There Goes the Neighborhood? Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws Leigh Linden Columbia University Jonah Rockoff

There Goes the Neighborhood?Estimates of the Impact of Crime Risk

on Property Values from Megan’s Laws

Leigh LindenColumbia University

Jonah RockoffColumbia 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 PercentIndecent Liberty with a Minor 71.6%Sex Offense 10.8%Rape 8.8%Attempted Rape or Attempted Sexual Offense 3.8%Incest Between Near Relatives 1.2%Kidnapping Against a Minor - 1st and 2nd Degree 0.8%Sexual Exploit of Minor 2.1%Felonious Restraint Against a Minor 0.4%Other 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 Evidence12

014

016

0H

ousi

ng P

rices

($1

,000

)

0 .05 .1 .15 .2 .25 .3Distance from Offender's Location (Miles)

Note: Results from local polynomial regressions (bandwidth=0.075 miles) of sale price on distancefrom offender's future/current location.

Figure 2a: Price Gradient of Distance from OffenderSales During Year Before and After ArrivalAfter Offender Arrival

Graphical Evidence cont’d12

014

016

0H

ousi

ng P

rices

($1

,000

)

0 .05 .1 .15 .2 .25 .3Distance from Offender's Location (Miles)

Before Offender Arrives After Offender Arrives

Note: Results from local polynomial regressions (bandwidth=0.075 miles) of sale price on distancefrom offender's future/current location.

Figure 2b: Price Gradient of Distance from OffenderSales During Year Before and After Arrival


013

014

015

0H

ousi

ng P

rices

($1

,000

)

-730 -365 0 365 730Days Relative to Sex Offender Arrival, Arrival on Day 0

Note: Results from local polynomial regressions (bandwidth=90 days) of sale price on daysbefore/after offender arrival.

Figure 3a: Price Trends Before and After Offenders' ArrivalsParcels Within Tenth Mile of Offender Location


013

014

015

016

0H

ouse

Pric

es (

$1,0

00)

-730 -365 0 365 730Days 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 daysbefore/after offender arrival.

Figure 3b: Price Trends Before and After Offenders' ArrivalsParcels Within 1/3 Mile of Offender Location.3

Illustration of Identification Strategy

Estimation of Price Impact

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

Control for neighborhood-year fixed effects Use houses between 0.1 and 0.3 miles as

counterfactual difference over time (D-in-D)

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Probability

of Sale†

(1) (2) (3) (4) (5) (6) (7)Within .1 Miles of Offender -0.340

(0.052)*

Within .1 Miles * Post-Arrival

Dist*≤.1 Miles* Post-Arrival(0.1 Miles = 1)

Within 1/3 Miles of Offender

Within 1/3 Miles * Post-Arrival

H 0 : Within .1 Miles*Post-Arrival = 0

Standard Errors Clustered by… Neighbor-hood

Sample Size 164,993

R2 0.03

Log(Sale Price)Pre-Arrival Log(Sale Price), Pre- and Post-Arrival

Probability

of Sale†

(1) (2) (3) (4) (5) (6) (7)Within .1 Miles of Offender -0.340 -0.007

(0.052)* (0.013)

Within .1 Miles * Post-Arrival






Neighbor-hood

Sample Size 164,993 164,968

R2 0.03 0.84


Probability

of Sale†

(1) (2) (3) (4) (5) (6) (7)Within .1 Miles of Offender -0.340 -0.007 -0.007

(0.052)* (0.013) (0.012)

Within .1 Miles * Post-Arrival -0.033(0.019)+





P-value = 0.0805


Neighbor-hood

Neighbor-hood

Sample Size 164,993 164,968 169,557

R2 0.03 0.84 0.84


Probability

of Sale†

(1) (2) (3) (4) (5) (6) (7)Within .1 Miles of Offender -0.340 -0.007 -0.007 <.001

(0.052)* (0.013) (0.012) (0.013)

Within .1 Miles * Post-Arrival -0.033 -0.041(0.019)+ (0.020)*


Within 1/3 Miles of Offender -0.010(0.007)

Within 1/3 Miles * Post-Arrival 0.010(0.010)


P-value = 0.0805

P-value = 0.0442


Neighbor-hood

Neighbor-hood

Neighbor-hood

Sample Size 164,993 164,968 169,557 169,557

R2 0.03 0.84 0.84 0.84


Probability

of Sale†

(1) (2) (3) (4) (5) (6) (7)Within .1 Miles of Offender -0.340 -0.007 -0.007 <.001 -0.006

