In 2023, the nationwide burglary rate in the United States was 250.7 cases per 100,000 of the population. This is a slight decrease from the previous year, when the burglary rate stood at 272.7 cases per 100,000 of the population.

In 2023, New Mexico had the highest burglary rate in the United States. That year, they had 517.9 occurrences per 100,000 residents. Washington followed with 481 incidents per 100,000 residents. What is burglary? Burglary in the United States is considered a felony or misdemeanor. It includes trespassing and theft, and going inside a building or car with the intent to commit any crime. Even if the crime is not necessarily theft, it is still illegal. Some states consider burglary committed during the day as housebreaking, not burglary. The Bureau of Justice Statistics defines it as unlawful or forcible entry into a building. There are four types of burglary in total: completed burglary, forcible entry, unlawful entry, and attempted forcible entry. Burglary in the United States Burglary affects all 50 states in the United States, as burglary was the third most common type of property crime in the United States in 2023. California had the highest number of reported burglaries in that same year, whereas New Hampshire had the lowest number. However, the overall reported burglary rate in the United States has decreased significantly since 1990.

In 2022, Costa Rica had the highest burglary rate worldwide, with ***** occurrences per 100,000 inhabitants. Other countries with the highest burglary rate were Sweden, Luxembourg and Dominica.

The average for 2017 based on 79 countries was 105 robberies per 100,000 people. The highest value was in Costa Rica: 1587 robberies per 100,000 people and the lowest value was in Oman: 1 robberies per 100,000 people. The indicator is available from 2003 to 2017. Below is a chart for all countries where data are available.

The National Crime Victimization Survey (NCVS) Series, previously called the National Crime Surveys (NCS), has been collecting data on personal and household victimization through an ongoing survey of a nationally-representative sample of residential addresses since 1972. The NCVS was designed with four primary objectives: (1) to develop detailed information about the victims and consequences of crime, (2) to estimate the number and types of crimes not reported to the police, (3) to provide uniform measures of selected types of crimes, and (4) to permit comparisons over time and types of areas. The survey categorizes crimes as "personal" or "property." Personal crimes include rape and sexual attack, robbery, aggravated and simple assault, and purse-snatching/pocket-picking, while property crimes include burglary, theft, and motor vehicle theft. Each respondent is asked a series of screen questions designed to determine whether she or he was victimized during the six-month period preceding the first day of the month of the interview. A "household respondent" is also asked to report on crimes against the household as a whole (e.g., burglary, motor vehicle theft). The data include type of crime, month, time, and location of the crime, relationship between victim and offender, characteristics of the offender, self-protective actions taken by the victim during the incident and results of those actions, consequences of the victimization, type of property lost, whether the crime was reported to police and reasons for reporting or not reporting, and offender use of weapons, drugs, and alcohol. Basic demographic information such as age, race, gender, and income is also collected, to enable analysis of crime by various subpopulations.

Around 54,632 private homes were burglarized in Mexico in 2023, down from 60,514 home burglaries registered a year earlier. In the past years, 2017 presented the highest number of house burglaries, the number of robberies in private homes have been decreasing since then. Business burglaries, however, had consistently increased from 2015 up until 2020.

When incidents happened, information about offenders, the victim’s perception of the incident, and what items were stolen. Annual data from the Crime Survey for England and Wales (CSEW).

Burglary rate of Ireland leapt by 8.73% from 168.1 cases per 100,000 population in 2021 to 182.8 cases per 100,000 population in 2022. Since the 36.23% drop in 2020, burglary rate dropped by 14.50% in 2022. “Burglary” means gaining unauthorised access to a part of a building/dwelling or other premises; including by use of force; with the intent to steal goods (breaking and entering). “Burglary” should include; where possible; theft from a house; appartment or other dwelling place; factory; shop or office; from a military establishment; or by using false keys. It should exclude theft from a car; from a container; from a vending machine; from a parking meter and from fenced meadow/compound. (UN-CTS M4.6)

Contains property crime victimizations. Property crimes include burglary, trespassing, motor vehicle theft, and other household theft. Households that did not report a property crime victimization are not included on this file. Victimizations that took place outside of the United States are excluded from this file.

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), police services in Ontario, 1998 to 2023.

This is the tenth report in an annual series combining crimes recorded by the police and interviews from the British Crime Survey (BCS) for the financial year 2010/11. Each source has different strengths and weaknesses but together they provide a more comprehensive picture of crime than could be obtained from either series alone. Additional explanatory notes are available in the User Guide to Home Office Crime Statistics.

Longer term datasets contain https://data.gov.uk/dataset/0e26ee1b-26b7-406e-a3b1-f3481b324977/local-police-recorded-crime-data" class="govuk-link">police recorded crime for police force areas and local authorities

https://data.gov.uk/dataset/ea7a5bd4-4c26-4ea3-b1ff-c5c0dfe9fcfd/crime-in-england-and-wales-2010-11" class="govuk-link">Crimes detected in England & Wales 2010/11 reports on the levels and trends in detections and detection rates in England and Wales.

The last annual crime statistics https://data.gov.uk/dataset/df7e3554-2a62-497a-bbd6-2c3982dba5a5/crime-in-england-and-wales-2009-10" class="govuk-link">Crime in England and Wales 2009/10 was published in July 2010.

See the https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/datasets/crimeinenglandandwalesannualsupplementarytables" class="govuk-link">Crime Survey supplementary tables on the nature of: burglary, vehicle-related theft, bicycle theft, household theft, personal and other theft and vandalism.

