How  Crime  Affects  Property  Values  in  San  Antonio,  TX  Atiya K. Mitchell

University of Texas-San Antonio, Public Administration Department, Graduate Program

Spring 2015

ABSTRACT  

 This paper uses monthly real estate data obtained from the San Antonio Board of Realtors and

crime data from the Uniform Crime Reports (UCR), which is recorded monthly by the San Antonio

Police Department (SAPD), Records Unit and published annually by the Federal Bureau of

Investigations (FBI), to study the relationship of crime rates to median home prices in San Antonio,

Texas. I use correlation analysis, instead of the hedonic price model, to determine the strength of

interdependence of home values and specific crime variables for violent crime and property crime.

Variables for violent crime include homicide, rape, robbery, and aggravated assault, and they include

burglary, larceny theft, and vehicle theft for property crimes. Results show that violent crime rates in

general have a stronger connection to home prices than the total of the property crimes that are

evaluated in this study. The conclusive findings of this study suggest that home values fall as crime

rates overall increase. I find, specifically, that among all property crimes burglary rates have the

strongest impact on prices. In contrast to this, violent crimes overall show a significantly positive

relationship to prices, where the rate of rape offenses wields major influences indicating that higher

rates of rape align with higher home prices.

Keywords: crime, property values, home prices

Introduction  

Owning a home in the United States is part of the “American Dream.” Through the

creation of government incentives like low interest rates and government programs, FHA loans,

VA loans, and down payment assistance this dream has become an achievable reality for a large

number of Americans who would otherwise be unable to afford it. For many families, a home is

considered to be their greatest investment. It holds the majority of their wealth, so its value

should be a matter not taken lightly. Some factors homebuyers consider are market conditions,

financial terms, and, more appropriately for this study, price. The market price for a home is

determined by the mutually agreed upon price that owners are willing to sell and buyers are

willing to pay. Aside from how much buyers are able to afford, a buyer’s desire to invest in a

home can be a complex equation built of numerous components directly reflecting the amount he

or she is willing to pay for a property. One of the major components in this equation is a

property’s location.

Buyers consider a home’s location in proximity to certain amenities and conveniences,

such as closeness to one’s workplace or someone they know, access to transportation or certain

highways, area traffic, and proximity to certain stores, entertainment, or shopping centers, and if

they have children they may be concerned with the ratings and reputations of school districts or

proximity to sex offenders. For the homes they identify, not only do buyers evaluate the

condition of the home, but they may also examine the condition of the neighborhood surrounding

the home.

It is very normal for people to want to feel safe and secure within their own homes. A

high-crime neighborhood perceived to be unsafe and unkempt could endanger these values

causing buyers to seek discounted home prices in exchange for the increased risk to their persons

and property. Property devaluation of this kind is a revolving catalyst compromising not only

family wealth but also local wealth. Increased crime significantly reduces tax revenue (Hellman

& Naroff, 1979). Properties are taxed according to their assessed values by cities, counties,

public school districts, and other entities, in order to provide public services, and with less

revenue one can assume cutbacks along with unfavorable adjustments to service quality. This

leads to poor school districts, poorly maintained facilities, and a lower quality of life for local

residences, all regularly having a strong relationship and leading to more crime, more vacant and

boarded buildings, and further devaluation. Crime rates influence not only where families choose

to live, but also where employers choose to locate their businesses, and having high crime rates

adversely affects the labor market reducing the availability of jobs in the area, especially high-

paying jobs (Braakmann, 2012). Because of this and more, crime within these areas becomes

venomous, plaguing the lives of those who are unable to escape its bounds.

Crime-ridden areas typically have higher unemployment rates and are composed of low-

income residences because those who are more affluent can afford to live elsewhere and they

prefer this option. Russell and his colleagues tie into this phenomenon describing it as the new

“feudalism” in which well-off families are financially able and willing to pay premiums to live in

richer neighborhoods with higher levels of perceived safety and security (Russell, Borick, &

Shafritz, 2012). Here they are often enclosed within gated-communities and are afforded better

public services such as private police, parks, and trash collection as well as the ability to send

their children to private schools. Critics claim that this trend further damages the quality of

service for those less fortunate relying on the lesser expensive alternatives.