(0.052)* (0.013) (0.012) (0.013) (0.012)

Within .1 Miles * Post-Arrival -0.033 -0.041 -0.036(0.019)+ (0.020)* (0.021)+



Within 1/3 Miles * Post-Arrival 0.010 0.010(0.010) (0.016)


P-value = 0.0805

P-value = 0.0442

P-value = 0.0813


Neighbor-hood

Neighbor-hood

Neighbor-hood

OffenderArea

Sample Size 164,993 164,968 169,557 169,557 9,086

R2 0.03 0.84 0.84 0.84 0.75


Probability

of Sale†

(1) (2) (3) (4) (5) (6) (7)Within .1 Miles of Offender -0.340 -0.007 -0.007 <.001 -0.006 -0.013

(0.052)* (0.013) (0.012) (0.013) (0.012) (0.014)

Within .1 Miles * Post-Arrival -0.033 -0.041 -0.036 -0.115(0.019)+ (0.020)* (0.021)+ (0.060)+

Dist*≤.1 Miles* Post-Arrival 0.11(0.1 Miles = 1) (0.065)+


Within 1/3 Miles * Post-Arrival 0.010 0.010 0.010(0.010) (0.016) (0.017)


P-value = 0.0805

P-value = 0.0442

P-value = 0.0813

P-value = 0.0579


Neighbor-hood

Neighbor-hood

Neighbor-hood

OffenderArea

OffenderArea

Sample Size 164,993 164,968 169,557 169,557 9,086 9,086

R2 0.03 0.84 0.84 0.84 0.75 0.75


Probability

of Sale†

(1) (2) (3) (4) (5) (6) (7)Within .1 Miles of Offender -0.340 -0.007 -0.007 <.001 -0.006 -0.013 -0.033

(0.052)* (0.013) (0.012) (0.013) (0.012) (0.014) (0.034)

Within .1 Miles * Post-Arrival -0.033 -0.041 -0.036 -0.115 0.125(0.019)+ (0.020)* (0.021)+ (0.060)+ (0.059)*

Dist*≤.1 Miles* Post-Arrival 0.11(0.1 Miles = 1) (0.065)+


Within 1/3 Miles * Post-Arrival 0.010 0.010 0.010 -0.055(0.010) (0.016) (0.017) (0.040)


P-value = 0.0805

P-value = 0.0442

P-value = 0.0813

P-value = 0.0579

P-value = 0.0364


Neighbor-hood

Neighbor-hood

Neighbor-hood

OffenderArea

OffenderArea

OffenderArea

Sample Size 164,993 164,968 169,557 169,557 9,086 9,086 1,519,364

R2 0.03 0.84 0.84 0.84 0.75 0.75 0.01


Offender Location & Property Value

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 Cost ($2004)

Sexual OffensesRape and Sexual Assault $113,732

Violent CrimesMurder/Manslaughter $3,843,363Assault $31,374Robbery $10,458Kidnapping $43,140

Non-violent CrimesBurglary $2,092Larceny $523Motor Vehicle Theft $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 :

Given the distribution of crime risk f(c), we can solve for the cost of crime vc

dccfcvdwUwU c )()(

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 CalculationEstimated

Victimization Cost

Baseline Assumptions $1,242,000


Victimization Cost


Lower Risk Avers ion (l=1) $2,186,100Higher Risk Avers ion (l=3) $890,000

Fewer Neighbors (60) $1,093,000More Neighbors (180) $1,320,000

Fewer Offenses by Neighbors (100% of NCVS) $2,485,000

More Offenses by Neighbors (300% of NCVS) $621,200


Victimization Cost


Lower Risk Avers ion (l=1) $2,186,100Higher Risk Avers ion (l=3) $890,000

Fewer Neighbors (60) $1,093,000More Neighbors (180) $1,320,000

Fewer Offenses by Neighbors (100% of NCVS) $2,485,000

More Offenses by Neighbors (300% of NCVS) $621,200

Systematic Overes timation 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

Documents

There Goes the Neighborhood? Estimates of the Impact of Crime Risk on Property Values from Megan’s Laws Leigh Linden Columbia University Jonah Rockoff