UNIVERSITY OF CINCINNATI
Date: 7-Oct-2010
I, Troy C Payne , hereby submit this original work as part of the requirements for the degree of: Doctor of Philosophy in Criminal Justice It is entitled: Does Changing Ownership Change Crime? An Analysis of Apartment
Ownership and Crime in Cincinnati
Student Signature: Troy C Payne
This work and its defense approved by: Committee Chair: John Eck, PhD John Eck, PhD
James Frank, PhD James Frank, PhD
Pamela Wilcox, PhD Pamela Wilcox, PhD
Elizabeth Groff, PhD Elizabeth Groff, PhD
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Abstract Crime at multi-family dwellings is an ongoing concern. Using concepts from environmental criminology, this dissertation adapts Madensen’s (2007) model of bar place management to apartments. One aspect of this model, the relationship between ownership change of an apartment building and crime, is examined. I found that while about half of apartments change ownership during the period 2002-2009, serial ownership change is rare.
Crime is heavily concentrated among apartments, with over half of crime occurring at just 10% of apartments – and these extreme values of crime tend to drive the multivariate analysis.
Ownership change and crime are associated with each other in a feedback system. Ownership change is more likely at apartments with a history of past crime, and ownership change is associated with a 10% increase in future crime counts. Neighborhood context has a complex relationship with significant variation between neighborhoods in both crime counts and in the relationship between ownership change and crime. In some neighborhoods, ownership change and crime are positively related; in other neighborhoods, the relationship is negative.
Even though my findings are sensitive to extreme values, methodology and model selection decisions, it is apparent that ownership change could be an important intervention point for crime prevention. Interventions such as landlord training should be targeted at high crime apartments which change ownership, while recognizing that the overwhelming majority of apartments and apartment owners have zero crime.
'
'' Acknowledgements Any errors of omission or commission in this dissertation are mine alone – and they would have been much more numerous without the input of others. First among these is my wife, Carrie Payne. Without Carrie, my academic career simply would not be possible. This work, and any other work I produce from here forward, simply could not have existed without her support over the past decade or so. I owe her more than I can say.
My fellow students at the School of Criminal Justice at the University of Cincinnati were also helpful. Specifically, Heidi Scherer provided key insights that enabled me to break through multiple mental blocks during the analysis. Her willingness to provide commentary via text, instant message, email, and phone was incredible. I suspect she will need less help than I did on her dissertation, but I stand ready to repay that debt.
John Campbell’s landlord training provided me with the opportunity to interact with landlords. His training and that interaction sparked the specific idea that eventually lead to this dissertation. The City of Cincinnati and the Cincinnati Police Department invited Campbell to
Cincinnati to conduct those landlord trainings and were nice enough to invite me as well. Lt.
Brett Issac, Sgt. Maris Herold, and Officer Katie Werner of the Cincinnati Police Department were very patient with me. They taught me everything I know about how to provide timely, useful, actionable analysis to a police department.
Finally, each member of the faculty at the University of Cincinnati School of Criminal
Justice has given me different insights into research, teaching, and academic life. Even faculty not directly related to my particular research interests have provided invaluable insight and experience. I thank them all. My dissertation committee deserves special mention. Drs. Frank,
''' Wilcox, and Groff all provided useful feedback on this dissertation. My dissertation chair, John
E. Eck, has provided a critical eye at exactly the right moments, while giving me plenty of room to think for myself.
'4 Table of Contents Abstract ...... i Acknowledgements ...... iii Chapter 1: Introduction ...... 1 Overview ...... 2 Chapter 2: Crime theory and place ...... 4 Chapter 3: Crime concentration and place ...... 8 The need for facility-specific models and small units of analysis ...... 11 Chapter 4: Place management and apartments in the literature ...... 14 Chapter 5: A dynamic theory of place management of apartments ...... 17 Crime patterning and the consequences of poor place management over time ...... 24 Hypotheses ...... 27 Chapter 6: Data ...... 31 Setting ...... 31 Data ...... 31 8 ,". 0!#*1TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUS **1$-01#04'!#TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUT #-%0 .&7 ,"#,131TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUU 8',)',%-5,#01&'.Q!0'+#Q ,"*-! 2'-," 2 TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUU Operational definition of apartments ...... 34 Dependent variable ...... 35 Apartment-level measures ...... 36 5,#01&'.!& ,%#TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUX #!-,-+'!0#1-30!#1TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUY '8#TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUZ !0'-0! **1$-01#04'!#TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTUZ Neighborhood-level crime as context ...... 39 Analysis ...... 41 Chapter 7: Apartment characteristics and distribution of owner change ...... 42 Size ...... 42 Economic resources ...... 43 Neighborhood context ...... 43 Ownership change ...... 44 Ownership change is associated with past crime ...... 48 Summary ...... 50 Chapter 8: The distribution of crime ...... 51 Chapter 9: Does ownership change influence crime at apartments? ...... 62 Choosing the best model for these data ...... 62 3++ 07-$+-"#*1#*#!2'-,TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTXX '&##$$#!2-$-5,#01&'.!& ,%#-,!0'+#!-3,21S=->&0#13*21TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTXY A cautionary note: average effects can be deceptive ...... 75
Summary: ownership change increases crime ...... 75 Chapter 10: Does neighborhood-level crime condition apartment-level effects? ...... 78 Sensitivity to model specification ...... 78 Different effects in different neighborhoods ...... 81 Neighborhood-level effects: Summary ...... 88 A cautionary note: the influence of outliers ...... 88 Chapter 11: Conclusions and implications ...... 91 Methodology and future studies of crime counts ...... 95 Implications ...... 96 References ...... 99 Appendix I: Geocoding and matching Auditor data to crime data ...... 106 Appendix II: Calls for service by type ...... 108 Appendix III: Neighborhood characteristics ...... 111 Appendix IV: Distribution of apartments by neighborhood ...... 113 Appendix V: Correlation matrix of neighborhood indicators and number of apartments ...... 116 Appendix VI: Year of last sale by land use ...... 117 Appendix VII: Distribution of crime at apartments by neighborhood ...... 119 Appendix VIII: PRM, NBRM, ZIP, and ZINB estimates ...... 121 Appendix IX: ZINB results for models with days since last sale ...... 123 Appendix X: HLM models including only those neighborhoods with more than 10 apartments ...... 125 Appendix XI: HLM models with only one cross-level interaction ...... 127 ' *#-$4'%30#1 4'%30#SS'&#-07-$.* !#+ , %#+#,2 2 . 02+#,21TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTS[ 4'%30#TS) , %#+#,2Q1 *#Q ,"!0'+#TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTX 4'%30#US@#* 2'-,1&'.12#12#"TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTY 4'%30#VSB# 0-$* 121 *#$-0 . 02+#,21 1-$#!USQTRR[TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTVX 4'%30#WS!0#"'!2#"-""1-$-5,#01&'.!& ,%# 1 $3,!2'-,-$. 12!0'+#TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWR 4'%30#XS-,!#,20 2'-,-$ **!0'+# 2 . 02+#,21TRR[TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWT 4'%30#YS-,!#,20 2'-,-$"'1-0"#0 2 . 02+#,21TRR[TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWU 4'%30#ZS-,!#,20 2'-,-$.0-.#027!0'+# 2 . 02+#,21TRR[TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWV 4'%30#[S-,!#,20 2'-,-$4'-*#,2!0'+# 2 . 02+#,21TRR[TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWW 4'%30#SRS-,!#,20 2'-,-$ **!0'+# 2 . 02+#,215'2&VVS[3,'21TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWX 4'%30#SSS-,!#,20 2'-,-$ **!0'+# 2 . 02+#,215'2&TRVU[3,'21TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWY 4'%30#STS-,!#,20 2'-,-$ **!0'+# 2 . 02+#,215'2&VR&3,'21TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTWZ 4'%30#SUS-,!#,20 2'-,-$ **!0'+# 20#2 '*Q . 02+#,21-4#0TTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTW[ 4'%30#SVS-,!#,20 2'-,-$ **!0'+#1TRRXVTRRZTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTTXR
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'' Chapter 1: Introduction While most criminological literature focuses on the offender, a growing field, environmental criminology, focuses on the criminal event itself. Instead of asking why a person would want to commit crime in general, environmental criminology asks why a person would want to commit a particular crime, in a particular place, at a particular time, and under what circumstances (Cohen & Felson, 1979; Cornish & Clarke, 1986; Eck, 1994). This dissertation is rooted in this newer way of examining crime.
Crime at apartment buildings is an ongoing public concern. The nature of rental housing is such that its tenants are often lower income, with higher mobility rates than owner-occupied residences. So crime is more likely in rental housing than in owner occupied housing. The idea that landlords should actively manage their places and tenants in order to reduce crime has a certain appeal. Newspaper accounts of crime at apartments often discuss the link between competent management and crime as if it were fact (Bichler-Robertson, 1998). National training programs have been developed to give landlords the tools they need to manage their properties effectively (Campbell, 2000), and problem-oriented policing interventions have focused on landlords as well as on their tenants (Eck, 2002; Eck & Wartell, 1998; Green, 1995).
One case study has examined what happens when a slumlord purchases particular properties (Bichler-Robertson, 1998) but no systematic research has examined the impact of ownership change on a wide scale. This dissertation seeks to fill that gap. Using ownership data and crime data from Cincinnati, Ohio, I will test the relationship between ownership change and crime.
S Overview This dissertation begins with a review of crime theory and place research in Chapter 2.
Routine activities theory (Cohen & Felson, 1979) and rational choice theory (Cornish & Clarke,
1986) are briefly explained. The concept of place and place management is described (Eck,
1994; Felson, 1995) with an eye toward explaining why place managers should matter. Chapter
3 outlines the relationship between place and crime concentration. The variation in place management is proposed as one mechanism for the common finding that crime is highly concentrated at specific heterogeneous facilities (Groff, Weisburd & Yang, 2010; Sherman,
Gartin & Buerger, 1989; Weisburd, Bushway, Lum & Yang, 2004). Chapter 3 also explains why studies of place management, such as this one, should utilize small units of aggregation at one facility type (Eck, Clarke & Guerette, 2007; Madensen, 2007). Next, Chapter 4 discusses the extant literature regarding place management at apartments, which suggests that changes in place management are empirically related to crime.
Chapter 5 adapts the general model of place management proposed by Madensen (2007) to apartments. With minor exceptions, Madensen’s model, which was originally developed for bars, fits apartment place manager decisions very well. The consequences of poor place management over time are discussed in this chapter as well, followed by the primary hypotheses that will guide my analysis:
• H1: Ownership change of apartments is rare compared to ownership stability. • H2: Ownership change is more likely at apartments with a history of high crime than at apartments with a history of low crime. • H3: Crime at apartments is similar to a power-law distribution. Regardless of how apartments are partitioned, a relative handful of apartments produce a disproportionate amount of crime.
T • H4: Ownership change will be associated with increases in crime; these increases will be greater at places with a history of being crime problems than at places with no such history. • H5: The effect of ownership change varies among neighborhoods as a function of neighborhood-level crime.
Chapter 6 describes the data and methods that will be used to test the hypotheses outlined in Chapter 5. Ownership data from the Hamilton County Auditor will be combined with crime data from the Cincinnati Police Department using base maps provided by the Cincinnati Area
Geographic Information System. The variables that are available in this data and those that will be created are discussed. Chapter 7 discusses the characteristics of apartments in Cincinnati and examines whether past crime is associated with ownership change. Chapter 8 details the distribution of crime at apartments. Chapter 9 examines whether ownership change is associated with future crime using pooled count-based models. Chapter 10 details hierarchical models which address the question of neighborhood context. A final chapter provides implications and conclusions.
U Chapter 2: Crime theory and place Instead of focusing on what causes criminal events, criminologists have traditionally focused on what causes criminal tendencies. The central question has been, "What causes people to be criminal?" instead of "What causes people to commit this particular crime, in this particular way, against this particular victim, in this particular place?" Hirschi (1986) has described this as the difference between criminality – the propensity to commit crime – and a specific criminal event. The majority of criminology has historically focused on criminality while taking opportunities for crime as a given. For example, Gottfredson and Hirschi (1990) attribute crime to criminals’ general inability to delay gratification. They acknowledge that opportunity must be present in order for a crime to occur but argue that such opportunities are so prevalent as to be practically ubiquitous. The idea that opportunity is ubiquitousis a logical requirement for focusing exclusively on offenders. This assumption is seldom challenged in traditional criminology.
Despite this historical focus on criminality, a growing and separate field, environmental criminology, focuses on the criminal event itself. Studies in this field suggest that opportunity to commit crime is far from ubiquitous. Routine activities theory suggests that the overall patterns of people's lives affect crime rates by changing the ability of motivated offenders to find suitable targets in the absence of capable guardians (Cohen & Felson, 1979). While their initial test of routine activities theory was a macro-level study, focusing on the movement of American activities outside of the home in the post-World War II period, Cohen and Felson provided a theory that explicitly includes testable hypotheses about the criminal event itself (Felson, 1987).
If Cohen and Felson (1979) are correct, then a criminal event requires a motivated offender and a suitable target to converge in time and space in the absence of a capable guardian. Crime
V prevention therefore requires removing the offender, removing the target, or ensuring that a
capable guardian is present. Cohen and Felson allude to this by suggesting that "potential
victims of predatory crime may take evasive actions which encourage offenders to pursue targets
other than their own" (Cohen & Felson, 1979, p. 590).
Such "encouragement" works to reduce the risk of a particular criminal event occurring
because offenders are rational within the limits of their knowledge (Clarke & Cornish, 2000;
Cornish & Clarke, 1986). That is, offenders seek benefits for themselves and choose criminal
means to obtain those benefits. Those decisions are made to accrue some sort of benefit such as
money, material goods, status, or excitement. Both the decision to be a criminal generally and
the decision to commit a specific criminal act are alterable – crime is not an immutable fact.
Individual offenders make purposive and deliberate decisions to offend (Clarke & Cornish,
2000). Like Gottfredson and Hirschi (1990), Clarke and Cornish (2000) separate involvement
decisions (i.e., criminality) from decisions regarding specific criminal acts.1
Felson (1986) discusses the utility of combining elements of control theory, rational
choice, and routine activities theory to form models of criminal events. He describes crime as
the result of multiple decisions by an interconnected web of actors. This web naturally includes
the victim and offender, but can also include handlers that exert control over offenders and
guardians that protect targets. Felson (1986) writes mostly about informal social control –
guardians are rarely police officers in his model. Perhaps most important for the current
discussion, however, is that Felson’s suggestion that decisions made by persons other than the
1 Note that this is no different from separating decisions to begin any line of work and decisions to commit specific acts related to the line of work chosen. For example, the decision to enter graduate school can logically be separated from the decision to write a particular scholarly article. The correlates for both decisions may – or may not! – be similar.
W victim and the offender affect the likelihood of a criminal event occurring. Offenders may be willing to violate the law but are blocked in their ability to do so by the decisions of other actors.
Those actors extend beyond offenders and their handlers, and targets and their guardians.
A third category of controllers was added by Eck (1994): Place managers. While routine activities theory had previously been formulated as the convergence in time and space of a motivated offender and a target in the absence of a capable guardian, Cohen and Felson (1979) largely take place for granted. Places are not “scaled down neighborhoods and neither are neighborhoods simple aggregations of places” (Eck, 1994, p. 14). Places are relatively small areas, no larger than a block face. The utility of dealing in aggregates smaller than neighborhoods is that social control is not exercised at the areal level. People exercise social control and are controlled at places, not in neighborhoods.
Place managers are people who exercise control over places (Eck, 1994). Place managers control conduct at the places they manage, independent of offenders and targets. Often, place managers are owners or employees of the owner. Place management can be obvious, as when hotel employees do not allow prostitution (Reichert & Frey, 1985). Place management can also be less obvious through creation and maintenance of the physical environment, such as placing jewelry behind glass cases to deter theft (Abelson, 1992). Moreover, effective place management can enhance other controllers’ efforts at crime prevention. Effective place management therefore has both direct and indirect effects, making promotion of effective place management a particularly potent strategy for crime prevention (Eck, 1994).
Felson (1995) further elaborates on Eck's (1994) model by providing a typology of those who would discourage crime. Felson (1995) suggests that each controller (target guardian,
X offender handler, and place manager) can have differing levels of responsibility for crime prevention. In descending order of responsibility, Felson identifies four levels of responsibility:
1) personal; 2) assigned; 3) diffuse; or 4) general. An example of personal responsibility for place management is homeowners monitoring their own home and nearby properties. Persons with assigned responsibility have a job assignment that specifically includes crime prevention, such as a doorman, concierge, building manager, or parking lot attendant. Any employee has diffuse responsibility for crime discouragement. A hotel maid, for example, can deter crime just by being present (and through no specific action on her part). Finally, anyone using the place has a general responsibility for discouraging crime. However, discouragement is most likely to occur when people have personal or assigned responsibility (Felson, 1995; Sorensen, 1998).
Recent studies have shown that crime and its discouragement occurs in a social and spatial context. Unlike Shaw and McKay (1972), which viewed the breakdown of social controls
(i.e., “social disorganization”) as the inevitable result of areas transitioning from residential to business and industrial areas, modern contextual theories recognize that human decision-making is involved in crime. These decisions occur within a social and spatial context (see, e.g.,
Brantingham & Brantingham, 1993; Wilcox Rountree, Land & Miethe, 1994; Wilcox, Land &
Hunt, 2003). Multi-level theories and models therefore help to improve prediction of criminal events while recognizing that both individual decision-making and larger sociological constructs are important. However, individuals are not randomly distributed within contexts. Even in poor neighborhoods, for example, some persons are poorer than others. The immediate context within which crime occurs – the place – therefore has stronger influences on decision-making than more distal contextual variables.
Y Chapter 3: Crime concentration and place Even in high-crime neighborhoods, crime concentrates at a relative handful of locations.
Sherman, Gartin, and Buerger (1989) found that over half (50.4%) of all crime in Minneapolis occurred at just 3.3% of the city’s addresses. Some crimes were very concentrated. For example, all robberies occurred at just 2.2% of all places in the city. Longitudinal analyses conducted in Seattle (Groff et al., 2010; Weisburd et al., 2004) found similar concentrations, with 4-5% of street segments accounting for about 50% of crime. All crime occurred in about half (48-53%) of all street segments. These concentrations were remarkably stable over the 14 years studied by Weisburd and his co-authors. These findings suggest that crime is both rare and concentrated at particular places within cities, with more variation within than between neighborhoods.
Crime is also generally stable at locations within a city over time. Weisburd et al. (2004) use trajectory analysis to empirically identify groups of street segments with differing crime trends. Though they identify nineteen different trajectories of Seattle street segments via group- based modeling techniques, Weisburd et al. (2004) classify these trajectories into just three logical groups for the purpose of discussion: stable, increasing, and decreasing trajectories. The stable group constitutes 84.7% of Seattle’s street segments. These stable street segments have varying amounts of crime but are characterized by very little change over the 1989-2002 period under study by Weisburd et al. (2004, pp. 301). Decreasing trajectories make up 14.1% of the street segments. The smallest group of street segments – just 2.1% of street segments – experienced an increase in crime during the study period.