Criminals often choose to commit crimes within their own neighborhoods (Ihlanfeldt &

Mayock, 2010), and when communities are already laden with crime it becomes increasingly

difficult to maintain order and stability. Residences may tend to become disengaged and less

concerned with monitoring the community and may also become indifferent about maintaining

their homes and neighborhoods, all of which provides a “cozy” environment for more criminals

and increased activity. Delinquents may judge their rate of successfully executing an offense on

the levels of low surveillance and policing in target areas, accessibility, whether or not they deem

the property to be private or public territories, how successfully they can cross environmental

barriers (the landscape), symbolic barriers (exterior decorations showing pride in

homeownership), or other physical barriers (gates, security guards, locks, fences, alarms, or

walls) without being detected or apprehended (Brantingham, 1981). Upon these deciding factors,

areas with high foreclosure rates and vacant property become very attractive locations for

offenders, while more wealthy neighborhoods that normally have maximum barriers are

considered high risk with high rewards.

San Antonio, Texas is the seventh largest municipality ranking among the strongest

economies and fastest growing metropolitan areas in the nation. BuilderOnline.com’s local

housing data shows the area currently adds more than 7,000 new construction homes each year

and the San Antonio Board of Realtors (SABOR), which encompasses the San Antonio-New

Braunfels MSA, reports median home prices appreciating at an average of 6 to 7 percent

annually. According to the Uniform Crime Reports (UCR), national crime rates steadily

decreased each year between 2010 and 2013, while San Antonio recently showed spikes in

violent crime during 2013. In this paper, I examine whether or not crime rates influence home

prices in San Antonio, TX. Research has consistently uncovered a negative relationship between

crime and property values, and this study seeks to find similar evidence using crime and housing

data related to the San Antonio-New Braunfels MSA. This paper is constructed as follows:

section 1 is a review of the literature and discusses what we already know about crime in relation

to housing; section 2 describes the study’s theory and hypotheses; section 3 describes the data

used and methods of study; section 4 will cover the results; and the final section will present the

conclusion.

1.  Literature  Review  

Crime and Property Values

Out of the various types of crimes potentially impacting home values, Gibbons finds that

property related crime, such as vandalism, graffiti, and arson have a significantly negative

relationship to property values due to the heightened fear and anxiety of crime that they inspire,

but finds that there is “no measureable impact” on prices from burglary offenses (Gibbons,

2004). In addition to this, he concludes that an increase of one-tenth standard deviation of

property crimes reduces home values by almost 1 percent. He sees that unrepaired property

damage encourages crime and that because vandalism and graffiti are easily observed they are

perceived as indicators for neighborhood decline and increased crime (Gibbons, 2004).

Braakmann found that one additional violent crime leads to a 2 percent home price reduction

(Braakmann, 2012). Using an alternative measurement of crime, crime density, results showed

negative impacts to price from aggravated assault and robbery, reducing prices by 0.1 to 0.3

percent for every 1 percent increase, and positive effects from vandalism (Ihlanfeldt & Mayock,

2010). Pope and Pope did a study involving national crime rates and concluded that a 10 percent

reduction in violent crime rates increases values by 1.5 percent (Pope & Pope, 2012).

Perception of Crime upon Neighborhood Housing

“Fear is primarily an emotional response to the perceived likelihood of victimization,

based on the combined experiences of oneself and others in the neighborhood…[and] is a

response to the perception of residents that the area is becoming characterized by a

growing number of signs of disorder and incivility (such as loitering groups of

unsupervised teenagers, vandalism, graffiti, abandoned buildings, and public drug and

alcohol use) that indicate that the social order of the neighborhood is eroding” (Bursik,

1993).

