abstract
This study reports a finding of a significant effect on the selling
price of a single-family house given its proximity to a registered sex
offender's residence. The effect is an increasing function of
proximity that varies with offender classification and with the
community notification system employed. For more dangerous offenders,
the effect is significant for houses located up to 0.3 mile from an
offender. Houses located within 0.1 mile of an offender sold for 17.4 %
less, on average, than similar houses located farther away. For less
dangerous offenders, the significant effect extends to 0.2 mile from the
offender's residence, and the effect is smaller.
**********
Real estate literature is rich with papers reporting the results of
studies conducted to determine the effect of externalities on housing
value. (1) While the list of previously examined externalities is
extensive, a notable exception is the proximity of the residence of a
registered sex offender to the subject property. La Fond (2) has shown
that the direct and indirect costs of enacting and implementing sexual
predator laws are expensive for the public at large. Whether an
additional monetary burden must be borne by property owners in close
proximity to an offender's residence, however, has not been
investigated. Relatively recent additions to the law facilitate an
examination of this issue and enable us to begin filling this gap in the
literature. In 1996, Congress passed "Megan's Law," (3)
which required states to enact laws governing sex offender registration
and community notification. Today, every state has complied with the
federal requirement and has laws that require offenders to register with
police (or a government agency) and specify how registration information
is released to the public.
In this study, single-family house transactions that occurred
during 2000 in Montgomery County, Ohio, are examined to determine the
effect on selling price given the house's proximity to the
residence of a registered sex offender. A significant effect was
discovered. The effect is an increasing function of proximity that
varies with the community notification system employed. Where limited
disclosure was employed for more dangerous offenders, the negative
effect extends to 0.3 mile from an offender's residence. Compared
to comparable houses located farther away, houses located within 0.1
mile of an offender's residence sold, on average, for 17.4% less.
Where passive notification was employed for relatively less dangerous
offenders, the negative effect was significant for houses located up to
0.2 mile from the offender. Compared to comparable houses located
farther away, the houses located within 0.1 mile of an offender's
residence sold, on average, for 7.5% less. The results suggest that when
appraising a single-family house, appraisers may want to place more
reliance upon the cost approach and/or include an appropriate adjustment
to a comparable's sale price when using the sales comparison
approach.
This article first describes the systems for sex offender
classification and public notification employed in Ohio during the study
period. Next, the pricing implications of both the categories of sex
offenders and the notification systems are described. Then the data and
methodology are presented along with a brief explanation of the
geocoding process used to determine the distance between each sales
transaction and the nearest offender's residence. Finally, the
results and their implications for appraisers are discussed.
Sex Offender Classification and Community Notification in Ohio
Courts in Ohio classify sex offenders into one of three categories:
"sexual predator" "habitual sex offender," or
"sexually oriented offender." The classification depends on
the history of the offender and the court's opinion of the
likelihood that the offender will commit another offense. All convicted
sex offenders in Ohio are required to register with the sheriff's
office in the county in which they reside. While members of any category
may cause public concern, sexual predators are deemed to present the
greatest risk to the community. In Ohio, a sexual predator is defined as
a person who has been convicted of, or pleaded guilty to, a sexually
oriented offense and who is considered likely in the future to commit
additional sexually oriented offenses (4) A habitual sex offender is
defined as a person who has been convicted of, or pleaded guilty to,
committing a sexually oriented offense, and who has previously been
convicted of or pleaded guilty to one or more sexually oriented
offenses. (5) Finally, a sexually oriented offender is defined as a
person who has been convicted of, or pleaded guilty to, a sexually
oriented offense. (6)
Ohio law specifies that each county sheriff's office must
follow a practice sometimes referred to as "limited
disclosure," i.e., proactive notification that applies only to
sexual predators and some habitual sex offenders. Under this system, the
sheriff's office must notify a variety of parties within 72 hours
after the sexual predator or habitual sex offender moves into a
residence. Parties that are notified include owners of houses adjacent
to the offender's residence and school officials (who in turn
sometimes notify the parents of students). Interested parties may also
learn about sexual predators and habitual sex offenders through two
"passive notification" mechanisms. In some counties (including
the sample county here), information about predators may be viewed over
the Internet on the sheriff's office web site. In addition,
interested parties may personally request information about sex
offenders (in any category) from the sheriff's office. Passive
notification is the disclosure system used for sexually oriented
offenders and some habitual sex offenders. Under this system, interested
parties must initiate contact with the sheriff's office to discover
the location of an offender's residence.
Effectively, there are four categories of offenders in Ohio: (1)
sexual predators where limited disclosure applies, (2) habitual sex
offenders where limited disclosure applies, (3) habitual sex offenders
where passive notification applies, and (4) sexually oriented offenders
where passive notification applies. For the purpose of this study,
however, sex offenders were divided into two groups based on the
disclosure procedure: (1) sexual predators and habitual sex offenders
where limited disclosure applies, and (2) sexually oriented offenders
and habitual sex offenders where passive notification applies.
