28 Feb 2005
• Anderson Mutemererwa
Crime & Criminal Law
Empirical Studies
Modelling The Cost of Crime
•
•
A.K. Lynch, T. Clear & D. Rasmussen
Suggested 3 Approaches to Estimating
Cost of Crime
– 1. Asking victims to quantify and estimate
the costs they suffer such as the value of
stolen property, medical costs incurred,
forgone productivity as a results of
victimisation. Likely to understate the cost of
crime (value of non-monetary psychological
costs such as pain and suffering).
– 2. Establish an empirical relationship between
costs of crime and magnitude of jury awards
in similar cases. Juries tend to award
compensation that, in the juries’ view, make
the victim whole again. This approach gives a
more complete accounting of costs than the
survey method and tends to take care of the
short and long term productivity losses and
pain and suffering.
– The second approach assumes that the
relationship between direct costs and pain
and suffering costs is the same for all victims–
a biased sample of cases that go to trial
where the plaintiffs and the defendants likely
disagree about appropriate settlements.
– Further are the estimates of costs plausible?
Enormous pain and suffering estimates
contributes to the vast costs crimes in work
done by Cohen, Miller and Weirsema (1995).
– 3. Base the estimate of crime cost on the
relationship between crime levels and
property values. Hence people are willing to
pay a premium to stay in a neighbourhood
where crimes levels are low. Hence
differences in property prices, ceteris paribus,
will capture the willingness to pay for a lower
probability of being a victim of crime. Hence
an implicit price of spatial variations in crime
can be captured by implicit price models of
housing markets.
– This method of using implicit prices is appealing as
it tends to reflect the extent to which people are
willing to “vote with their feet” to get increased
public safety.
– The theory behind this approach is quite
established. Goods with many attributes such as
houses or cars can be decomposed into a bundle of
underlying characteristics each of which has an
implicit price. The selling price of a house is
determined by such things as age, number of
rooms, lot description (waterfront, size etc.),
neighbourhood attributes (poverty rates etc.) and
the levels and seriousness of crime in that area.
Changes in any of these will affect the dwelling's
selling price.
– Implicit prices (hedonic) models are used to
determine how changes in any of the abovementioned underlying attributes affect the market
prices for homes.
– Most of the studies show that crime lowered house
prices. A study by Thaler (1978) in Rochester, N.Y
found that average property crime lowered $1930.
Helman and Naroff (1979) and Rizzo (1979) used
census prices from Boston and Chicago
respectively and found estimate that a 1 per cent
rise in the overall crime rate lowers house prices
by 0.63 per cent.
– Cohen (1990) however, claims that these
studies are biased as they rely on reported
crime rather than the cost of crime (hence a
burglary is treated the same as larceny while
unarmed robbery is treated the same as
murder). Seriousness of crime was thus
ignored
– Lynch and Rasmussen (1998) explore
Cohen’s hypothesis and find that the number
of property crimes has a positive impact on
house values in a hedonic model while an
alternative cost of crime model has a
significantly negative effect on house values.
Estimating the Costs of Crime
• Used a hedonic model.
• Data come from the proportion of singlefamily dwellings sold in Jacksonville (FL)
between 1 July 1994 and 30 June 1995.
• The selling price of a house in regressed
on house and lot characteristics,
neighbourhood characteristics, and crime
occurring in the area.
• Pi = f(Si,Ni,Ck)
– where Pi is selling price of house
– Si is vector of structural and lot characteristics
– Ni is a vector of neighbourhood characteristics
– Ck is the estimated cost of crime in the
relevant police beat area.
• Jacksonville, FL has population of 712
000.