Incident-based crime statistics (actual incidents, rate per 100,000 population, percentage change in rate, unfounded incidents, percent unfounded, total cleared, cleared by charge, cleared otherwise, persons charged, adults charged, youth charged / not charged), by detailed violations (violent, property, traffic, drugs, other Federal Statutes), Canada, provinces, territories, Census Metropolitan Areas and Canadian Forces Military Police, 1998 to 2023.

Context

This dataset was actually made to check the correlations between a housing price index and its crime rate. Rise and fall of housing prices can be due to various factors with obvious reasons being the facilities of the house and its neighborhood. Think of a place like Detroit where there are hoodlums and you don't want to end up buying a house in the wrong place. This data set will serve as historical data for crime rate data and this in turn can be used to predict whether the housing price will rise or fall. Rise in housing price will suggest decrease in crime rate over the years and vice versa.

Content

The headers are self explanatory. index_nsa is the housing price non seasonal index.

Acknowledgements

Thank you to my team who helped in achieving this.

Inspiration

https://www.kaggle.com/marshallproject/crime-rates https://catalog.data.gov/dataset/fhfa-house-price-indexes-hpis Data was collected from these 2 sources and merged to get the resulting dataset.

These data investigate the behaviors and attitudes of active residential burglars, not presently incarcerated, operating in St. Louis, Missouri. Through personal interviews, information was gathered on the burglars' motivation and feelings about committing crimes, peer pressure, burglary methods, and stolen goods disposal. Respondents were asked to describe their first residential burglary, to recreate verbally the most recent residential burglary they had committed, to discuss their perceptions of the risk values involved with burglary, and to describe the process through which they selected potential targets for burglaries. In-depth, semistructured interviews lasting from one and a half to three hours were conducted in which participants were allowed to speak freely and informally to the investigator. These interviews were tape-recorded and transcribed verbatim, and some were later annotated with content-related markers or "tags" to facilitate analysis. Information was also elicited on age, race, sex, marital status, employment status, drug history, and criminal offense history.

*This dataset is updated nightly. Crime data represents the initial information that is provided by individuals calling for police assistance. Please note that the dataset only contains the last 5 years. Remaining information is often amended for accuracy after an Officer arrives and investigates the reported incident. Most often, the changes are made to more accurately reflect the official legal definition of the crimes reported. An example of this is for someone to report that they have been "robbed," when their home was broken into while they were away. The official definition of "robbery" is to take something by force. An unoccupied home being broken into, is actually defined as a "burglary," or a "breaking and entering." While there are mechanisms in place to make each initial call as accurate as possible, some events require evaluation upon arrival. Caution should be used when making assumptions based solely on the data provided, as they may not represent the official crime reports.

This project was designed to isolate the effects that individual crimes have on wage rates and housing prices, as gauged by individuals' and households' decisionmaking preferences changing over time. Additionally, this project sought to compute a dollar value that individuals would bear in their wages and housing costs to reduce the rates of specific crimes. The study used multiple decades of information obtained from counties across the United States to create a panel dataset. This approach was designed to compensate for the problem of collinearity by tracking how housing and occupation choices within particular locations changed over the decade considering all amenities or disamenities, including specific crime rates. Census data were obtained for this project from the Integrated Public Use Microdata Series (IPUMS) constructed by Ruggles and Sobek (1997). Crime data were obtained from the Federal Bureau of Investigation's Uniform Crime Reports (UCR). Other data were collected from the American Chamber of Commerce Researchers Association, County and City Data Book, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Independent variables for the Wages Data (Part 1) include years of education, school enrollment, sex, ability to speak English well, race, veteran status, employment status, and occupation and industry. Independent variables for the Housing Data (Part 2) include number of bedrooms, number of other rooms, building age, whether unit was a condominium or detached single-family house, acreage, and whether the unit had a kitchen, plumbing, public sewers, and water service. Both files include the following variables as separating factors: census geographic division, cost-of-living index, percentage unemployed, percentage vacant housing, labor force employed in manufacturing, living near a coastline, living or working in the central city, per capita local taxes, per capita intergovernmental revenue, per capita property taxes, population density, and commute time to work. Lastly, the following variables measured amenities or disamenities: average precipitation, temperature, windspeed, sunshine, humidity, teacher-pupil ratio, number of Superfund sites, total suspended particulate in air, and rates of murder, rape, robbery, aggravated assault, burglary, larceny, auto theft, violent crimes, and property crimes.

The User Guide to Home Office Crime Statistics is designed to be a useful reference guide with explanatory notes regarding the issues and classifications which are key to the production and presentation of the crime statistics.

Following the transfer of responsibility for the publication of crime statistics to the Office for National Statistics (ONS) from 1 April 2012, a new reference guide is available within the Guidance and Methodology section on the Office for National Statistics Crime in England and Wales) website.

This study considers semiparametric spatial autoregressive models that allow for endogenous regressors, as well as the heterogenous effects of these regressors across spatial units. For the model estimation, we propose a semiparametric series generalized method of moments estimator. We establish that the proposed estimator is both consistent and asymptotically normal. As an empirical illustration, we apply the proposed model and method to Tokyo crime data to estimate how the existence of a neighborhood police substation (NPS) affects the household burglary rate. The results indicate that the presence of an NPS helps reduce household burglaries, and that the effects of some variables are heterogenous with respect to residential distribution patterns. Furthermore, we show that using a model that does not adjust for the endogeneity of NPS does not allow us to observe the significant relationship between NPS and the household burglary rate. Supplementary materials for this article are available online.