Z The overall stability of crime at street segments over time is an important finding because
it suggests that common causal factors may be present at crime-prone places over time. Even at
increasing and decreasing street segments, there was stability in the scale of increase or decrease.
That is, high-crime places at the beginning of the study period were generally high-crime at the
end of the study period, and low-crime places at the beginning of the study period were generally
low-crime at the end of the study period.
Groff, Weisburd, and Yang (2010) continue this line of analysis by examining the spatial
distribution of temporal crime trajectories using similar data.2 Groff et al. (2010) found that
street segments near one another can have very different patterns of crime over time. Large
areas of Seattle consist of predominantly crime free street segments – partly because nearly half
(49.5%) of street segments fall into this category. Other areas of the city consist of “extreme
spatial heterogeneity” (Groff et al., 2010). The “hot spots” of crime are therefore highly
localized and persist over time (Spelman, 1995).
That crime concentrates into “hot spots” has been one of the driving forces behind
geographically-focused policing and crime prevention. The chance of an intervention to have an
effect greatly increases when police focus on high-crime areas instead of conducting unfocused
interventions (National Research Council, 2004). A consistent series of studies have shown that
geographically focused interventions work to reduce crime. Furthermore, crime is not likely to
simply move around the corner (Weisburd et al., 2006). In fact, displacement is less likely than
the diffusion of crime prevention benefits to areas nearby intervention areas (Braga, 2001; Braga,
2 Groff et al. (2010) use a slightly different definition of street segments, using geographic street segments instead of Weisburd et al.’s (2004) hundred-block definition. Groff et al. (2010) were also able to utilize an additional year of crime data. These differences did not produce substantive differences in the trajectories identified by group-based modeling.
[ Kennedy, Waring & Piehl, 2001; Braga et al., 1999; Green, 1995; Guerette & Bowers, 2009;
Weisburd et al., 2006).
While Sherman, Gartin and Buerger (1989), Weisburd et al. (2004), and Groff et al.
(2010) studied heterogeneous facilities, other research has found that crime concentrates in homogenous facility types as well. In fact, Eck, Clarke, and Guerette (2007) hypothesize that crime is highly concentrated within any facility type (apartments, bars, convenience stores, motels, etc), with a relative handful of places producing a large percentage of crime. Eck, et al.
(2007) further suggest that these concentrations persist no matter how facilities and crime are subdivided, provided that adequate numbers of facilities and crime exist to conduct analyses.
This suggests that there are identifiable differences between those facilities at which a disproportionate amount of crime occurs and the overwhelming majority of facilities.
The concentration among homogenous facilities could explain positive correlations between, for example, bars and crime. Though widely cited as finding that bars increase crime,
Roncek and Maier (1991) actually found that some bars increase crime. Thirty percent of blocks with bars had crime counts that were below the median number of crimes for all blocks. The positive coefficients for the presence of bars in their linear regression and tobit analyses could be due to a relative handful of very criminogenic bars instead of a general criminogenic feature common to all bars. While Roncek and Maier (1991) admit this in their conclusion, they attribute it to “culture” differences among bars. That “culture” is more appropriately termed social control of the place – or place management.
Variation in the attractiveness of places for committing crime is one mechanism that could explain concentrations of crime at places (Madensen, 2007). Eck (1994) demonstrated this
SR to be the case with drug dealing. In order for crime to occur at a place, the place must be attractive to criminals3 and must be located within the awareness space of offenders
(Brantingham & Brantingham, 1993).
Studies have shown that place features attract criminals – and that most of these features are the end result of decisions of the individuals who own or manage the place (Clarke, 1997;
Eck, 2002; Madensen, 2007). Put simply, a lack of effective place management can create crime opportunities and attract offenders (Clarke & Eck, 2005). Moreover, with notable exceptions – e.g., security personnel – crime control is not a primary goal of most place managers (Eck, 1994;
Felson, 1986; Madensen, 2007). Instead, crime control is situated in a constellation of other decisions, most of which are unrelated to crime (Eck, 1994; Madensen, 2007).
The need for facility-specific models and small units of analysis Place management in bars was documented by Madensen (2007). One key element of
Madensen’s (2007) model is that place management must be studied within the context of a particular facility type. Just as “crime” is not specific enough for the purposes of situational crime prevention (Clarke, 1995) and problem-solving (Goldstein, 1990), “high-crime place” is not specific enough for place-based analysis. Each facility type (bar, convenience store, apartment, etc) may have a different set of incentives for crime control, necessitating the study of each facility type independently until similarities can be found (or reasonably inferred).
This need for a facility-specific model may explain why place management is often overlooked in criminological studies, even among scholars who use a routine activity approach to explain empirical findings. Neither Weisburd et al. (2004) nor Groff et al. (2010), for
3 Though, as Eck (1994) notes, offenders need not seek the most attractive place. A place need only be minimally suitable for crime.
SS example, discuss place management when speculating on the underlying causes of crime at street segments despite discussing the confluence of motivated offenders and suitable targets in the absence of capable guardians (Cohen and Felson, 1979) as one possible mechanism that could cause the empirical patterns identified in their Seattle data. It is just as likely that street segments that experience high crime are characterized by a lack of effective place management.
Indeed, the causes of crime may be more concentrated than Weisburd et al. (2004) and
Groff et al.’s (2010) findings suggest. If the patterns found by Eck, Clarke, and Guerette (2007) hold for any aggregate (as they claim) then it is likely that only a handful of addresses on any given street segment cause a majority of the crime on that segment. By focusing solely on an aggregate – be it street segment or neighborhood – we are likely to obscure causal processes at the individual place level. As computing power increases, the ability to examine very small units of aggregation opens new possibilities for theory and practice (Brantingham, Brantingham,
Vajihollahi & Wuschke, 2009).
The purpose of this study is to examine one possible cause for crime at places. For that purpose, focusing on a single facility type at a small unit of aggregation is necessary.
Specifically, the focus here is apartment buildings. Apartment buildings were chosen for two reasons. First, apartments can generate large amounts of crime (Eck, 1994; Clark and Bichler-
Robertson, 1998) . Routine activities theory could explain this, since apartment buildings would provide for more opportunities for crime than single-family residences simply due to the larger number of people residing in apartment buildings. This is true for “victimless” crimes as well.
For example, Eck (1994) found that 60% of all building-based drug markets were in apartments.
ST Second, like bars (Madensen, 2007), apartments provide something of an ideal case for examining the effects of place management on crime. Apartment owners (or their agents) have an explicit financial interest in maintaining their properties. This financial interest is transparent
– decisions made by apartment owners are geared towards making a profit (Charles Tassell, personal communication). In short, apartment owners are business persons, seeking to minimize cost and maximize profit. The extent to which crime plays a role in this search for profit will be explored in the dynamic model of apartment management presented in the Chapter 5.
SU Chapter 4: Place management and apartments in the literature Surprisingly little literature exists to guide explorations of place management at privately-owned apartments. Similar to bars (Madensen, 2007), a search of the business literature returned no results for apartments or rental housing and crime. Criminological literature regarding crime at apartments is sparse as well, but does exist. Most studies of apartment place management in the literature are evaluations of specific interventions, most commonly surrounding nuisance abatement or drug reduction. Because specific interventions are not the focus of this dissertation, the following is not a comprehensive review of what
“works” to reduce crime at apartments. Instead, this review is intended only to demonstrate that place managers not only have an important theoretical role (as discussed above) but also that empirical research has shown that place manager behavior is associated with crime.
Green (1995) found reductions in drug activity as a result of a multi-modal intervention at problem places with little displacement in Oakland. Oakland’s intervention was a multi-agency approach, focusing on nuisance locations. While not focused exclusively on apartments, three- quarters of the intervention sites were renter-occupied, and 87% were residential. One of the key elements of Oakland’s intervention was a landlord training program focusing on crime reduction.
Rental properties are particularly good candidates for “third party policing,” or coercion of place managers to actively control disorderly and criminal behavior at their properties
(Buerger, 1998). In a randomized control trial, for example, Eck and Wartell (1998) found that intervening with landlords of places that had been targets of drug enforcement led to a reduction in subsequent crime. The most effective intervention was a face-to-face meeting with the landlord, but even a letter led to a crime reduction compared with controls. These interventions
SV led to few physical improvements, but changes in tenant screening and eviction procedures were
found (Eck & Wartell, 1998). These sorts of interventions can be coupled with training
landlords how to manage their properties more effectively.
There is at least some public expectation of landlord responsibility for crime. Bichler-
Robertson (1998) explored newspaper accounts of the link between apartment management and
crime. She found that there was a strong belief among the public that apartment managers could
reduce crime (as measured by a content analysis of newspaper articles). Moreover, landlord
training programs aimed at improving landlord management skills had been attempted in 13
states. Over half (51%) of the newspaper articles studied by Bichler-Robertson (1998) discussed
city ordinances aimed at increasing the power of building inspectors or imposing legal
responsibilities on landlords.4
This surge of news coverage of landlord trainings corresponds with a national effort by
the Bureau of Justice Assistance to develop a comprehensive landlord training program for crime
prevention. Beginning in the late 1990’s, John Campbell created a landlord training program
that incorporates elements of crime prevention through environmental design, tenant screening,
managing tenant behavior, and fair eviction procedures (Campbell, 2000). This training program
has been conducted in many jurisdictions, including Cincinnati in late 2009.
I could find only one study that examined the effects of ownership change, which is the
primary focus of this dissertation. Clarke and Bichler-Robertson (1998) presented a case study
of one ownership change and one owner forced to hire competent property managers. In the case 4 Such an ordinance was enacted in Cincinnati as CMC 761. CMC 761 allows the Cincinnati Police Department to recoup costs associated with enforcement at places with three or more calls for service per month. Despite being enacted in November, 2006, CPD largely did not enforce this ordinance until the middle of June 2009 due to operational difficulties in collating data and an ongoing (as of this writing) legal dispute over the language of the ordinance.
SW of ownership change, the number of average yearly arrests increased dramatically when a particular slumlord purchased properties compared to controls. In the second case study, an owner was forced to hire competent property managers after numerous problems led to the threat of nuisance abatement civil procedures. A crime reduction was seen compared to controls.
Together, the existing studies strongly suggest that place management at apartments can reduce crime. In a review of crime prevention efforts at places, Eck (2002) found that the strongest effect of place-focused interventions was at rental properties, often through coercing the owner to take action. These studies should be put in the context of profit. Eck and Wartell
(1998) found that 37.9% of apartment owners that had drug enforcement at their properties could not afford to spend anything to improve their properties. Furthermore, nearly half of the owners had difficulty making a profit at these properties. This suggests that properties that are problems for police and public safety may also be problems for the owners as well – these properties are simply not profitable. Immediate concerns over profit are likely to outweigh the long-term benefits from proper maintenance. Just as a leaky roof is expensive to fix but more expensive to not fix in the long run, crime problems are expensive to manage but more expensive to not manage in the long run (Campbell, 2000). Eck explains this as "decisions to emphasize some goals at the expense of crime control help to explain why some places have a disproportionate amount of deviance compared to other places" (Eck, 1994, p. 35). The next chapter outlines these decisions utilizing a dynamic model of place management first proposed by Madensen
(2007).
SX Chapter 5: A dynamic theory of place management of apartments The model of apartment place management presented below adapts Madensen’s (2007, p.
34) dynamic theory of place management in bars to another facility type. Madensen suggests that her model is a general framework and provides a generalized model as well (2007, p. 160).
Her model is indeed flexible. Critical to understanding this framework is its dynamic nature –
“place management is… not a static condition” (Madensen, 2007, p. 32). Previous management decisions have downstream consequences, but many characteristics operate simultaneously and with feedback. A theory of place management must “explain why managers made particular decisions in the past and what circumstances prompted those decisions” (Madensen, 2007, p.
32). Madensen’s framework implicitly separates decisions into logical blocks. My application of her theory to apartments, in Figure 1, makes these logical blocks of decisions more explicit. I have separated place management decisions into five categories: 1) Constraints on purchase; 2)
Management decisions not easily altered; 3) Management decisions that are easily altered; 4)
The immediate setting; 5) Constraints on reaction. Each of these is discussed below.
Like bars, initial management decisions regarding apartments determine how the place is used and who uses it. The decision to purchase a particular apartment property is influenced and constrained by several external factors. First, the property must be available for sale. If not explicitly on the market, the owner of a desired building must be willing to sell in order for a new owner to take possession. The physical condition of the property (including building age, maintenance, utility costs) and location also influence the decision to purchase. The ability to finance the purchase is also essential. Apartment buildings are most often purchased with some form of credit or investments. Small buildings can be financed by local banks through mortgages. Large buildings require several investors to pool resources together to finance the
SY purchase. Finally, the existing lessees (tenants) must be considered, as the potential new owner will be purchasing them as well. A set of good tenants is an income stream that is factored into the price of the building. Poorly phrased leases and undesirable tenants can substantially reduce the value of a building. Vacant buildings are also less valuable than buildings with few vacancies. This decision-making process is diagrammed in Figure 1 below.
SZ
Figure 1: Theory of place management at apartments
S[ Though Madensen’s model of bars fits apartments well, there is one exception.
Madensen described the owner’s decision as to the theme of the bar (sports, gay, and so forth).
Unlike bars, however, apartment complexes rarely have a theme – fair housing laws generally
prohibit the sorts of themes that Madensen (2007) describes at bars and nightclubs. There is an
exception to this: apartment buildings can be designated as “seniors-only.” In general, family
status (i.e., having children) is a protected class in housing. However, federal fair housing law
allows discriminating against families with children when all residents at a property are 62 or
older, or when at least 80% of the occupied units have one person who is 55 or older (42 USC
3607, Fair Housing Act). Therefore, an apartment building may have a seniors-only “theme,”
but in general apartments do not have explicit themes that influence decisions in the way that
bars do.
The remainder of Madensen’s (2007) model applies to apartments much as it does to
bars. The property characteristics, staff, amenities, apartment location, and marketing strategies
all interact to influence the types of tenants inhabiting apartments and events that occur at
apartment buildings. While some of these decisions are difficult – or impossible – to change,
others are more easily altered.
Property characteristics include the building age, architecture, and maintenance of the
property. Older buildings with uncommon architecture are likely to attract different types of
tenants than new construction. Well-maintained properties are likely to attract different types of
tenants than poorly maintained properties.5 The amenities offered by an apartment building are
also important. Amenities include swimming pools, workout facilities, laundry facilities, 5 Indeed, when I moved to Cincinnati to pursue my graduate degree, my wife and I had some difficulty finding an apartment. We often made decisions regarding whether we would speak with a leasing agent after just seconds of evaluating property characteristics. We quickly determined that her standards are quite higher than mine.
TR outdoor seating (picnic tables, etc), and shuttle service to shopping areas, transportation hubs, or college campuses. Additionally, the location of an apartment building has a large impact on the amenities required to be competitive, the physical characteristics of the building, and likely tenants. The physical characteristics and location of buildings are not easily changed by management. Many amenities (e.g., swimming pools or off street parking) are expensive to install and remove as well.
More easily altered by management are staff, tenant screening, and marketing strategy decisions. Staff at apartment buildings is an important characteristic, as staff acts as the owner’s agent on the property. Large apartment buildings may have full-time staff, such as a property manager and/or maintenance personnel. Such staffing is prohibitively expensive at medium and small buildings, however (Eck, 1994). Part-time staff may be responsible for several medium- sized properties at once. The smallest buildings will not even have that – their owners are likely to hire contractors when work needs to be performed. Whether the presence of staff affects crime depends on what the staff do. Passive staff who fail to correct problem behaviors may be worse than no staff at all (Campbell, 2000). Staff who are explicitly tasked with crime prevention are more likely to perform crime prevention (Felson, 1995).
Tenant screening is critical to keeping illegal activity out of rental property (Campbell,
2000). The ability of landlords to actively screen out undesirable tenants is often the difference between business success and failure for the landlord. Failure to screen tenants on financial criteria, for example, can result in tenants unable to pay rent. Similarly, failure to screen tenants on criminal history can result in tenants unwilling to follow the law. One of the most powerful methods of tenant screening is creating an environment where undesirable tenants simply do not want to live (Campbell, 2000). Drug dealers, for example, would rather not live in a place where
TS the landlord is actively involved in the day-to-day activities of tenants (Eck, 1994).
Demonstrating a level of care to potential tenants can therefore result in undesirable tenants
simply going elsewhere of their own accord.6
Careful screening can be part of a marketing strategy to attract desirable tenants.
Marking strategies can also include off-site advertising (such as in local apartment guides,
newspapers, and internet sites), on-site advertising such as vacancy signs, and inducements for
current tenants to refer new tenants. Private owners of multi-family housing may also decide to
accept housing choice vouchers. Housing choice vouchers are commonly referred to as Section
8 (because they are defined by 24 CFR Part 892 § 8). Section 8 is the primary federal housing
assistance program. Housing vouchers are issued to low-income tenants by their local public
housing authority. The tenants must then find a landlord who will accept the voucher. The
public housing authority conducts a physical inspection and approves the landlord’s lease. The
housing authority then pays a portion of the rent directly to the landlord ("Housing Choice
Vouchers Fact Sheet," 2010). Landlords are not required to accept Section 8 vouchers, though
many do in order to guarantee that a portion of the rent will be paid. The decision to accept
vouchers is therefore often a financial one on the part of the landlord.
The immediate setting of events is determined by the types of tenants attracted to the
apartment building and the events that occur at the apartment. Just as with bars, this immediate
setting is determined largely by the management decisions most proximate to the setting:
marketing strategies used, tenant screening, the presence and actions of staff, the apartment
location, amenities available, and physical characteristics of the apartment building. The 6 Landlord training conducted by John Campbell, Campbell and DeLong Resources, Inc., discusses these concepts in much greater detail. See http://www.cdri.com/community-problem-solving/landlordproperty-management- training.html and Campbell (2000).
TT reaction by the owner (and/or the owner’s agent) to events can either be part of the immediate setting (e.g., a maintenance employee takes immediate action against minor children of a tenant smoking marijuana on the property) or a more distal influence (e.g., lease terms which specifically prohibit drug use on the property added to future leases as a result of minor children of tenants smoking marijuana on the property). The owner’s reaction – or lack thereof – to events can therefore alter future events through multiple, complex paths.
An owner’s reaction to events can include changing previous decisions, such as adding staff or changing the job description of staff, changing the target market for the apartment, or screening tenants differently. These changes are relatively easy, while changing the physical characteristics of the building, amenities, and location are relatively difficult. An owner’s reaction to events on the property can also include selling the property to another party. We shall return to that possible reaction in the next section – it has implications for the patterns of crime at apartment buildings over time.