Meaningful analysis based on offender classification is problematic in
the present study because there are only six habitual sex offenders in
the database.
Price Implications of Offender Classification and Notification
Systems
Intuitively, it would appear that if a house is located in close
proximity to a sex offender, selling price effects should be negative.
Few people would elect to live next door to a felon of any type.
Although convicted arsonists and murderers also may pose a risk to a
community, current law makes it easier for market participants to
identify the location of sex offenders and build that information into
transaction prices. (7) Recidivism by sex offenders is well documented
in the literature. (8) Analysis of data collected by the United States
Department of Justice (DOJ) suggests that proximity to a sex
offender's residence increases one's risk of becoming a
victim. Approximately every five years, DOJ administers a comprehensive
questionnaire to a nationally representative sample of prison inmates.
Sex offenders accounted for 8.5% of state prisoners included in the most
recent survey, the 1997 Survey of Inmates in State Correctional
Facilities (9) The results indicate that while sex offenders commit
their crimes over substantial geographic areas, they tend to perpetrate
their crimes close to home. Sex offenders reported that 85.1% of their
crimes were committed in the same city in which they resided at the time
of arrest (compared to 80.2% for other offenders), and 64.9% of sex
offenders reported committing their offense in their own neighborhoods
(compared to 44.6% for other offenders), (10)
It is plausible that larger price discounts would be associated
with the proximity of a house to a more dangerous offender compared to
proximity to a less dangerous offender. It also is reasonable that
larger discounts would be associated with a notification system where
government authorities take an active role in the process compared to a
system where they do not. No published studies, however, have documented
that the public recognizes or fails to recognize the difference between
offender classifications. Therefore, it is uncertain whether the public
distinguishes between offender classifications, and whether any
differences in selling price discovered here are due to the relative
risk posed by the offender or due to the notification system employed.
If the public does not distinguish between offender classifications, any
selling price difference attributed to difference in offender
classification should disappear when the same notification system is
used for all offenders.
A selling price results from the negotiations between the seller
and buyer. The presence of an offender may motivate owners to accept a
low offer to consummate a sale, and the model employed here will capture
that effect. However, in order to estimate the actual selling price
effect of proximity to an offender, both price and marketing time should
be investigated because, from the seller's perspective, extra time
on the market lowers the present value of the selling price.
Unfortunately, the transaction data set used in this study does not
include reliable time on the market information. (11) Because marketing
time is not included in the model used in this study, the selling price
effect discovered may understate the effective selling price effect. In
fact, if a sufficient number of owners wait for an undiscounted offer,
failure to include time on the market would make it impossible to detect
any selling price effect. While knowledgeable buyers may refuse to bid
on a house located in close proximity to an offender's residence,
or may lower their bid, the price offered by uninformed buyers will be
unaffected. Therefore, without time on the market in the model, if
almost all owners are willing to search until an uninformed buyer is
located, no selling price effect should be detected.
The existence of knowledgeable buyers is a critical element in
order for selling price discounts to occur. This study could not
determine the percentage of knowledgeable buyers in the data sample, but
information obtained from the sheriff's office indicates that they
are present. Although the sheriff's office does not track web site
hits, they reported receiving approximately 3,000 personal requests for
information during the study year (about 10% of which came from real
Data
Two data sets are used in this study. The first contains
information about registered sex offenders, including their addresses
and classifications. This information was obtained from the Montgomery
County Sheriff's Office. Their database contained information on 22
sexual predators where limited notification applied, 4 habitual sex
offenders where limited disclosure applied, 2 habitual offenders where
passive notification applied, and 221 sexually oriented sex offenders
where passive notification applied. (12) Therefore, the offender data
used in this study contains 26 offenders where limited disclosure
applied and 223 offenders where passive notification applied.
The second data set consists of 3,208 single-family houses located
in Montgomery County, Ohio, that sold during 2000. (13) Montgomery
County contains 461.7 square miles and has a population of 558,427.
There are about 226,182 occupied household units in the county and
142,371 of these are owner occupied. Dayton (population 167,475) is the
county seat. Another 216,100 people live in the next nine largest cities
in the county. Approximately 5% of the population reside in rural areas.
Transaction data and property characteristics for sold houses were
obtained from public records offices in Montgomery County and the Dayton
Area Board of REALTORS[R]. Descriptive statistics of the transactions in
this study are presented in Table 1.
