Rental housing and crime: the role of property ownership and management

Rephann, Terance J.

doi:10.1007/s00168-008-0215-1

Cited by 25 publications

(20 citation statements)

References 26 publications

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“…Third, urban neighborhoods are often marked with high levels of crime and danger (Leventhal & Brooks-Gunn, 2004). At the analytical level of the residential city block, prior research has linked this crime and danger directly to a lack of homeownership (e.g., Glaeser & Sacerdote, 1999;Rephann, 1999Rephann, , 2009. Compared to renters, homeowners are less mobile (Dietz & Haurin, 2003) and thus have better established neighbor social networks (DiPasquale & Glaeser, 1999;Rohe & Stewart, 1996;Rohe, VanZandt, & McCarthy, 2002).…”

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confidence: 99%

Can Neighbor Attributes Predict School Absences?

Gottfried

2013

Urban Education

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Recent evidence suggests that the neighborhood context, particularly for urban youth, can influence a range of outcomes. This study makes contributions to the field by examining how the neighborhood context directly relates to missing school. To do so, this study employs a large-scale, longitudinal data set of multilevel observations for entire elementary-and middleschool student cohorts in an urban district over 8 academic years. By linking urban school district administrative data with U.S. Census data, this study provides unique insight into how the characteristics of the neighbors on a student's residential block (determined by his or her exact home address) predict school absences. The results indicate significant relationships between school absences and multiple categories of neighbor attributes, as delineated across measures of poverty, family structure, homeownership status, and race. These results are also distinguishable based on student gender, race and ethnicity, and socioeconomic status.

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confidence: 99%

Can Neighbor Attributes Predict School Absences?

Gottfried

2013

Urban Education

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“…Similar to the counterpart hypothesis at the neighborhood level (see hypothesis 4), burglars may prefer burglarizing renter-occupied residences rather than owner-occupied residences. Because renters tend to move more often than homeowners, rental status may inhibit guardianship and reduce the risk of detection since it could be more difficult for neighbors to establish whether visitors are legitimate or not (Andresen 2011;Rephann 2009). We hypothesize that:…”

Section: Hypothesismentioning

confidence: 99%

“Location, Location, Location”: Effects of Neighborhood and House Attributes on Burglars’ Target Selection

Vandeviver

Bernasco

2019

J Quant Criminol

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Objectives To empirically test whether offenders consider environmental features at multiple spatial scales when selecting a target and examine the simultaneous effect of neighborhood-level and residence-level attributes on residential burglars' choice of residence to burglarize. Methods We combine data on 679 burglaries by 577 burglars committed between 2005 and 2014 with data on approximately 138,000 residences in 193 residential neighborhoods in Ghent, Belgium. Using a discrete spatial choice approach, we estimate the combined effect of neighborhood-level and residence-level attributes on burglars' target choice in a conditional logit model. Results Burglars prefer burglarizing residences in neighborhoods with lower residential density. Burglars also favor burglarizing detached residences, residences in single-unit buildings, and renter-occupied residences. Furthermore, burglars are more likely to target residences in neighborhoods that they previously and recently targeted for burglary, and residences nearby their home. We find significant cross-level interactions between neighborhood and residence attributes in burglary target selection. Conclusions Both area-level and target-level attributes are found to affect burglars' target choices. Our results offer support for theoretical accounts of burglary target selection that characterize it as being informed both by attributes of individual properties and attributes of the environment as well as combinations thereof. This spatial decision-making model implies that environmental information at multiple and increasingly finer scales of spatial resolution informs crime site selection.

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“…External factors including location, proximity to other hotels, transportation convenience, overall safety image (Huang, Kwag, and Streib 1998; P. Jones and Groenenboom 2002; Rephann 2009) are recognized as significant factors which are related to criminal activity. Internal factors like hotel rental features like the number of rooms, length of stay (Hendri 2012), and security design (Huang, Kwag, and Streib 1998) have also affected the crime rate.…”

Section: Introductionmentioning

confidence: 99%

The Sharing Economy: A Geographically Weighted Regression Approach to Examine Crime and the Shared Lodging Sector

Pennington‐Gray

Kim

2018

Journal of Travel Research

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The sharing economy has gained great market share within the lodging sector by offering cost-effective accommodation solutions. However, it is also troubled by increasing criminal incidents. This study examined the global relationship between the density of Airbnb and crimes in Florida, explored how the relationships vary at the county level. The results suggested that crime-lodging associations vary by listing types but not crime types. Only the Shared Room type consistently exhibited positive associations with both property and violent crimes, while Private Room and Entire Home exhibited negative associations. Local variations were identified by geographically weighted regression, which could be explained by the local tourism development and ethnic diversity degree. We suggested equal efforts in preventing both property and violent crimes in home sharing business. Also regional differences need to be considered when responding to shared lodging crimes.

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Rental housing and crime: the role of property ownership and management

Abstract: K42, R29,

Cited by 25 publications

References 26 publications

Can Neighbor Attributes Predict School Absences?

Can Neighbor Attributes Predict School Absences?

“Location, Location, Location”: Effects of Neighborhood and House Attributes on Burglars’ Target Selection

The Sharing Economy: A Geographically Weighted Regression Approach to Examine Crime and the Shared Lodging Sector

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