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6
Forecasting Crime:
A City-Level Analysis
John V. Pepper
It’s tough to make predictions, especially about the future.
Yogi Berra
INTRODUCTION
Over the past three decades, a handful of criminologists have tried
unsuccessfully to forecast aggregate crime rates. Long-run forecasts have
been notoriously poor. Crime rates have risen when forecasted to fall (e.g.,
the mid-1980s) and have fallen when predicted to rise (e.g., the 1990s).
Despite the need these difficulties suggest, there is little relevant research to
guide future forecasting efforts. Without a developed body of methodologi-
cal and applied research in forecasting crime rates, errors of the past are
likely to be repeated.
In this light, I explore some of the practical issues involved in forecasting
city-level crime rates using a common panel dataset. In particular, I focus
on the problem of predicting future crime rates from observed data, not the
problem of predicting how different policy levers impact crime. Although
clearly important, causal questions are distinct from the forecasting ques-
tion considered in this chapter. Research on cause and effect must address
the fundamental identification problem that arises when trying to predict
outcomes under some hypothetical regime, say new sentencing or policing
practices. My more modest objective is to examine whether historical time-
series data can be used to provide accurate forecasts of future crime rates.
To do this, I analyze forecasts from a number of basic and parsimoni-
ously specified mean regression models. While the problem of effectively
and
L and McCall (2001) and Levitt (2004) review and critique the crime forecasting
literature.
177

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178 UNDERSTANDING CRIME TRENDS
forecasting crime may ultimately require more complex models, there is
ample precedent for applying simple alternatives (Baltagi, 2006; Diebold,
1998). I thus focus on basic linear models that do not allow for structural
breaks in the time-series process, do not incorporate cross-state or cross-
crime interactions, and include only a small number of observed covariates.
Finally, I focus on point rather than interval forecasts. Sampling variability
plays a key role in forecasting, but a natural starting point is to examine the
sensitivity of point forecasts to different modeling assumptions. Thus, my
focus is on forecasting variability across different models. Adding confidence
intervals will only increase the uncertainty associated with these forecasts.
I begin by considering the problem of forecasting the national homicide
rate. This homicide series lies at the center of much of the controversy sur-
rounding the few earlier forecasting exercises that have proven so futile.
Using annual data on homicide rates, I estimate a basic autoregressive
model that captures some important features of the time-series variation
in homicide rates and does reasonably well at shorter run forecasts. As for
the longer run forecasts, the statistical models clearly predict a sharp drop
in crime during the 1990s, but they fail to forecast the steep rise in crime
during the late 1980s.
After illustrating the basic approach using the national homicide series,
I then focus on the problem of forecasting city-level crime rates. Using panel
data on annual city-level crime rates for the period 1980-2000, I again
estimate a series of autoregressive lag models for four different crimes:
homicide, robbery, burglary, and motor vehicle theft (MVT). Data for
2001-2004 are used for out-of-sample analyses.
The key objective is to compare the performance of various city-level
forecasting models. First, I examine basic panel data models with and with-
out covariates and with and without autoregressive lags. Most importantly,
I contrast the homogeneous panel data model with heterogeneous models
in which the process can vary arbitrarily across cities. I also consider two
naïve models, one in which the forecast simply equals the city-level mean
or fixed effect—the best constant forecast—and the other in which the
forecast equals the last observed rate—a random walk forecast. In addi-
tion to considering the basic plausibility of the various model estimates,
I examine differences in prediction accuracy and bias over 1-, 2-, 4-, and
10-year forecast horizons.
Diebold refers to this idea as the parsimony principle; all else equal, simple models are pref-
erable to complex models. Certainly, imposing correct restrictions on a model should improve
the forecasting performance, but even incorrect restrictions may be useful in finite samples.
Simple models can be more precisely estimated and may lessen the likelihood of overfitting the
observed data at the expense of effective forecasting of unrealized outcomes. Finally, empirical
evidence from other settings reveals that simpler models can do at least as well and possibly
better at forecasting than more complex alternatives.