Geocoding
An important variable in the study is the distance between each
sold house and the residence of the nearest offender. To determine this
distance, both the sold house and the offender data sets were geocoded
over the Internet using software provided by Tele Atlas. (14) This
produced the latitudes and longitudes for observations in both data
sets. In addition, Tele Atlas rated each geocoded location for accuracy
based upon how well the property description submitted matched the
information in their database, and indicated the level of accuracy for
each observation by attaching a quality rating ranging from 1 to 6. All
geocodes that did not meet the highest quality rating of 1 were
eliminated from the study. (15)
Next, using the geocoded latitudes and longitudes, ArcView[R]
software was used to calculate the distance from each address in the
sold house data set to the nearest address in the offender data set.
(16) This distance was calculated twice: once from each sold house to
the nearest offender who was subject to limited disclosure, and again
from each sold house to the nearest offender subject to passive
notification. In determining the closest offender, it was required that
the offender be in residence at least one week before the purchase
contract was signed.
In addition, the geocoded points were plotted on maps using the
ArcView[R] software. A map of the location of sold houses and the
residences of offenders subject to limited disclosure is presented in
Figure 1, and a map of the location of sold houses and the residences of
offenders subject to passive notification is shown in Figure 2. These
maps show that the clustering of sold houses and offenders'
residences are similar, The source of the shape file used to display
Montgomery County in both exhibits was the 1990 Topologically Integrated
Geographic Encoding and Referencing System (TIGER[R]) files from the U.
S. Census. (17)
[FIGURES 1-2 OMITTED]
Methodology
There are three basic steps in the methodology. The first two steps
are used to test the following two null hypotheses:
[H.sub.O]: Ceteris paribus, the selling price of a single-family
house is unaffected by its proximity to the residence of a registered
sex offender where limited disclosure applies.
[H.sub.O]: Ceteris paribus, the selling price of a single-family
house is unaffected by its proximity to the residence of a registered
sex offender where passive notification applies.
Stepwise Regression
The objective of the first step of the methodology is to identify
variables to be included in a hedonic regression. To accomplish this
task, a battery of property characteristic variables is subjected to
stepwise regression. (18) Each variable must maintain a p-value of.05 or
less in order to enter and remain in the model.
In the second step of the methodology, a binary variable, PROX, is
added to the model, and a two-level analysis of covariance (ANCOVA)
procedure is performed on the expanded model. (19) PROX is assigned a
value of 1 if the subject house was located within a specified area
relative to the nearest offender, or 0 if the subject house was located
outside the specified area. A series of non-overlapping concentric rings
(rings) is used to define the area where PROX equals 1. In the first
iteration, PROX equals 1 if the subject house is located 0.1 mile or
less from the nearest offender. In subsequent iterations, the maximum
radius is expanded by increments of 0.1 mile and observations from
previously tested rings are eliminated from the sample. For example,
when the maximum radius is set at 0.2 mile (the second ring), houses
located no more than 0.2 mile but more than 0.1 mile from an offender
are compared to houses located farther away than 0.2 mile from the
offender. (20) The iterative process is conducted twice, first for
offenders subject to limited disclosure and again for offenders subject
to passive notification, to determine the point at which PROX becomes
and remains insignificant at the 95% confidence level. When estimating
the model for offenders subject to passive notification, any
observations located within the distance found to be significant for an
offender subject to limited disclosure are eliminated from the sample in
order to eliminate the effect of the more dangerous offender. (21) A
significant negative estimated coefficient for PROX indicates that the
average selling price of houses located within the specified ring is
less than the average selling price of houses located farther away.
In the final step of the methodology, the percentage selling price
effect for each ring is calculated by dividing the dollar price effect
by the average selling price of houses located within the ring. The
stepwise regression indicates that the basic hedonic model contains
thirty-four variables, as shown in Equation (1). (22)
(1) SP = [alpha] + [[beta].sub.1]SQFT + [[beta].sub.2]AGE +
[[beta].sub.3]LOT + [[beta].sub.4]FIRE + [[beta].sub.5]BATH3 +
[[beta].sub.6]OWN + [[beta].sub.7]FULL + [[beta].sub.8]WINTER +
[SIGMA][[beta].sup.34.sub.n=9]LOC + [xi]
where:
SP = the selling price of the subject property
[alpha] = the intercept
[[beta].sub.n] = the estimated coefficients
SQFT= the amount of living space in square feet
AGE = the age of the house in years
LOT = the size of the lot in acres
FIRE = the number of fireplaces in the house
BATH3 = a binary variable equal to 1 if the house has three or more
bathrooms, equal to 0 otherwise (23)
OWN = a binary variable equal to 1 if the house is owner occupied,
equal to 0 otherwise (24)
FULL = a binary variable equal to 1 if the house has a full
basement, equal to otherwise
WINTER = a binary variable equal to 1 if the house sold during
December, January, or February, equal to 0 Otherwise (25)
LOC = a vector of binary variables equal to 1 if the house is
located in a particular tax district of 48 tax districts, equal to 0
otherwise (26)
[xi] = the error term
A joint test for functional 10nrta and homoskedasticity of the
error term that follows the approach of White (27) was conducted on
Equation (1) using the SPEC option in PROC REG available in SAS. (28)
Because of the large number of observations in the sample, the estimated
variance-covariance matrix degenerated into singularity rendering the
test results suspect. Therefore, the joint test was conducted again
using only the nonbinary independent variables. Although the singularity
problem was eliminated by this adjustment, the null hypothesis of
homoskedasticity was rejected for LOT, SQFT, and AGE. A variety of
transforms on both the dependent and independent variables were tested;
in each case the null hypothesis of homoskedasticity of the error term
was rejected. It was apparent from the large number of degrees of
freedom in the test results that the large number of observations in the
data set caused rejection of the null hypothesis even for slight
deviation from homoskedasticity. Therefore, nonquantitative methods for
detection of homoskedasticity (i.e., residual analysis) were utilized.
