CRIM1020 Victimology – Week 7 Notes: Traditional Crimes (Personal & Property)

Part II: Data on traditional crimes

• Aims (Part II): Identify personal and property crime data on ‘traditional crimes’ using two main data sources:
  • Reported crime data: crimes reported to or discovered and recorded by police. Use the term “reported” when referring to this data in writing. Note: reported data cannot account for the ‘dark figure of crime’ (crimes not reported to police).
  • Self-report data: public responses to crime and victimisation surveys.
• Important caveat about data types:
  • The ‘golden rule’: we do not observe a change in crime directly from the data; we infer a change in crime from the data.
  • Self-report data helps triangulate official (police) data to capture otherwise hidden victimisation.
• Part III introduces Property Victimisation and notes that other crime types (fraud, identity theft, mass-marketing fraud, cybercrimes against businesses) can be context-specific in a digital society.

Crime categories

• Personal crimes (violent crimes) – examples and scope (NSW BOCSAR data listed for context):
  • Murder
  • Robbery
  • Assault
  • Rape/sexual assault
• Property crimes: break and enter (burglary), theft, motor vehicle theft, arson
  • Note: burglary and arson are described as property and person offences (cross-reference with slides 21–22)
• Summary point: Personal crimes/violent crimes vs property crimes are two primary categories used in reporting and analysis (NSW BOCSAR data).

Crime Data Types and Key Conceptual Points

• Two main data types criminologists use today:
  • Reported crime data: to or discovered by police; often preferred for trend analysis in official statistics.
  • Self-report data: obtained from surveys and victimisation studies; captures unreported offences.
• Key interpretive issues:
  • The “dark figure of crime”: many crimes go unreported; self-report data helps estimate true prevalence.
  • The “golden rule” of crime data interpretation: observed data reflect reported changes; we infer actual changes in crime from observed patterns.
• Data sources mentioned:
  • NSW BOCSAR Recorded Crime Statistics, Quarterly Update, March 2025
  • NSW BOCSAR: NSW violent and property offences statistics (long-term trends)

Figures: Long-term trends in NSW (violent and property offences)

• Figure 1.1: NSW long-term trend in violent offences
  • Measure: incident rate per 100,000 population
  • Violent offences include: murder, attempted murder, manslaughter, assault (domestic violence related and non-domestic), assault of police, robbery (with/without weapons), sexual assault and sexual offences
  • Data source: NSW BOCSAR Recorded Crime Statistics – Quarterly Update March 2025
  • Visual takeaway: trend lines showing increases/decreases over time (with a moving average line)
• Figure 1.2: NSW long-term trend in property offences
  • Measure: incident rate per 100,000 population
  • Property offences include: break and enter (dwelling and non-dwelling), motor vehicle theft, steal from motor vehicle, steal from retail store, steal from dwelling, steal from person, stock theft, and other theft
  • Data source: NSW BOCSAR Recorded Crime Statistics – Quarterly Update March 2025

Two-year changes (to March 2025) and SARO impact

• Recorded criminal incidents for major offences over 24 months to March 2025 (NSW):
  • Domestic violence-related assault up by +3.6 ext{ exttt{%}}
  • Sexual assault up by +6.4 ext{ exttt{%}}
  • Steal from retail store up by +4.3 ext{ exttt{%}}
  • Steal from motor vehicle declined by -9.1 ext{ exttt{%}}
• Sexual Assault Reporting Option (SARO): introduced Jan 2023; online reporting surged, comprising 22.8 ext{ exttt{%}} of all sexual assault reports in the year to March 2025 and driving 40.5 ext{ exttt{%}} of the total increase over the past decade.
• Commentary by NSW BOCSAR Executive Director Jackie Fitzgerald: these figures highlight growing willingness of victims to speak out, supported by more accessible and confidential reporting pathways.
  • Source: NSW BOCSAR, Quarterly Update, March 2025

Homicide, victimhood indicators, and broader context (literature and sources)

• Homicide in Australia: Miles H and Bricknell S (2025) Homicide in Australia 2023–24, Australian Institute of Criminology (AIC). DOI: 10.52922/sr77826.
• Crime victimisation in Australia 2023–24: Australian Bureau of Statistics (ABS) 2025; ABS report on crime victimisation for the 2023–24 financial year.
• Family, domestic and sexual violence: Australian Institute of Health and Welfare (AIHW) 2025; key findings.
• Indigenous status as a victimhood indicator: (Miles & Bricknell, 2025) and related ABS/AIHW materials.
• Foundational/interpretive points:
  • Victimisation data from multiple sources helps understand disparities and structural determinants of crime.
  • Data are not value-neutral; the way data are collected and used can have distributive effects on different victim groups.
• References (for context in lecture and readings): Pleasence & McDonald (2013); ABS (2025); AIHW (2025); Cross, Dragiewicz & Richard (2018); Miles & Bricknell (2025); McNally & Newman (2008); Newman (2010); NSW BOCSAR (2025); Weatherburn (2011).