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FORECASTING CRIME 179
I found considerable variability in the parameters and forecasting per-
formance across models, cities, crimes, and horizons. While there is evi-
dence of heterogeneity across cities, heterogeneous models do not perform
notably better than the homogeneous alternatives. A naïve random walk
forecasting model performs quite well for shorter run forecast horizons, but
the regression models are superior for longer horizon forecasts.
Finally, I use the basic homogeneous panel data models to provide
point forecasts for city-level crime rates in 2005, 2006, and 2009. This
out-of-sample forecasting exercise reveals predictions that are sensitive to
the covariate specification. All models generally indicate modest changes in
city-level crime rates over the next several years. However, forecasts found
using one model imply that city-level crime rates will tend to increase over
the remainder of the decade, whereas forecasts from another model imply
that crime rates will fall.
In closing, I draw conclusions about the limitations of forecasting in
general and the specific problems associated with forecasting crime. Fore-
casting city-level crime rates appears to be a volatile exercise, with few
generalizable lessons for how best to proceed.
NATIONAL HOMICIDE RATE TRENDS
While my primary interest is to forecast city-level crime rates, I begin
by considering the national time series in homicide rates. Some of the basic
issues involved in forecasting crime can be illustrated effectively by consid-
ering this single national time series. Attempts to forecast this series in the
1980s and 1990s have been notoriously inaccurate.
Using data on annual homicide rates per 100,000 persons from the
National Center for Health Statistics, I display the annual time series in
the log rate for 1935-2002 in Figure 6-1. The series appears to be quite
persistent over time, with some periods of fluctuation and notable turns.
From 1935 until around 1960, the homicide rate tended downward and
then began sharply rising, reaching a peak of just over 10 homicides per
100,000 (log rate of 2.31) in 1974. Over the next 15 years, homicide rates
fluctuated between 8 and 10 per 100,000 (log rates between 2.13 and 2.33)
and then unexpectedly began to sharply and steadily fall in the 1990s.
By the end of the century, the homicide rate hit a 34-year low of 6.1 per
100,000 (log rate of 1.81).
ata
D come from the National Center for Health Statistics and were downloaded in January
2007 from the Bureau of Justice Statistics Historical Crime Data Series at http://www.ojp.
usdoj.gov/bjs/glance/tables/hmrttab.htm. The victims of the terrorist attacks of September 11,
2001, are not included in this analysis. Some concerns have been raised about the reliability of
the annual time-series data on crime prior to 1960, but the effect on homicide trends is thought
to be minimal. For further discussion of these issues, see Zahn and McCall (1999).

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180 UNDERSTANDING CRIME TRENDS
2.4
2.2
National Homicide Rate
2.0
1.8
1.6
1.4
1940 1960 1980 2000
Year
FIGURE 6-1 National annual homicide rate, 1935-2002.
Figure 6-1, edtable
A variety of demographic, economic, and criminal justice factors are
known to be correlated with this series and have been used to predict
aggregate crime rates. Demographic characteristics of the population—
namely, gender, age, and race distributions—have all played a primary
role in crime forecasting models (see Land and McCall, 2001). Criminal
justice policies, including the number of police and the incarceration rates,
are also thought to be important factors in explaining aggregate crime
rates and trends. Macroeconomic variables appear to play only a modest
role in explaining aggregate crime rates, especially for violent crimes such
as homicide (Levitt, 2004).
For this study, I use two primary covariates, the percentage of the
population who are 18-year-old men and the fraction of the population
(per 100,000) that is incarcerated. Figure 6-2 displays the time series for
these two random variables along with the homicide rate series. All three
series are normalized to be relative to a 1935 base. This figure reveals that
ata
D on the population size and demographics comes from the U.S. Census Bureau, and
year-end incarceration counts for prisoners sentenced to more than one year were obtained
from the Bureau of Justice Statistics. The national incarceration series can be found at http://
www.census.gov/statab/hist/HS-24.pdf.

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FORECASTING CRIME 181
1.4
1.2
Ratio, 1935 Base Year
1.0
.8
.6
1940 1960 1980 2000
Year
Log-Homicide Log-Incarceration
Males, 18 years
FIGURE 6-2 Homicide, incarceration, and demographics, 1935-2002.
Figure 6-2, editable
the fraction of young men (18-year-olds) is closely related to the homicide
rate. In contrast, the variation in incarceration rates does not mirror the
analogous variation in crime rates. Rather, incarceration rates tended to
increase over the entire century, with the sharpest increases beginning in
the mid-1970s. The notable exceptions are during peak draft years during
World War II and the Vietnam War.
I follow the convention in the literature by taking the natural logarithm
of the crime and incarceration rates. I estimate the regression models using
the annual data for 1935-2000, leaving out pre-1935 data because accurate
homicide rate and covariate information is not readily available, and the
post-2000 data to assess forecasting performance.
The means and standard deviations of the variables used in the analy-
sis are displayed in Table 6-1. Figures for 2001-2002 are separated out,
as these data are not used to estimate the model. Notice the difference
between the historical series for 1935-2000, in which mean log-homicide
rate equals 7.26 per 100,000 persons, and the 2001-2002 rate, which is
over one point less.