Intercept
Weighted Crime High Crime
index hedonic
area-interaction
model
coefficient
estimates
10.59*
Weighted Crime High Crime areaindex hedonic
interaction
model estimates coefficient
Propertyspecific
Variables
Square
footage
0.0005*
0.00001566
Lot size
0.0335*
0.1695
Bedrooms
-0.0238***
0.0107
Log (age of
homes)
-0.0643*
0.0211
Weighted Crime
index hedonic
model estimates
High Crime areainteraction
coefficient
0.0542
0.0174
Covered Parking 0.0920
Spaces
Fenced Property 0.0137
0.0109
Propertyspecific
Variables
Pool
0.0703
Weighted Crime High Crime
index hedonic
area-interaction
model
coefficient
estimates
Propertyspecific
Variables
Fireplace
0.618
Central heating 0.1245
Central Air
0.1192
conditioning
Vacant
-0.0912
-0.0095
0.0241
-0.0333
0.0533
Weighted Crime High Crime
index hedonic
area-interaction
model
coefficient
estimates
Propertyspecific
Variables
Assumable
mortgage
Waterfront
Property
Gated
Community
0.0136
0.0209
0.1542
0.0104
0.1690
-0.1486
Weighted
High Crime
Crime index
area-interaction
hedonic model coefficient
estimates
Neighbourhood
Variables
Population
-0.0001197
0.000008497
Percentage
white
0.2957
-0.0979
Percentage
Hispanic
Percentage
3.6638
0.6278
0.4786
2.0079
Weighted Crime High Crime areaindex hedonic
interaction
model
coefficient
estimates
Neighbourhood
Variables
Percentage
bachelors degree
Percentage owneroccupied
Percentage kids
(under 17)
Percentage young
adult (18-24)
0.0791
-1.0494
-0.1064
0.4255
-1.3653
2.2850
-1.0836
2.9819
Weighted Crime High Crime areaindex hedonic
interaction
model estimates coefficient
Neighbourhood
Variables
Percentage older -0.0527
(over 55)
Median household 0.000003908
income
Median-average
-0.156158E-12
Income
Population Growth -0.3941
(1990-96)
2.7457
0.000002
2.003E10
0.3276
Weighted Crime High Crime
index hedonic
area-interaction
model
coefficient
estimates
Crime
Variables
Log (cost of
-0.0163
violent crimes in
$)
Log (cost of
-0.0289
property crimes
in $)
High crime area -3.0699
Discussion of Results
• Older homes and those that are vacant at the
time of sale are sold at a discount
• The lower the number of bedrooms (for a given
square footage) the higher the price of the
property (larger bedrooms)
• Population with one mile of an observation is an
implicit measure of population density and has a
negative impact on sales. Other factors that
reduce sales price of dwellings are number of
kids under 18, a measure of income dispersion
(the square of the difference between median
average income).
Discussion of Results
• The sales price of houses is higher, ceteris
paribus, in neighbourhoods with more whites
than Hispanic residents and higher in those with
white collar residents.
• The column on high-crime interaction basically
shows the interaction of each of the
characteristics in the middle with high crime.
This column shows the amount by which each
coefficient rises in high crime areas.
Discussion of Results
• The cost of crime coefficients have the expected
negative signs, with cost of violent crime being
significant at the 10 per cent level of significance.
They however, are modest in magnitude.
• Property crime is very significant at the 1 per cent
level of significance.
• Most striking is that the coefficients reveal a very
modest impact on sales. Hence a 10 per cent
decrease in the cost of property crime raises the
price of a home by $275 and a similar decrease in
violent crime increases expected sales prices by
$155 (probably because victims tend to know the
offenders in the USA).
Discussion of Results
• Four neighbourhood characteristics of high
crime area: percentage white collar,
percentage over 55, percentage owneroccupied and the measure of income
dispersion are statistically significant with
the excepted signs. Population stability
seems to be important in successful
neighbourhoods, a factor fostered by
homeowners, older households and is
therefore associated with higher sales
prices.
Conclusions
• The study shows that the cost of crime seems to
have a trivial effect on the price of an average
house in Jacksonville, FL.
• The average homeowner’s implicit price for a 10
per cent decline in crime is $15, markedly
different from the actual cost of crime.
• Total cost of crime per capita in Jacksonville, FL
is about $933 per year.
•
Conclusions
• Two factors probably account for the
differences between implicit prices and costs
of crime.
– (a) probability of being victimised is less than 1
and affected by the behaviour of the household
(private investment in protection, diligence etc.)
– (b) the option of zero risk is not available and
the implicit price estimate is based on
differences in public safety which can be
achieved within the relatively safe areas which
are characteristic of most housing markets.
Conclusions
• Final conclusions
– Weighting crimes by their seriousness
significantly improves models of the cost of
crime via implicit price models.
– Although, cost of crime using estimated from
jury awards may be criticised, in implicit
models they appear superior to alternative
measures of crime which do not weigh crime
by its seriousness.