Quoted from James Wilson in Brantingham’s Envrironmental Criminology, “Predatory

crime does not merely victimize individuals, it impedes and, in the extreme case, even prevents

the formation and maintenance of community” (Brantingham, 1981). A logical assumption from

this would be the existence of distressed property values in relation to crime. Real estate

comprises the majority of wealth and is a prime source of revenue for governments (Bursik,

1993; Hellman & Naroff, 1979), and governments and individuals alike should be concerned

with how crime affects their property. Though nearly all researchers agree with the adverse

affects of crime on neighborhoods, some have reported that the greatest impacts occur not

necessarily from crime, but from the perception of crime, or more specifically the increased fear

and anxiety of victimization (Buonanno, Montolio, & Raya-Vílchez, 2013; Bursik, 1993). This

fear is deterrence for many potential buyers and causes neighborhood residences that can afford

relocation to move to other areas.

Combining data from the housing market and a victimization survey in Barcelona, Spain,

Buonanno calculates that a one standard deviation in the increased perception of security leads to

a 0.57 percent increase in values, while areas perceived to be less safe are highly discounted by

an average of 1.27 percent (Buonanno et al., 2013). One study shows that those victimized the

least, women and elderly people, have the highest perceptions of fear and anxiety compared to

those who are actually victimized the most, which are African-American males. This may be

because women and elderly people perceive themselves to be more vulnerable, and for women, it

is common for them to believe that rape could easily follow an offense (Bursik, 1993).

Constructing methods to measure fear is challenging and differentiated amongst researchers and

will, therefore, not be addressed by this study.

Reasons  for  Crime  That  Indirectly  Affect  Values  

Home sales prices can be negatively impacted based upon the proportion of children

under 18 living in the neighborhood (Lynch & Rasmussen, 2001). As it relates to age, delinquent

behavior peaks between the ages of around 12 to 17 (Bursik, 1993), and most crimes are

committed between the ages of 18 and 24 (Lynch & Rasmussen, 2001). According to the Bureau

of Justice Statistic, as cited by Bursik and Rasmick, children between 12 and 14 years old are

victims of violence and theft at a much higher rate than adults over 25, which makes

neighborhood flight more likely for families with children (Bursik, 1993).

A descriptive analysis of crime demographically very often shows a high correlation

between crime and black people. Consequently, some tend to assume that a person’s race is

indicative of their propensity for violence, and in this case the perception on the surface is that

blacks commit the majority of crime and are therefore threatening. This stereotype is especially

perceived to be true by whites, and is often the reason for, what Bursik and Grasmick call, “white

flight” to other neighborhoods (Bursik, 1993; Harris, 1999). On the contrary, others have found

that it is not merely the presence of blacks that results in high crime neighborhoods, but rather

crime is already present and blacks, who statistically have higher unemployment rates, lower

wages, and lower educational attainment, are forced to live in lower income neighborhoods with

high crime and therefore cannot afford to leave (Bursik, 1993; Harris, 1999). For these reasons

many whites consider black neighborhoods to be undesirable, and this causes property values to

diminish further (Bursik, 1993; Harris, 1999). Harris finds a clear relationship between the

negative appreciation and the proportion of blacks and further reports that its cause could result

from either racial discrimination or socioeconomic status of blacks depending on the ratio of

owner-occupied homes (Harris, 1999).

Areas with high foreclosure rates and vacant property are attractive locations for

offenders. Foreclosure rates show a significantly increased relationship with crime—burglary,

aggravated assault, and larceny—only after the appearance of at least three foreclosures on the

block and even more crime after the foreclosed property is abandoned (Ellen, Lacoe, &

Sharygin, 2013). A one percentage point increase in these rates alone could cause a 10.1 percent

increase in burglary rates (Goodstein & Lee, 2010).

2.  Theory  &  Hypotheses.    

 A number of researchers have approached the topic of crime and its influences on

property values and have concluded a negative correlation between the two. The differences in

strength of correlation vary slightly depending upon the type of crime. In previous studies,

property crime, as opposed to violent crime, has proven to be a stronger indicator of whether or

not buyers will desire to purchase homes in these locations. Naturally buyers who choose to bear

the increased risks of living in a high crime neighborhood expect discounted home prices, while

some are willing to pay higher premiums to reside in what they perceive as safer areas where

crime is reduced and better controlled. I expect to uncover similar results, reflecting a basic

negative relationship between crime rates and home values, where prices decrease with an

increase in crime and vice versa.

Hypothesis (1): Increases in crime rates will decrease median home prices.