Some apartment owners address crime problems; others do not. There are constraints on the desire and ability of place managers to address crime problems. The constraints identified by
Madensen (2007) for bars apply to apartments as well. First, many apartment managers are simply unaware of solutions to crime problems. Many owners of rental property have no training in managing rental property. These owners entered the rental property market as an investment, much as some people buy stocks (Campbell 2000; John Campbell personal communication; Eck, 1994). These owners can be educated by local real estate investment associations – but often are not. Eck, Clarke, and Gurette (2007) suggest that the cost of change is important as well. Just as with bars (Madensen, 2007), it is probably the cost of change relative to existing resources that is important for apartment owners. Many owners simply do
TU not have the capital required to make large changes to the physical characteristics of a property, for example (Eck and Wartell, 1998). Smaller properties often do not earn enough profit to allow full-time staff (Eck, 1994). External pressure is determined by the extent to which outsiders exert influence. News reports of absentee landlords can spur governmental regulators to act (see, e.g., Whitaker, 2010).
Finally, whether the owner experiences direct harm and is making a profit is a constraint on action. To the extent that owners are not harmed by their tenant’s actions, they are unlikely to take action that is costly. Eck (1994) found that landlords rarely directly profit from drug dealing, but drug dealers that pay the rent on time every month are more desirable than vacant apartments to some owners. These owners may remain willfully ignorant of their tenant’s actions so long as rent payments continue.
In summary, with relatively minor changes, Madensen’s (2007) model is easily adaptable to a new facility type: apartments. Just like her model, my model of apartments involves a dynamic set of feedback and recursive relationships. Early decisions have implications for later decisions in complex ways. The functional form of relationships depicted remains unspecified, and thresholds may exist. Like Madensen’s model, traditional multivariate analyses which result in partial correlations may not be the best way to explore the plausibility of the theory (see
Madensen, 2007, pp. 41 for an extended discussion). One way to explore the plausibility of a theory of crime is to imagine what the patterning of crime would look like if the theory is true.
Crime patterning and the consequences of poor place management over time One possible consequence of the model illustrated in Figure 1 is that owners sell problem properties that they cannot afford to manage. Clarke and Bichler-Robertson (1998) demonstrated that changes in crime can occur when a building changes ownership. Theirs was a
TV case study, examining the impact on crime of one slumlord and one owner who was forced,
under the threat of legal action, to change their management practices at several buildings.
While small-N case studies are valuable (Eck, 2006), they cannot determine if the relationships
examined are widespread. More generally, changes in ownership can result in changes in place
management because different owners likely have different constraints on their reactions. For
example, different owners likely have differing levels of knowledge of solutions that work.
Owners also vary by their existing resources. Finally, owners may also vary in their evaluation
of the relative costs and benefits to active place management.
Place management is not the only variable on which owners are likely to vary. Owners
also vary in their ability to evaluate the profitability of a property prior to the purchase. In fact,
the business model of savvy landlords often depends on the poor skills of other, less savvy
landlords (Charles Tassell, personal communication).7 Because many landlords are not trained
in any systematic way (Campbell, 2000; Eck, 1994), they must learn how to evaluate and
manage property through experiencing losses.
From the landlord’s point of view, each successive loss on a property is a learning
experience. Lessons learned from each loss can be applied to future purchases – or used to
determine that property management is not a good line of business. From a single property’s
point of view, however, the cycle can be never-ending. Each landlord that buys a particular
problem property is likely to be less experienced and less able to handle problems than the last,
causing a spiral of passive management, disorderly or criminal tenants, falling property values,
7 A non-crime example of this involved a building for which the existing owner was paying $40,000 per year to heat the building, causing a loss which forced the existing owner to sell. Tassell, an experienced landlord, knew how to reduce the heating bill to levels that would allow the building to make a sizable profit. In this way, landlords with experience are able to profit from the inexperience of other landlords.
TW and, ultimately, high crime at the location. It is important to note that this “death spiral” does not require malice on the part of landlords – most landlords are truly ignorant, having had no training in how to deal with problem tenants (Eck, 1994; Campbell, 2000; John Campbell, personal communication). While malice is not required, a relative handful of landlords do seem to have a business model that simply extracts value from properties (see, e.g., Kelley, 2010). Regardless of any specific mens rea on the part of landlords, the consequences for the property are the same.
This spiral is diagrammed as Figure 2 below.
Figure 2: Management, sale, and crime When multiple such locations are situated near one another, the end result is neighborhood disorder and decline. This explanation for neighborhood decline is different from the more sociological explanations (see, e.g., Shaw & McKay, 1972; Skogan, 1990; Wilson &
TX Kelling, 1982) which largely ignore the persons responsible for behavior at the places where it occurs (Madensen, 2007; Snodgrass, 1976).
This dissertation focuses on one element of the model: consequences from the sale of apartments. Based on the discussion above, I will investigate whether the sale of apartments changes the amount of crime at the property. Because the relationships are complex and have feedback, only a small subset can be examined. Specifically, my data allow me to examine whether ownership change is associated with crime, and whether past crime is associated with ownership change. The relationships tested are diagrammed as Figure 3. Below, I state five hypotheses describing the relationship between property ownership changes and crime changes and give justifications for each.
Figure 3: Relationships tested Hypotheses Five hypotheses emerge from the discussion above. These hypotheses are summarized in
Table 1. If supported, these hypotheses provide one possible mechanism through which the crime trends found by Weisburd et al. (2004) and Groff et al. (2010) occur: ownership change.
First, I hypothesize that ownership change of apartments is rare. Transferring property is time consuming and costly. Purchasing a property is often a long-term investment. While some
TY owners may have a business model of “flipping” properties (purchasing with the intent to sell quickly at a profit), most owners are unlikely to do so because of the costs involved.
Table 1: Hypotheses
H1 Ownership change is rare compared to ownership stability. H2 Ownership change is more likely at apartments with a history of high crime than at low crime apartments. H3 Regardless of how apartments are partitioned, a relative handful of apartments produce a disproportionate amount of crime. Crime at apartments is distributed like a power-law. H4 Ownership change will be associated with increases in crime; these increases will be greater at places with a history of being crime problems than places with no such history. H5 The effect of ownership change on apartment-level crime varies among neighborhoods as a function of neighborhood-level crime.
Second, when ownership change does occur, I contend that the change will be more likely at properties with a history of high crime than at low-crime apartments. There are several reasons for this. High-crime properties are likely to be less profitable than otherwise comparable low crime properties. Also, as suggested earlier, owners of high-crime properties are probably less business savvy than other owners, on average. The combination of police enforcement, external pressure for neighborhood groups, and the owner’s lack of knowledge of solutions creates a situation where selling high-crime apartment buildings may often be more palatable than spending resources to solve the problem.
My third hypothesis is that crime is distributed similar to a power-law at apartments.
Eck, Clarke, and Guerette (2007) suggest that crime counts at any facility are not normally distributed. Instead, a relative handful of places produce a disproportionate amount of crime.
Eck et al. (2007) give several examples of this phenomenon, which should exist for my data as
TZ well: Regardless of how apartments in Cincinnati are partitioned, a relative handful will produce a disproportionate amount of crime.
My fourth hypothesis is that ownership change is associated with increases in crime.
There are two reasons for this. First, increases will be more likely and greater at places that have a previous history of being crime problems than at places with no such history. Apartments with a high-crime history are likely to enter a downward spiral, where each subsequent owner sells to a less knowledgeable owner. On rare occasion, a new owner will rehabilitate the high crime place, but this will be the exception because most potential buyers will be reticent about taking on additional rehabilitation costs. This will cause bad situations to get worse. Second, apartments with a history of no or very little crime are likely to see small increases, on average.
These increases are due to the overall low base rate of crime. That is, because crime is very rare in general, the only crime change that can occur at low crime apartments is an increase. Though this implies a ratcheting up of crime at apartment buildings, these small increases at low crime apartments are likely to be addressable by new owners and will therefore be short lived.
Finally, the effect of ownership change varies across neighborhoods as a function of neighborhood-level crime. That is, there is variability in the effect of ownership change on apartment-level crime among neighborhoods which is explained by neighborhood crime. If macro theories of crime are correct, higher neighborhood crime should lead to higher crime counts at apartments.
In summary, I have stated five testable hypotheses that link apartment sales to crime changes. In combination they suggest that crime changes at apartment building places are a function of ownership change in the context of previous crime levels and neighborhoods. Going
T[ back to the Madensen model applied to apartment buildings, crime provides a context for understanding at least one important set of place management decisions (sales and purchase) and the consequences of these decisions.
UR Chapter 6: Data I examined the consequences of ownership change using data from three sources: Land parcel ownership data from the Hamilton County (Ohio) Auditor, crime data provided by the
Cincinnati Police Department, and base map data provided by the Cincinnati Area Geographic
Information System (CAGIS). I describe Cincinnati and each data source below, followed by the measures that I will use in the analysis. Next, I discuss my methods for linking these various data sources together.
Setting Cincinnati had a population of 331,285 in 2000 and is Ohio’s third largest city.
Cincinnati is located on 79.6 square miles in Hamilton County, southwestern Ohio. The Ohio
River forms the southern border. Just over half (53%) of Cincinnati’s residents are white.
African Americans form the largest minority group with 42.9% of the population. There were
48,375 single-family homes in Cincinnati in 2000, with a median value of $93,000.
Cincinnati has 52 neighborhoods. There is a sense of neighborhood pride among the locals. When asked where they live, city residents ordinarily refer to the name of their neighborhood. Cincinnati neighborhoods vary substantially in land area, population, and a variety of Census indicators. Crime also varies substantially among neighborhoods. This variation among neighborhoods makes Cincinnati well suited to examining multi-level models.
These neighborhoods have borders recognizable by locals – to be clear, the neighborhoods used in my analysis are not Census aggregates.
Data Land parcels. The Hamilton County Auditor provided electronic files containing information on land parcels in the county for 2002, 2005, 2007, 2008, and 2009. The Auditor
US archived information every third year until 2007 – the intervening years of data were discarded by the Auditor. After 2007, the Auditor has kept annual archives. While certainly not error-free, this is the most comprehensive source for land parcel-level data in Cincinnati. Each record in this data is one land parcel. Each parcel has several attributes, including street address of the parcel, owner name, owner mailing address, total market value for tax purposes, date of last sale, acreage, delinquent taxes, and a dummy indicator for foreclosure. The Auditor’s data contains information on 349,047 county land parcels in 2009, 120,794 are within Cincinnati city limits.
Calls for service. The Cincinnati Police Department provided dispatched calls for service data for 2006-2009. The original source for these data is Cincinnati Police Department’s computer-aided dispatch (CAD) system. These data include both citizen and officer-initiated incidents, which corrects for one of the limitations on calls for service information found in the
PSS data by Klinger and Bridges (1997). The PSS data was collected in the late 1970’s, well before modern computer systems made it possible to integrate officer and citizen-initiated incidents into one comprehensive data source. Another limitation of the PSS data was the likelihood of misclassification incident types by either citizens or dispatchers (Klinger &
Bridges, 1997). The data provided by CPD include feedback from the officers on the scene, making such misclassifications unlikely in my data. This feedback also means that Cincinnati calls for service data are an official data source that contains definitions of the situation as determined by officers on the scene. Therefore, my calls for service data constitute an official measure of crime.
Each record in the CPD calls for service data is one incident. In 2009, there were
288,574 calls for service in Cincinnati. Each call for service record contains the date, a short description of the call (disturbance, theft, robbery, noise, etc) and address. Calls for service are
UT used instead of Uniform Crime Reports crime data because of the more inclusive nature of calls for service as a measure of failed place management at a property. For example, many disorder calls, such as noise, are clearly the responsibility of the landlord to address yet do not appear in part I and part II UCR data.
Geography and Census. Geographic data were provided by the Cincinnati Area
Geographic Information System (CAGIS). Specifically, CAGIS provided Cincinnati neighborhood boundaries, street networks, and land parcel layers in ArcGIS shapefile format.
Linking ownership, crime, and location data. There are two possible methods for merging calls for service and land parcel information. The most straightforward method is a simple string match on address between the two data sources. While expedient, this method will fail to match crime data to land parcel information when the two are spelled differently. Even within the same data source, this potential exists. Recent work with the Cincinnati Police
Department has shown that this is not a hypothetical problem. Until November, 2007, CPD had listed one apartment’s address as “400 W NINTH ST” in their computer-aided dispatch system.
After November, 2007, the same place is listed as “400 W 9TH ST” in CPD’s data. A simple string match would consider these two spellings to be different places and is therefore not ideal.
The other method for linking calls for service and land parcel information is to use a streets-based address locator in ArcMap 9.2. ArcMap’s address locator algorithm would geocode the two spellings of Ninth Street above to the same location. ArcMap also has tolerance for misspellings and other inconsistencies (e.g., if some address records have two spaces between the number and street while other records have one space). The disadvantage of this method is that streets-based address location in ArcMap depends on interpolation to locate a point in space.
UU There is therefore a degree of error in precisely where each point is located (see Bichler &
Balchak, 2007 for an extended discussion of error in geolocation).
Both matching methods are therefore likely to cause error. The simple string match method is very likely to cause errors in merging crime and land parcel data with one another.
The streets-based address locator method is likely to cause errors in geolocation, but will facilitate merging crime and address parcel data more accurately than a simple string match would. For my analysis, linking is far more important than precision in location. Consequently, geocoding both crime and land parcel data using the same address locator is the approach I have selected. Crime and ownership data were combined using the matched address generated by geocoding as the key field between data sources and data years. See ..#,"'6-S #-!-"',%
,"+ 2!&',%3"'2-0" 2 2-!0'+#" 2 for geocoding details, such as the percent matched from each data file.
Operational definition of apartments Apartments are defined as privately-owned multi-family dwellings housing four or more families. Land use codes in the Auditor data identify apartments by number of units: Land use code 401, 4-19 units; land use code 402, 20-39 units; land use code 403, 40+ units; land use code
404, retail, apartments over. Duplexes and triplexes are excluded from analysis for two reasons.
First, there is a higher likelihood of the owner occupying one unit. Decision-making process behind the sale of owner-occupied and investment properties are likely to be different because of different constraints on their decisions. Duplexes and triplexes have also been excluded for conceptual clarity: this is an analysis of apartments, which are qualitatively different from duplexes and triplexes. Land use codes identify duplexes (520) and triplexes (530).
UV Publicly owned buildings were excluded for similar reasons: the decision-making processes involved in the purchase and sale of public property are qualitatively different than decisions made by private landowners. Metropolitan Housing Authority-owned land parcels are identified by land use code (645) and by owner name in the Auditor data.
Dependent variable There were 288,574 dispatched calls for service city-wide in 2009. There are 96 different descriptions in calls for service in the CAD data for 2009. These descriptions are just a few words long and occasionally ambiguous. The most frequent call type (8.1% of the calls in
2009), for example, is “make investigation.” No further information is given about the nature of the investigation. Such ambiguous call types are excluded from this analysis. Traffic and parking offenses are also excluded, because apartment owners rarely have control over whether traffic accidents occur in front of their buildings.
All remaining calls for service were identified as either property, violent, and disorder.
Examples of property calls for service include theft reports, breaking and entering, and criminal damage. Examples of violent calls for service include domestic violence, menacing, and assault.
Examples of disorder calls for service include noise, neighbor trouble, fireworks complaint, drug use/sale, and family trouble (non-violent). The full categorization is documented in ..#,"'6--S
**1$-01#04'!# 727.#.
The count of calls for service at each apartment during 2009 is the dependent variable in the analysis. Crimes were counted as occurring at an apartment building when the address listed in the police data matched the address listed for the apartment.8 The count of each type of crime
8 Crimes were matched using the “Match_addr” field created during the ArcGIS geocoding process.
UW (property, violent, and disorder) will be examined separately as well, to test whether owner
change has different effects on each crime type.
Apartment-level measures Ownership change. Ownership change is the independent variable of interest. I have
found no previous literature regarding how ownership change should be measured. My analysis
will therefore explore two different methods of measuring ownership change. If the two
measures lead to similar conclusions, then we can be more confident in the conclusions from the
analysis. First, I will use the elapsed days since the most recent date of sale. This measure
should be negatively associated with crime counts such that more time since the last sale leads to
less crime. This relationship is hypothesized to be contingent on prior crime, with prior crime
being directly related to current crime.
Second, ownership change can be measured as a count of the number of ownership
changes over the study period. In addition to date of last sale, the owner name and mailing
address are compared to determine if there was an ownership change in the time between each
pair of dates available from the Auditor. Auditor data is available for 2002, 2005, 2007, 2008,
and 2009 allowing for five possible values of this count, ranging from zero to four, which
represent the number of periods over which ownership change occurred.9
For the purpose of measuring changes in place management, ownership changes must be
substantive. Owner changes in name only generally would not indicate a change in place
management practices. Adding or removing a spouse to the title, for example, is unlikely to
9 This somewhat awkward phrasing is used because the Auditor’s data contains only the last sale date, not the number of sales during the year. Therefore, I cannot determine the number of ownership changes during any comparison period. Instead, I can only determine whether an ownership change occurred from one year of Auditor data to the next.
UX result in different place management practices. Similarly, transferring a property from personal assets to a corporation is unlikely to result in changes in place management. To avoid counting such non-substantive owner name changes as an ownership change, the sale amount and mailing address were examined. Property transfers with a sale amount of zero were counted as an ownership change only when both the owner name and mailing address change occurred as well.
Economic resources. As described in Chapter 5 above, (and by Eck, 1994; Eck and
Wartell, 1998; Clark and Bichler-Robertson, 1998; Madensen, 2007) economic resources form a threshold for managerial action. While I do not have data relating to each owner’s capital reserves, I do have three indicators of economic resources that are related to the property. The assessed value of the property is one such measure. This is the value of the property for tax purposes. Higher values are hypothesized to have less crime for two reasons: 1) Higher-value properties are more likely to be bought and sold by owners with more resources at their disposal than lower-value properties; 2) Higher-value properties are more likely to exist in lower-crime neighborhoods. The first is an assumption for which I have no data; the second is an assumption that can be tested with my data. If higher-value properties are more likely to exist in lower-crime neighborhoods, then controlling for neighborhood-level factors in multi-level models should dramatically reduce the effect of individual property values.
The Auditor’s data also includes a dichotomous indicator for foreclosure. There is some evidence to suggest that higher neighborhood foreclosure rates of single-family homes are associated with higher violent (but not property) crime (Immergluck and Smith, 2006).
However, I could find no literature regarding the effect of individual foreclosures on crimes at individual places, nor is there literature regarding the effect of foreclosure of multi-family housing on crime. Foreclosure is hypothesized to be positively related to crime for two reasons.
UY First, owners who manage their finances so poorly as to wind up in foreclosure are likely to manage other elements of their business such as managing tenant behavior poorly as well.
Second, after foreclosure, banks often manage properties as cheaply as possible. In many cases they simply erect a fence and board up the property (Charles Tassell, personal communication).
The result is a vacant property, which may attract crime.
The Auditor’s data includes a measure of delinquent taxes as well. Higher delinquent taxes are hypothesized to be positively related to crime. Owners who cannot afford to pay their taxes on time are unlikely to have the resources to effectively manage their places through tenant screening or environmental design.
