Transcript How Do People Respond to the Fear of Crime? Evidence from

There Goes the Neighborhood?
Estimates of the Impact of Crime Risk on
Property Values from Megan’s Laws
Leigh Linden
Columbia University
Jonah Rockoff
Columbia Business School
Broad Motivation
 Crime
is a costly local disamenity

Most violent crime occurs less than one mile
from victims’ homes

Local governments spend $50 billion a year on
police protection
 Optimal
expenditure on anti-crime policies
depends on the demand for crime reduction
Why Focus on Sex Offenders?


“Megan’s Laws” require offenders to register and
that their addresses be made public

Laws challenged and upheld by supreme court

Some state and local governments prohibit sex
offenders from living in specific areas
Law creates opportunity to measure distaste for
increased crime risk at the local level
The Hedonic Method
 Estimate
demand for neighborhood
characteristics through property values

Rosen (1974), Bartik (1987), Epple (1987)…
 Technique
used to evaluate demand for
amenities like school quality, public safety,
environmental hazards, etc.

Davis (2004), Chay and Greenstone (2005)…
Crime and Property Values
 Houses
in high crime areas should, all
else equal, sell for lower prices
 Identification
problem: high crime areas
may have other characteristics that are
unobservable to the econometrician
 Difficult
to overcome potential omitted
variables bias in cross sectional studies

Larson et al. (2003) on sex offenders
Our Study
 Combine
housing market data with
information from sex offender registrations

Allows us to use variation in the threat of crime
within small homogenous groups of homes

The timing of a sex offender’s arrival allows us
to control for baseline property values
Megan’s Laws
 Federal
law (1994) requires registration of
sex offenders at the state level

Amended law (1996) requires dissemination
 NC
law (1996) well suited to our study

Date offender moved into current address

Stringent requirements (e.g., 10 day limit)

High quality data: only 2% fail to register
Types of Crimes Committed (NC)
Crime Committed
Indecent Liberty with a Minor
Sex Offense
Rape
Attempted Rape or Attempted Sexual Offense
Incest Between Near Relatives
Kidnapping Against a Minor - 1st and 2nd Degree
Sexual Exploit of Minor
Felonious Restraint Against a Minor
Other
Percent
71.6%
10.8%
8.8%
3.8%
1.2%
0.8%
2.1%
0.4%
0.5%
Data Sources

NC Sex Offender Registry (January 2005)



Locations and move-in dates
Mecklenburg County Tax Data (March, 2005)

GIS data to map offender locations

House characteristics (e.g., sq. feet, # rooms)
Mecklenburg County Sales Data (1994 – 2004)

Only use sales of single family homes
Offender Areas (0.3 mile radius)
Graphical Evidence
120
140
160
Figure 2a: Price Gradient of Distance from Offender
Sales During Year After
BeforeOffender
and After Arrival
Arrival
0
.05
.1
.15
.2
Distance from Offender's Location (Miles)
.25
.3
Note: Results from local polynomial regressions (bandwidth=0.075 miles) of sale price on distance
from offender's future/current location.
Graphical Evidence cont’d
120
140
160
Figure 2b: Price Gradient of Distance from Offender
Sales During Year Before and After Arrival
0
.05
.1
.15
.2
Distance from Offender's Location (Miles)
Before Offender Arrives
.25
.3
After Offender Arrives
Note: Results from local polynomial regressions (bandwidth=0.075 miles) of sale price on distance
from offender's future/current location.
Figure 3a: Price Trends Before and After Offenders' Arrivals
Parcels Within Tenth Mile of Offender Location
120
130
140
150
Graphical Evidence cont’d
-730
-365
0
365
Days Relative to Sex Offender Arrival, Arrival on Day 0
Note: Results from local polynomial regressions (bandwidth=90 days) of sale price on days
before/after offender arrival.
730
Figure 3b: Price Trends Before and After Offenders' Arrivals
.3 Mile of Offender Location
Parcels Within 1/3
120
130
140
150
160
Graphical Evidence cont’d
-730
-365
0
365
Days Relative to Sex Offender Arrival, Arrival on Day 0
<.1 Miles
.1 to .3 Miles
Note: Results from local polynomial regressions (bandwidth=90 days) of sale price on days
before/after offender arrival.
730
Illustration of Identification Strategy
Estimation of Price Impact
logPijt    jt  bX i   0 Di  1Di * Postit   ijt
1
10