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182 UNDERSTANDING CRIME TRENDS
TABLE 6-1 Means and Standard Deviations by Selected Years for the
National Homicide Rate Series Data
1935-2000 2001-2002
Variable Mean SD Mean
Homicide rate 7.26 2.00 6.10
Log-homicide rate 1.94 0.28 1.81
Log-incarceration rate 4.98 0.48 6.16
Fraction male, 18 0.008 0.001 0.007
N 66 2
The Best Linear Predictor
To forecast the homicide series in Figure 6-1, I fit the following auto
regressive regression model:
yt = γ 1yt −1 + γ 2 yt − 2 + xt β + ε t , (1)
where yt is the log-homicide rate in year t, xt is a 1xK vector of observed
covariates, and εt is an iid unobserved random variable assumed to be
uncorrelated with xt. Finally, {γ, β} are unobserved covariates that are
consistently estimated using least squares.
Table 6-2 displays estimates and standard errors from two variations
on this specification: Model A includes the AR(2) lags and Model C pres-
ents estimates from the full unrestricted specification. Consistent with Fig-
ure 6-1, there is a strong autoregressive component to the series, with the
period t homicide rate being strongly associated with the lagged rates. In the
unrestricted Model C, the regression coefficients associated with the incar-
ceration rate are positive, small, and statistically insignificant. Likewise,
the coefficient on the demographic variable is statistically insignificant and
relatively small in magnitude.
In-Sample Forecasts
How well does this model do at forecasting crime in the 1980s and
1990s? Figure 6-3 presents the predicted series under different starting dates
everal
S statistical tests were used to aid in the selection of the specification in Equation
(1). Based on a visual inspection of the correlogram and on an augmented Dickey-Fuller test,
I found no evidence of a unit root in the homicide series. Thus, there appears to be no need
to difference this series. The AR(2) model was then selected using the AIC and BIC criteria,
among the class of ARIMA(3,0,3) models. Finally, McDowall (2002) provides evidence favor-
ing a linear specification over a number of nonlinear alternatives.

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FORECASTING CRIME 183
TABLE 6-2 National Homicide Rate Regression Model Estimates and
Standard Errors
Model A Model C
yt–1 1.46 1.42
(0.12) (0.12)
yt–2 –0.50 –0.48
(0.12) (0.13)
Ln(inc) 0.01
(0.03)
Fraction male, 18 11.38
(11.25)
RMSE 0.06 0.06
R2 0.96 0.96
N 64 64
NOTE: Ln(inc) = log-incarceration rate; yt = log-homicide rate in year t.
for the forecast. In Panel A, the forecasted series begins in 1981 (i.e., 1980
is assumed to be the last observed year), in Panel B the forecasts begin in
1986, in Panel C the forecasts begin in 1991, and finally, in Panel D, the
series begins in 1996. In each case, the forecasts are dynamic in yt–1 and
yt–2; the forecasted lagged homicide rates, not the actual rates, are used.
Importantly, these forecasts are not dynamic in the covariates; for Model
C, actual covariate data are used for all forecasts.
The forecasts beginning in 1981 (Panel A) and 1986 (Panel B) have the
same qualitative errors found in the predictions made nearly three decades
ago. In particular, the model forecasts a steady drop in homicide rates
throughout the 1980s, yet the actual rates rose in the late 1980s.
Ultimately, the ability of this model to effectively forecast crime depends
on observed relationships continuing into future periods. The model can-
not effectively capture new phenomena, such as the rise or fall of new drug
markets. What, then, should forecasters have predicted at the start of the
1990s? Is one to believe that the mid-1980s were just a deviation from the
norm, or that there had been a regime shift? Normal deviations and turns in
a series are notoriously difficult to predict, and the 1980s might be nothing
more. If so, then the historical time series might have been used to accu-
rately forecast crime in the 1990s, even if it mischaracterized crime trends in
the late 1980s. Instead, however, the forecasting errors in the 1980s might
have reflected a structural change in the time-series process that cannot be
identified by the historical data.
With hindsight, one can see that the forecasts made for the 1990s based
on the historical series are relatively accurate. Crime is forecasted to fall

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184 UNDERSTANDING CRIME TRENDS
A 2.4
2.2
Log-Homicide Rate
2.0
1.8
1970 1980 1990 2000
Year
Log-Homicide Rate Model A Forecasts: Lag Only
Model B Forecasts: Full Model
B 2.4
Figure 6-3a, editable
2.2
Log-Homicide Rate
2.0
1.8
1970 1980 1990 2000
Year
Log-Homicide Rate Model A Forecasts: Lag Only
Model B Forecasts: Full Model
FIGURE 6-3 Realized and forecasted homicide rates.
Figure 6-3b, editable