Size. Larger properties house more people, which increases the potential for crime.
There is greater natural surveillance at larger properties, such that crime could be more likely to be reported. At the same time, larger properties allow for on-site managers and maintenance staff, which increases guardianship and place management. Small properties have less tolerance for vacancy, making owners more willing to overlook the background of tenants (Eck, 1994).
The functional form of the relationship between size and calls for service will therefore be explored – it is possible that a curvilinear relationship exists. Two measures of size are available. First, the land use code denotes whether apartments have 4-19, 20-39, or 40+ units.
This rather coarse measure of the number of units is the only available measure of the number of units in apartment buildings. The second available measure of size is total acreage of the land parcel.
Prior calls for service. Both the dynamic model of place management at apartments adapted from Madensen (2007) and my hypotheses described above include feedback from prior
UZ actions that inform current decision-making. Put differently, the past is one context within which decisions about the future are made. Prior calls for service at each apartment will be measured as a three-year average of calls. Using a multi-year average makes this measure less sensitive to idiosyncratic single-year processes. A multi-year average also captures a sustained condition of higher (or lower) crime better than a single-year measure.
Neighborhood-level crime as context Several studies suggest that contextual variables are important in models of crime and in crime theories (see, e.g., Wilcox Rountree, Land, & Miethe, 1994; Wilcox, Land, & Hunt, 2003).
Including contextual variables in multi-level models not only improves explanatory power, it corrects for clustered variance associated with nested data (Rabe-Hesketh & Skrondal, 2008;
Raudenbush & Bryk, 2002). Given the differences in Cincinnati neighborhoods generally, it stands to reason that calls for service at apartments will vary across Cincinnati neighborhoods.
Various contextual measures have been used in prior research – primarily indices created from census variables (e.g., Wilcox, Land, & Hunt, 2003) and land use (e.g., Stucky &
Ottensmann, 2009). Census indicators are less than ideal for the present analysis for three reasons. First, census aggregates ignore neighborhood boundaries. In Cincinnati, neighborhoods are identifiable as one moves through the city. They are constructed from the historical and social reality on the ground. Census aggregates, on the other hand, are created for the purposes of taking the decennial census. Matching census aggregates to neighborhoods therefore requires a degree of error. Second, census data is produced only once every ten years. It is too crude of a measure to use for one-year analyses of crime data. Finally, census indicators are typically included in models of crime because they are associated with processes that cause crime. This
U[ last reason applies to land use as well – it is included in models due to hypothesized relationships to crime.
My data allow me to measure neighborhood-level crime directly. I do not need to measure crime potential indirectly through other measures. For my analysis, the important contextual variable is neighborhood-level calls for service. Calls for service at all properties
(regardless of whether or not they are apartments) will be aggregated to the neighborhood level and entered as a level-2 covariate in a multi-level model. Additionally, the ratio of apartments to single-family homes in each neighborhood will be included. This ratio captures the context of the residential housing stock in the neighborhood, a potentially important factor. ' *#T summarizes the variables that will be included in the analyses.
Table 2: Variables
Concept Measure Hypothesized relationship to CFS Owner change Elapsed days since last sale (-) Serial owner change Count of property sales over (+) study period, (entered as a series of dummies) Economic resources Market value, in dollars (-) Economic resources Dichotomous indicator for (+) foreclosure Economic resources Delinquent taxes, in dollars (+) Size Land use code (entered as a Curvilinear, medium-sized series of dummies) places have higher crime than small apartments and large apartments Size Acreage Curvilinear, medium-sized places have higher crime than small apartments and large apartments Prior crime Three-year average of past (+) CFS at property Neighborhood context Calls for service at all (+) properties aggregated to neighborhood
VR Neighborhood context Ratio of apartments to single (+) family dwellings
Analysis The analysis will proceed in multiple chapters based on the hypotheses described in
Chapter 5. Chapter 7 will examine apartment characteristics, including the distribution of ownership change of apartments. What do apartments in Cincinnati look like? Is ownership change rare? Chapter 8 examines the distribution of calls for service at apartments. If studies of heterogeneous facilities (Sherman et al., 1989; Weisburd et al., 2004) are any guide, and if some facilities are more risky than others (Eck et al., 2007), calls for service at Cincinnati apartments should be highly concentrated at a relative handful of apartments. Chapter 9 presents pooled count-based multivariate regressions which examine the effect of ownership change net on crime net of other factors. Chapter 10 presents multi-level Poisson-based models of 2008 calls for service. Poisson models will be used for these analyses because the nature of count data violates many assumptions of OLS (Osgood, 2000). Multi-level models will be estimated to correct for clustered variance associated with nested data (Rabe-Hesketh & Skrondal, 2008; Raudenbush &
Bryk, 2002). A final chapter offers conclusions and implications for policy.
Together, these analyses will allow testing the hypotheses that ownership is rare (H1), that ownership change is more likely at apartments with a history of high crime (H2),that crime is highly concentrated at a handful of apartments (H3), that crime is associated with ownership change (H4), and that apartment-level crime differs by neighborhood as a function of neighborhood-level crime (H5).
VS Chapter 7: Apartment characteristics and distribution of owner change Housing stock varies from place to place in the United States. As anyone who has
relocated can attest, apartments in one place may or may not be similar to apartments elsewhere.
Before examining the effect of apartment ownership change on crime, I will examine the
characteristics of apartments included in this study. This information is based on apartments
located within Cincinnati city limits.10
Size Apartments are identified in the Auditor's data by land use code. Land use codes 401 (4-
19 units), 402 (20-39 units), 403 (40+ units), and 404 (retail, apartments over) identify
apartments. There were 6,701 land parcels classified as apartments in Cincinnati city limits in
2009. ' *#U shows apartments by land use classification. The majority of apartments in
Cincinnati are small, with 74.9% having fewer than 20 units.11 Apartments are also small by
acreage; the mean acres per apartment parcel is 0.34.
Table 3: Apartment land parcels by land use classification, 2009
Number of Units Freq. Percent
4-19 units 5,020 74.91 20-39 units 384 5.73 40+ units 292 4.36 Retail, apartments over 1,005 15.0
Total 6,701 100
10 Apartments in Hamilton County but outside of Cincinnati municipal boundaries are excluded because comprehensive county-wide crime data are not available. 11 If duplexes were included, even more of the multi-family dwellings would be small. There were approximately 12,000 duplexes in 2009.
VT Economic resources The value of apartment land plus all improvements is the market value of the parcel. In
2009, the median total market value for apartment land parcels was $140,070. ' *#V shows
the median value by land use classification. As one would expect, the median value of
apartments in Cincinnati varies by the size, with larger buildings having higher median values.
Apartments with retail below have the lowest median value. This may be due to their location --
21.59% of apartments over retail exist in Over-the-Rhine, a very low income neighborhood.
Table 4: Median Market Value by Land Use Code, 2009
Median value Number of Units (dollars)
4-19 units 138,715 20-39 units 540,000 40+ units 1,081,940 Retail, apartments over 85,000
Total 140,070
Foreclosure was relatively rare in 2009, with 207 apartments (3.09%) in foreclosure.
Delinquent taxes were more common, with 10% of apartments having more than $100 in
delinquent taxes. Less than 4% of apartments had delinquent taxes totaling more than $5,000,
however, suggesting that serious tax delinquency is not common.
Neighborhood context Cincinnati has 52 neighborhoods. This study uses these socially defined neighborhoods
as a context within which apartments are located.12 Cincinnati neighborhoods vary considerably
in land area, population, and Census indicators. ..#,"'6---S>#'%& -0&--"!& 0 !2#0'12'!1
lists various Census indicators by neighborhood.
12 Note that “neighborhoods,” as used here, do not refer to Census aggregates. Instead, neighborhoods are socially defined spaces that are well recognized by Cincinnati locals. The Cincinnati Area GIS (CAGIS) provided the neighborhood shapefile used for my analysis.
VU Apartments are not equally distributed throughout the city's 52 neighborhoods. The top 5 neighborhoods have nearly a third (31%) of the city’s apartments; the top 10 neighborhoods have nearly one half (49%) of the city’s apartments. Westwood, the largest neighborhood by land area, has nearly ten percent of all apartments in the city. Over-the-Rhine, an inner-city neighborhood commonly associated with high rates of crime and other social problems, has over seven percent of the apartments in the city. The number of apartments in each neighborhood by land use classification is included as ..#,"'6-DS'120' 32'-,-$ . 02+#,21 7
,#'%& -0&--". This appendix also includes Figure 22, a map showing the spatial distribution of apartments in Cincinnati.
The only census indicator that is significantly correlated to the total number of apartments per neighborhood is total population (r=0.69, p<0.05). Otherwise, bivariate correlations between indicators from the 2000 Census and the total number of apartments are generally small and not significant at p<0.05 (see ..#,"'6DS-00#* 2'-,+ 20'6-$
,#'%& -0&--"',"'! 2-01 ,",3+ #0-$ . 02+#,21 for the full correlation matrix).
Ownership change Ownership of apartments is not static in Cincinnati. Apartment buildings are bought and sold each year. I use two measures of parcel ownership change. The first is a count of the number of ownership changes. Hamilton County Auditor data were available for 2002, 2005,
2007, 2008, and 2009. The number of ownership changes was added across years to produce a scale ranging from zero to four. When comparisons could not be made due to incomplete data, the value of this variable is missing. ' *#W shows the frequency distribution of this measure.
VV Table 5: Frequency distribution of ownership changes >3+ #0-$ -5,#01&'. 40#/T !#0!#,2 !& ,%#1
R UQUU[ V[TZU S TQSYT UTTVS T XX[ [T[Z U SVV TTSW V SW RTTT +'11',% UXT WTV
'-2 * XQYRS SRR
The second measure of owner change, days since last sale, was calculated by subtracting
the date of last sale from the end date of the data, December 31, 2009. The mean number of days
since last sale is 2,995.05 (s=2,731.21), or just over 8 years. 4'%30#V shows the percent of
apartments sold each year.13 Over a quarter (27.23%) of apartments have been sold in the past
three years. Half (50.45%) have been sold in the past five years. Almost all apartments were
sold within the last 30 years.
13 Of the 6,701 apartments in the 2009 Auditor data, 98 (1.46%) had missing data for last sale date.
VW
Figure 4: Year of last sale for apartments as of Dec 31, 2009 The concept I seek to measure with these variables is a change in place management, measured indirectly through a change in place manager. The Hamilton County Auditor’s record keeping practices occasionally make that measurement difficult. Some changes in owner names do not lead to a change in place manager. For example, marriage often leads to a change in the owner name from “John Smith” to “John and Joan Smith.” A similar situation occurs when ownership of a building is transferred from personal assets to corporate assets. The concept I am measuring – change place management practices – is unlikely to occur when the place manager changes in name only.
My data do not allow a perfect fix for the problem. I can, however, make certain assumptions. My data include the mailing address and sale amount for 2002, 2005, 2007, 2008,
VX and 2009. Owner names, mailing addresses, and sale amounts were compared to determine
whether a substantive change in owner occurred from one year of data to the next. Owner
changes were counted as substantive when both the owner name changed and either the mailing
address changed or the sale amount was non-zero. This solution corrects the ownership change
scale for many of the changes in name only.
Unfortunately, I lack the comprehensive historical data necessary to make the same
correction for the days since last sale measure. For example, I have no data available to
determine if a sale in 2001 was a substantive change in ownership of a parcel. The 2001 owner
name, mailing address, and sale amount are not available. The days since last sale measure is
therefore uncorrected for changes in name only. The construct validity of the days since last sale
measure may be somewhat compromised by counting sales that did not result in a change in
place manager (Shadish, Cook, and Campbell, 2002). However, the two measures are related as
one would expect. ' *#X shows the mean days since last sale for each category of owner
change.
Table 6: Mean days since last sale by number of owner changes >3+ #0-$ )# ," 71 -5,#01&'. 1',!#* 12 !& ,%#1 1 *# R VYXVTSZV S SUZRTYUX T YSTTXZXS U UTRT[UYW V TRTTRXXY
As the number of owner changes increases, the mean number of days since last sale
decreases. This gives some confidence that the two measures are in the same conceptual
domain. Like most measures of social science concepts, neither measure of ownership change is
VY perfect. The number of owner changes has a limited range and only covers changes from 2002-
2009, while days since last sale likely has some conceptual slippage. Each measure will be entered into separate models in subsequent chapters.
Ownership change is associated with past crime The reasons why a particular property go on the market are likely complex. The decision to sell a property could be a reaction to events at the property. To determine if crime is a partial explanation for selling apartments, past crime was entered into a logistic regression. The outcome variable is a property sale between 2008 and 2009.
Odds ratios from this regression are presented in ' *#Y. The average number of crimes
2006-2008 is a significant predictor for ownership change. Also significant – and negative – is the total market value of the property, meaning that more expensive properties were less likely to be sold than less expensive properties. No measure of apartment size was significant, though apartments over retail were significantly less likely to be sold than apartments with 4-19 units.
VZ Table 7: Logistic regression of ownership change
Ownership change 2008-2009
Market value (2009) 0.999** (-2.75) Past crime 1.012* (2.48) Foreclosure (2009) 0.930 (-0.26) Acres 1.036 (0.79) 20-39 units1 0.946 (-0.24) 40+ units1 0.697 (-0.64) Apts over retail1 0.642*** (-2.96) Delq tax 1.000 (-1.86) (-0.16) N 6240 Pseudo R2 0.013 chi2 39.00 p-value 0.000 1. Number of units was entered as a series of dummies, with 4-19 units as the excluded category. Exponentiated coefficients; t statistics in parentheses. Robust standard errors taking neighborhood clustering into account were used to calculate t. *p< 0.05, **p< 0.01, ***p< 0.001
The odds ratio of past crime indicates the effect a one unit increase in crime has on the
probability of an apartment changing ownership. In interpreting such odds ratios, it is helpful to
visualize the effect of change over the range of the independent variable. 4'%30#W shows the
predicted odds of ownership change as past crime varies from its minimum (0) to its maximum
(197.67), holding all other variables constant at their means. While a one-unit change in past
crime has a relatively small effect on the odds of an ownership change occurring, ownership
change is much more likely at the extremely criminogenic properties in Cincinnati.
V[
Figure 5: Predicted odds of ownership change as a function of past crime
Summary Cincinnati apartments are overwhelmingly small, with three quarters having fewer than
20 units. The median value of apartments increases with the apartment size, though apartments over retail establishments have relatively low values. Neighborhood census indicators are largely unrelated to the total number of apartments. While most apartments have been sold at least once in the past 30 years, 49.8% were not sold during the years for which data are available.
The average last sale date was just over 8 years ago. A history of crime problems is associated with ownership change. The next chapter details the distribution of crime at apartments.
WR Chapter 8: The distribution of crime There were 26,026 crimes at Cincinnati apartments in 2009.14 The mean number of crimes at apartments was 3.88 (s=11.58), with a median count of 1 and a range of 326. Like crime counts at other facility types, crime at Cincinnati apartments is heavily skewed. Eck,
Clarke, and Guerette (2007) describe crime at any given facility type (bars, motels, schools, apartments, etc) as following a “J-curve” or power-law type distribution. They predict that crime at any facility type will take on a similar distribution. Their method ranks individual facilities by the count of crimes. When a bar chart is drawn, the result is resembles a reclining capital J.
4'%30#X is this type of bar chart showing all included crimes at apartments in 2009.
4'%30#X includes one bar for each apartment along the horizontal axis (each bar appears to touch the next because of limited space on the page) with the count of crimes plotted on the vertical axis. As predicted by Eck et al. (2007), crime is indeed highly concentrated. The top
10% of apartments (shown at the left of 4'%30#X) generate 63% of the crimes. Over two-thirds of apartment buildings had two or fewer crimes. Forty-three percent of apartments had zero crimes in 2009.
14 See Appendix I: Geocoding and matching Auditor data to crime data for geocoding details.
WS '-.SR$-$ . 02+#,21 !!-3,2$-0XU$-$TRR[!0'+#
VU$-$ ** . 02+#,21& "no!0'+#
Figure 6: Concentration of all crime at apartments 2009
This J-curve distribution holds when we disaggregate by crime type. 4'%30#Y, 4'%30#Z, and 4'%30#[ show similar charts for disorder, property, and violent crime. While the concentration varies by crime type, the shape of the distribution is similar. Even for the most common crime type, disorder, 55% of all apartments had zero police calls for service.
WT '-.SR$-$ . 02+#,21 !!-3,2$-0XXTW$-$TRR["'1-0"#0
WW$-$ ** . 02+#,21& "no"'1-0"#0
Figure 7: Concentration of disorder at apartments 2009
WU '-.SR$-$ . 02+#,21 !!-3,2$-0V[TZ$-$TRR[.0-.#027!0'+#
XZTX$-$ ** . 02+#,21& "no.0-.#027!0'+#
Figure 8: Concentration of property crime at apartments 2009
WV '-.SR$-$ . 02+#,21 !!-3,2$-0XXT[$-$TRR[4'-*#,2!0'+#
YTTU$-$ ** . 02+#,21& "no4'-*#,2!0'+#
Figure 9: Concentration of violent crime at apartments 2009 When confronted about excessive crime at their apartments, apartment owners often claim that crime is a simple function of size, measured by number of units. However, a similar distribution exists when disaggregating by apartment size. 4'%30#SR, 4'%30#SS, 4'%30#ST, and
4'%30#SU show all crime at each land use classification (4-19 units; 20-39 units; 40+ units, and retail, apartments over). Within every grouping by size, a disproportionate amount of crime occurs at a handful of apartments while a sizable percentage of apartments have zero crime. Ten percent of apartments in each size group account for more than half of crime within that group.
WW '-.SR$-$ . 02+#,215'2&VVS[3,'21 !!-3,2$-0WYTZ$-$ !0'+#', . 02+#,215'2&VVS[3,'21
VVTX$-$ . 02+#,215'2&VVS[3,'21& "no !0'+#
Figure 10: Concentration of all crime at apartments with 4-19 units
WX '-.SR$-$ . 02+#,215'2&TRVU[3,'21 !!-3,2$-0WUTT$ -$!0'+#1', . 02+#,215'2&TRVU[3,'21
URTY$-$ ** . 02+#,215'2&TRVU[3,'21 & "no!0'+#
Figure 11: Concentration of all crime at apartments with 20-39 units
WY '-.SR$-$ . 02+#,215'2&VR&3,'21 !!-3,2$-0XUTW$-$ !0'+# 2 . 02+#,215'2&VR&3,'21
URTW$-$ . 02+#,215'2&VR&3,'21& "no !0'+#
Figure 12: Concentration of all crime at apartments with 40+ units
WZ '-.SR$-$0#2 '*Q . 02+#,21-4#0 !!-3,2$-0WXT[$-$TRR[ !0'+#
VXTU$-$0#2 '*Q . 02+#,21-4#0& "no !0'+#
Figure 13: Concentration of all crime at retail, apartments over These patterns persist over time as well. 4'%30#SV shows the number of crimes over a three-year period, 2006-2008. Once again, the top ten percent of apartments account for over half of crime.
