In 2023, the District of Columbia had the highest rate of motor vehicle theft in the United States, with 1,070.9 cases per 100,000 inhabitants. Washington, Nevada, Colorado, and Maryland rounded out the top five states for motor vehicle theft in that year. Nationwide, the rate of motor vehicle theft stood at 318.7 cases per 100,000 residents.

In 2023, the nationwide rate of motor vehicle theft in the United States was 318.7 reported cases per 100,000 population. While this is an increase from the previous year, it is a significant decrease from the rate in 1990, which stood at 657.8 motor vehicle thefts per 100,000 of the population.

In 2023, an estimated 1,067,522 reported motor vehicle theft cases occurred in the United States. This is an increase from the previous year, when there were an estimated 948,119 cases of motor vehicle theft nationwide.

When incidents happened, where it took place, the victim’s perception of the incident, and what items were stolen or damaged. Annual data from the Crime Survey for England and Wales (CSEW).

In 2023, the federal state of California recorded the most motor vehicle thefts in the United States, with a total of 199,592 reported cases of motor vehicle theft. This was followed by Texas with 125,045 cases. Washington, Illinois, and Colorado rounded out the top five states for motor vehicle theft in that year.

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) from 2006 to the end of last year (2017). For additional details, please see the attached data dictionary in the ‘About’ section.

This trends and best practices evaluation geared toward motor vehicle theft prevention with a particular focus on the Watch Your Car (WYC) program was conducted between October 2002 and March 2004. On-site and telephone interviews were conducted with administrators from 11 of 13 WYC member states. Surveys were mailed to the administrators of auto theft prevention programs in 36 non-WYC states and the 10 cities with the highest motor vehicle theft rates. Completed surveys were returned from 16 non-WYC states and five of the high auto theft rate cities. Part 1, the survey for Watch Your Car (WYC) program members, includes questions about how respondents learned about the WYC program, their WYC related program activities, the outcomes of their program, ways in which they might have done things differently if given the opportunity, and summary questions that asked WYC program administrators for their opinions about various aspects of the overall WYC program. The survey for the nonmember states, Part 2, and cities, Part 3, collected information about motor vehicle theft prevention within the respondent's state or city and asked questions about the respondent's knowledge of, and opinions about, the Watch Your Car program.

This dataset includes all auto theft occurrences by reported date and related offences since 2014.Auto Theft DashboardDownload DocumentationThis data is provided at the offence and/or vehicle level, therefore one occurrence number may have several rows of data associated to the various MCIs used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at PSITAdministration@ChicagoPolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data are updated daily. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

The Criminal Justice Research Division of the San Diego Association of Governments (SANDAG) received funds from the National Institute of Justice to assist the Regional Auto Theft Task (RATT) force and evaluate the effectiveness of the program. The project involved the development of a computer system to enhance the crime analysis and mapping capabilities of RATT. Following the implementation of the new technology, the effectiveness of task force efforts was evaluated. The primary goal of the research project was to examine the effectiveness of RATT in reducing auto thefts relative to the traditional law enforcement response. In addition, the use of enhanced crime analysis information for targeting RATT investigations was assessed. This project addressed the following research questions: (1) What were the characteristics of vehicle theft rings in San Diego and how were the stolen vehicles and/or parts used, transported, and distributed? (2) What types of vehicles were targeted by vehicle theft rings and what was the modus operandi of suspects? (3) What was the extent of violence involved in motor vehicle theft incidents? (4) What was the relationship between the locations of vehicle thefts and recoveries? (5) How did investigators identify motor vehicle thefts that warranted investigation by the task force? (6) Were the characteristics of motor vehicle theft cases investigated through RATT different than other cases reported throughout the county? (7) What investigative techniques were effective in apprehending and prosecuting suspects involved in major vehicle theft operations? (8) What was the impact of enhanced crime analysis information on targeting decisions? and (9) How could public education be used to reduce the risk of motor vehicle theft? For Part 1 (Auto Theft Tracking Data), data were collected from administrative records to track auto theft cases in San Diego County. The data were used to identify targets of enforcement efforts (e.g., auto theft rings, career auto thieves), techniques or strategies used, the length of investigations, involvement of outside agencies, property recovered, condition of recoveries, and consequences to offenders that resulted from the activities of the investigations. Data were compiled for all 194 cases investigated by RATT in fiscal year 1993 to 1994 (the experimental group) and compared to a random sample of 823 cases investigated through the traditional law enforcement response during the same time period (the comparison group). The research staff also conducted interviews with task force management (Parts 2 and 3, Investigative Operations Committee Initial Interview Data and Investigative Operations Committee Follow-Up Interview Data) and other task force members (Parts 4 and 5, Staff Initial Interview Data and Staff Follow-Up Interview Data) at two time periods to address the following issues: (1) task force goals, (2) targets, (3) methods of identifying targets, (4) differences between RATT strategies and the traditional law enforcement response to auto theft, (5) strategies employed, (6) geographic concentrations of auto theft, (7) factors that enhance or impede investigations, (8) opinions regarding effective approaches, (9) coordination among agencies, (10) suggestions for improving task force operations, (11) characteristics of auto theft rings, (12) training received, (13) resources and information needed, (14) measures of success, and (15) suggestions for public education efforts. Variables in Part 1 include the total number of vehicles and suspects involved in an incident, whether informants were used to solve the case, whether the stolen vehicle was used to buy parts, drugs, or weapons, whether there was a search warrant or an arrest warrant, whether officers used surveillance equipment, addresses of theft and recovery locations, date of theft and recovery, make and model of the stolen car, condition of vehicle when recovered, property recovered, whether an arrest was made, the arresting agency, date of arrest, arrest charges, number and type of charges filed, disposition, conviction charges, number of convictions, and sentence. Demographic variables include the age, sex, and race of the suspect, if known. Variables in Parts 2 and 3 include the goals of RATT, how the program evolved, the role of the IOC, how often the IOC met, the relationship of the IOC and the executive committee, how RATT was unique, why RATT was successful, how RATT could be improved, how RATT was funded, and ways in which auto theft could be reduced. Variables in Parts 4 and 5 include the goals of RATT, sources of information about vehicle thefts, strategies used to solve auto theft cases, _location of most vehicle thefts, how motor vehicle thefts were impacted by RATT, impediments to the RATT program, suggestions for improving the program, ways in which auto theft could be reduced, and methods to educate citizens about auto theft. In addition, Part 5 also has variables about the type of officers' training, usefulness of maps and other data, descriptions of auto theft rings in terms of the age, race, and gender of its members, and types of cars stolen by rings.