Examination of residual plots indicated that eight (high price) outliers
were present in the data, but very little heteroskedasticity. The
outliers were eliminated from the data, and the functional form that
resulted in the highest [R.sup.2], linear, was selected. The final
residual analysis indicated that heteroskedasticiry and nonlinearity
were not problems.
A collinearity diagnostics program that follows the approach of
Belsley, Kuh, and Welch, (29) available on SAS, was conducted. The
results indicate a moderate degree of multicollinearity is present in
the data, but not enough to be harmful in the sense that the estimates
of the regression are highly imprecise or unstable. The highest
condition number was 13.79 and the highest proportion of variation for
any variable was .44 (the second highest proportion of variance for any
variable was .22).
A critical assumption of ANCOVA over and above the assumptions made
in regression analysis is that of homogeneity of regression.
Specifically, that the slopes of all the regression lines in simple
regression (or the slope of the hyperplanes in multiple regression) are
equal with respect to the qualitative variable being tested (i.e.,
PROX). In other words, there should be no interaction between PROX and
covariates. The interaction was tested and found to be insignificant for
all variables except SQFT. The interaction for SQFT and PROX was
investigated and found to be of magnitude and not of direction. Using
the method outlined by Tabachnick and Fidell, (30) SQFT was transformed
into a blocking variable and the ANCOVA model was reestimated. (31) No
significant change occurred in either the estimated coefficient or
p-value for PROX. Therefore, the robustness of ANCOVA indicated the
model was appropriate.
Results of the ANCOVA Procedure
The results of the ANCOVA procedure, where PROX is set at the
maximum significant radii, are shown in Table 2. In Table 2, the
explanatory variables are listed in the first column; the respective
estimated coefficients for proximity to offenders subject to limited
disclosure and passive notification are shown in the second and fourth
columns respectively. The p-value for each variable is shown in the
third and fifth columns. Examination of Table 2 reveals that the model
fits the data well. The adjusted [R.sup.2] indicates that the model
explains over 72% of the variation in selling price. Previous hedonic
studies have found that selling price tends to be negatively related to
AGE and WINTER, and positively related to SQFT, LOT, FIRE, FULL, and
BATH3. (32) The sign of each property characteristic variable in the
model is consistent with previous research. Because over 37% of all
houses in the sample are not owner occupied, OWN was included to control
for any price difference that may be attributable to the occupancy
intentions of the purchaser. The positive sign on OWN is subject to
multiple interpretations. It indicates that buyers who intend to live in
the property pay more than buyers who plan to rent it to others while
living elsewhere themselves. This could mean that nonoccupant owners are
systematically more aware of the presence of nearby offenders and factor
that information into their purchase offers. Another possible
explanation is that absentee owners may be purchasing houses in poor
condition. The study did not prove this because property condition was
not a variable in the model, but OWN may be serving as a proxy for
property condition.
Focusing on the variable of interest, PROX, the results of the
ANCOVA procedure enable the rejection of both null hypotheses. Note that
the estimated coefficient for PROX is negative for offenders subject to
both notification systems. The negative sign means that there was a
significant negative effect on the selling price of single-family houses
in the sample due to their proximity to the residence of a sex offender.
Specifically, it means that the average selling price for houses located
within the specified rings is significantly less than the average
selling price for comparable houses located farther away from the
offender. The ANCOVA procedure indicated that a significant selling
price effect occurs for houses located up to 0.3 mile from the residence
of an offender subject to limited disclosure. The ANCOVA procedure also
showed a significant selling price effect occurs for houses located up
to 0.2 mile from the residence of an offender subject to passive
notification. If the maximum radii are extended beyond these distances,
no significant difference is observable in an average selling price for
houses located within the specified ring and those located farther away.