Hypothesis (2): Property crime rates will impact median home prices significantly more

that violent crime rates.

3.  Data  &  Methods  

Five general measurements of crime have been used in previous studies: data gathered by

law enforcement, surveys of offending, surveys of fear and victimization, ethnographic

community studies, and records of crime-related police service calls (Bursik, 1993). Using data

from the city of San Antonio, I have collected monthly crime statistics data from the Uniform

Crime Reports (UCR), dating from January 2011 to December 2014. Every year the United

States Federal Bureau of Investigation (FBI) collects crime statistics from local law enforcement

agencies around the country, which is then published publicly. Reporting to the program is

voluntary, however, it is mandated for agencies in the State of Texas to report crime statistics to

the Texas Department of Public Safety as well as the FBI. The specific crimes gathered through

the UCR are categorized under violent crimes (including homicide, forcible rape, robbery, and

aggravated assault) and property crimes (including burglary, larceny theft, and vehicle theft)

(SAPD, 2014). In this analysis, rates have been standardized to account for the variances in

growth over time that may distort crime rates. The number of monthly crimes reported are

calculated per 1,000,000 people of annual population estimates, which are recorded in the UCR

in 2011 as 1,355,339, in 2012 as 1,380,123, and in 2013 as 1,399,725 (FBI, 2015). 2014

estimates of 1,416,291 were obtained from the San Antonio Economic Development Foundation

(SAEDF, 2015).

There are many variables that determine a home’s price based upon the home’s

desirability. Previous researchers have attempted to capture the influences of these variables on

price using a hedonic price analysis model, which is a common econometric function used to

discern each variable’s individual degree of impact. By this manner, researchers could control

for other variables working outside the context of their study, such as the impact on price from

foreclosures and home characteristics. Property appraisers use a similar comprehensive method

when measuring property values through their appraisals. This study does not replicate these

methods directly. However, I employ a more simplistic approach to determining a home’s actual

market value by capturing the price at which buyers are willing to pay for a home and owners are

willing to sell, also known as a home’s market sales price. This data is obtained through

SABOR, which stores archival real estate data, containing median prices and total units sold of

single-family residences for cities within this region. This particular data is published in the

SABOR Regional Sales and Price Activity reports (SABOR, 2015). Monthly prices are converted

to December 2014 USD according to the monthly Consumer Price Index (CPI) inflation rate,

reported by the Bureau of Labor Statistics ("Inflation Calculator," 2015). CPI measures the

“changes in the prices paid by urban consumers for a representative basket of goods and

services” (BLS, 2015).

The independent variable, crime rates, is lagged one month in order to examine how the

previous month’s crime rate influenced the current month’s home price. For example, people

purchasing homes in January will view crime rates in December. The data is first examined

statistically and graphically prior to analysis. To test how property values are impacted by crime,

I compare crime against median sales prices (in 2014 USD) of homes sold within the city each

month between January 2011 and December 2014 using correlation analysis. I anticipate

significant correlations, similar to prior studies, between the various types of crime and property

values, using the individual crime categories as the independent variable influencing median

home prices, the dependent variable.

4. Results The following charts are visual representations of all variables covering the period for

January 2011 to December 2014. The first figures show a line graph depicting how the individual

variables have fluctuated over time and how they compare against the other variables. The

second figure is a table displaying the descriptive statistics of the data, including the number of

records, mean, range, and standard deviation for each variable. The next group of figures shows

how the variables correlate with one another graphically through a scatter plot matrix. The last

figure is a table for the correlation analysis explaining numerically the strength and direction of

influence for each variables relationship with median prices.

Figure 1.1

Figure 1.2

Figure 1.3

Figure 1.4

* All years are in December 2014 USD.

Figure 2 Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

MedPrice 48 $132,144 $167,481 $149,106.27 $9,786.150

Homicide 48 2 9 5.33 1.826

Rape 48 14 79 41.60 15.355

Robbery 48 83 191 114.35 19.953

AggAssault 48 188 477 296.54 57.058

TotViolent 48 327 732 457.77 73.775

Burglary 48 662 1111 874.85 124.176

LarcenyTheft 48 2610 4140 3555.56 325.383

VehicleTheft 48 259 514 389.67 53.604

TotProperty 48 3661 5466 4820.02 426.992

TotCrime 48 3988 5985 5277.69 457.401

Valid N (listwise) 48 * All variables have been standardized to account for population growth and inflation. Rates are per 1Million people, and median prices are in December 2014 USD.