W[ '-.SR$-$ . 02+#,21 !!-3,2$-0WWTU$-$!0'+#$0-+TRRXV TRRZ
TW$-$ . 02+#,21& "no !0'+#$0-+TRRXV TRRZ
Figure 14: Concentration of all crimes 2006-2008 As predicted by Eck et al. (2007), this pattern is present regardless of how the data are partitioned (given enough facilities to allow analysis). Graphs for each neighborhood and each crime type (not shown) all display high concentration of crime at a relative handful of locations with a high percentage of zero-count locations. The mean percent of crimes in the top ten percent of apartments is 49.87% (s=22.22%), while the mean percent of apartments with zero crimes is 41.71% (s=15.44%). ..#,"'6D--S'120' 32'-,-$!0'+# 2 . 02+#,21 7
,#'%& -0&--" lists the percentage of crime in the top ten percent of apartments by neighborhood and the percentage of apartment buildings with zero crime.
XR The distribution of crimes at apartments – including the large number of zero-count apartments – has implications for multivariate modeling. The next chapter discusses these implications and presents Poisson-based count models of crime at apartments in Cincinnati.
XS Chapter 9: Does ownership change influence crime at apartments? In this chapter, I use multivariate models to determine if ownership change influences crime at apartments. While it is possible to use linear regression models for count outcomes,
Long and Freese state that, “this can result in inefficient, inconsistent, and biased estimates”
(2006:349). Researchers should use Poisson-based models designed for counts when examining counts (Long, 1997; Osgood, 2000). The difficulty with Poisson-based models is in 1) choosing which Poisson-based model is appropriate; and 2) interpretation of results. I examined several
Poisson-based models but this chapter interprets results only for the best model. All models were estimated using Stata/MP 11.1 (born 16 Jun 2010). This chapter begins with a discussion of which Poisson-based count model is most appropriate for these data. Results from zero- inflated negative binomial models are then discussed.
Choosing the best model for these data I considered four Poisson-based models: the Poisson regression model (PRM), the negative binomial regression model (NBRM), and zero-inflated variants of both. 15 The PRM assumes that the conditional variance and condition mean are equal, while the NBRM adds a dispersion parameter that allows the variance to exceed the mean. Overdispersion should exist in my data due to the large number of zero-count apartments. Overdispersion can cause PRM estimates to be inefficient with downward-biased standard errors (Long, 1997). Standard tests of an independent variable’s strength may therefore overestimate the strength of variables in overdispersed PRM models.
15 Other Poisson-based methods include corrections for zero-truncated data (i.e., zeros are excluded from the sample) and are not considered here due to the large number of zero-crime apartments and because these apartments are substantively important.
XT Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models are variations of count models that allow zero-crime apartments to be generated by different processes than non-zero-crime apartments. Zero-inflated models assume there are two latent, unobserved groups of apartments: one that has no probability of crime, and one that has a nonzero probability of having a positive crime count. These models are estimated in three steps, beginning with modeling the membership into each group, then modeling counts in the group that could have a positive crime count, and finally computing observed probabilities by mixing the probabilities of each group. (Long, 1997; Long and Freese, 2006). The result is two sets of estimates: one for the count-based model and one for the logit model predicting group membership.
Model fit is generally used to choose from among these four models (Long, 1997). The discussion below is based on fitting a full model with all crime as the dependent variable, number of owner changes as the independent variable, and all control variables entered.16
Comparisons of model fit for the other dependent variables (disorder, property, violent) and the other independent variable of interest (number of owner changes) produced the same substantive result.
An informal method for assessing model fit is to compare the observed and predicted counts from each model, with the goal of minimizing the difference between the observed and predicted counts. ' *#Z lists the maximum difference in observed and predicted counts for each model for crime counts zero through nine, along with the mean absolute difference. The
NBRM model produces the smallest mean difference of 0.006 and ties the ZINB model for the smallest maximum difference.
16 This chapter discusses pooled models, i.e., the neighborhood-level measures are added as a covariate in single- level models. The next chapter discusses multi-level models.
XU Table 8: Comparison of differences between observed and predicted values for counts 0-9
Maximum At Mean Model Difference Value Diff PRM 0.384 0 0.089 ZIP 0.141 1 0.046 ZINB 0.027 1 0.007 NBRM -0.027 1 0.006
An extension of this method is to plot the differences in observed and predicted values
for selected counts of the dependent variable. 4'%30#SW is such a plot showing observed minus
predicted values for each model for crime counts zero through nine. The x-axis is the number of
crimes. The y-axis is the difference between observed and predicted crime counts. Points above
zero on the y-axis are underpredicted and points below zero on the y-axis are overpredicted.
4'%30#SW shows that the PRM grossly underpredicts zero-crime apartments and
overpredicts crime counts in the middle of the range shown. The ZIP model underpredicts crime
counts of one and two and overpredicts other counts at the end of the scale. The NBRM and
ZINB models do a better job of predicting crime counts at apartments than either Poisson model.
From these informal assessments, the negative binomial-based models are appropriate. This is
confirmed by a significant result for overdispersion when fitting the NBRM.17
17 2 = − This test is computed as G 2(ln L NBRM ln L PRM ) . Stata reports this test as chibar2(01) after fitting a negative binomial model. Here, chibar2(01) = 2.8x104, p<0.0001 for the NBRM model.
XV
Figure 15: Choosing between four models A more formal method of model selection is to compare measures of fit statistics, such as the Akaike information criterion (AIC) or Bayesian information criterion (BIC)18 from each model. Lower values of AIC and BIC are preferred over higher values. ' *#[ compares the
18 The AIC used here is defined as: {− 2 ln Lˆ(M ) + 2P } AIC = k k ; N ˆ where L(M k ) is the likelihood of the model and Pk is the number of parameters in the model.
The BIC used here is defined as: = − ˆ − BIC 2ln L(M k ) df k ln N ; where the degrees of freedom equal N minus the number of parameters in the model. Other definitions of these statistics exist (see Long and Freese, 2006) and lead to the same conclusion.
XW AIC and BIC across all four count-based models. Comparing the AIC and BIC across models,
the ZINB model is the best fit for my data. One final test suggests the ZINB is preferred over the
NBRM as well: the Vuong (1989) test for non-nested models strongly favors ZINB over NBRM
with a value of 19.4.19
Table 9: Model fit statistics PRM ZIP NBRM ZINB
AIC 8.157 6.039 4.024 3.805
BIC -3547.720 -16686.487 -29334.862 -30616.643
Summary of model selection The informal method of comparing mean deviations of predicted and observed counts
strongly suggested that negative binomial models – those which allow the conditional mean and
conditional variance to differ – are preferred over Poisson models. That comparison suggested
that the NBRM and ZINB were similar. Formal tests using AIC, BIC, and Vuong statistics,
however, strongly support using ZINB models. However, Long and Freese (2006:409) caution
that model selection should not be guided by model fit alone: “when fitting a series of models
with no theoretical rationale, it is easy to overfit the data.”
19 ˆ As defined by Long and Freese (2006), the Vuong test considers two models where Pr1 ( yi | xi ) is the predicted ˆ probability of observing y in the first model, and Pr2 (yi | x i ) is the predicted probability of observing y in the I ˆ Y L Pr1 (yi | xi ) L second model. Then, m = lnJ Z ; with a mean of m and standard deviation of s . The Vuong i L ˆ L m KPr2 (yi | xi )[ statistic tests the hypothesis that E (m) = 0 by calculating: Nm V = . sm Values of V ± 1.96 indicate a significant difference between the two models.
XX Here, ZINB models make substantive sense. Some apartments have such a low
probability of crime that the probability is effectively zero. Other apartments have a non-zero
probability of crime. Explicitly modeling the process that could create the excess zero-count
apartments is important, and that is precisely what zero-inflated models do.20 The next section
presents and interprets results from these ZINB regressions.
The effect of ownership change on crime counts: ZINB results After dropping cases with missing values21 the number of apartments remaining in the
sample is 6,240. ' *#SR displays summary statistics for each of the study variables.
Table 10: Descriptive statistics of study variables
Variable Obs Mean Std. Dev. Min Max
Dependent Disorder 6240 2.243269 6.436128 0 146 variables Property 6240 0.666827 1.612001 0 33 Violent 6240 0.81266 2.648627 0 65 All 6240 3.722756 9.847567 0 215
Variables Days since sale 6240 3040.915 2753.648 0 23060 of interest # Owner changes1 0 3,243 1 2,169 2 669 3 144 4 15
Control Number of variables units2 4-19 Units 4,742 20-39 Units 347 40+ Units 255
20 Unfortunately, this creates problems for multi-level modeling, as multi-level statistics packages cannot estimate multi-level ZINB models. I will return to this issue in the next chapter. 21 The most common missing variable was owner change, with 362 missing values due to missing Auditor data on years prior to 2009 for those land parcels.
XY Apartments over retail 896 Acres 6240 0.327 1.105 0 35 Acres2 6240 1.327 25.400 0 1225 Foreclosure (dummy) 6240 0.029647 0.169626 0 1 Delinquent taxes (dollars) 6240 843.0987 7065.08 0 328077.6 Total market value (dollars) 6240 246959.6 683023.3 0 2.55E+07 Neighborhood crime3 6240 5581.383 3757.676 189 13276 Neighborhood ratio of apartments to single family dwellings 6240 0.481581 1.308123 0.012712 12.5 1. Owner change is an ordinal variable entered as a series of dummy variables. Zero changes is the excluded category. 2. Number of units is an ordinal variable entered as a series of dummy variables. 4-19 units is the excluded category. 3. Count of all included crimes in the entire neighborhood, including crimes not at apartments.
Days since last sale and number of owner changes were entered into separate models for each of the dependent variables (2009 counts for disorder, property, violent, all combined) resulting in eight ZINB models. Days since last sale was not significant in any of the models, perhaps due to construct validity. Recall from Chapter 7 that the number of ownership changes was corrected for changes in name only; no such correction was possible for days since last sale.
The result is that days since last sale includes changes in ownership that did not result in a substantive change in place management. Full results from these ZINB models with days since last sale as the independent variable of interest appear in ..#,"'6-FS=->&0#13*21$-0+-"#*1
5'2&" 711',!#* 121 *# but will not be discussed further here.
' *#SS displays exponentiated coefficients from ZINB models for each dependent variable with the number of owner changes as the independent variable of interest. ZINB models produce two sets of coefficients. The first predicts counts for those observations in the not
XZ always zero group. These coefficients are labeled count in ' *#SS and are interpreted
similarly to negative binomial coefficients. The second set of coefficients predicts membership
in the always zero group. These coefficients are labeled always zero in ' *#SS and are
interpreted similarly to logit coefficients. The z-tests reported in ' *#SS were computed using
robust standard errors that take neighborhood clustering into account but do not allow
coefficients to vary across neighborhoods.
Table 11: ZINB pooled regression results
(1) (2) (3) (4) Disorder Property Violent All combined Count 1 Owner Chng 1.107* 1.011 1.182* 1.106* (2.33) (0.23) (2.27) (2.48) 2 Owner Chng 0.972 1.029 1.090 1.023 (-0.28) (0.47) (0.61) (0.26) 3 Owner Chng 1.027 0.776 0.947 0.985 (0.13) (-1.67) (-0.25) (-0.08) 4 Owner Chng 0.834 0.994 0.302* 0.721 (-0.37) (-0.01) (-2.26) (-0.82) Past Crime 1.074*** 1.035*** 1.054*** 1.081*** (10.74) (9.52) (13.00) (13.00) Foreclosure 0.972 0.870 0.895 0.905 (-0.21) (-1.21) (-0.55) (-0.76) Delq Tax 1.000 1.000 1.000 1.000 (0.43) (-1.64) (-0.52) (-0.80) Market Value 1.000 1.000 1.000 1.000 (-0.04) (1.56) (-0.54) (-0.08) 20-39 Units 1.436*** 2.017*** 1.357*** 1.466*** (4.34) (8.42) (4.14) (6.97) 40+ Units 1.468** 2.251*** 1.362** 1.498*** (2.79) (5.85) (2.60) (3.55) Apts Over 0.838 1.439*** 0.906 0.941 Retail (-1.67) (7.53) (-0.85) (-0.91) Acres 0.991 0.950 0.996 0.998 (-0.16) (-1.39) (-0.09) (-0.04) Acres^2 1.001 1.002 1.001 1.000 (0.53) (1.32) (0.52) (0.32) Neigh Crime 1.000** 1.000 1.000 1.000* (2.63) (1.50) (0.35) (2.45) Apts:SingFam 1.019 1.000 1.052 1.009
X[ (1.61) (0.01) (1.04) (1.15) Always zero 1 Owner Chng 1.168 0.813 1.210 1.113 (1.11) (-1.20) (1.18) (0.83) 2 Owner Chng 1.585 0.815 1.034 1.319 (1.69) (-0.80) (0.12) (1.40) 3 Owner Chng 1.057 0.545 1.006 0.703 (0.11) (-0.72) (0.01) (-0.70) 4 Owner Chng 0.810 33.030* 0.919 1.536 (-0.54) (2.01) (-0.08) (1.42) Past Crime 0.113*** 0.157*** 0.298*** 0.0414*** (-4.96) (-3.66) (-8.23) (-5.05) Foreclosure 1.062 2.504 1.783 1.934* (0.20) (1.57) (1.21) (2.47) Delq Tax 1.000* 1.000 1.000 1.000 (2.29) (-1.53) (0.89) (-1.16) Market Value 1.000 1.000 1.000** 1.000** (1.14) (-0.14) (3.21) (2.94) 20-39 Units 3.108*** 2.785 1.117 2.646** (3.53) (1.83) (0.37) (2.73) 40+ Units 2.273 4.361* 0.785 1.838 (1.91) (2.16) (-0.46) (1.62) Apts 1.372 0.994 1.416 1.182 (1.45) (-0.04) (1.49) (0.82) Acres 0.760 0.789 0.648** 0.856 (-1.26) (-0.86) (-2.81) (-0.82) Acres^2 1.024* 1.020 1.001 1.006 (2.14) (1.62) (0.15) (0.58) Neigh Crime 1.000 1.000* 1.000*** 1.000** (-1.83) (-2.41) (-3.43) (-2.70) Apts:SingFam 1.165 1.342*** 1.110 1.152 (1.48) (6.10) (1.66) (1.40) N 6240 6240 6240 6240 N_zero 3430 4285 4510 2687 ll -9386.725 -5767.035 -5624.708 -11844.108 chi2 457.706 290.891 740.955 493.486 p-value 0.000 0.000 0.000 0.000 Exponentiated coefficients; z statistics in parentheses. Robust standard errors taking neighborhood clustering into account were used to calculate z. *p< 0.05, **p< 0.01, ***p< 0.001
Past crime and size (measured by land use code) are the most consistently significant
predictors across all the models. Higher values of past crime both increase the count of current
crime (top panel of ' *#SS) and decreases the probability of being in the always zero group
YR (bottom panel of ' *#SS). Size, as measured by land use code, is significant across all count models. Land use codes were entered as dummy variables, with 4-19 units as the excluded category. The coefficients for 20-39 units and 40+ units are similar, suggesting that there is a threshold effect for size. The only land use code that is not significant across all models is the indicator for apartments over retail, which is significant only for property crime. This could be due to property crime (e.g., shoplifting) in the retail portion of mixed-use buildings – my data do not allow me to determine whether the crime occurred in the retail or residential portion of the address.
Having one ownership change is positive and significant in all count models except property crime. For the all crime model, one ownership change increases the expected crime count by a factor of 1.106, or about 10.6%, all else being equal. Two and three owner changes are not significant in any model, while four ownership changes is negatively related to the violent crime count and positively related to membership in the always zero group. There are only 15 apartments that had four ownership changes, however, so those coefficients should be interpreted with caution.
While examining coefficients is a good starting point for interpreting ZINB results, the model is non-linear. It is therefore helpful to examine predicted counts as values of independent variables change, particularly for continuous variables. 4'%30#SX plots the predicted value of all crime for 2009 on the vertical axis, with past crime on the horizontal axis. Apartments with 4-19 units are symbolized as squares; apartments with 20-39 units are symbolized as circles. Hollow,
YS blue symbols indicate no ownership change; filled, red symbols indicate one ownership change.
All other variables were held at their means to produce these predicted counts.22
! 12!0'+#'1highly.0#"'!2'4#-$!300#,2!0'+#
. 02+#,215'2&TRVU[3,'21 !'0!*#1 ,". 12!0'+#& 4#+-0# #6.#!2#"!0'+#2& , . 02+#,21 5'2&VVS[3,'211/3 0#1
. 02+#,215'2&-5,#01&'.!& ,%# $'**#"Q0#"17+ -*1& 4#SR$&'%� .0#"'!2#"!-3,21-$!0'+#
Figure 16: Predicted values of 2009 crime as past crime count varies from 0 to 25
4'%30#SX clearly shows that past crime is highly predictive of current crime. When past
crime is zero, the predicted value of crime is very close to zero, regardless of apartment size or
ownership change. It is not until the value of past crime increases that there are differences by
size and owner change. The largest change in expected count attributable to ownership change
occurs at apartments with 20-39 units with past crime of 25. There, apartments with one
22 Apartments with 40+ units have predicted values of crime so similar to apartments with 20-39 units that it is difficult to differentiate them when plotted. This suggests that any size effect is for buildings more than 4-19 units, with little distinction between apartments with 20-39 units and those with 40+ units.
YT ownership change experience an increase of 2.373 in the expected crime count compared to apartments with no ownership changes. Also clear from 4'%30#SX is that the effect of size is greater than that of ownership change.
Other than past crime, the substantive effect of other variables on the expected crime count is small. This can be observed by creating plots similar to 4'%30#SX with different variables plotted along the x-axis. For example, neighborhood-level crime is a significant predictor in the count model for all crime. Yet the substantive impact of this neighborhood crime is slight. Figure 14 plots the full range of neighborhood crime on the x-axis (min=198; max=13,276). The triangle, blue line is the predicted value of 2009 crime when the past crime at the apartment is zero. The diamond, red line is the predicted value of 2009 crime when the past crime at the apartment is 25. Once again, past crime at the apartment is highly predictive of future crime.
YU %'%&*#4#*1-$. 12!0'+#
8-5*#4#*-$. 12!0'+#
Figure 17: Predicted crime counts as neighborhood crime varies over its range Past crime at the apartment-level conditions the effect of neighborhood crime. When past crime is zero, increasing neighborhood crime from its minimum to its maximum produces a change of 0.532 expected crimes (holding all other variables constant at their means). When past crime is 25, however, changing neighborhood crime from its minimum to its maximum increases the expected crime count by 3.178 (once again, holding other variables constant at their means).
That is, the effect of 13,276 neighborhood-level crimes is an expected increase of about one half of a crime at apartments with zero past crimes, and an expected increase of just over 3 crimes at apartments with a history of past crime.