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FORECASTING CRIME 185
C 2.4
2.2
Log-Homicide Rate
2.0
1.8
1970 1980 1990 2000
Year
Log-Homicide Rate Model A Forecasts: Lag Only
Model B Forecasts: Full Model
D 2.4
Figure 6-3c, editable
Log-Homicide Rate
2.2
2.0
1.8
1970 1980 1990 2000
Year
Log-Homicide Rate Model A Forecasts: Lag Only
Model B Forecasts: Full Model
FIGURE 6-3 Continued
Figure 6-3d, editable

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186 UNDERSTANDING CRIME TRENDS
(see Figure 6-3c), although the regression models miss the steepness of the
realized decline. Thus, the historical time series, as modeled in Equation (1),
are sufficient to predict the direction if not the full magnitude of the drop
in homicide rates during the 1990s. These general patterns are consistent
with the hypothesized notion of a short “bubble” in the homicide rate that
was induced by violence associated with the crack cocaine epidemic in the
1980s (Land and McCall, 2001).
A more systematic evaluation is found by measuring the errors associ-
ated with different fixed forecast horizons. In particular, I compute the two-
and five-year-ahead forecasts for each year from 1980 to 2002. Given these
predictions, I then report measures of forecast bias and accuracy. I compute
mean error (ME) as an indicator of the statistical bias of the forecasts and
the root mean squared error (RMSE) and mean absolute error (MAE) as
measures of the accuracy of the forecasts (Congressional Budget Office,
2005). The MAE and RMSE show the size of the error without regard to
sign, with RMSE giving more weight to larger errors. If small errors are less
important, the RMSE error will give the best indication of accuracy. Also,
as a different indictor of systematic one-sided error or forecasting bias, I
compute the fraction of positive errors (FPE).
Table 6-3 displays the realized log-homicide rates and the two- and five-
year-ahead forecasts for each year from 1980 to 2002. I also include fore-
casts derived from a naïve random walk model that uses the last observed
rate to predict future outcomes. So, in the two-period-ahead analysis, the
naïve forecast would be the rate observed two periods prior, and likewise
the five-period-ahead forecast is the rate in period t–5. The bottom rows
of Table 6-3 display the four summary measures of bias and accuracy of
the forecasts.
Several general conclusions emerge from the results displayed in this
table. First, as expected, the two-period-ahead forecasts are more accurate
than the five-period-ahead counterparts. The RMSE for the two-period-
ahead forecasts is about 0.10 and the MAE is around 0.09, whereas for
the five-period-ahead forecasts these measures are around 0.18 and 0.15,
respectively. For comparison, the RMSE for the in-sample predictions is
about 0.06 (see Table 6-2). Second, in general, the forecasting models out-
perform the naïve random walk model, especially for the longer run fore-
casts. For the shorter two-period-ahead forecast, the naïve model performs
nearly as well as the AR(2) model in Equation (1). For the shorter horizons,
the differences in forecasting performance across these three models appear
small and, to a large degree, may reflect sampling variability. Finally, during
this 23-year period, the forecasting models consistently underpredict during
the period from 1989 to 1994 and overpredict homicide rates after 1995.

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FORECASTING CRIME 187
Out-of-Sample Forecasts
In Figure 6-4, I display the actual log-homicide series and the one-step-
ahead predictions for each year from 1970 to 2000. These in-sample pre-
dictions nearly match the realized crime rates; the regression model closely
fits the observed data.
I also display dynamic forecasts of the homicide rate for 2001-2010.
For these forecasts, I assume that last observed year is 2000. In this setting,
forecasts of the homicide series are sensitive to variations in the choice of
explanatory variables included in the regression model. Both models predict
relatively modest changes to the homicide rate over the period, yet they
have different qualitative implications. The Model A forecasts imply that
homicide rates will continue to fall during the period, whereas the Model C
forecasts suggest that homicide rates will increase.
FORECASTING CITY-LEVEL CRIME RATES
To forecast city-level crime rates, the Committee on Law and Justice
provided a panel dataset of annual crime rates in the 101 largest U.S. cities
(approximately all cities with greater than 200,000 persons) over the period
1980-2004. The data consist of rates of homicide, robbery, burglary, and
motor vehicle theft as measured by the Federal Bureau of Investigations
Uniform Crime Reports. The data also include annual measures of drug-
related arrest, state-level incarceration rates, and the number of police per
100,000 population. I supplemented these data with annual county-level
demographic information on the fraction of the population who are men
ages 20-29 and ages 30-39 and the natural logarithm of the total county
population. As with the national series, I follow the convention in the
literature by taking the natural logarithm of the crime, incarceration, and
policing rates.
Using these data, I provide out-of-sample city-level forecasts for 2005
and 2006. When providing out-of-sample forecasts, one must either pre-
dict contemporaneous covariates or use lagged covariates in the forecasting
model. I use lagged covariates. That is, to address the practical problem that
arises when forecasting using covariates, I lag all covariates by two periods.
Given this lag structure, I estimate the models using data for 1982-
2000, leaving out pre-1982 data to incorporate the lagged covariates and
the post-2000 data to assess forecasting performance. Thus, for each of the
For the Model C forecasts, observed covariate data from 2001 and 2002 are used in the
corresponding forecasts. Unobserved covariate data for 2003-2010 are assumed to be un-
changed from the 2002 realizations.
ost of the crime data from Kansas City are missing. Thus, while there are 101 cities in
M
this sample, Kansas City is dropped from most of the analysis.