This dataset includes all Theft from Motor Vehicle occurrences by reported date and related offences since 2014. The Theft from Motor Vehicle offences include Theft from Motor Vehicle Under and Theft from Motor Vehicle Over.Theft from Motor Vehicle DashboardDownload DocumentationThis data is provided at the offence and/or victim level, therefore one occurrence number may have several rows of data associated to the various offences used to categorize the occurrence.The downloadable datasets display the REPORT_DATE and OCC_DATE fields in UTC timezone.This data does not include occurrences that have been deemed unfounded. The definition of unfounded according to Statistics Canada is: “It has been determined through police investigation that the offence reported did not occur, nor was it attempted” (Statistics Canada, 2020).**The dataset is intended to provide communities with information regarding public safety and awareness. The data supplied to the Toronto Police Service by the reporting parties is preliminary and may not have been fully verified at the time of publishing the dataset. The location of crime occurrences have been deliberately offset to the nearest road intersection node to protect the privacy of parties involved in the occurrence. All location data must be considered as an approximate location of the occurrence and users are advised not to interpret any of these locations as related to a specific address or individual.NOTE: Due to the offset of occurrence location, the numbers by Division and Neighbourhood may not reflect the exact count of occurrences reported within these geographies. Therefore, the Toronto Police Service does not guarantee the accuracy, completeness, timeliness of the data and it should not be compared to any other source of crime data.By accessing these datasets, the user agrees to full acknowledgement of the Open Government Licence - Ontario.In accordance with the Municipal Freedom of Information and Protection of Privacy Act, the Toronto Police Service has taken the necessary measures to protect the privacy of individuals involved in the reported occurrences. No personal information related to any of the parties involved in the occurrence will be released as open data. ** Statistics Canada. 2020. Uniform Crime Reporting Manual. Surveys and Statistical Programs. Canadian Centre for Justice Statistics.

In 2022, Chile recorded the highest car theft rate in the world, with nearly *** incidents per 100,000 inhabitants. Other countries with notably high rates included Uruguay, Israel, and Luxembourg.

Private car theft rate of Germany shot up by 25.68% from 47.0 cases per 100,000 population in 2021 to 59.0 cases per 100,000 population in 2022. Since the 11.83% slump in 2020, private car theft rate leapt by 14.64% in 2022. Private Cars' means motor vehicles, excluding motorcycles, commercial vehicles, buses, lorries, construction and agricultural vehicles.(UN-CTS M4.5)

Private car theft rate of Philippines sank by 25.25% from 6.1 cases per 100,000 population in 2017 to 4.5 cases per 100,000 population in 2018. Since the 5.91% jump in 2014, private car theft rate plummeted by 65.41% in 2018. Private Cars' means motor vehicles, excluding motorcycles, commercial vehicles, buses, lorries, construction and agricultural vehicles.(UN-CTS M4.5)

1800C_Hsinchu County Government Police Department Car Theft Statistics Form

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) for all complete quarters so far this year (2019). For additional details, please see the attached data dictionary in the ‘About’ section.

Private car theft rate of Finland fell by 1.64% from 101.0 cases per 100,000 population in 2021 to 99.3 cases per 100,000 population in 2022. Since the 15.34% jump in 2020, private car theft rate plummeted by 15.07% in 2022. Private Cars' means motor vehicles, excluding motorcycles, commercial vehicles, buses, lorries, construction and agricultural vehicles.(UN-CTS M4.5)

China: Motor vehicle thefts per 100,000 people: The latest value from is thefts per 100,000 people, unavailable from thefts per 100,000 people in . In comparison, the world average is 0.00 thefts per 100,000 people, based on data from countries. Historically, the average for China from to is thefts per 100,000 people. The minimum value, thefts per 100,000 people, was reached in while the maximum of thefts per 100,000 people was recorded in .

This dataset includes all valid felony, misdemeanor, and violation crimes reported to the New York City Police Department (NYPD) from 2006 to the end of last year (2019). For additional details, please see the attached data dictionary in the ‘About’ section.

In recent years, the car robbery rate in Brazil has been decreasing. In 2023, approximately 112.3 out of every 100,000 automobiles were stolen in the country, which represents a decrease from an average of over 128.1 auto thefts per 100,000 vehicles recorded one year before. That year, São Paulo was the Brazilian state that had the highest number of car thefts and break-ins.