Comparing the effect of neighborhood crime to owner change helps to illustrate the relatively weak effect of neighborhood crime in these models. For apartments with high past
YV crime (past crime = 25), an ownership change increased expected count of crime by 2.373, compared to apartments with no ownership change. In order to get the same increase in the expected crime count, neighborhood crime has to increase from its minimum to 10,190. That is, one ownership change at the apartment level increases the expected crime count by the same amount (2.373) as over ten thousand neighborhood-level crimes.
A cautionary note: average effects can be deceptive The regression results presented above reflect the average effect of each of the variables.
As explored in Chapter 8, however, crime counts are highly skewed. ' *#ST shows the mean crime counts for each category of ownership change.
Table 12: Crime counts by category of ownership change Number of Mean crime sd crime count Percent of zero- owner changes count count apartments 0 3.207 8.422 44.56 1 4.454 11.950 40.80 2 3.976 8.918 42.90 3 4.222 9.254 43.06 4 2.667 5.863 53.33
While the mean crime count is higher for apartments with one ownership change compared to apartments with no owner changes, the modal count of crime is zero regardless of the number of owner changes. The number of ownership changes is another way of categorizing apartments – a facility subtype – with a highly skewed distribution of crime within each category, just as we saw for apartments overall.
Summary: ownership change increases crime The ZINB pooled regression models showed that past crime is the strongest predictor of current crime. Size is important as well, with 4-19 unit apartments having lower crime than 20-
YW 39 and 40+ unit apartments – though the difference between 20-39 units and 40+ is slight.
Ownership change increases the crime count by just over 10% in the all crime models, all else equal but is not significant in the property crime model. The remaining variables had slight substantive impact, even when statistically significant.
This analysis and its results have several implications. First, the choice of count-based models is important, particularly when modeling count outcomes with excessive zeros such as crime counts. Second, models of place-level crime that do not include a measure of past crime are likely misspecified. The measure of past crime used here, a three-year average of prior crime, was so important to the analysis that ZINB models had difficulty converging without it.
One important finding from these models is that ownership change does not produce a reduction in crime. In fact, ownership change made things worse. Except for apartments with no past crime, sale to a new owner made crime worse regardless of apartment size. The exception is property crime, which may be more sensitive to physical characteristics that are costly to change (e.g., new windows or door locks). Disorder and violent crime are likely more sensitive to changes in behavioral rules and tenant screening, which are relatively easy to change with new ownership. Indeed, my findings suggest these management practices change for the worse, on average.
Ideally, high-crime apartments would be purchased by excellent place managers. If excellent place managers bought these crime-ridden apartments, we would expect ownership change to be negatively related to crime. Unfortunately, Cincinnati is not an ideal city: where there is a history of past crime, ownership change produces higher crime counts. Many apartment owners are unaware of good place management practices, and may even be unaware of crime problems prior to purchasing properties (Campbell, 2000; Campbell, personal
YX communication). In such situations, an ownership change is likely to result in an even less experienced place manager taking control of the apartment.
The models presented so far have been pooled regressions. While the assumption of independent errors has been relaxed by using robust standard errors, the intercepts and slopes have not been allowed to vary across neighborhoods. The next chapter turns to multi-level analysis that allows for exactly that.
YY Chapter 10: Does neighborhood-level crime condition apartment-level effects? Chapter 9 found that the average effect of neighborhood-level crime on apartment-level crime is statistically significant, though this effect is small, on average. Neighborhood-level crime could interact with apartment-level predictors, however. If this is the case, then apartment-level variables, including ownership change, may influence crime at apartments differently, depending on the amount of crime in the surrounding neighborhood. This chapter examines whether neighborhoods provide a context that alters the effects of place-level causes of crime at apartments in Cincinnati using multi-level models.
Sensitivity to model specification Recall from chapter 9 that there are multiple Poisson-based models that can be used with count data. The best fit for my data was the zero-inflated negative binomial (ZINB) model.
Unfortunately, no commonly available software package can estimate multi-level ZINB models.
However, the second-best fit for my data, the negative binomial regression model (NBRM), can be estimated in hierarchical models. Because the model specification is different from the results discussed earlier, it is worthwhile to present single-level NBRM results before discussing multi-level models. ' *#SU shows results from single-level NBRM models with robust standard errors which take neighborhood clustering into account.
YZ Table 13: NBRM Pooled Regression Coefficients
(1) (2) (3) (4) Disorder Property Violent All combined
1 Owner Chng 1.098 1.074 1.163* 1.110* (1.63) (1.50) (2.09) (2.09) 2 Owner Chng 0.974 1.109 1.191 1.063 (-0.24) (1.32) (1.29) (0.60) 3 Owner Chng 1.121 0.902 1.210 1.101 (0.48) (-0.73) (0.73) (0.49) 4 Owner Chng 0.723 0.694 0.361 0.607 (-1.05) (-0.76) (-1.50) (-1.88) Past Crime 1.140*** 1.075*** 1.123*** 1.136*** (11.82) (10.50) (10.64) (11.41) Foreclosure 0.911 0.698*** 0.747 0.814 (-0.61) (-3.69) (-1.30) (-1.40) Delq tax 1.000 1.000 1.000 1.000 (-0.71) (-0.93) (-0.85) (-0.98) 20-39 Units 1.093 1.641*** 1.100 1.146 (0.70) (5.93) (0.80) (1.23) 40+ Units 1.160 1.786*** 1.148 1.208 (0.93) (3.84) (1.07) (1.51) Apts over 0.760* 1.371*** 0.789* 0.884 Retail (-2.51) (4.82) (-1.99) (-1.48) Acres 1.008 0.995 1.037 1.013 (0.14) (-0.12) (0.64) (0.25) Acres2 1.000 1.000 0.999 1.000 (-0.06) (0.10) (-0.37) (-0.21) Neigh Crime 1.000*** 1.000** 1.000* 1.000*** (4.13) (3.20) (2.16) (4.10) Apts:SingFam 0.985 0.971 1.010 0.981 (-0.73) (-0.98) (0.30) (-1.18) N 6240 6240 6240 6240 ll -9944.564 -6124.835 -6032.879 -12541.618 chi2 366.887 337.531 351.656 342.833 p 0.000 0.000 0.000 0.000 Exponentiated coefficients; z statistics in parentheses; shaded cells differ from ZINB models presented in chapter 9 * p < 0.05, ** p < 0.01, *** p < 0.001
Y[ Shaded cells indicate a difference in significance from the ZINB models. While there were differences in significance, no predictor changed signs. Still, there are substantive differences in the results from the NBRM models presented in ' *#SU and the ZINB models presented in chapter 9. The effect of one owner change is no longer significant for disorder. The coefficients are similar, however (1.098 for NBRM; 1.107 for ZINB) and the other coefficients for one ownership change are similar as well. Other differences are more substantial. In ZINB models, the effect of apartment size (measured by land use code) was significant and strong across all dependent variables. In the NBRM models, these effects are no longer as consistent, with size being significant only in the property crime model. The effect of neighborhood crime is significant across all NBRM models as well. In the ZINB models, neighborhood crime was not a significant predictor of property or violent crime.
Combined, these differences between the NBRM and ZINB models suggest two things.
First, the relationships examined are sensitive to the model used. Theoretically, it makes sense to use ZINB models. There are a large number of zero-count apartments and multiple processes may create those crimeless apartments. Model fit statistics discussed in chapter 9 agree with that approach. Because the problem of excess zeros is likely to be more pronounced for dependent variables with a higher proportion of zeros, I will limit examinations of multi-level models to only the all crime measure (property, violent, and disorder combined), which has the fewest number of zeros.
Second, the coefficients presented in chapter 9 should not be directly compared to the multi-level models’ coefficients discussed below. Trends can be compared, however. Despite the model selection and comparison problems, the nested nature of my data suggest a multi-level approach. This is especially true given the substantively small impact of neighborhood crime
ZR found in chapter 9. It could be that the effect of neighborhood crime is expressed primarily through a conditioning effect on apartment-level variables.
Consequently, our confidence in results based on a hierarchical models using NBRM must be tempered by the knowledge that had I been able to use ZINB instead, the results may have been different. I will return to this problem in the final chapter, because it has implications far beyond this study.
Different effects in different neighborhoods Multi-level modeling proceeds in stages, adding variables at the two units of analysis in sequence (Raudenbush and Bryk, 2002). The first stage is to determine if there is substantial variation among neighborhood crime levels. If there is not, we do not need to proceed further with multilevel modeling. ' *#SV shows results from these models23, beginning with an unconditional model to determine if there is variation among neighborhoods in column 1. The significant chi-square value (334.437) for the level-2 intercept suggests there is variation in crime among neighborhoods to use an hierarchical model. However, the intra-class correlation coefficient is relatively low, suggesting that most of the variation is among apartments within neighborhoods instead of among neighborhoods.
Just as in the ZINB models presented in chapter 9, the effects of size and past crime24 at the apartment-level are significant and consistent across the models. The effect of size varies
23 Additional models not shown in Table 14 were estimated as well. Appendix X shows results from models including only neighborhoods with ten or more apartments. Appendix XI shows results from models that include only one cross-level interaction at a time. All of these alternative models produced very similar results to those displayed in Table 14. 24 The effect of past crime was fixed across all models due to high colinearity with the y-intercept when the effect was allowed to vary randomly across neighborhoods – the models would not converge when past crime was allowed to vary. As described in chapter 8, the distribution of crime within neighborhoods is highly skewed, with just a handful of high-crime apartments within each neighborhood. Examination of neighborhood-specific scatterplots suggested that extreme values within each neighborhood were very influential and differed across neighborhoods. Theoretically, there is little reason to expect the effect of past crime on current crime to vary across neighborhoods. Practically, the models cannot run with random past crime. Fixing (averaging) the effect of past crime across neighborhoods removed the colinearity and allowed the models to run.
ZS across neighborhoods (see the variance components in columns 4 and 5 of ' *#SV), but the interaction term with neighborhood crime is not significant (column 5). This model fails to explain much of the variation in the effect of size across neighborhoods.
Tracing the effect of neighborhood crime across the models, neighborhood crime is significant when neighborhood-level variables are added to the model without apartment-level variables (column 2). Adding apartment-level variables to the model (panel 3) renders neighborhood crime non-significant (column 3). Allowing the effects of apartment-level variables to vary randomly across neighborhoods (column 4) brings neighborhood crime back to significance. Neighborhood crime is not significant in models where it is allowed to interact with the apartment-level effects that vary across neighborhoods (column 5).
ZT Table 14: HLM models predicting all crime
(1) (2) (3) (4) (5) Fixed effect Exp(b) Exp(b) Exp(b) Exp(b) Exp(b) Intercept 3.693*** 3.491*** 3.036*** 2.894*** 2.860*** 1 Owner Chng 1.147*** 1.121 1.097 2 Owner Chng 1.051 1.083 1.040 3 Owner Chng 1.056 1.120 1.103 4 Owner Chng 0.931 0.939 0.948 Past Crime 1.024*** 1.029*** 1.030*** Foreclosure 0.914 0.867 0.865 Delq tax 1.000 1.000 1.000 20-39 Units 2.651*** 2.319*** 2.062*** 40+ Units 2.189*** 2.608*** 2.794*** Apts over Retail 0.790*** 0.787** 0.797*** Acres 0.948** 0.904* 0.899*** Acres2 1.002** 1.004*** 1.004*** Neigh Crime 1.000060** 1.00004 1.00004* 1.00004 Apts:SingFam 0.982 1.0044 0.978 0.957 Cross-level Interactions Neigh crime * 1 Owner Chng 1.000006 2 Owner Chng 0.999966 3 Owner Chng 0.999934 20-39 Units 1.000032 40+ units 0.999888 Variance components Variance Variance Variance Variance Variance Chi-square Chi-square Chi-square Chi-square Chi-square Level-2 intercept 0.257 0.23085 0.174 0.150 0.158 334.437*** 257.316*** 363.390*** 107.400*** 158.123*** 1 Owner Chng 0.064 0.066 51.264** 48.083** 2 Owner Chng 0.363 0.186 144.303*** 70.133*** 3 Owner Chng 0.621 0.569 43.663* 36.844* 20-39 Units 0.152 0.199 65.211*** 71.980*** 40+ Units 0.839 0.597 272.083*** 163.658*** Apts over Retail 0.048 14.473 Level-1error 19.535 19.507 8.323 7.200 7.302 Intra-class correlation 0.013 0.012 0.020 0.020 0.021 Poisson-based models estimated in HLM 6.08 with an overdispersion parameter. Exponentiated coefficients; * p < 0.05, ** p < 0.01, *** p < 0.001
ZU A single ownership change is significant when the effect is averaged across all neighborhoods (column 3). However, when it is allowed to vary across neighborhoods (columns
4 and 5) it is not significant. There are two reasons for this. The first is technical: the degrees of freedom are reduced from 6220 in the fixed models to 48 in the random coefficient models
(columns 4 and 5). When coefficients are allowed to vary across neighborhoods, the point estimate is calculated as an average of the effect of ownership change in each neighborhood (50 neighborhoods) instead of an average effect across all apartments (6240 apartments).
Examination of the neighborhood-specific regression lines suggests a second reason:
Some neighborhoods have a negative coefficient for ownership change – in some neighborhoods, ownership change reduces crime. 4'%30#SZ plots these estimated relationships from model 5.
One line is plotted per neighborhood. To show a possible interaction with neighborhood crime, neighborhoods with above-median neighborhood-level crimes are drawn in red; blue lines are below the median of neighborhood crime. Overall, however, the effect between ownership change and crime appears positive: ownership change increases crime.
ZV 6.77 Neighborhood crime: below median Neighborhood crime: above median
5.10
3.43 2009 Crime 2009
1.76
0.09 1 Ownership Change
Figure 18: Effect of ownership change by neighborhood The non-significant effect of ownership change in the random coefficients models suggests that the coefficient of ownership change is indistinguishable from zero. Yet the purpose of modeling multi-level effects was to determine if neighborhood context conditions apartment- level effects. Twelve neighborhoods had a negative relationship between ownership change and crime. The average exponentiated coefficient for one ownership change in these 12 neighborhoods is 0.904, or about a 10% reduction in crime due to ownership change (all else equal). Ownership change is no more likely in these neighborhoods with a negative coefficient for ownership change than in those neighborhoods with a positive coefficient (t=-0.027 df=48; p=0.979). Average neighborhood-level crime was not significantly different in neighborhoods
ZW with negative coefficients for ownership change compared to neighborhoods with a positive coefficient for neighborhood change (t=1.270, df=48; p=0.210). Apartment-level crime was also not significantly different between the two groups of neighborhoods (t=0.6971, df=48; p=0.489).
It could be that ownership changes in these 12 neighborhoods are somehow systematically different than apartment ownership changes in the other 38 neighborhoods. The neighborhoods with a negative relationship between ownership change and crime are: Camp
Washington, Carthage, CBD/Riverfront, College Hill, East Price Hill, East Walnut Hills, East
Westwood, Mount Washington, Northside, Over-the-Rhine, Pendleton, and Pleasant Ridge.
These neighborhoods are shaded in 4'%30#S[.
ZX
Figure 19: Cincinnati neighborhoods with negative coefficients for ownership change
ZY Neighborhood-level effects: Summary This analysis suggests something of a mixed result for the effect of neighborhood context on apartment-level crime. Clearly, there are significant differences across neighborhoods in crime and in the slopes of the measures studied. Neighborhood-level crime, however, does not predict those slopes.
Just as with the ZINB models in chapter 9, apartment-level effects are the strongest predictors in multi-level models. Past crime and size are the best predictors in the models. One ownership change is significant and increases crime when its effect is fixed across neighborhoods. However, the effect of ownership change varies across neighborhoods, with some neighborhoods having the opposite effect. When the effect of ownership change is allowed to vary randomly, its effect is no longer significant due to some neighborhoods having negative slopes for ownership change.
A cautionary note: the influence of outliers Recall from Chapter 8, that the distribution of crime within each neighborhood is highly skewed. Past crime could not be allowed to vary across neighborhoods in multi-level models due to the influence of extreme values – high crime apartments – that are not randomly distributed throughout the 50 neighborhoods. The skewness of the all crime count varies substantially across all neighborhoods, with a minimum of 0.012 and a maximum of 10.333.
Among neighborhoods where ownership change increases crime, the skewness of crime is 9.952.
Among the 12 neighborhoods where ownership change decreases crime, the skewness is 6.809.
One way to visualize the outliers is with a box plot. 4'%30#TR shows box plots of the dependent variable, all crime at apartments, for different categories of ownership change. These are plotted separately for neighborhoods with a negative slope for ownership change and for
ZZ neighborhoods with a positive slope for ownership change. A plot with all apartments is included and labeled Total.
The distribution of outliers is different for neighborhoods where ownership change reduced crime. In these neighborhoods, the extreme crime counts occur at apartments where no change in ownership occurred (upper left in the figure). In the majority of neighborhoods, however – those where ownership change increased crime – the extreme crime counts occurred where there was a change in ownership (upper right in the figure). When the two plots are combined (lower panel), it is clear that the crime counts at apartments that experienced an owner change are more extreme than the counts at apartments which did not experience an ownership change.
Z[
Figure 20: Extreme values of crime are different among neighborhoods Extreme values influence the coefficients in all of my models. The traditional method of dealing with such cases is often to delete them and rerun the models. I have rejected that approach because those cases contain valuable information. The majority of crime occurs at properties that could reasonably be called outliers. Moreover, the distribution of crime counts at apartments follows a power-function regardless of how the data are partitioned. In every neighborhood, by every category, crime is highly concentrated at a relative handful of places.
This is something which will be discussed in more detail in the next chapter.
[R Chapter 11: Conclusions and implications This dissertation seeks to fill a gap in the place management literature. While previous studies have focused on the concentration of crime at heterogeneous facilities (Groff et al.,
2010; Sherman et al., 1989; Weisburd et al., 2004), particular crimes or interventions at apartments (e.g., Eck, 1994; Eck and Wartell, 1998; Green, 1995) and the effects of a single owner purchasing property (Clarke and Bicher-Robertson, 1998), no study has examined systematically the effects of ownership changes at one facility type in an entire city. As
Madensen’s (2007) theory of place management implies, purchasing and sales decisions are just as much a part of place management as the details of lease provisions, or decisions about locks, lighting, and parking rules. This dissertation is the first systematic attempt to addresses that gap.
It used data I obtained from the Hamilton County (Ohio) Auditor, Cincinnati Police Department, and Cincinnati Area Geographic Information System describing 6,701 apartment buildings. My goal was to examine whether ownership change influences crime at apartments.
I tested five hypotheses. First, I hypothesized that apartment ownership change is rare. I found that ownership change is not quite as rare as I had anticipated. Half (49.8%) of apartments had been sold during the period 2002-2009. Most of those (32.4% of the total) were sold just once, however, and the mean time since last sale was just over 8 years. Only 15 properties were sold 4 times in the period 2002-2009. Serial ownership change is rare: most properties do not turn over frequently, though in a long enough time span all will change owners.