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200 UNDERSTANDING CRIME TRENDS
5.2
5.0
Log-Robbery Rate
4.8
4.6
4.4
1985 1990 1995 2000 2005
Year
Log-Robbery Rate Homogenous Model Forecasts
Heterogeneous Model Forecasts
FIGURE 6-5c Realized and forecasted log-robbery rates, Madison.
7.5
Figure 6.5c
7.0
Log-Robbery Rate
6.5
6.0
5.5
5.0
1985 1990 1995 2000 2005
Year
Log-Robbery Rate Homogenous Model Forecasts
Heterogeneous Model Forecasts
FIGURE 6-5d Realized and forecasted log-robbery rates, New York.
6.5d

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FORECASTING CRIME 201
6.6
6.5
Log-Robbery Rate
6.4
6.3
6.2
6.1
1985 1990 1995 2000 2005
Year
Log-Robbery Rate Homogenous Model Forecasts
Heterogeneous Model Forecasts
FIGURE 6-5e Realized and forecasted log-robbery rates, Richmond.
Figure 6-5e, editable
7.0
Log-Robbery Rate
6.5
6.0
1985 1990 1995 2000 2005
Year
Log-Robbery Rate Homogenous Model Forecasts
Heterogeneous Model Forecasts
FIGURE 6-5f Realized and forecasted log-robbery rates, San Francisco.
Figure 6-5f, editable

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202 UNDERSTANDING CRIME TRENDS
robbery rates over the four-year period, whereas the heterogeneous model
leads to the opposite conclusion. Realized robbery rates over this four-year
period closely track the forecasts from the homogeneous model in Denver
and from the heterogeneous model in San Francisco, and they lie between
the two forecasts for New York.
Clearly, the heterogeneous model does not provide uniformly superior
out-of-sample forecasts. Table 6-10 displays the RMSE across all cities
for these different models and different forecast horizons. In addition to
analyzing the forecasting performance of the models in Equations (2) and
(3), I also consider two naïve models, one in which the forecast equals the
city-level mean or fixed effect—the best constant forecast—and the other
in which the forecast equals the last observed rate—the random walk fore-
cast. Finally, I display the RMSE from the one-step-ahead forecasts that, in
practice, is only feasible if the period t-1 realization is known (or perfectly
forecasted).
Each model is used to provide forecasts of annual crime rates for three
different overlapping horizons, 2003-2004, 2001-2004, and 1995-2004,
and three different starting points, 2002, 2000, and 1994. Thus, dynamic
forecasts starting in 1994 are used to make 10-year-ahead predictions for
the 2004 crime rate. Importantly, these forecasts are not dynamic in the
covariates; for Model C, actual covariate data are used for all forecasts,
even those that go beyond two-year-ahead predictions.
Many of the findings reported in this table confirm the earlier results. In
particular, for shorter run forecasts, the restricted Model A seems to do at
least as well as the unrestricted Model C, and both of these homogeneous
models provide slightly less accurate forecasts than the naïve random walk
model. As before, these differences are modest and may simply reflect sam-
pling variability rather than true differences in forecasting performance.
In both cases, however, these patterns are not consistent across forecast
horizons; models that work relatively well for the shorter run do not neces-
sarily provide accurate forecasts for longer horizons. Long-horizon random
walk forecasts, for example, perform poorly. The RMSE for the random
walk forecasts of homicide rates for 1995-2004, for instance, is 0.52, much
greater than the RMSE of 0.41 found using the sample average (i.e., the best
constant predictor) and Model A, in which the RMSE is 0.39.
Likewise, for longer run forecasting problems, the unrestricted Model C
provides more accurate forecasts than the restricted alternative. For exam-
ple, the RMSE for the 1995-2004 forecasts of the homicide rate using the
unrestricted Model C is 0.33, 0.06 less than the analogous RMSE of the
forecasts made from the restricted Model A. This finding, however, may
reflect the fact that the long-run (over two years forward) Model C fore-
casts utilize realized covariate data. In practice, the necessary covariate data
will not be observed.