Police reporting patterns for personal and household crime (ABS NSW data)

• Personal crime (2023–2024): proportion of victims who reported the most recent incident to police:
  • Physical assault: 49 ext{ exttt{%}}
  • Face-to-face threatened assault: 39 ext{ exttt{%}}
  • Non-face-to-face threatened assault: 33 ext{ exttt{%}}
  • Robbery: 45 ext{ exttt{%}}
• Household crime (2023–2024): proportion of victims who reported the most recent incident to police:
  • Break in: 75 ext{ exttt{%}}
  • Attempted break in: 48 ext{ exttt{%}}
  • Motor vehicle theft: 84 ext{ exttt{%}}
  • Theft from a motor vehicle: 51 ext{ exttt{%}}
  • Malicious property damage: 55 ext{ exttt{%}}
  • Other theft: 37 ext{ exttt{%}}
• Interpretation: reporting rates vary by crime type and context; high for high-severity or tangible losses, lower for more discreet or insurance-covered losses.

Summary and implications for Victimology practice

• Personal crimes tend to attract more media and policy attention due to potential for severe harm and visibility.
• Property crimes are more prevalent and have substantial societal costs (fear, insurance, property loss) despite often receiving less sensational media coverage.
• Triangulation of reported and self-reported data is essential for understanding true prevalence and for informing resource allocation and policy responses.
• Data interpretation must consider potential biases, reporting effects, and structural determinants of victimisation across population groups.

Part III: Property Victimisation

Property Victimisation: overview

• Property crime is a form of criminal victimisation that interfaces with the criminal justice system.
• Common examples of property crime include: burglary, theft, motor vehicle theft, arson.
• Focus here is on how property victimisation plays out in terms of fear, loss, and justice system engagement, as well as the types of offences most relevant to victims.

Burglary

• Burglary is a major source of fear and is often framed as a violation of the home and sense of safety.
• Not confined to the home; may occur when an offender enters any structure with intent to commit a crime (felony such as assault/rape or theft).
• No explicit requirement for use of force to qualify as burglary.
• Implications for victims: perceived vulnerability, housing security, and fear extend beyond the immediate incident.

Arson (in NSW)

• Legal framework (Crimes Act 1900 (NSW)):
  • Destroy or damage property: Section 195
  • Intend to cause injury: Section 196
  • Dishonestly destroy or damage property: Section 197
  • Intend to endanger life: Section 198
• Bushfires context:
  • Section 203E: offence to set a fire and be reckless as to the spread to vegetation on public land or land belonging to another.
  • Maximum penalty for arson-related offences: $21 ext{ years}$ imprisonment.
  • If death results from arson, offender may be charged with murder or manslaughter.
• Source: NSW legislation (current in force).

Other Types of Property Offending

• Offending with vestiges in traditional property crimes plus emerging technology:
  • Fraud
  • Identity theft
  • Mass-marketing fraud

Fraud

• Definition: An act in which attempts are made to deceive with promises of goods, services, or financial benefits that do not exist, were never intended to be provided, or were misrepresented.
• Nature: An enduring form of crime, increasingly perpetrated in novel ways; victims may not immediately recognise victimisation.
• Key component: deception.
• NSW legislation (Fraud): Part 4A–4C, including Division 2 Fraud and related offences (Section 192E et seq.).
  • 192E Fraud: (1) A person who, by any deception, dishonestly—
    (a) obtains property belonging to another, or
    (b) obtains any financial advantage or causes financial disadvantage,
    is guilty of the offence of fraud.
  • Maximum penalty: $10 ext{ years}$ imprisonment.
  • (2) Dishonesty can apply even if the person is willing to pay for the property.
  • (3) A conviction for fraud can apply to deficiencies in money or property accumulated over time.
  • (4) Fraud as an alternative verdict to larceny; larceny can be an alternative verdict to fraud.
• Common types of fraud (illustrative types presented in lecture slides):
  • Home-repair schemes: up-front cash; targeting elderly; scams around home repairs.
  • Appliance or auto repair schemes: similar to home repair scams.
  • Insurance fraud: victim is sold worthless or misrepresented insurance.
  • Award scams: victim pays a fee to process a supposed prize
• Common types of fraud (category breakdown):
  • Membership fraud: clubs/businesses that do not exist or quickly go out of business.
  • Financial/investment fraud: misleading/false information by a financial adviser.
  • Charity fraud: soliciting funds for a non-existent or non-handed charity.
  • 900-number phone scams: high-cost phone charges.
  • Romance fraud: victims defrauded during a fraudulent romantic relationship (Cross, Dragiewicz et al. 2018).

