022, US motor vehicle thefts cost $8.9 billion, rising 24% as losses and premiums climbed.
Related reading
Economic Costs and Trends
Economic Costs and Trends Interpretation
National Incidence Rates
National Incidence Rates Interpretation
More related reading
Offender Demographics
Offender Demographics Interpretation
State and Local Variations
State and Local Variations Interpretation
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Vehicle Types and Models
Vehicle Types and Models Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Thomas Lindqvist. (2026, February 13). Motor Vehicle Theft Statistics. Gitnux. https://gitnux.org/motor-vehicle-theft-statistics
Thomas Lindqvist. "Motor Vehicle Theft Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/motor-vehicle-theft-statistics.
Thomas Lindqvist. 2026. "Motor Vehicle Theft Statistics." Gitnux. https://gitnux.org/motor-vehicle-theft-statistics.
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