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TABLE 6-10 Root Mean Squared Forecast Error for Different Prediction Horizons and Models, All Cities
Homicide Robbery Burglary MVT
Model 2003-04 2001-04 1995-04 2003-04 2001-04 1995-04 2003-04 2001-04 1995-04 2003-04 2001-04 1995-04
Homogeneous models
Lag, No Cov, 2002 0.37 0.24 0.16 0.23
Lag, No Cov, 2000 0.43 0.39 0.32 0.26 0.22 0.19 0.32 0.26
Lag, No Cov, 1995 0.44 0.43 0.39 0.41 0.38 0.31 0.31 0.29 0.25 0.42 0.38 0.32
Lag, No Cov, t–1 0.35 0.34 0.32 0.21 0.18 0.16 0.17 0.15 0.14 0.21 0.19 0.17
No Lag, Cov 0.42 0.39 0.35 0.36 0.34 0.27 0.33 0.32 0.24 0.48 0.46 0.37
Lag, Cov, 2002 0.38 0.24 0.23 0.28
Lag, Cov, 2000 0.42 0.38 0.33 0.28 0.33 0.28 0.43 0.36
Lag, Cov, 1995 0.40 0.36 0.33 0.37 0.33 0.25 0.30 0.27 0.21 0.49 0.43 0.32
Lag, Cov, t–1 0.37 0.35 0.31 0.21 0.20 0.16 0.21 0.19 0.16 0.25 0.23 0.19
Naïve, 2002 0.37 0.20 0.16 0.22
Naïve, 2000 0.42 0.40 0.26 0.21 0.22 0.19 0.30 0.24
Naïve, 1995 0.61 0.60 0.52 0.57 0.54 0.45 0.50 0.48 0.39 0.55 0.51 0.42
Average 0.46 0.45 0.41 0.45 0.42 0.34 0.58 0.57 0.48 0.45 0.42 0.37
Heterogeneous models
Lag, No Cov, 2002 0.19 0.19 0.16 0.19
Lag, No Cov, 2000 0.23 0.20 0.21 0.17 0.20 0.16 0.23 0.20
Lag, No Cov, 1995 0.36 0.33 0.30 0.36 0.33 0.29 0.24 0.24 0.22 0.36 0.33 0.30
Lag, No Cov, t–1 0.18 0.16 0.16 0.17 0.15 0.14 0.15 0.13 0.13 0.18 0.16 0.16
NOTES:
Lag: Autoregressive lag inluded in the regression.
No Cov/Cov Indicates if covariates are included.
1995, 2000, 2002 Last observed year for dynamic forecasts.
t–1 Year t–1 is assumed to be observed. This is the one step-ahead forecast.
Naïve Forecast equals the crime rate in the “last observed” year, namely 2002, 2000, and 1995.
203
Average Forecast is the city-specific average crime rate from 1980-2000.

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204 UNDERSTANDING CRIME TRENDS
Finally, the added flexibility of the heterogeneous forecasting model in
Equation (3) leads to some improvements in forecasting accuracy. As might
be expected, the results are especially striking for homicide, in which there
is evidence of much heterogeneity in the parameter estimates. Assuming
the 2002 log-homicide rate is the last observed data point, the RMSE for
the 2003-2004 forecasts is 0.37 using the homogeneous Model A and 0.19
using the heterogeneous alternative.
For the other crimes, however, the forecasting gains from the hetero-
geneous model are less pronounced. For example, the RMSE for forecasts
of burglary rates in 2003-2004 is 0.16 for both models, and the analogous
RMSE for motor vehicle theft is 0.23 for the homogeneous model and
0.19 for the heterogeneous alternative. Except for the homicide series, the
efficiency gains from the homogeneous model appear to nearly offset any
biases due to heterogeneities.
Out-of-Sample Forecasts
As noted above, I forecasted city-level crime rates using the observations
through 2004. For this illustration, I use the panel data models from Equa-
tion (2) to provide forecasts of city-level crime rates for 2005 and 2006. I
also use Model A to forecasts crime rates in 2009. Without covariate data
over this period, long-run Model C forecasts are not feasible.
In Table 6-11, I present these out-of-sample forecasts for the six selected
cities analyzed throughout this chapter. Except for New York City, the fore-
casted changes across these six cities are generally modest. For example,
the log-robbery rates in Denver, Knoxville, and Madison are all predicted
to change by less than 0.03 points over the five-year period from 2004
to 2009. During the preceding five years, 1999-2004, log-robbery rates
increased by 0.23 in Denver and 0.04 in Madison and decreased by 0.14
in Knoxville.
The specific changes vary by city and by crime. To see this, notice the
five-year-ahead forecasts. In San Francisco, log-robbery rates are forecasted
to increase by 0.38 points, whereas forecasts for the other three crime rates
are slightly less than the 2004 levels. In Madison, log-homicide rates are
forecasted to increase by 0.31 and log-MVT rates by 0.15, whereas the
log-crime rates for both robbery and burglary are forecasted to drop over
the same period.
Finally, there are notable differences in the predictions made from the
two models. In several cases, Model A implies an increase in crime, whereas
Model C predicts a slight drop, and in nearly every case the Model A fore-
casts exceed the Model C counterparts.
Overall patterns regarding these forecasts can be found by examining
Table 6-12, which displays summary measures for the forecasts in every