To show the effect on selling price as the distance from the
offender's residence increases, partial ANCOVA procedure results
are summarized in Table 3. The results for proximity to offenders
subject to limited disclosure are shown in the upper portion of the
tablet and the results for proximity to offenders subject to passive
notification are shown in the lower portion of the table. PROX (in
miles) is shown in the first column. The number of sold houses within
each ring (n) is shown in the second column. The dollar price effect due
to proximity to an offender is shown in the third column. The p-value
for the significance of the difference between selling prices for houses
located inside the ring compared to those located farther away is shown
in the fourth column. Finally, the percentage price effect, which is the
dollar price effect for each ring divided by the average selling price
of houses sold within the ring, is shown in the fifth column.
Focusing on the upper portion of Table 3, it is shown that the
price effect is significant for houses located up to 0.3 mile from the
residence of an offender subject to limited disclosure. Compared to
comparable houses located farther away, houses located within 0.1 mile
of an offender's residence sold, on average, for 17.4% less. The
effect drops as distance from the offender's residence increases.
On average, houses located between 0.1 and 0.2 mile from an
offender's residence sold for 10.2% less compared to houses located
farther from the offender. Also, houses located between 0.2 and 0.3 mile
from an offender's residence sold, on average, for 9.3% less.
Approximately 7.7% (247) of all the houses in the sample were located
within 0.3 mile of an offender subject to limited disclosure. Note that
the number of observations in each ring increases as the minimum ring
radius is increased (by a constant 0.1 mile). This phenomenon occurs
because the area within the expanded ring is larger than the areas
within the rings located closer to the offender.
Focusing on the lower portion of Table 3, it is shown that the
price effect is significant for houses located up to 0.2 mile from the
residence of an offender subject to passive notification. Compared to
comparable houses located farther away, houses located within 0.1 mile
of an offender's residence sold, on average, for 7.5% less. Again,
the effect drops as distance from the offender's residence
increases. On average, houses located between 0.1 and 0.2 mile from an
offender's residence sold for 5% less compared to houses located
farther away from an offender. Approximately 25% (802) of all houses in
the sample were located within 0.2 mile of an offender subject to
passive notification. (33) Because the sample market included almost ten
times the number of offenders subject to passive notification as
offenders subject to limited disclosure, it is not surprising that more
houses in the sample were located within the significant price effect
area for the former classification.
Summary and Conclusions
This study shows that a monetary burden must be borne by house
sellers in close proximity to a registered sex offender's
residence. Examining single-family house transactions that occurred in
Montgomery County, Ohio, during 2000, a significant negative effect upon
selling price is discovered due to a house's proximity to the
residence of a registered sex offender. The effect is an increasing
function of proximity that varies with the community notification system
employed, which in turn depends on the risk the offender poses to the
community. Limited disclosure is the notification system used for
offenders deemed to present a relatively greater risk to the community.
Under this system, the sheriff's office notifies owners of houses
adjacent to the offender's residence and school officials (who
sometimes notify the parents of students). Passive notification is the
disclosure system used for offenders deemed to present relatively less
risk. Under this system, interested parties must contact the
sheriff's office to discover the location of an offender's
residence.
It is intuitive that larger discounts would be associated with the
proximity of a house to a more dangerous offender compared to proximity
to a less dangerous offender. It also is a reasonable assumption that
larger discounts would be associated with a notification system where
authorities take an active role in the process compared to a system
where they do not. Because we are unsure if the public distinguishes
between offender classifications, we cannot be certain whether the
difference in selling price effect discovered here is due to the
relative risk posed by the offender, or if it is due to the notification
system employed. If the public does not distinguish between offender
classifications, differences in selling price effects should not be
present when the same notification system is used for all offenders.
Perhaps this could be tested in a state that employs the same
notification system for all offenders.
The study results are consistent with both of the above
possibilities. Where limited disclosure applies, significant selling
price effects are greater and extend farther from an offender's
residence than when passive notification applies. Where limited
disclosure is employed, the significant negative effect extends to 0.3
mile from an offender's residence. Compared to comparable houses
located farther away from an offender, on average, houses located within
0.1 mile of an offender sold for 17.4% less. Houses located between 0.1
and 0.2 mile from an offender sold for 10.2% less, and houses located
between 0.2 and 0.3 mile from an offender sold for 9.3% less.
Where passive notification is employed, the significant negative
effect extends to 0.2 mile. Compared to comparable houses located
farther away, on average, houses located within 0.1 mile of an
offender's residence sold for 7.5% less, and houses located between
0.1 and 0.2 mile from an offender sold for 5% less. Despite the
relatively compact areas where significant price effects occur, a
substantial number of the houses in the sample were located close enough
to an offender that they may have been affected. Approximately 7.7% of
the houses were located within 0.3 mile of an offender subject to
limited disclosure, and approximately 25% of the houses were located
within 0.2 mile of an offender subject to passive disclosure.
