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Place management in neighborhood context: an analysis of crime at apartments in Cincinnati


The present study explores the importance of apartment management decisions for crime counts at apartments, and estimates whether the associations between these management decisions and crime vary according to neighborhood context. These issues are explored through multilevel Poisson-based regression modeling of manager survey data from a Cincinnati-based sample of 238 apartments nested within 29 neighborhoods. Results indicate that place management decisions were not, on average, associated with less crime at apartments. However, numerous management variables showed significantly different associations with crime at varying levels of neighborhood disadvantage. The results reinforce the propositions of multilevel opportunity theory suggesting that place management is likely to have different effects based on the broader neighborhood context. Multi-faceted approaches to place management in disadvantaged contexts are suggested.

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  1. 1.

    The exception to this rule are apartments designated for "seniors only" living. Generally, family status is a protected class under the Federal Fair Housing Act, unless the apartment building is designated as housing for older persons. Under this provision, apartments can discriminate against tenants with children if the building is occupied solely by persons aged 62 or older, or if at least one tenant that is 55 or older is living in at least 80% of the occupied units (42 USC 3607, Fair Housing Act).

  2. 2.

    Property offenses include: Breaking and Entering, Burglary, Motor Vehicle Theft, Receiving Stolen Property, and Theft. Violent offenses include: Aggravated Assault, Simple Assault, Kidnapping, Murder, Robbery, and Shootings. Disorder offenses include: Menacing, Prostitution, Trespassing, Vandalism, and Weapons Offenses (such as carrying firearms).

  3. 3.

    In that study, 40 apartments with unique owners were randomly selected across neighborhood groups in Cincinnati to allow for multilevel analysis. Additionally, in order to adequately pick up high-crime locations, all 98 unique-owner, high-crime apartments in the city (defined as more than 9 crimes in the year preceding sampling) were also added to the sample. This process yielded a total of 1,451 apartments (see Eck et al. 2010, for more details).

  4. 4.

    Cincinnati has 52 recognized neighborhoods, but several had very few apartments. For sampling purposes, adjacent neighborhoods with small numbers of apartments were combined (see Eck et al. 2010, for more details).

  5. 5.

    In the United States, the Housing Choice Voucher program (colloquially known as Sect. 8) is a federal program that gives low income families, the elderly, and the disabled assistance in paying for safe and affordable housing in the private market. Those who qualify for housing assistance through the Sect. 8 program are responsible to find adequate housing and come to an agreement with the landlord, who will receive funds directly from the U.S. Department of Housing and Urban Development (HUD) for the individual family's rent. More information about the Housing Voucher Choice Program can be found at

  6. 6.

    1000 feet is approximately double the average block length in the city (457 ft).

  7. 7.

    A spatial lag is included if theory or data analysis supports the likelihood of spatial autocorrelation regarding crime. Theoretically, there is reason to suspect contagion or diffusion processes, but we also examined whether there was statistical evidence of spatial autocorrelation in our data. In doing so, we initially tried to use “threshold distance” to create spatial lag variables, and examine the level of autocorrelation at the location level, but we reached a computational limit with GeoDA and ArcGIS software. Instead, we examined the level of spatial autocorrelation at the census block level (with each type of crime count aggregated to the census block). Global Moran’s I statistics were as follows: total crimes (0.31), property crimes (0.27), violent crimes (0.32), and disorder crimes (0.30). All indices indicate significant autocorrelation of crime counts across census blocks (p < 0.0001).

  8. 8.

    The percent of cases with imputed values is as follows: attract right renters (4%), hire managers (1%), frequency of visiting property (3%), hired security personnel (1%), hired maintenance workers (2%), financial background check (2%), criminal background check (1%), requiring a lease (< 1%), previous landlord contact (3%), number of evictions (1%), percent Sect. 8 (1%), advertised for rental (2%), and number of units (2%).

  9. 9.

    During this procedure, missing values of a continuous variable with a restricted range are filled in using a predictive mean matching imputation method. Briefly, we illustrate the sequential imputation technique (using chained equations and predictive mean matching) with 3 variables: v1 (binary), v2 (limited metric), and v3 (limited metric). In this scenario, we impute missing values for v1 using a logistic regression (v1 regressed on v2 and v3), impute missing values for v2 using predictive mean matching of v2 on v1 and v3, and impute missing values for v3 using a predictive mean matching of v3 on v1 and v2. Such a chain of equations are created for all variables with missing values.

  10. 10.

    In a multiple imputation procedure using 10 imputed datasets, the HLM program produces 11 total outputs—one for each of the 10 datasets and one that represents the averages of the values from the analyses of the 10 imputed datasets (with adjusted standard errors). While we present the average-values models herein, estimates from each of the imputed datasets are available from the authors upon request.

  11. 11.

    It should be noted that we examined the possibility of multicollinearity among level-1 variables using VIF (variance inflation factor) values. The diagnostic analyses revealed that multicollinearity was not a problem (maximum VIF is less than 2).

  12. 12.

    Since our models are Possion-based, regression coefficients should be read as one-unit increase/decrease in the independent variable corresponds with increase/decrease in the logs of expected crime counts. Also, in models not shown, we found that an initially-significant effect of disadvantage was accounted for by level-1 variables (i.e., compositional effects).


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This study was funded, in part, by the National Institute of Justice (2005-IJ-CX-0030, John E. Eck, PI). The authors would like to thank John Eck, Bonnie S. Fisher, Tamara D. Herold, and Heidi Scherer for their efforts in the design and execution of the collection of data analyzed herein and for shaping our views on place management. We would also like to thank Daniel Reinhard for his comments on an early draft of this paper. This paper would not have been possible without their efforts. However, we accept full responsibility for any errors contained herein.

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Correspondence to Andrew M. Gilchrist.

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See Table 4.

Table 4 Neighborhood-level descriptive statistics

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Gilchrist, A.M., Deryol, R., Payne, T.C. et al. Place management in neighborhood context: an analysis of crime at apartments in Cincinnati. Secur J 32, 501–522 (2019).

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  • Place management
  • Crime prevention
  • Multilevel opportunity theory
  • Neighborhood disadvantage
  • Apartment complexes