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FORECASTING CRIME 205
TABLE 6-11 Homogeneous Model Forecasts for Selected Cities,
2005-2009
2005 2006 2009
Model Model Model
2009-
2004 A C A C A 2004
Homicide
Denver 2.73 2.65 2.55 2.61 2.47 2.59 –0.15
Knoxville 2.43 2.51 2.25 2.54 2.20 2.56 0.13
Madison 0.34 0.51 0.25 0.59 0.22 0.64 0.31
New York 1.95 2.47 2.70 2.88 0.93
Richmond 3.86 3.82 3.66 3.81 3.58 3.79 –0.06
San Francisco 2.45 2.45 2.41 2.45 2.40 2.45 –0.01
Robbery
Denver 5.54 5.55 5.58 5.56 5.59 5.58 0.03
Knoxville 5.69 5.70 5.60 5.71 5.54 5.72 0.02
Madison 4.89 4.88 4.72 4.88 4.60 4.87 –0.02
New York 5.71 5.94 6.12 6.44 0.73
Richmond 6.53 6.51 6.35 6.49 6.22 6.47 –0.06
San Francisco 5.99 6.11 6.19 6.20 6.32 6.37 0.38
Burglary
Denver 7.17 7.14 7.23 7.11 7.27 7.03 –0.14
Knoxville 7.27 7.24 7.01 7.21 6.99 7.14 –0.12
Madison 6.49 6.47 6.41 6.45 6.36 6.41 –0.08
New York 5.78 5.80 5.82 5.88 0.11
Richmond 7.24 7.24 7.21 7.25 7.19 7.26 0.02
San Francisco 6.69 6.67 6.76 6.66 6.81 6.62 –0.07
MVT
Denver 7.20 7.17 7.18 7.14 7.15 7.09 –0.11
Knoxville 6.67 6.69 6.61 6.71 6.57 6.75 0.08
Madison 5.54 5.58 5.44 5.61 5.37 5.69 0.15
New York 5.56 5.74 5.89 6.22 0.66
Richmond 7.01 7.09 6.89 7.08 6.73 7.06 –0.03
San Francisco 6.97 6.95 7.01 6.93 7.03 6.90 –0.07
city. In particular, for each crime and each forecast period, I report the
mean forecast, the mean predicted change, the fraction of positive predicted
changes, the IQR of the predicted change, and the mean absolute predicted
change.
The results in this table confirm that Models A and C provide different
pictures about what to expect for crime in large cities over this period. Fore-
casts made using Model A generally imply modest increases (e.g., homicide)

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206
TABLE 6-12 Homogeneous Model Forecasts Summary, All Cities, 2005-2009
2005 2006 2009
2004 Model A Model C Model A Model C Model A
Homicide
Mean forecast 2.29 2.42 2.17 2.49 2.14 2.53
Mean change from 2004 0.14 –0.07 0.20 –0.10 0.24
Fraction positive change 0.75 0.35 0.75 0.34 0.75
IQR [0.01, 0.26] [–0.23, 0.07] [0.01, 0.37] [–0.30, 0.10] [0.01, 0.46]
Mean absolute change 0.19 0.20 0.27 0.27 0.33
Robbery
Mean forecast 5.67 5.73 5.57 5.78 5.54 5.87
Mean change from 2004 0.06 –0.04 0.11 –0.07 0.20
Fraction positive change 0.82 0.35 0.82 0.36 0.82
IQR [0.01, 0.12] [–0.12, 0.01] [0.02, 0.21] [–0.20, 0.02] [0.04, 0.37]
Mean absolute change 0.08 0.09 0.13 0.15 0.23
Burglary
Mean forecast 6.99 6.99 6.91 6.99 6.88 6.99
Mean change from 2004 0.00 –0.06 0.00 –0.09 0.00
Fraction positive change 0.54 0.28 0.54 0.28 0.54
IQR [–0.01, 0.01] [–0.10, 0.01] [–.0.02, 0.02] [–0.18, 0.01] [–.0.04, 0.05]
Mean absolute change 0.01 0.09 0.03 0.15 0.06
MVT
Mean forecast 6.66 6.68 6.59 6.70 6.52 6.74
Mean change from 2004 0.02 –0.08 0.03 –0.14 0.07
Fraction positive change 0.68 0.19 0.68 0.17 0.68
IQR [–0.02, 0.05] [–0.14, –0.03] [–0.04, 0.10] [–0.25, –0.05] [–0.08, 0.20]
Mean absolute change 0.05 0.11 0.09 0.20 0.18