The study results may actually understate the true financial effect
of proximity to an offender's residence because the model did not
include a variable for time on the market. From the seller's
perspective, extra time on the market lowers the present value of the
selling price. The presence of an offender may motivate some owners to
accept a low offer to consummate a sale, and the model employed here
captures that effect. However, if the owners want an undiscounted price
for their house, they may have to extend their search time because
knowledgeable buyers will either refuse to make an offer or lower their
offer to account for the presence of the offender. To the degree that
owners wait for an undiscounted offer from an uninformed buyer, failure
to include time on the market will mask the true effect of proximity to
an offender on the effective selling price. An examination of additional
markets with reliable time on the market data seems a logical extension
to this research effort.
To keep the problem tractable, two important assumptions were made
concerning the impact of offender proximity on the selling price of
nearby houses: that the presence of a more dangerous offender dominates,
and that the presence of the nearest offender dominates. This does not
imply that the presence of additional offenders located farther away has
no effect. Future research efforts could examine the effect of proximity
to multiple offenders in the same classification as well as interaction
effects between offender classifications.
Implications for Residential Appraisers
What are the implications of this study for residential appraisers?
First, this problem is likely to become more widespread because the
number of registered offenders is growing. As a result, appraisers may
want to modify the appraisal process. As a prerequisite, it is suggested
that the appraiser ascertain the local price effect, if any,
attributable to proximity to a sex offender. If no effect is present,
maintain the status quo. We suspect, however, that the findings
presented in this article are not unique. If a price effect is
discovered, it is suggested that when estimating value with the sales
comparison approach, an adjustment to comparable selling price may be
warranted to account for offender proximity. In certain cases, it also
may be prudent to place more reliance on the cost approach.
In valuing a single-family house, many appraisers place heavy
reliance on the sales comparison approach. In fact, it is not unusual
for the final estimate of value to equal the indicated value derived
from this approach (with the cost approach used primarily as a device to
ensure the reasonableness of the sales comparison's indicated
value). This practice can be maintained if the price effect due to
offender proximity is identical for the subject property and each
comparable property. If this is not the case, appraisers must modify
their methodology to accurately estimate value using the sales
comparison approach. The potential effect of proximity to an offender
must be calculated for the subject property, as well as the effect
included in the transaction price for each comparable. Then, each
comparable's sale price should be adjusted to account for the
difference in offender price effects between the subject and the
comparable.
The actions of an appraiser in response to this study also depend,
in part, on the purpose of the appraisal. For example, the adjustment
described in the preceding paragraph is warranted if the purpose is to
support a mortgage loan, or if the appraisal is being prepared for an
individual contemplating the acquisition of a house for investment
purposes. However, if the appraisal is to establish value for the
origination of an insurance policy or for supporting an insurance claim,
it is suggested that the cost approach be assigned more importance in
arriving at the final value estimate. After all, if the
above-recommended adjustment to the sales comparison approach results in
a lower value estimate, this does not reduce the replacement cost of the
property. Also, to the extent the offender proximity effect is reflected
in the comparable sale prices, a lower value estimate could result
whether or not the adjustment is made.
Finally, it should be noted that most of the offenders in this
study did not change their residence during the study year. However,
because offenders are free to move (and report to authorities their new
location), the financial burden associated with an offender's
presence may be transitory for a particular house owner. A determination
of exactly how long it takes for the negative price effect to disappear
after the offender leaves remains a topic for further research.
Table 1 Descriptive Statistics of Sample Transactions