Second, I hypothesized that ownership change is more frequent at places with a history of being crime problems. Logistic regression of ownership change in 2009 showed that the average of past crime from 2006-2008 was a significant predictor of ownership change. Increasing past
[S crime from zero to 25 changed the odds of an ownership change by 0.02 (from 0.07 to 0.09).
Past crime had a more pronounced effect on ownership change at the higher end of its range – the predicted odds of ownership change at the highest crime apartments are 0.44.
Third, I hypothesized that regardless of how apartments are partitioned, a relative handful of apartments would produce a disproportionate amount of crime. Crime counts at Cincinnati apartments follow a power-law like distribution, as predicted by Eck et al. (2007). The worst
10% of apartments accounted for 63% of 2009 crime at apartments in Cincinnati. This pattern holds regardless of how the apartments are partitioned. Within each category of size, for example, the top 10% of apartments had over half of the crime. Crime is concentrated at a relatively few apartment buildings in each neighborhood. And crime is highly concentrated among apartments that had an ownership change.
Fourth, I hypothesized that ownership change is associated with increases in crime, and that this relationship is stronger at places with a history of past crime. One ownership change is associated with an increase in crime counts. Zero-inflated negative binomial models found that one ownership change increases the expected crime count by about 10%, net of other factors.
This effect is most pronounced at apartments with high values for past crime and for apartments with more than 20 units. The strongest predictor of crime is past crime at the same location, however.
Finally, I hypothesized that neighborhood context would have an effect on crime, and that the influence of ownership change may be sensitive to neighborhood context. My analysis produced a complex set of findings. Overall, neighborhood crime had little or no direct effect on crime at the place-level, once apartment-level factors were controlled. However, neighborhood
[T membership did have a large impact on how ownership change influences crime. In some neighborhoods, ownership change increases crime; in other neighborhoods, ownership change decreases crime. Extreme values are largely to blame for the difference. In neighborhoods where ownership change increased crime, extreme values of crime occurred at places with an ownership change. In neighborhoods where ownership change reduced crime, extreme values occurred at places with no ownership change. In short, neighborhood influences are almost entirely contextual, and this influence is considerable. The relevant context appears to be the skewness of crime and unsold places.
It is helpful to return to Figure 2, which depicted these relationships and is reproduced below as Figure 21. My analysis has shown that the relationship between crime and property sales exists. The other relationships depicted are plausible explanations for that association between crime and ownership change – but my data did not include measures that would allow for testing those relationships...
[U Figure 21: Management, sale, and crime revisited
Both crime and ownership change are concentrated. These two phenomena are connected: past crime increases the odds of a property changing ownership. In some neighborhoods, this change is likely to make crime worse. This implies a positive feedback system that leads to spirals of decline at the place level that accumulate at the neighborhood- level. As that occurs, each place-level decline contributes to the context of surrounding places, exacerbating management problems elsewhere. Yet in other neighborhoods, ownership change can lead to crime declines. This implies a feedback system that prevents place and neighborhood decline is possible. As I explain below, these conclusions are based on statistical models that are highly sensitive to extreme cases and model selection.
[V Methodology and future studies of crime counts My analyses began with one research question: Does ownership change of apartments affect crime? My findings suggest that ownership change does cause an increase in crime counts at apartments. However, the multivariate analyses used here are sensitive to extreme values and model specification. It could be that the relationships I found would not be seen if I had used different methods.
With few extreme values, it is difficult to determine if the coefficients found in my models are real effects of the measures or idiosyncratic processes limited to a handful of places.
That said, the extremely criminogenic apartments are substantively important to understanding crime at apartments. So much crime occurs at these extremes that if we ignore them, then we ignore the most interesting places. Consequently, deleting the apartments with high crime from the dataset is not a solution to the problem.
The purpose of this dissertation was to explore the plausibility of ownership change as a causal variable. With that accomplished, future studies should use different designs and methods to determine not only if ownership change matters, but if so, how – and under what circumstances. One method for doing so is to compare crime at apartments for a time period before and after ownership changes. Is there a difference in crime counts in the 12 months before and 12 months after an ownership change? I plan on examining that question in future work with data similar to that used in this dissertation. But a more useful question remains: If ownership change is associated with a difference in crime, is that change the result of different management practices before and after the change?
Answering this last question will require primary data collection at apartments, ideally including surveys of the place managers or owners. Such studies are difficult, expensive, and
[W require fieldwork. Mail surveys typically receive low response rates particularly when businesses are surveyed (Dillman, 2000). For example, a recent study of Cincinnati apartments had a response rate of 21.5% to a mailed survey of place management practices (Eck et al.,
2009), and no other contact method existed for the majority of apartment owners. This is particularly troublesome since most of the action occurs at extreme apartments, not the typical apartments (they have no crime). And with low response rates we have little confidence that the extreme values will show up in the sample with sufficient frequency. Since wide-scale surveys are unlikely to yield usable data, it may be that very small-n case studies are what is needed
(Eck, 2006).
Implications Together, my findings suggest that ownership change is an important variable in crime counts and that there is feedback between the two. Even with the methodological challenges I have mentioned, this finding bolsters the growing literature which suggests that place management is a significant element in the causation of criminal events. It is, however, curious that while one owner change is (generally) a significant predictor, two, three, and four ownership changes are not. It is possible that place managers for properties that change owners frequently have a super-controller (Sampson, Eck, and Dunham, 2010): realtors and real estate markets.
Realtors may be exerting influence over the owners of apartments that are frequently on the market. It may also be that too few places with serial change exist to detect an effect of serial change.
While other variables in my models were significant as well, past crime was the most substantively important predictor. The very strong relationship between past crime and current crime is not terribly surprising, given prior studies which show stability in crime counts at street
[X segments over time (Weisburd et al. 2004; Groff et al. 2010). My findings therefore suggest targeting crime prevention resources at 1) apartments with past crime; 2) apartments with more than 20 units; and 3) apartments with ownership changes in neighborhoods where high crime apartments are frequently sold. Table 15 shows the percentage of apartments with one ownership change in neighborhoods. In neighborhoods where ownership change increases crime, a higher proportion of high-crime apartments were sold compared to neighborhoods where ownership change decreases crime. In short, when the apartments being sold are high- crime apartments, ownership change is likely to make the crime problem worse.
Table 15: Percent of apartments with 1 ownership change Crime Ownership change increases Ownership change decreases crime (positive slope) crime (negative slope)
High (>2) 40.01 34.56
Low (<2) 32.04 36.67
Neighborhood context is important, but neighborhood effects cannot be adequately explained with area measures of crime. It may be that some feature of the neighborhood causes the variation crime among neighborhoods, and variation in the effect of ownership change. The most likely candidate feature is the skewness of the place-crime distribution. Extreme values of crime are not randomly distributed among Cincinnati’s neighborhoods and those extreme values drive the analysis. If so, then we should look not to neighborhood crime means for an explanation, but to the extremes. If apartments do enter a spiral of decline after being sold, then transfer of ownership at high-crime apartments may represent a prime opportunity for intervention. These findings are too new and too sensitive to research methodology to suggest
[Y specific policies at the moment, but future research could reveal significant crime prevention benefits from such highly-targeted interventions.
Perhaps the most surprising finding is one that required no complicated statistics and no caveats: the majority of apartments have zero or one crime over a one-year period. That includes disorder offenses and other minor offenses in my data that would not appear in the Uniform
Crime Reports. This has clear policy implications. When tackling problems at apartments, police departments and neighborhood groups would be well-served to focus their efforts at apartments with the worst problems. Focusing on the apartments with the highest prior crime counts is likely to provide the greatest reduction. At the same time, jurisdictions should remember that the majority of places are not problems. Partnerships with the majority of apartment owners who are managing their places well should be part of any apartment-focused strategy. Ongoing positive contacts with landlords and public recognition of landlords with good management practices are a logical extension of any apartment-based crime prevention program.
Apartment owners themselves may be in the best position to educate their peers in the value of managing for crime prevention.
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SRW Appendix I: Geocoding and matching Auditor data to crime data Each year of Hamilton County Auditor data was geocoded using a county-wide address locator created from a streets layer provided by CAGIS. Apartments were then selected by attribute based on land use code (land use code 401, 402, 403, and 404). Apartments were interactively rematched to correct for inconsistencies. For example, “North Bend Rd” was parsed incorrectly during the automatic geocode. Many of the unmatched apartment parcels simply lacked complete address information – either the street name or number was missing.
After interactive matching, apartments inside city limits were selected by location, with a 500’ buffer added to the Cincinnati city limits.
Year County Parcels County % County City Apartments Apartments Apartments Geocoded
2009 349,047 8,908 95.80 6,898
2008 348,727 9,058 95.03 6,957
2007 349,249 9,105 95.62 6,957
2005 345,939 9,589 93.09 7,226
2002 351,311 9,397 96.22 7,293
Calls for service data was geocoded using the same address locator as the Auditor data.
The data provided by the Cincinnati Police Department contained only city (not county) crimes.
Unmatched calls for service were matched interactively and corrections were made where possible. Wide-scale corrections included in the Auditor data (e.g., for North Bend Rd) were
SRX applied to the crime data as well. Crimes were matched to Auditor data using the “Match_addr” field created during the ArcGIS geocoding process
Year Number of CFS % CFS matched
2009 288,574 93.01
2008 287,268 93.14
2007 300,440 93.77
2006 312,974 94.21
SRY Appendix II: Calls for service by type Calls for service by type Count Description Classification 20072 FAMILY TROUBLE (NON-VIOLENT) DISORDER 16471 DISORDERLY PERSON (INCLUDES CROWD) DISORDER 10740 NOISE COMPLAINT DISORDER 9963 SUSPICIOUS PERSON OR AUTO DISORDER 4929 TRESPASSER DISORDER 4659 NEIGHBOR TROUBLE DISORDER 4591 DRUG USE/SALE DISORDER 3619 MENTALLY IMPAIRED - NON VIOLENT DISORDER 3019 ANIMAL COMPLAINT DISORDER 2546 MENTALLH IMPAIRED - VIOLENT DISORDER 2212 DISORD GROUP (4 OR MORE) DISORDER 2205 PERSON DOWN, NOT COMBATIVE, NOT SICK/INJURED DISORDER 1475 COMPLAINT OF PANHANDLERS DISORDER 972 JUVENILE COMPLAINT DISORDER 748 SICK PERSON DISORDER 675 FIREWORKS COMPLAINT DISORDER 659 PLACE FOUND OPEN DISORDER 602 COMPLAINT OF PROSTITUTES DISORDER 405 CURFEW VIOLATION DISORDER 368 PROWLER DISORDER 108 PERSON DOWN AND OUT DISORDER 33 PERSON INJURED DISORDER 7898 NON RESIDENT ALARM PROPERTY 6251 RESIDENT ALARM PROPERTY 5876 THEFT REPORT PROPERTY 5572 BE REPORT PROPERTY 4392 THEFT JUST OCCURRED PROPERTY 3388 AUTO THEFT REPORT PROPERTY 3012 BE IN PROG/JO PROPERTY 2221 CRIMINAL DAMAGE JUST OCCURRED PROPERTY 1475 CRIMINAL DAMAGE REPORT PROPERTY 865 PROPERTY FOUND PROPERTY 665 AUTO THEFT JUST OCCURRED PROPERTY 23 CON GAME PROPERTY 11 PROPERTY LOST PROPERTY 5713 ASSAULT JUST OCCURRED VIOLENT 4309 FIGHT IN PROGRESS VIOLENT 3640 DOMESTIC VIOLENCE IN PROGRESS VIOLENT 3236 MENACING JUST OCCURRED VIOLENT 3200 POSSIBLE SHOTS FIRED VIOLENT 2528 ASSAULT WITH INJURIES VIOLENT 2406 PERSON W/GUN VIOLENT
SRZ 2038 ROBBERY JUST OCCURRED VIOLENT 1732 HOLDUP ALARM (ALL EXCEPT SIG66) VIOLENT 1236 CRITICAL MISSING VIOLENT 1126 PERSON W/WEAPON (INCLUDES KNIFE) VIOLENT 1044 NON-CRITICAL MISSING VIOLENT 678 ASSAULT REPORT VIOLENT 533 SHOOTING HAS OCCURRED VIOLENT 487 POSSIBLE DOA VIOLENT 396 SEX OFFENSE JUST OCCURRED (NOT RAPE) VIOLENT 390 MENACING REPORT VIOLENT 313 ROBBERY REPORT VIOLENT 270 RETURNED NON-CRITICAL MISSING VIOLENT 268 CUTTING HAS OCCURRED VIOLENT 218 DOMESTIC VIOLENCE REPORT VIOLENT 172 RETURNED CRITICAL MISSING VIOLENT 145 ROBBERY WITH INJURIES VIOLENT 128 RAPE JUST OCCURRED VIOLENT 115 RAPE REPORT VIOLENT 106 ASSAULT PERSON INJURED VIOLENT 91 STALKING IN PROGRESS VIOLENT 89 ABDUCTION VIOLENT 87 PERSON SHOT VIOLENT 84 SEX OFFENSE REPORT (NOT RAPE) VIOLENT 68 PERSON CUT VIOLENT 57 AUTOMATED HOLDUP ALARM VIOLENT 48 BOMB THREAT, EXPLOSIVE DEVICE VIOLENT 45 HOSTAGE SITUATION VIOLENT 40 STALKING REPORT VIOLENT 15 ROBBERY PERSON INJURED VIOLENT 11 RAPE WITH INJURIES VIOLENT 5 BIO/CHEMICAL THREAT VIOLENT 2 BARRICADED PERSON VIOLENT 21968 MAKE INVESTIGATION EXCLUDED 14856 CAR IN VIOLATION EXCLUDED 14846 AUTO ACC/NO INJURY EXCLUDED 8669 TRAFFIC HAZZARD EXCLUDED 8463 911 DISCONNECT EXCLUDED 6566 POSSIBLE WANTED SUBJECT EXCLUDED 4213 WARRANT SERVICE EXCLUDED 4005 UNKNOWN TROUBLE EXCLUDED 3799 PRISONER ADULT/JUV (INCLUDES OLD PRISA & PRISJ) EXCLUDED 2838 ATTEMPT TO LOCATE EXCLUDED 1934 AUTO ACCIDENT - INJURIES EXCLUDED 1730 CHILD VICTIM EXCLUDED 1259 911 SILENT CALL EXCLUDED 571 KEYS LOCKED IN AUTO EXCLUDED 521 FIRE REQUEST POLICE EXCLUDED 382 TELPHONE HARRASSMENT EXCLUDED
