# RECORD eb83365b4894fa9c413cfdff9bdf069881dad3ad
# TITLE: Examining the spatial relationship between environmental health factors and house prices
# YEAR: 2018.0  VENUE: Journal of European Real Estate Research  DOI: 10.1108/jerer-01-2018-0008
# AUTHORS: M. McCord; Sean Macintyre; P. Bidanset; D. Lo; P. Davis
# PAGES: 49  CANDIDATE TABLE PAGES (1-based): [45, 46, 48, 49]
# ABSTRACT: 
Purpose
Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.


Design/methodology/approach
A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).


Findings
The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.


Originality/value
Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.



===== PAGE 1 (score 2, numeric-paren 0, table-words 0, var-words 2) =====

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Examining the spatial relationship between environmental health factors and house
prices: NO2 problem?
McCord, M., MacIntyre, J., Bidanset, P., Lo, D., & Davis, P. (2018). Examining the spatial relationship between
environmental health factors and house prices: NO2 problem? Journal of European Real Estate Research,
11(3), 353-398. https://doi.org/10.1108/JERER-01-2018-0008
Link to publication record in Ulster University Research Portal
Published in:
Journal of European Real Estate Research
Publication Status:
Published (in print/issue): 05/11/2018
DOI:
10.1108/JERER-01-2018-0008
Document Version
Author Accepted version
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CC BY-NC
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Download date: 20/10/2026


===== PAGE 45 (score 39, numeric-paren 36, table-words 0, var-words 3) =====

Journal of European Real Estate Research
Linear 
Log-linear 
β 
t 
β 
t 
%effect 
(Constant) 
64520.655 
7.651*** 
11.208 
152.631*** 73716.83 
AREA 
984.173 
40.962*** 
.008 
36.788*** 
0.80% 
APP 
26783.922 
11.888*** 
.232 
11.815*** 
26.11% 
DET 
44882.795 
20.154*** 
.349 
18.017*** 
41.76% 
SDT 
12213.843 
9.105*** 
.165 
14.084*** 
17.94% 
PRE1919 
-12068.371 
-8.082*** 
-.082 
-6.332*** 
-7.87% 
POST1980 
4313.548 
1.910* 
.092 
4.663*** 
9.64% 
POSTWAR 
-5143.561 
-3.402*** 
.000 
.033 
0.00% 
EARLYMODER 
-5168.802 
-2.731*** 
.008 
.470 
0.80% 
ELECHEAT 
-1801.579 
-.956 
-.024 
-1.453 
-2.37% 
GASHEAT 
-235.123 
-.224 
-.004 
-.485 
-0.40% 
SOLIDHEAT 
-20.006 
-.012 
.002 
.144 
0.20% 
SOC 
-11022.089 
-6.185*** 
-.138 
-8.920*** 
-12.89% 
NOGAR 
-1338.239 
-1.198 
-.018 
-1.837* 
-1.78% 
Air Q Low  
-5459.501 
-2.477** 
-.066 
-3.425*** 
-6.39% 
Air Q High  
4677.732 
1.957** 
.050 
2.424** 
5.13% 
Air Q Very High  
29829.619 
4.988*** 
.349 
6.704*** 
41.76% 
PM8_9 
6568.690 
2.602*** 
.061 
2.769*** 
6.29% 
PM10_11 
-2325.647 
-1.243 
-.023 
-1.419 
-2.27% 
PM11_12 
-11669.558 
-4.199*** 
-.098 
-4.061*** 
-9.34% 
PM12_13 
-11885.036 
-1.452 
-.101 
-1.982** 
-9.67% 
PM13_14 
-13055.665 
-1.386 
-.118 
-2.434** 
-11.13% 
AQMA Within300 
-773.144 
-.192 
.013 
.372 
1.31% 
AQMA Inside 
2624.339 
1.527 
.037 
2.489** 
3.77% 
Road Noise V Low 
-128.152 
-.096 
-.012 
-1.043 
-1.19% 
Road Noise Low 
2914.370 
1.394 
.026 
1.456 
2.63% 
Road Noise Moderate 
-1833.483 
-.621 
-.002 
-.066 
-0.20% 
Road Noise High 
3667.620 
.900 
.002 
.050 
0.20% 
Road Noise V High 
918.180 
.187 
.037 
.863 
3.77% 
Road Noise Extreme 
1290.596 
.204 
.008 
.151 
0.80% 
Road_25 
-4728.319 
-1.285 
-.023 
-.711 
-2.27% 
Road_50 
-2478.698 
-.923 
-.021 
-.886 
-2.08% 
Road_75 
-2109.971 
-.913 
-.021 
-1.029 
-2.08% 
Road_100 
1617.957 
.846 
.017 
1.025 
1.71% 
Road_125 
-1048.081 
-.575 
.005 
.289 
0.50% 
Road_150 
2384.342 
1.316 
.017 
1.074 
1.71% 
Road_175 
970.000 
.423 
.004 
.204 
0.40% 
Road_200 
-2612.830 
-1.313 
-.007 
-.407 
-0.70% 
Road_225 
7514.066 
3.218*** 
.055 
2.685*** 
5.65% 
Road_250 
4800.880 
2.338** 
.037 
2.082** 
3.77% 
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===== PAGE 46 (score 34, numeric-paren 24, table-words 3, var-words 4) =====