Standard
Variable Mean Deviation Minimum Maximum
Selling price (dollars) 83,075 56,662 10,000 595,000
Living space (square feet) 1,355 581 320 5,947
House age (years) 62 23 2 192
Lot size (acres) .361 .984 0.14 16.4
Bathrooms (number) 1.339 .563 0.5 5
Distance to nearest limited
disclosure 1.659 1.676 .004 13.284
offender (miles)
Distance to nearest passive
notification 0.574 0.714 .002 10.318
offender (miles)
Table 2 ANCOVA Results
Limited Disclosure Passive Noticification
Offenders (PROX set at Offenders (PROX set at
at > .2 & <= .3 mile > .1 & <= .2 mile)
Estimated Estimated
Variable Coefficient P > t Coefficient P > t
Intercept 31,736 < .0001 29,578 < .0001
PROX -7,188 .0126 -4,303 .0115
SQFT 45 < .0001 46 < .0001
AGE -385 < .0001 -365 < .0001
LOT 7,622 < .0001 7,404 < .0001
FIRE 9,573 <. 0001 9,542 < .0001
OWN 8,155 < .0001 7,896 < .0001
WINTER -3,438 .0132 -4,020 .0084
BATH3 34,707 < .0001 33,016 < .0001
FULL 5,622 < .0001 6,228 < .0001
LOC5 11,556 < .0001 11,066 < .0001
LOC7 -20,700 < .0001 -19,756 < .0001
LOC11 -22,650 < .0001 -23,391 < .0001
LOC14 -8,547 .0283 -7,955 .0506
LOC17 12,610 .0259 12,065 .0416
LOC18 24,834 < .0001 23,421 < .0001
LOC19 -26,103 < .0001 -26,753 < .0001
LOC20 -16,260 < .0001 -18,168 < .0001
LOC21 -16,099 < .0001 -16,772 < .0001
LOC22 -14,594 < .0001 -14,239 .0002
LOC25 -31,452 < .0001 -32,470 < .0001
LOC26 -22,024 < .0001 -20,955 < .0001
LOC28 14,084 .0029 12,716 .0089
LOC29 -29,222 < .0001 -27,283 < .0001
LOC30 -29,740 < .0001 -25,596 .0002
LOC31 -24,554 < .0001 -24,347 < .0001
LOC33 -25,978 .0004 -26,864 .0004
LOC34 -26,114 < .0001 -26,592 < .0001
LOC37 -25,539 < .0001 -26,321 < .0001
LOC39 21,148 < .0001 19,742 < .0001
LOC41 77,149 < .0001 81,136 < .0001
LOC42 14,547 .0027 13,375 .0073
LOC45 15,693 < .0001 14,138 .0005
LOC47 9,274 .0013 8,186 .0071
LOC48 30,216 < .0001 28,510 < .0001
LOC49 22,942 .0171 21,150 .0323
Adjusted [R.sup.2] Adjusted [R.sup.2]
=.7267 =.7320
Table 3 Selling Price Effect
Proximity to Offender Subject to
Limited Disclosure
Dollar Percentage
PROX (in miles) n Price Effect P > t Price Effect
<= 0.1 31 -11,864 .0301 17.4
> 0.1-0.2 92 -7,475 .0207 10.2
> 0.2-0.3 124 -7,188 .0126 9.3
> 0.3-0.4 135 -5,104 .0690 6.4
> 0.4-0.5 153 -703 .7970 0.8
Proximity to Offender Subject to
Passive Notification
Dollar Percentage
PROX (in miles) n Price Effect P > t Price Effect
<= 0.1 238 -4,208 .0492 7.5
> 0.1-0.2 463 -4,303 .0115 5.0
> 0.2-0.3 486 -3,465 .0574 3.8
> 0.3-0.4 369 -3,843 .0717 3.9
> 0.4-0.5 271 -1,932 .4765 1.8
The authors thank the Ohio Link and the Paul Lawrence Dunbar
Library at Wright State University for their generous support by
providing the ESRI software through a site licensing arrangement. We
also thank the Ohio GIS-Net for providing GIS advice, the Department of
Urban Affairs and Geography at Wright State University for their
assistance in this study, and the reviewers who commented on this paper.
(1.) In general, previous studies find that if an externality is
perceived as favorable, it has a positive effect on the value of the
subject property; if the externality is perceived as unfavorable, it has
a negative effect. Negative price effects have been demonstrated for
houses in close proximity to other negative externalities including a
variety of environmental hazards. For a review of the environmental
hazard literature, see Melissa A. Boyle and Katherine A. Kiel, "A
Survey of House Price Hedonic Studies of the Impact of Environmental
Externalities," Journal of Real Estate Literature 9, no. 2 (2001):
117-144.
(2.) John Q. La Fond, "The Costs of Enacting a Sexual Predator
Law, Psychology, Public Policy and Law 4 (1998): 468-504.
(3.) P.L. 104-145, [section]1, 110 Stat. 1345. One of the stimuli
for this law was the case of Megan Kanka, who in 1994 was raped and
killed by a repeat sex offender who, unknown to Megan's parents,
lived across the street from her home.
(4.) Ohio Revised Code [section] 2950.01; offenses included in this
statute are rape, sexual battery, gross sexual imposition, kidnapping,
abduction, unlawful restraint, criminal child enticement, corruption of
a minor, compelling prostitution, endangering children (under age 18),
pandering obscenity, pandering sexually oriented material involving a
minor, and illegal use of a minor in nudity-oriented material. Sexual
predators must report to the sheriff's office every 90 days for
life.
(5.) Habitual sex offenders must report to the sheriff's
office once annually for 20 years.
(6.) Sexually oriented offenders must report to the sheriff's
office once annually for 10 years.
(7.) Unlike in some other states (e.g., Alaska), house sellers in
Ohio are not required to report the presence of sex offenders on the
mandatory seller disclosure form.