Figure 3.1 Scatter Plot Matrix—Median Prices, Rape and Homicide

Figure 3.2 Scatter Plot Matrix—Median Prices, Robbery, and Aggravated Assault

Figure 3.3 Scatter Plot Matrix—Median Prices and Property Crime

Figure 3.4 Scatter Plot Matrix—Median Prices, Total Violent and Property Crimes, and

Total Crimes

Figure 4.1 Correlation Analysis—Median Prices and Violent Crime Correlations

Homicide Rape Robbery Agg

Assault Tot

Violent Med Price

Pearson Correlation 0.07 .719** -0.067 0.205 .293*

Sig. (2-tailed) 0.638 0 0.651 0.162 0.043

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

c. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Figure 4.2 Correlation Analysis—Median Prices and Property Crime

Correlations

Burglary Larceny

Theft Vehicle Theft

Tot Property

Med Price

Pearson Correlation -.559** -0.138 0.199 -0.243

Sig. (2-tailed) 0 0.349 0.176 0.096

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

c. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Figure 4.3 Correlation Analysis—Median Prices and Total Crime

Correlations

Tot

Crime Med Price

Pearson Correlation -0.179

Sig. (2-tailed) 0.223 **. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed). c. Unless otherwise noted, bootstrap results are based on 1000 bootstrap samples

Through mere observation of the data, I find extreme values in which violent crime rates

increased significantly for robbery and aggravated assault occurring in December 2013, when

total violent crime rates were outrageously high. Another extreme value for aggravated assault is

found in October of that same year. These spikes are likely due to the winter holiday season.

Property crime rates for larceny-theft and vehicle theft saw significant decreases in February,

which is typically the coldest month of the year. These decreases for larceny-theft appear in

February of 2011, 2013, and 2014, and for vehicle theft they appear in January and February

2011. Additionally, total violent crime rates share 8.6 percent of the variation in median home

prices, while total property crime rates share on 5.9 percent. 11.6 percent of variance is shared by

total property crime and total violent crime.

The data lacks normality in distribution, so to account for biases the analysis was based

on a bootstrap sampling method, by which smaller random samples were drawn from the data to

obtain 1000 parameter estimates. Median prices show a positive relationship with rape and

aggravated assault where the rate of rapes and assaults increase as home prices increase.

Although the connection between median prices and aggravated assault rates is relatively weak,

these results slightly contradict my first hypothesis along with other research indicating that

aggravated assaults and violent crime in general impact prices negatively (Braakmann, 2012;

Ihlanfeldt & Mayock, 2010; Pope & Pope, 2012). The relationship between rape and home

values was not covered in preview literature acquired for this study, however their link is

undisputable and shows statistical significance below 0.001 as seen in figure 4.1. This means that

the chances of obtaining such a high correlation coefficient of 0.719 if there was no relationship

between the two is next to zero. A little more than half (51.6 percent) of the variance in rape

crime rates explains the variance in median home prices, meaning that the other half of the

variances in prices is explained by other variables. Although this analysis does not confer the

direction of causality, it would be highly unreasonable to believe that an increase in rapes in a

specific neighborhood would increase the demand for people to purchase homes there causing

home values to rise. Therefore, it is safe to assume that the rate of rapes graduate as a result of

appreciating home prices, or that rape offenses are more likely to occur in more affluent

neighborhoods.

There is not much of a relationship at all between homicides and home prices. The rates

of homicide are so very low compared to other crimes that its little to no impact on home prices

is to be expected.

Corresponding favorably to my first hypothesis and with research done by Ihlanfeldt and

Mayok (2010), robbery shows a slightly negative relationship to prices as seen in figures 3.2 and

4.1, meaning a slight increase in robberies shows a slight decrease in home values or a slight

increase in home values shows slight decreases in robberies. Overall, violent crime rates have a

strong affiliation with median home prices and run parallel with inclines and declines in home

values. This finding, as aforementioned, contrasts with that of other studies showing a positive

relationship that is statistically significant at the 0.05 level. The opposite conclusion is true for

property crimes.

