Spatial Heterogeneity and the Geographic
Distribution of Airport Noise
The authors thank Lesli Ott for excellent research assistance and the Atlanta Department of Aviation for noise contour data. We thank meeting participants at the Federal Reserve System Committee on Regional Economic Analysis, especially Anil Kumar, for their comments. Nancy Lozano-Gracia also provided helpful comments on an earlier version of the paper. The
views expressed are those of the authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors.
Jeffrey P. CohenAssociate Professor of Economics
University of Hartford
Cletus C. CoughlinVice President and Deputy Director of
ResearchFederal Reserve Bank of St. Louis
ASSA Meeting – January 2010Atlanta, GA
Motivation & Major QuestionsMotivation
Possibility that airport noise depends on
• neighborhood demographic characteristics
• proximity to airport (noise and distance are not necessarily
correlated)
• prices of houses
Major Questions
• Which of the preceding variables are significant
determinants of noise? Does estimation incorporating
spatial heterogeneity generate results that differ from
estimation ignoring spatial heterogeneity?
• How do the results differ across locations? Why?
ApproachOur approach
• Locally Weighted Regressions (LWR) (non-parametric
spatial specification)
• This is opposed to spatial autocorrelation and/or
spatially lagged variables (parametric approach)
Advantage of LRW
• Allows for non-uniformity in relationships between
variables
Background: Airport NoiseFederal Register (2000)
Annoyance: the adverse psychological response to noise
• 12 percent of people subjected to a DNL of 65 decibels
report that they are “highly annoyed”
• 3 percent are highly annoyed when subjected to a DNL of
55 decibels
• 40 percent are highly annoyed at a DNL of 75 decibels
Nelson (2004)
Since 1979 federal agencies have regarded land subject to 65
to 74 decibels as “normally” incompatible with residential use
and land subject to less than 65 decibels as “normally”
compatible with residential land use
Airport Noise, Proximity & Housing Prices• Standard finding is that airport noise reduces housing prices:
McMillen (2004): 9 percent reduction for houses near Chicago O’Hare for 65 db or more
Over time, noise levels around O’Hare have decreased Espey and Lopez (2000): 2 percent reduction for houses near Reno
Cannon for 65 db or more Lipscomb (2003): no effect for houses in College Park, GA, which
is near Atlanta Hartsfield-Jackson
• Proximity: must control for proximity to accurately measure effect of noise
• Proximity tends to have a positive effect: Tomkins, Topham, Twomey, and Ward (1998): Manchester McMillen (2004): O’Hare Lipscomb (2003): Hartsfield-Jackson
Other Studies of Airport NoiseCohen and Coughlin (2008): Atlanta, GA airport
• noise assumed to be exogenous – common
assumption
• houses exposed to 70 db noise faced sale prices that
were 20 percent lower than houses in the buffer
zone
Sobotta, et. al. (2007): Phoenix, AZ airport
• modeled noise as dependent variable
• found higher Hispanic neighborhoods were exposed
to significantly more noise
• Estimation by Ordered Probit
ModelY = f (X, Z, u)
Where
Y =
noise (ordered categorical variable ; can be <65db, 65db, 70db)
X =
{ log(distance), log(age), % Hispanic, % black, median household income }
Z =
{ log(fitted sale price) }
u =
normally distributed error term, zero mean and constant variance
Ordered ProbitFor ordered probit we re-define noise:
Y Noise
0 < 65 db
1 65 db but < 70 db
2 70 db
Alternative Estimation ApproachLocally Weighted Regressions (LWR)
• addresses spatial nature of the data
Background
• Spatial Econometrics (parametric approach)
• LWR (non-parametric approach)
Locally Weighted Regressions (LWR)More tractable for ordered probit
Advantages
• non-parametric approach to address spatial variation
• allows for non-linearity in relationships between
independent variables and dependent variables
• for ordered probit, can be estimated by a “pseudo
maximum likelihood” approach
Locally Weighted Regressions (LWR)With 3 regimes in ordered probit, pseudo log-likelihood
function is:
where is standard normal c.d.f.