SR[ 274 AUTO ACC/INJURY EXCLUDED 155 AUTO CRASH INTO BUILDING EXCLUDED 113 POLICE OFFICER NEEDS ASSISTANCE EXCLUDED 82 TRAFFIC POST EXCLUDED 81 ATTEMPT SUICIDE EXCLUDED 57 TRAFFIC TIE UP EXCLUDED 18 FIRE NEEDS ASSISTANCE EXCLUDED 11 AWARE ALARM (JURIS) EXCLUDED 11 JURISMONITOR ALARM EXCLUDED 10 AIR CRASH EXCLUDED 10 INACTIVITY ALARM EXCLUDED 7 CHEMICAL SPILL EXCLUDED 1 DROWNING-LARGE BODY OF WATER EXCLUDED
SSR Appendix III: Neighborhood characteristics Proportion population under Proportion Area 2000 Number of poverty population Number Neighborhood (sq mi) Population Households line nonwhite 1 Avondale 1.40 10,732 4,476 0.401 0.969 2 Bond Hill 2.30 10,127 4,359 0.209 0.956 3 California 1.63 395 157 . 0.005 4 Camp Washington 1.22 1,506 502 0.353 0.291 5 Carthage 1.31 2,495 1,095 0.159 0.133 6 CBD/Riverfront 1.07 3,809 1,905 0.265 0.429 7 Clifton 2.12 9,118 4,800 0.164 0.260 8 College Hill 3.69 14,950 6,664 0.114 0.538 Columbia 9 Tusculum 0.87 2,011 943 . 0.109 10 Corryville 0.52 3,781 1,808 0.342 0.586 11 Fairview 0.77 9,803 4,924 0.376 0.302 12 East End 4.06 1,249 512 0.244 0.175 13 East Price Hill 2.37 14,562 5,614 0.251 0.211 14 East Walnut Hills 0.71 3,936 2,137 0.209 0.444 15 East Westwood 0.76 3,766 1,683 . 0.756 16 English Woods 0.14 1,286 514 . 0.959 17 Evanston 1.38 11,009 3,688 0.357 0.754 18 Fay Apartments 0.32 2,359 854 0.606 0.981 19 Hartwell 1.17 5,022 2,519 0.133 0.262 20 University Heights 0.63 5,205 1,431 0.340 0.299 21 Hyde Park 2.74 12,452 6,484 0.054 0.083 22 Kennedy Heights 1.01 5,276 2,393 0.080 0.789 23 Linwood 0.74 932 399 . 0.026 24 Lower Price Hill 0.69 1,301 439 0.540 0.174 25 Madisonville 2.39 10,774 4,660 0.128 0.646 26 Millvale 0.29 2,856 1,049 . 0.966
SSS Proportion population under Proportion Area 2000 Number of poverty population Number Neighborhood (sq mi) Population Households line nonwhite 27 Mount Adams 0.53 1,631 1,071 0.083 0.039 28 Mount Airy 3.24 9,603 3,995 0.152 0.519 29 Mount Auburn 0.72 6,790 2,833 0.260 0.762 30 Mount Lookout 1.55 6,362 2,958 0.039 0.032 31 Mount Washington 3.83 11,736 5,931 0.061 0.068 32 North Avondale 1.33 7,990 3,537 0.255 0.804 33 North Fairmount 0.61 7,990 3,537 0.255 0.804 34 Northside 2.15 9,098 3,874 0.224 0.436 35 Oakley 2.33 11,082 6,269 0.076 0.125 36 Over-the-Rhine 0.50 6,553 3,124 0.598 0.781 37 Paddock Hills 0.51 2,242 1,065 . 0.682 38 Pendleton 0.07 1,129 487 0.365 0.853 39 Pleasant Ridge 1.77 8,842 4,250 0.106 0.387 40 Queensgate 1.46 714 165 0.443 0.766 41 Riverside 2.38 2,555 998 0.204 0.122 42 Roselawn 1.29 6,325 3,061 0.155 0.795 43 Sayler Park 1.59 6,325 3,061 0.155 0.795 44 Sedamsville 0.27 456 142 . 0.250 South 45 Cumminsville 0.43 1,058 374 0.543 0.963 46 South Fairmount 1.32 4,881 1,968 0.339 0.464 47 Walnut Hills 1.27 7,907 3,981 0.369 0.827 48 West End 0.87 7,435 3,421 0.545 0.940 49 West Price Hill 3.14 20,068 8,365 0.130 0.153 50 Westwood 5.72 31,053 14,482 0.131 0.321 51 Winton Hills 2.38 6,412 2,372 0.636 0.898 52 Winton Place 1.99 2,337 939 0.100 0.512
SST Appendix IV: Distribution of apartments by neighborhood Retail, 4-19 20-39 40+ apartments Neighborhood units units units over Total
AVONDALE 161 1913 6 199 BOND HILL 274 7 1 16 298 CALIFORNIA 1 . . 2 3 CAMP WASHINGTON 21 . 1 35 57 CARTHAGE 24 1 1 34 60 CBD/RIVERFRONT 8 1 8 58 75 CLIFTON 191 2414 21 250 COLLEGE HILL 148 18 14 12 192 COLUMBIA TUSCULUM 9 . 2 2 13 CORRYVILLE 64 11 3 27 105 CUF 140 48 22 174 EAST END 15 . . 10 25 EAST PRICE HILL 138 10 16 43 207 EAST WALNUT HILLS 47 41 12 64 EAST WESTWOOD 18 6 4 2 30 ENGLISH WOODS . . 5 . 5 EVANSTON 133 11 1 29 174 HARTWELL 52 6 3 2 63 HEIGHTS 60 101 38 109 HYDE PARK 189 16 11 8 224 KENNEDY HEIGHTS 128 5 3 4 140 LINWOOD 7 . . 1 8 LOWER PRICE HILL 39 . . 16 55 MADISONVILLE 85 15 7 16 123 MILLVALE 1 .. . 1 MOUNT ADAMS 32 1 3 9 45 MOUNT AIRY 123 10 20 2 155 MOUNT AUBURN 126 7 12 22 167 MOUNT LOOKOUT 79 3 . 3 85 MOUNT WASHINGTON 149 17 14 4 184 NORTH AVONDALE 164 20 8 9 201 NORTH FAIRMOUNT 14 . . 5 19
SSU NORTHSIDE 67 5 4 70 146 OAKLEY 256 912 20 297 OVER-THE-RHINE 294 9 3 217 523 PADDOCK HILLS 86 7 1 17 111 PENDLETON 70 . . 8 78 PLEASANT RIDGE 263 13 9 17 302 QUEENSGATE . 1 1 . 2 RIVERSIDE 9 23 . 14 ROSELAWN 222 4 10 6 242 SAYLER PARK 32 1 . 4 37 SEDAMSVILLE 4 . 1 5 10 SOUTH CUMMINSVILLE . . 1 5 6 SOUTH FAIRMOUNT 54 3 4 24 85 WALNUT HILLS 156 23 7 40 226 WEST END 73 . 3 38 114 WEST PRICE HILL 213 26 10 21 270 WESTWOOD 545 51 45 26 667 WINTON HILLS 19 4 3 11 37 WINTON PLACE 17 . 1 6 24
Total 5,020 384292 1,005 6,701
SSV 0)#01& "#1',"'! 2# +-0# . 02+#,21.#0 3,'2 0# T >#'%& -0&--"* #*1 ! , #$-3,"',2&# b,3+ #0c!-*3+,-$ ..#,"'6---T
Figure 22: Distribution of apartments by neighborhood, normalized by neighborhood area
SSW
Appendix V: Correlation matrix of neighborhood indicators and number of apartments
Proport ion Number Number Number Number Proporti vacant Proportion Proportion female Number of 4-19 of 20-39 of 40+ of retail, Populatio on Proportion stable househ Proportion aged 5-21 headed households with of apts units units units apts over n poverty residence olds nonwhite years children numapts 1 4-19 units 0.9741* 1 20-39 units 0.7307* 0.7459* 1 40+units 0.6939* 0.7267* 0.7795* 1 Retail, apts 0.4789* 0.2705 -0.0226 -0.0748 1 Pop 0.6916* 0.7168* 0.8170* 0.8219* -0.0314 1 Pr(poverty) -0.1273 -0.1702 -0.1464 -0.3344* 0.4125* -0.3887* 1 Pr(stable) -0.0055 0.0662 0.1821 0.0777 -0.3067 0.1672 -0.4035* 1 Pr(vacant) 0.0548 -0.1199 -0.076 -0.1715 0.5685* -0.3146* 0.5263* -0.206 1 Pr(nonwhite) 0.0218 0.0974 -0.0571 -0.1829 0.1277 -0.1522 0.5453* -0.0222 0.2146 1 Pr(5-21_ -0.1584 -0.1537 0.0583 -0.1819 0.084 -0.1239 0.6086* -0.2401 0.2236 0.4408* 1 Pr(fhhwch) -0.156 -0.1177 0.0197 -0.1049 0.0861 -0.2084 0.6544* -0.0258 0.2012 0.6477* 0.6229* 1
SSX Appendix VI: Year of last sale by land use Retail, Year of last 4-19 20-39 40+ apartments sale units units units over Total
1900 73 6 3 16 98 1946 1 . . . 1 1961 1 . . . 1 1970 32 2 8 6 48 1971 1 . . 1 2 1972 2 . . . 2 1974 1 . . . 1 1975 2 . . 1 3 1976 10 . . 4 14 1977 10 . 3 4 17 1978 10 . . 2 12 1979 12 . . 5 17 1980 10 . 1 2 13 1981 4 1 . . 5 1982 2 1 3 2 8 1983 28 3 5 7 43 1984 26 1 . 4 31 1985 37 4 1 6 48 1986 42 3 3 14 62 1987 41 3 2 7 53 1988 48 2 . 9 59 1989 45 1 . 10 56 1990 189 7 5 28 229 1991 58 5 12 14 89 1992 52 3 1 10 66 1993 70 2 2 16 90 1994 91 4 2 13 110 1995 85 10 4 28 127 1996 138 9 17 23 187 1997 153 9 14 35 211 1998 139 4 4 38 185 1999 181 18 8 42 249 2000 160 17 8 46 231 2001 165 10 2 31 208 2002 268 17 16 68 369 2003 278 22 14 62 376
SSY 2004 356 33 18 54 461 2005 390 30 42 65 527 2006 429 32 27 80 568 2007 451 61 39 90 641 2008 412 34 18 88 552 2009 517 30 10 74 631
Total 5,020 384292 1,005 6,701
SSZ
Appendix VII: Distribution of crime at apartments by neighborhood Percent of Percent of crime in the top apartments Number of Number of crimes in Zero-crime 10% of with zero Neighborhood Apartments neighborhood apartments apartments crime Avondale 199 1649 48 35.23% 24.12% Bond Hill 298 464 177 37.93% 59.40% California 3 16 1 100.00% 33.33% Camp Washington 57 211 17 60.19% 29.82% Carthage 60 231 34 26.41% 56.67% CBD/Riverfront 75 296 34 52.03% 45.33% Clifton 250 557 110 48.47% 44.00% College Hill 192 839 66 41.60% 34.38% Columbia Tusculum 13 17 6 64.71% 46.15% Corryville 105 538 34 32.53% 32.38% CUF 174 676 65 39.79% 37.36% East End 25 34 12 70.59% 48.00% East Price Hill 207 1298 66 38.37% 31.88% East Walnut Hills 64 188 31 35.64% 48.44% East Westwood 30 369 6 43.09% 20.00% English Woods 5 365 1 100.00% 20.00% Evanston 174 503 106 26.24% 60.92% Hartwell 63 261 24 40.61% 38.10% Heights 109 336 32 57.44% 29.36% Hyde Park 224 207 153 34.78% 68.30% Kennedy Heights 140 389 61 50.64% 43.57% Linwood 8 5 4 100.00% 50.00% Lower Price Hill 55 310 19 56.45% 34.55% Madisonville 123 354 58 42.37% 47.15% Millvale 1 18 0 100.00% 0.00% Mount Adams 45 28 27 53.57% 60.00% Mount Airy 155 807 44 53.78% 28.39% Mount Auburn 167 484 79 45.04% 47.31% Mount Lookout 85 59 50 54.24% 58.82% Mount Washington 184 920 60 38.26% 32.61% North Avondale 201 1331 89 29.38% 44.28% North Fairmount 19 63 5 80.95% 26.32% Northside 146 433 45 55.89% 30.82% Oakley 297 378 196 27.78% 65.99% Over the Rhine 523 2471 222 40.02% 42.45% Paddock Hills 111 201 82 16.92% 73.87% Pendleton 78 361 26 56.51% 33.33% Pleasant Ridge 302 503 168 34.39% 55.63% SS[ Queensgate 2 86 0 100.00% 0.00% Riverside 14 44 6 40.91% 42.86% Roselawn 242 664 88 55.27% 36.36% Sayler Park 37 115 18 41.74% 48.65% Sedamsville 10 45 5 26.67% 50.00% South Cumminsville 6 27 3 100.00% 50.00% South Fairmount 85 552 30 41.49% 35.29% Walnut Hills 226 1316 82 33.89% 36.28% West End 114 745 58 20.00% 50.88% West Price Hill 270 1122 101 47.86% 37.41% Westwood 667 3055 224 44.22% 33.58% Winton Hills 37 36 27 30.56% 72.97% Winton Place 24 49 12 38.78% 50.00%
STR Appendix VIII: PRM, NBRM, ZIP, and ZINB estimates
Variable PRM NBRM ZIP ZINB
all09 Mean count of crime, 2006- 2008 1.026 1.141.023 1.086 193.92 33.41157.34 28.99 Elapsed Days Since Last Sale 1 1 1 1 -6.85 -3.1-3.65 -1.55 Foreclosure in 2009? 1.002 0.79 1.07 0.886 0.04 -2.011.66 -1.06 Delinquent Taxes 1 1 1 1 6.02 -0.412.99 -0.23 Market value 1 1 1 1 24.5 0.8921.32 2.51 Neighborhood-level crime 1 1 1 1 18.8 6.817.97 3.54 Ratio Apts:Single Family 0.979 0.97 0.995 0.996 -3.98 -2.18-1.03 -0.32 Constant 2.528 1.1954.633 2.125 62.45 4.08100.47 17.33
lnalpha Constant 1.729 1.037 20.43 1.13
inflate Mean count of crime, 2006-2008 0.568 0.04 -23.85-11.38 Elapsed Days Since Last Sale 1 1 -0.290.88 Foreclosure in 2009? 1.46 1.94 1.881.65
Delinquent taxes 1 1 0.79-0.96 Market value 1 1 1.183.21 Neighborhood-level crime 1 1 -3.55-3.71 Ratio Apts:Single Family 1.089 1.167 3.252.79 Constant 2.4913.705 13.049.73 STS
Statistics alpha 1.729 N 6240 62406240 6240 - - - - ll 2.68E+04 1.25E+04 1.98E+04 1.19E+04 bic 53651.95 25174.4339679.29 23894.64 aic 53598.04 25113.7839571.47 23780.08
legend: b/t
STT Appendix IX: ZINB results for models with days since last sale (1) (2) (3) (4) Disorder Property Violent All Combined main Days Since 1.000 1.000 1.000 1.000 Last Sale (-0.46) (-0.35) (-1.70) (-1.27) Past Crime 1.074*** 1.035*** 1.054*** 1.081*** (11.07) (9.56) (13.85) (13.34) Foreclosure 0.979 0.879 0.911 0.912 (-0.15) (-1.12) (-0.46) (-0.70) Delq Tax 1.000 1.000 1.000 1.000 (0.35) (-1.63) (-0.72) (-0.90) Market Value 1.000 1.000 1.000 1.000 (0.04) (1.57) (-0.36) (-0.02) 20-39 Units 1.434*** 2.028*** 1.350*** 1.464*** (4.49) (8.37) (4.11) (7.04) 40+ Units 1.485** 2.246*** 1.361* 1.510*** (2.80) (5.93) (2.51) (3.56) Apts 0.830 1.443*** 0.896 0.933 (-1.76) (7.80) (-0.97) (-1.01) Acres 0.987 0.951 0.994 0.995 (-0.23) (-1.36) (-0.12) (-0.10) Acres^2 1.001 1.002 1.001 1.000 (0.59) (1.30) (0.56) (0.38) Neigh Crime 1.000** 1.000 1.000 1.000* (2.70) (1.45) (0.42) (2.51) Apts:SingFam 1.018 1.001 1.054 1.010 (1.63) (0.02) (1.09) (1.28) inflate Days Since 1.000 1.000 1.000 1.000 Last Sale (1.28) (1.61) (-1.21) (0.83) Past Crime 0.112*** 0.157*** 0.301*** 0.0407*** (-4.91) (-3.65) (-8.69) (-5.20) Foreclosure 1.073 2.548 1.812 1.959* (0.24) (1.63) (1.28) (2.52) Delq Tax 1.000* 1.000 1.000 1.000 (2.36) (-1.50) (0.77) (-1.14) Market Value 1.000 1.000 1.000** 1.000** (1.20) (-0.20) (3.12) (3.09) 20-39 Units 3.218*** 2.858 1.117 2.706** (3.58) (1.87) (0.38) (2.75) 40+ Units 2.171 4.365* 0.779 1.794 (1.84) (2.15) (-0.48) (1.54)
STU Apts 1.306 0.995 1.391 1.152 (1.24) (-0.03) (1.43) (0.69) Acres 0.756 0.793 0.649** 0.853 (-1.29) (-0.86) (-2.73) (-0.82) Acres^2 1.024* 1.020 1.001 1.005 (2.17) (1.64) (0.20) (0.56) Neigh Crime 1.000 1.000* 1.000*** 1.000** (-1.81) (-2.37) (-3.41) (-2.76) Apts:SingFam 1.175 1.338*** 1.111 1.163 (1.48) (5.77) (1.70) (1.42) N 6240 6240 6240 6240 N_zero 3430 4285 4510 2687 ll -9390.985 -5769.218 -5628.317 -11847.121 chi2 437.058 304.420 715.373 449.041 p 0.000 0.000 0.000 0.000 Exponentiated coefficients; z statistics in parentheses *p< 0.05, **p< 0.01, ***p< 0.001
STV Appendix X: HLM models including only those neighborhoods with more than 10 apartments Hierarchial models can be unstable when aggregates with fewer than ten level-1 units are
included (Raudenbush and Byrk, 2002). The results presented below excluded the 21 apartments
in neighborhoods with fewer than ten apartments. Linwood, South Cumminsville, English
Woods, California, and Millvale were excluded below. The results are similar to those reported
in Chapter 10, with no changes in significance or direction.
(1) (2) (3) (4) (5) Fixed effect Exp(b) Exp(b) Exp(b) Exp(b) Exp(b) Intercept 3.655*** 3.491*** 3.012*** 2.884*** 2.846*** 1 Owner Chng 1.147*** 1.123 1.104 2 Owner Chng 1.050 1.097 1.028 3 Owner Chng 1.057 1.122 1.079 4 Owner Chng 0.932 0.941 0.957 Past Crime 1.024*** 1.030*** 1.030*** Foreclosure 0.927 0.877 0.880 Delq tax 1.000 1.000 1.000 20-39 Units 2.645*** 2.322*** 2.099*** 40+ Units 2.159*** 2.582*** 2.694*** Apts over Retail 0.792*** 0.787** 0.797*** Acres 0.952* 0.907*** 0.903*** Acres2 1.002* 1.004*** 1.004*** Neigh Crime 1.000060** 1.00004 1.00004* 1.00004 Apts:SingFam 0.982 1.0052 0.978 0.960 Cross-level Interactions Neigh crime * 1 Owner Chng 1.000008 2 Owner Chng 0.999959 3 Owner Chng 0.999957 20-39 Units 1.000037 40+ units 0.999905 Variance components Variance Variance Variance Variance Variance Chi-square Chi-square Chi-square Chi-square Chi-square Level-2 intercept 0.259 0.231 0.173 0.146 0.151 333.133*** 252.217*** 357.944*** 106.775*** 109.065*** 1 Owner Chng 0.064 0.066 51.179** 45.491** 2 Owner Chng 0.363 0.197
STW 144.190*** 74.947*** 3 Owner Chng 0.640 0.624 43.794* 35.292* 20-39 Units 0.151 0.185 65.002*** 67.825*** 40+ Units 0.883 0.649 268.068*** 168.075*** Apts over Retail 0.052 14.474 Level-1error 19.575 19.544 8.331 7.211 7.286 Intra-class correlation 0.013 0.012 0.021 0.020 0.021 Poisson-based models estimated in HLM 6.08 with an overdispersion parameter. Exponentiated coefficients; * p < 0.05, ** p < 0.01, *** p < 0.001
STX Appendix XI: HLM models with only one cross-level interaction Including multiple cross-level interaction terms in the same model can introduce
colinearity. The models below included only one cross-level interaction per run, with very
similar results to those reported in Chapter 10.
(1) (2) (3) (4) (5) Fixed effect Exp(b) Exp(b) Exp(b) Exp(b) Exp(b) Intercept 2.899*** 2.890 *** 2.894 2.903*** 2.894*** 1 Owner Chng 1.101 1.119 1.122*** 1.122 1.115 2 Owner Chng 1.079 1.121 1.083 1.076 1.087 3 Owner Chng 1.099 1.121 1.121 1.139 1.024 4 Owner Chng 0.941 0.935 0.941 0.994 0.934 Past Crime 1.023*** 1.030*** 1.030*** 1.030*** 1.030*** Foreclosure 0.867 0.867 0.867 0.865 0.863 Delq tax 1.000 1.000 1.000 1.000 1.000 20-39 Units 2.316*** 2.323*** 2.321*** 2.190*** 2.328*** 40+ Units 2.599*** 2.580*** 2.606*** 2.572*** 2.734*** Apts over Retail 0.782** 0.789** 0.788*** 0.779** 0.801*** Acres 0.904*** 0.904*** 0.904* 0.904*** 0.904*** Acres2 1.004*** 1.004*** 1.003* 1.004*** 1.004*** Neigh Crime 1.00003 1.00004* 1.00004 1.00004 1.00004 Apts:SingFam 0.978 0.982 0.977 0.978 0.985 Cross-level Interactions Neigh crime * 1 Owner Chng 1.00001 2 Owner Chng 0.999963 3 Owner Chng 1.000007 20-39 Units 1.00003 40+ units 0.999917 Variance components Variance Variance Variance Variance Variance Chi-square Chi-square Chi-square Chi-square Chi-square Level-2 intercept 0.151 0.150 0.150 0.151 0.155 107.507*** 107.352*** 107.337*** 108.048*** 157.508*** 1 Owner Chng 0.070 0.064 0.064 0.065 0.063 53.965*** 51.286** 51.273** 51.302** 54.270** 2 Owner Chng 0.360 0.350 0.362 0.364 0..349 144.087*** 140.358*** 144.240*** 144.38*** 145.780*** 3 Owner Chng 0.627 0.621 0.680 0.611 0.673 43.774* 43.673* 45.968* 43.620* 48.788* 20-39 Units 0.155 0.152 0.151 0.171 0.164 65.307*** 64.995*** 65.174*** 67.845*** 67.656***
STY 40+ Units 0.834 0.822 0.837 0.838 0.610 271.431*** 272.522*** 271.931*** 271.666*** 177.686*** Level-1error 7.20 7.21 7.20 7.21 7.22 Intra-class correlation 0.021 0.021 0.021 0.021 0.021 Poisson-based models estimated in HLM 6.08 with an overdispersion parameter. Exponentiated coefficients; * p < 0.05, ** p < 0.01, *** p < 0.001
STZ