Identity Theft

• Terminology and definitional background:
  • Identity theft is a widely used term; no single universal definition; commonly defined as using another’s personal information to facilitate fraud.
  • McNally & Newman (2008): concise definition – an instance in which an individual’s personal information is used by another to facilitate an act of fraud.
• Detailed and policy-grounded concepts:
  • Identity theft: theft or assumption of a pre-existing identity (or significant part thereof), with or without consent, living or deceased.
  • Identity crime: generic term for activities where a perpetrator uses a fabricated, manipulated, or stolen identity to facilitate crimes.
  • Australian context: standard definitions developed by the Australian Federal Police (AFP) Proof of Identity Steering Committee for policing Australia-wide.
• NSW legal framework (Part 4AB Identity offences, sections 1921–192M and related provisions):
  • 1921 Definitions
  • 192J Dealing with identification information
  • 192K Possession of identification information
  • 192L Possession of equipment to manufacture identification documents or related items
  • 192M Miscellaneous provisions
  • 193 (Renumbered as 192A): Money-laundering-related provisions (contextual to identity crime)
  • 193A–193F: Money laundering and related offences
• Key concepts for victim support and enforcement:
  • Possession and misuse of identification information (names, addresses, birth dates, driver licenses, passports, biometric data, credit/debit card data, financial account details, passwords, etc.)
  • The definition of identification information encompasses a broad range of data that could facilitate fraud.

Mass Marketing Fraud

• Definition (US Department of Justice, 2015): any fraud scheme using mass-communication techniques (internet, phones, mail, or mass meetings) to present solicitations, conduct fraudulent transactions, or move proceeds to financial institutions.
• Two main types:
  • Small amounts from a large number of victims
  • Large amounts from a small number of victims (often wealthier individuals)

Cybercrime Against Business

• General definition: cybercrime involves computers or networks as tools, locations, or targets of crime (Newman, 2010).
• Newman (2010) outlines seven types:
  1) Hacking into a computer system
  2) Privacy violations (spying on victim’s computer use)
  3) Identity theft
  4) Phishing
  5) Information theft (for resale)
  6) Denial of Service (DoS) attacks
  7) Virus attacks
• Additional criminal activities mentioned: distribution of child pornography (not elaborated in detail, but noted as another category in cyber contexts).

Summary: Property offences versus personal crimes

• Personal crimes often attract more media coverage and may involve severe injury or death; however, property crimes are more prevalent and have a wide-reaching impact on fear, insurance costs, and economic losses, running into billions of dollars per year.
• There is a strong case for greater attention to victims of property crime and for developing resource responses to mitigate their plights.

Connections to practice and policy

• Triangulation of data sources (police-reported vs self-reported) is necessary to understand true victimisation patterns and to allocate resources effectively.
• Data collection and usage have distributive effects on victims; ethical considerations must guide how data are collected, interpreted, and applied in policy.

References (Key sources cited in the slides)

• Pleasence P and McDonald H (2013) Crime in context: Criminal victimization, offending, multiple disadvantage, and the experience of civil legal problems, Law and Justice Foundation of NSW, no. 33, pp. 1–6.
• Australian Bureau of Statistics (2025) Crime Victimisation, 2023-2024 financial year, ABS. Available from: https://www.abs.gov.au/statistics/people/crime-and-justice/crime-victimisation/latest-release
• Australian Institute of Health and Welfare (2025) Family, domestic and sexual violence, AIHW. Available from: https://www.aihw.gov.au/family-domestic-and-sexual-violence/population-groups/key-findings
• Cross C, Dragiewicz M and Richard K (2018) Understanding romance fraud: Insights from domestic violence research, The British Journal of Criminology, 58(6):1303-1322.
• Miles H and Bricknell S (2025) Homicide in Australia 2023–24, Australian Institute of Criminology, Canberra, Available from: https://www.aic.gov.au/publications/sr/sr52. DOI: 10.52922/sr77826
• McNally MM and Newman GR (2008) Perspectives on identity theft, Criminal Justice Press, Monsey, NY.
• Newman RC (2010) Computer security: protecting digital resources, Jones and Bartlett Publishers, Sudbury, MA.
• NSW Bureau of Crime Statistics and Research (2025) Recorded crime statistics quarterly update March 2025, available from: https://bocsar.nsw.gov.au/research-evaluations/2025/nsw-recorded-crime-statistics-quarterly-update-mar-2025.html
• Weatherburn D (2011) Uses and abuses of crime statistics, Contemporary Issues in Crime and Justice, NSW Bureau of Crime Statistics and Research, 153:1-16.