is parameter vector for observation I
Doj = 1 if obs. j = 0, = 0 otherwise
D1j = 1 if obs. j = 1, = 0 otherwise
D2j = 1 if obs. j = 2, = 0 otherwise
• impact of spatial weights enters in a non-parametric way
0 1 2log ( ' ) log ( ' ) log ( 'j ij j i j j i i j j i i jw D X D y X D y X
i
WeightsSomewhat different – we use Gaussian weight function:
where is standard normal density function
= distance between house i and house j
= standard deviation of distances between
house i and all other houses j
= bandwidth
• We use cross-validation to choose preferred b: vary b to be 0.4, 0.6, 0.8, 1.0
b = 0.4 maximizes the pseudo log-likelihood function
ijij
i
dW
s b
ijd
is
b
DataAtlanta airport
Noise Contours:Atlanta Department of Aviation
Housing Prices & Characteristics:2003
Demographics:% Hispanic, % black, medianhousehold income from U.S.Census Bureau, 2000.
Summary Statistics – 508 Observations Count Percentage
House Sales in the buffer zone – 2003 contours 343 67.5
House Sales in 65 db zone -- 2003 contours 146 28.7
House Sales in 70 db zone -- 2003 contours 19 3.7
House Sales in Atlanta 49 9.6
House Sales in College Park 147 28.9
House Sales in Conley 60 11.8
House Sales in East Point 66 13.0
House Sales in Forest Park 136 26.8
House Sales in Hapeville 50 9.8
1 story 425 83.7
2 or more stories 83 16.3
2 or less bedrooms 138 27.2
3 bedrooms 258 50.8
4 bedrooms 99 19.5
5 or more bedrooms 13 2.6
1 bathroom 246 48.4
2 bathrooms 151 29.7
3 or more bathrooms 111 21.9
0 or 1 fireplace 494 97.2
2 or more fireplaces 14 2.8
Summary Statistics – 508 Observations Mean Range
Price (dollars) 128,442 32,378-460,500
Distance (miles) 3.29 1.06-6.06
Acres 0.37 0.03-3.88
Age (years) 39.85 0-100
B1kHH00 (percent) 56.96 0-97.5
HispHH00 (percent) 8.64 0-30.1
MedHHInc (hundreds of dollars) 319.4 116.7-606.3
Comparison of OP and OPLWR ResultsVariable Standard Ordered
Probit1
Locally WeightedOrdered Probit2
AgeLog -0.236(-4.15)
-0.401(0.192)
[-1.187,-0.179]
DistanceLog -1.564(-2.96)
0.291(0.665)
[-0.873,2.036]
PriceLog-fitted -0.421(-1.44)
-0.415(0.008)
[-0.436,-0.386]
B1kHH00 0.030(8.14)
0.039(0.010)
[-0.094,0.058]
HispHH00 0.034(3.20)
-4.928(13.920)
[-117.043,0.112]
MedHHInc 0.003(4.15)
0.002(0.001)
[0.000,0.008]
Log likelihoodObservations
-311.40508
-1539.52508
1 Parameter estimates with t-statistics in parenthesis.
2 The average of the 508 parameter estimates for the variable is listed on the first of the three lines, the standard deviation in parenthesis is on the middle line, and the range of parameter estimates in brackets is provided on the third line. The log-likelihood value is the sum of the log likelihoods for the 508 regressions. Bandwidth = 0.4.
OPLWR• Heterogeneity in the parameters for different
houses
• Sign switches: log of distance
% Hispanic in block group
• Examine the geographic aspects of these variables
more clearly
Distance Coefficients and Location of Houses
b = .4
b = .8
Hispanic Coefficients and Location of Houses
Positive Coefficients
Negative Coefficients
Conclusions• spatial effects matter
• ignoring them can lead to biased results of
assessing determinants of noise
• find evidence of heterogeneity in effects of
Hispanic population, and distance from airport,
on probability of being in the “buffer zone”
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