1
10
Control for many housing characteristics


Sq. feet, bedrooms, bathrooms, age, # stories, air
conditioning, external wall type, building quality
Use all sales in county to estimate b
Control for neighborhood-year fixed effects
 Use houses between 0.1 and 0.3 miles as
counterfactual difference over time (D-in-D)


logPijt    jt  bX i  0 Di   0 Di
 D
1
3
10
i
3
10
  1Di
1
10
1
10
* Post  
it

ijt
Offender Location & Property Value
Log(Sale
Log(Sale Price)
Price)
Pre-Arrival
Pre-Arrival
Within .1 Miles of Offender
Offender
(1)
(1)
-0.340
-0.340
(0.052)*
(0.052)*
(2)
(2)
-0.007
-0.007
(0.013)
(0.013)
Within .1 Miles * Post-Arrival
Post-Arrival
Probability
Probability
Log(Sale
Log(SalePrice),
Price),PrePre-and
andPost-Arrival
Post-Arrival
(3)
(3)
-0.007
-0.007
(0.012)
(0.012)
ofofSale
Sale† †
(4)
(4)
<.001
<.001
(0.013)
(0.013)
(5)
(5)
-0.006
(0.012)
(6)
(6)
-0.013
(0.014)
(7)
(7)
-0.033
(0.034)
-0.033
-0.041
-0.033
-0.041
(0.019)+
(0.019)+ (0.020)*
(0.020)*
-0.036
(0.021)+
-0.115
(0.060)+
0.125
(0.059)*
Dist*≤.1 Miles* Post-Arrival
Post-Arrival
(0.1 Miles = 1)
Within 1/3 Miles of Offender
Offender
0.11
(0.065)+
-0.010
-0.010
(0.007)
(0.007)
Within 1/3 Miles * Post-Arrival
Post-Arrival
H 0 : Within .1 Miles*
Miles*
Post-Arrival = 00
0.010
0.010
(0.010)
(0.010)
0.010
(0.016)
0.010
(0.017)
-0.055
(0.040)
P-value
P-value==
0.0805
0.0805
P-value
P-value==
0.0442
0.0442
P-value =
0.0813
P-value =
0.0579
P-value =
0.0364
Standard Errors Clustered
Clustered by…
by…
NeighborNeighborhood
hood
NeighborNeighborhood
hood
NeighborNeighborhood
hood
NeighborNeighborhood
hood
Offender
Area
Offender
Area
Offender
Area
Sample Size
164,993
164,993
164,968
164,968
169,557
169,557
169,557
169,557
9,086
9,086
1,519,364
0.03
0.03
0.84
0.84
0.84
0.84
0.84
0.84
0.75
0.75
0.01
2
R2
Price Response and Cost of Crime
 Estimates
suggest the discount for living
near offender is ~$5.5k for median house
 If
effects are driven by rise in risk of
victimization to neighbors, we can use
them to estimate welfare costs to victims
 Compare
estimates with those from DOJ
studies that use other data and methods
Victimization Cost Estimates (DOJ)
Type of Crime
Sexual Offenses
Rape and Sexual Assault
Cost ($2004)
$113,732
Violent Crimes
Murder/Manslaughter
Assault
Robbery
Kidnapping
$3,843,363
$31,374
$10,458
$43,140
Non-violent Crimes
Burglary
Larceny
Motor Vehicle Theft
$2,092
$523
$5,229
“Back of Envelope” Methodology
 Households
can live far from an offender or
live close, get a price discount, and face risk
 Indifference
of marginal household :
U ( w)   U ( w  d  vc c) f c dc
 Given
the distribution of crime risk f(c), we
can solve for the cost of crime vc
Measuring Risk to Neighbors

Need an estimate of risk due to living in close
proximity to a convicted sex offender

Use data to create a probability distribution with
which neighbors are victimized

Data on arrests of prisoners released in 1994

NCVS estimates of crimes reported to police

FBI UCR clearance rates (arrests per report)

FBI UCR data on victim-criminal relationship

NC data on # households near offender
Cost of Crime Estimates
Assumptions in Calculation
Estimated
Victimization Cost
Baseline Assumptions
$1,242,000
Lower Risk Aversion (l=1)
Higher Risk Aversion (l=3)
$2,186,100
$890,000
Fewer Neighbors (60)
More Neighbors (180)
$1,093,000
$1,320,000
Fewer Offenses by Neighbors (100% of NCVS)
$2,485,000
More Offenses by Neighbors (300% of NCVS)
$621,200
Systematic Overestimation of Risk: Housholds
Neglect to Realize that Risk is Spread Among
Neighbors
$90,300
Conclusions

Proximity to a sex offender causes a significant
decline in property value (~4%)

Effects are extremely localized (0.1 mile)

Implies large costs relative to DOJ estimates

A number of potential explanations:
1. DOJ estimates are too low
2. Misperception of true crime risk
3. Utility loss independent of risk increase