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FORECASTING CRIME 207
or little overall change (e.g., burglary) in city-level crime rates throughout
the reminder of this decade. Forecasts made using Model C paint a different
picture, with crime rates continuing to fall, in general, over the period. For
example, the IQR in the forecasted change in log-robbery rates from 2004
to 2006 is [0.02, 0.21] when using Model A but is [–0.20, 0.02] when using
Model C. Likewise, the fraction of cities forecasted to see increases in the
robbery rate is 82 percent under Model A but only 36 percent in Model C.
Finally, Model C generally predicts slightly larger absolute changes in the
crime rates, and both predict much larger absolute changes in the homicide
rates than the other three crimes.
Forecasts of the log-crime rate series are sensitive to variation in the
choice of explanatory variables in the regression model. That is, whether
one concludes that city-level crime rates will increase or decrease based on
models of this type depends on which control variables are included.
This variability in the forecasts is difficult to reconcile given the cur-
rent state of the literature. As far as I can tell, there is almost no research
on how best to forecast crime, and there is much disagreement about the
proper set of covariates to include. The limited results presented here sug-
gest that Model A provides somewhat more accurate forecasts for one- and
two-year horizons. If true, this would imply that city-level crime rates
will tend to increase over the period. Yet these results also reveal that, for
short-run forecasts, the naïve random walk model provides slightly more
accurate forecasts than either panel data model. That is, for these short-
run forecasts, one might not be able to do better than the predicting that
tomorrow will look like today.
CONCLUSION
In this chapter, I compare the forecasting performance of a basic homo-
geneous model to the heterogeneous counterpart using the city-level panel
data provided by the Committee on Law and Justice. The results reveal the
fragility of the forecasting exercise. Seemingly minor changes to a model
can produce qualitatively different forecasts, and models that appear to
provide sound forecasts in some scenarios do poorly in others. In the end,
the naïve random walk forecasts that tomorrow will be like today do well
relative to the linear time-series models, especially for shorter run forecast
horizons.
Two factors contribute to the variability and uncertainty illustrated here.
First, forecasting is an inherently difficult undertaking. Social phenomena
such as crime can sometimes evolve in subtle but substantial ways that are
very difficult to identify using historical data and can take a long time to
understand. Forecasts are invariably error ridden around turning points,

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208 UNDERSTANDING CRIME TRENDS
especially when these movements are largely the result of external events
that are themselves unpredictable.
Second, little serious attention has been devoted to crime rate fore-
casting, and there is no well-developed research program on the problem.
Effective forecasts of social processes that evolve over time would seem to
require a scientific process that evolves as well. Certainly, periodic efforts
to forecast crime or analyze forecasting models cannot hope to provide
meaningful guidance.
For further headway to be made, a focused and sustained research
effort is needed. This research would necessarily include an applied com-
ponent, providing and assessing crime rate forecasts at regularly scheduled
intervals. To make notable advances, there would also need to be a sus-
tained methodological research program aimed at developing and assess-
ing the performance of different forecasting approaches. In this chapter, I
consider a very limited set of models and estimators. There are many other
forecasting approaches that could be considered. Baltagi (2006) for exam-
ple, assesses a variety of forecasting models and estimators using the same
structure as those considered in this chapter. More sophisticated models
that incorporate, for example, structural breaks, cross-state or cross-crime
interactions, and a larger set of observed covariates might also be evalu-
ated. Model-averaging techniques similar to those described by Durlauf,
Navarro, and Rivers (Chapter 7 in this volume) have been shown to be
effective at reducing forecasting errors in other settings. Finally, one might
consider using entirely different approaches, such as the prediction market
forecasting techniques described by Gürkaynak and Wolfers (2006).
ACKNOWLEDGMENTS
I have benefited from the comments of Phil Cook, Richard Rosenfeld,
Jose Fernandez, Elizabeth Wittner, several anonymous referees, the Uni-
versity of Virginia Public Economics Lunch Group, and participants at the
Committee on Law and Justice Workshop on Understanding Crime Trends.
I thank Rosemary Liu for assistance in formatting and organizing the data
file. The data used in this chapter were assembled by Rob Fornango and
made available by the Committee on Law and Justice. The author remains
solely responsible for how the data have been used and interpreted.
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FORECASTING CRIME 209
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