(8.) See for example, Dennis M. Doren, "Recidivism Base Rates,
Predictions of Sex Offender Recidivism, and the 'Sexual
Predator' Commitment Laws," Behavioral Sciences & the Law
16 (1998): 97-114; D. M. Greenberg, "Sexual Recidivism in Sex
Offenders," Canadian Journal of Psychiatry 43 (1998): 459-465;
Michael P. Hagan and Karyn L. Gust-Brey, "A Ten-Year Longitudinal
Study of Adolescent Rapists Upon Return to the Community,"
International Journal of Offender Therapy and Comparative Criminology
43, no. 4 (1999): 448-458; R. K. Hanson, R. A. Steffy, and R. Gauthier,
"Long-Term Recidivism of Child Molesters," Journal of
Consulting and Clinical Psychology 61 (1993): 646-652; R. A. Prentky, et
al., "Recidivism Rates Among Child Molesters and Rapists: A
Methodological Analysis," Law and Human Behavior 21 (1997):
635-659; V. L. Quinsey, M. E. Rice, and G. T. Harris, "Actuarial
Prediction of Sexual Recidivism," Journal of Interpersonal Violence
10 (1995): 85-105; and M. C. Seto and H. E. Barbaree, "Psychopathy,
Treatment Behavior, and Sex Offender Recidivism," Journal of
Interpersonal Violence 14 (1999): 1235-1248.
(9.) U.S. Department of Justice, Office of Justice Programs, Bureau
of Justice Statistics.
(10.) These figures were obtained by personal communication between
the authors and employees of the Bureau of Justice Statistics, United
States Department of Justice.
(11.) The local real estate board reports days on the market only
for the most recent listing contract; time for expired listings is not
included in the figure they report.
(12.) Not all of the offenders lived in Montgomery County for the
entire year. Twenty-five of the 26 offenders where limited disclosure
applied lived in the county at year-end. Ten of this group did not live
in the county at the beginning of the year. All 223 offenders where
passive notification applied lived in the county at the end of the year,
but 61 of this group did not reside in the county (or had not yet become
registered sex offenders) at the beginning of the year.
(13.) The Dayton Area Board of REALTORS[R] reported 5,614
single-family home sales during the study period. Ambiguous geocoding
resulted in 115 observations being discarded from the sample. The
remainder were eliminated because of incomplete data.
(14.) Tele Atlas can be found at www.geocode.com. Tele Atlas
provides the "Block Face Match" (BFM), which represents the
best match rather than parcel level accuracy. In essence, rather than
specifying the latitude and longitude at a particular point on each
property (e.g., front center) the geocode derived from a BFM is actually
a geometric estimation. Tele Atlas stores the beginning and ending
address range for a block, and knows the number parity (odd or even).
For example, the geocode assigned to 150 Eagle Street, would be roughly
halfway between the presumed beginning and ending address range of 100
and 198. The interactive web site for Tele Atlas was used in this study
for the geocoding because it provides a high level of location
confidence. It can accurately position every point in the data set to
six decimal points of a degree, and in the Montgomery County, Ohio area,
this accuracy translates to less than 20 inches. It is also fast,
repeatable, commonly used in geographic information systems (GIS) work,
and accessible to anyone conducting a study. There is a charge for the
service, but the cost per address is low.
(15.) If points with quality ratings of 2 to 6 are included, the
accuracy of the study suffers because the system places poorly geocoded
points at the centroid of the zip codes. This would result in a cluster
of sold houses (and/or offenders) located in one place, which is
obviously not the case. No sexual predators or habitual offenders were
eliminated from the study due to bad geocodes.However, 4 sexually
oriented offenders and 11S sales transactions were eliminated for this
reason.
(16.) ArcView[R] GIS (Redlands, Calif.: Environmental Systems
Research Institute (ESRI), Inc.). In determining the proximity measures
used in Equation (1), it was required that the offender had been in
residence for at least one week before the purchase contract was signed.
At some point in the year, an offender may have lived closer to a sold
property than the one used to calculate the proximity measure. If the
offender was not in residence prior to the sale, there is no way to
attribute any price effect to them.
(17.) TIGER[R] Line Files (Washington, D. C.: United States
Department of Commerce, Bureau of the Census, 1992).
(18.) Stepwise regression is the appropriate process to use in this
case because the major objective is not to predict the value of the
dependent variable; the major objective is the analysis of the
independent variables, in particular PROX. In a predictive model,
multicollinearity inhibits the analysis of independent variable effects
due to the instability of the regression coefficient of each independent
variable. In stepwise regression, an independent variable enters the
model only if it explains variation in the dependent variable that is
not already explained by variables in the model. Therefore, the
predictability of the dependent variable is maximized while
multicollinearity is minimized. Of course, predictability could be
higher by including all possible independent variables in the model
because the addition of any variable cannot lower the [r.sup.2]. But the
possible presence of multicollinearity in this situation makes the
interpretation of an independent variable problematic.
(19.) Analyzing explanatory variables in a regression model where
all explanatory variables are qualitative (e.g., 0, 1) is basically
equivalent to performing an ANO