Outside of vehicle theft, which shows only a slightly positive correlation with prices

(figures 3.3 and 4.2), property crimes tend to negatively correlate with median home prices

showing higher rates of property crime associated with lower home values. This outcome aligns

with results found in previous research (Gibbons, 2004). The strongest link to prices under

property crimes comes from burglary rates, suggesting that burglaries are more likely to be found

in areas with low home values.

Burglary rates show a genuine and conclusive association with median home prices,

displaying a negative correlation coefficient of -0.559 that is absolutely statistically significant

below 0.001 levels (figure 4.2). From this information I can assume a few things: 1) more

affluent areas present greater barriers and higher risks of capture for burglars (Brantingham,

1981; Russell et al., 2012); 2) increased burglaries heighten the fear of victimization, and those

who can afford to leave these neighborhoods flee to other communities perceived to be safer

causing home values in the former area to drop; and 3) lower income areas are less likely to

afford better public services, have less vigilance, and are ripe with crime providing benefits to

criminals with reduced risks for capture (Brantingham, 1981). This general finding is supported

by the claims that buyers who choose to bear the increased risks of living in a high crime

neighborhood expect discounted home prices, while some are willing to pay higher premiums to

reside in what they perceive as safer areas where crime is reduced and better controlled (Russell

et al., 2012).

According to the analysis the variance in home prices can be explained by about a third

(31.2 percent) of the variance in burglary rates. This now means that if 27.8 percent of the

variance is explained by rape, then the variances in burglary and rape together account for 55

percent of the variance in median home prices, which means that the remaining 45 percent of this

variable can be explained by other factors.

Total crime rates altogether do show a negative correlation (-0.179) to median home

price, confirming our first hypothesis to be true, in which increases in crime rates do show

decreases in median home prices. However, this finding is not statistically significant. Ihlanfeldt

and Mayok (2010) report meaningful associations for aggravated assault and robbery with prices,

whereas I have found no compelling relationships of the sort. Unlike previous studies that

suggest property crimes have a greater influence on home values than violent crimes, I find that

violent crimes have a more significant relationship with price than property crimes, and it holds a

statistically significant correlation at a level of 0.05 (figure 4.1). This is likely due to the heavy

sway of rape rates in the city and the fact that the data does not account for property vandalism

and graffiti offenses, in which case I believe the results may have turned out differently.

Conclusively, I have proven the last hypothesis to be false, which means that property crimes are

not stronger indicators than violent crimes of whether or not buyers will desire to purchase

homes in specific neighborhoods of San Antonio, Texas.

5. Conclusion  

Instead of revealing a solid relationship between crime and prices, I find that there are

specific variables of crime—burglary and rape—that show remarkable influences on home

values. Unlike Gibbons who found “no measurable impact on prices from burglary offenses

(Gibbons, 2004), I have found a strong correlation between burglary and home values in San

Antonio, where the direction of burglary rates diverges from home prices. In addition to this, the

findings demonstrate that rape rates run in a direction congruent with home prices.

Because this study does not account for certain exogenous variables—such as interest

rates, federal funds rates, unemployment, foreclosures, vacancies, age, race and other economic

or socioeconomic data—that are known to manipulate real estate market activity, this study, of

course, poses limitations. It does not examine the ranges in distance from crime pockets existing

within the city, which would allow for a better understanding of movement and price changes

reflected by crime. Monthly crime rate is judged according to the annual population in San

Antonio. Although the population counts are just estimates and not exact numbers, the accuracy

for crime rates is limited by the absence of monthly counting.

A more thorough picture of how property crimes in general affect prices could have been

gained if data were collected on other types of property crime—like vandalism, graffiti, and

arson—that physically affect the condition of the property, however this data was not readily

available from my source.

The type of strong correlation found for rape and home values is surprising and begs for

further research. One can speculate about several reasons for why and how it exists, and if the

relationship does not hold true in other places, then what would make San Antonio so unique? It

is possible that rape rates are consistent in all income groups and that wealthier individuals are

more likely to report a rape than those in low-income areas. If this is so, more research should be

conducted to reveal the root causes and solutions for abating the rate of offenses.

Local governments and special districts should be very concerned about how properties

within their jurisdictions are being affected and how to better maintain them in order to preserve

values. In so doing, they would be preserving their revenues through property taxes along with

the jobs and public services that it sustains. If crime drags down home values, then it likely

causes economic progress to grow at a slower pace. For cities to attract more families to specific

neighborhoods, the frequency of crime and the damage it brings to the local economy and to the

lives of local citizens could bring forth more changes to policies to combat these offenses.

Policies targeting former criminals and rehabilitation programs could allow offenders to

successfully reintegrate into the workforce or start businesses to make them economically, and

legally, productive once more. Working to replace delinquent activities with valuable ones such

as these could temper some of the negative effects mentioned, which would eventually raise

more revenue for local governments and augment the quality of life for their constituents.

Appendix

Definitions (as defined by the FBI)

Murder and Non-negligent Manslaughter

“The FBI’s Uniform Crime Reporting (UCR) Program defines murder and non-

negligent manslaughter as the willful (non-negligent) killing of one human being by

another. The classification of this offense is based solely on police investigation as

opposed to the determination of a court, medical examiner, coroner, jury, or other judicial

body. The UCR Program does not include the following situations in this offense

classification: deaths caused by negligence, suicide, or accident; justifiable homicides;

and attempts to murder or assaults to murder, which are scored as aggravated assaults.

Forcible Rape

Forcible rape, as defined in the FBI’s Uniform Crime Reporting (UCR) Program,

is the carnal knowledge of a female forcibly and against her will. Attempts or assaults to

commit rape by force or threat of force are also included; however, statutory rape

(without force) and other sex offenses are excluded.

Robbery

The FBI’s Uniform Crime Reporting (UCR) Program defines robbery as the

taking or attempting to take anything of value from the care, custody, or control of a

person or persons by force or threat of force or violence and/or by putting the victim in

fear.

Aggravated Assault

The FBI’s Uniform Crime Reporting (UCR) Program defines aggravated assault

as an unlawful attack by one person upon another for the purpose of inflicting severe or

aggravated bodily injury. The UCR Program further specifies that this type of assault is

usually accompanied by the use of a weapon or by other means likely to produce death or

great bodily harm. Attempted aggravated assault that involves the display of—or threat to

use—a gun, knife, or other weapon is included in this crime category because serious

personal injury would likely result if the assault were completed. When aggravated

assault and larceny-theft occur together, the offense falls under the category of robbery.

Burglary

The FBI’s Uniform Crime Reporting (UCR) Program defines burglary as the

unlawful entry of a structure to commit a felony or theft. To classify an offense as a

burglary, the use of force to gain entry need not have occurred. The UCR Program has

three subclassifications for burglary: forcible entry, unlawful entry where no force is

used, and attempted forcible entry.

Larceny-Theft

The FBI’s Uniform Crime Reporting (UCR) Program defines larceny-theft as the

unlawful taking, carrying, leading, or riding away of property from the possession or

constructive possession of another. Examples are thefts of bicycles, motor vehicle parts

and accessories, shoplifting, pocket-picking, or the stealing of any property or article that

is not taken by force and violence or by fraud. Attempted larcenies are

included. Embezzlement, confidence games, forgery, check fraud, etc., are excluded.

Vehicle Theft

In the FBI’s Uniform Crime Reporting (UCR) Program, motor vehicle theft is

defined as the theft or attempted theft of a motor vehicle. In the UCR Program, a motor

vehicle is a self-propelled vehicle that runs on land surfaces and not on rails. Examples of

motor vehicles include sport utility vehicles, automobiles, trucks, buses, motorcycles,

motor scooters, all-terrain vehicles, and snowmobiles. Motor vehicle theft does not

include farm equipment, bulldozers, airplanes, construction equipment, or water craft

such as motorboats, sailboats, houseboats, or jet skis. The taking of a motor vehicle for

temporary use by persons having lawful access is excluded from this definition” (FBI,

2015).

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