Journal of European Real Estate Research
Rail_25 
915.277 
.197 
.073 
1.795* 
7.57% 
Rail Noise Low 
-27487.45 
-2.415*** 
-0.441 
-4.160*** 
-35.63% 
Rail Noise Mod 
-5678.95 
-0.460 
0.004 
0.032 
0.37% 
Rail Noise High 
-24811.37 
-2.179** 
-0.365 
-3.445*** 
-30.58% 
Rail Noise V high 
19069.76 
1.670* 
0.151 
1.425 
16.35% 
Rail_50 
-11206.596 
-3.087*** 
-.041 
-1.302 
-4.02% 
Rail_75 
-5504.543 
-1.718* 
-.013 
-.452 
-1.29% 
Rail_100 
-17789.479 
-3.683*** 
-.103 
-2.459** 
-9.79% 
Rail_125 
-12567.617 
-1.939** 
-.045 
-.801 
-4.40% 
Rail_150 
3099.302 
.584 
.065 
1.400 
6.72% 
Rail_175 
-373.185 
-.075 
.011 
.243 
1.11% 
Rail_200 
-7930.388 
-1.569 
-.103 
-2.330** 
-9.79% 
Rail_225 
-7541.898 
-1.668 
-.036 
-.925 
-3.54% 
Rail_250 
-5447.877 
-1.121 
-.027 
-.635 
-2.66% 
Airport Noise Low 
-3105.489 
-1.517 
-.010 
-.557 
-1.00% 
Airport Noise Mod 
-4698.304 
-1.221 
-.032 
-.959 
-3.15% 
Airport Noise High 
-6349.414 
-1.024 
-.079 
-1.454 
-7.60% 
Airport400_1000 
-16552.490 
-2.385** 
-.056 
-.931 
-5.45% 
Airport1001_1500 
-10388.389 
-1.807* 
-.026 
-.527 
-2.57% 
Airport1501_2000 
-9955.022 
-2.219** 
-.086 
-2.196** 
-8.24% 
Airport2001_2500 
-10345.695 
-3.125*** 
-.076 
-2.637*** 
-7.32% 
Industry Noise Base 
-12765.213 
-1.786* 
-.149 
-2.389** 
-13.84% 
Industry Distance400 
-7160.737 
-3.119*** 
-.084 
-4.216*** 
-8.06% 
Industry Distance200 
3415.499 
.851 
.006 
.166 
0.60% 
R2 
0.809 
 
0.815 
 
 
Adjusted R2 
0.800 
 
0.807 
 
 
AIC 
58347.98 
 
57201.67 
 
 
F 
91.10*** 
 
95.09*** 
 
 
n 
2,501 
 
2,501 
 
 
NB: Spatial wards available in Appendices. Time variable available on request. 
 Denotes: ***99% level; **95% level; *90% level 
 
 
<<<Table VI GWR coefficients>>>  
Parameters 
min 
1st Qu. 
Median 
3rd Qu. 
Max 
Intercept 
10.04488 
10.317 
10.50732 
10.75813 
11.4698 
AREA 
0.003709 
0.006883 
0.007629 
0.008845 
0.0125 
APP 
0.044103 
0.195197 
0.302798 
0.398363 
0.6095 
DET 
-0.37527 
0.287017 
0.335969 
0.382142 
0.6255 
SDT 
0.006307 
0.118544 
0.154686 
0.194263 
0.4962 
PRE1919 
-0.29011 
-0.09524 
-0.0422 
0.000211 
0.0791 
POST1980 
-0.16995 
0.034962 
0.088613 
0.144219 
0.3708 
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===== PAGE 47 (score 2, numeric-paren 0, table-words 0, var-words 2) =====

Journal of European Real Estate Research
POSTWAR 
-0.22547 
-0.05829 
-0.01578 
0.033793 
0.1665 
EARLYMODER 
-0.33133 
-0.03715 
0.036268 
0.087848 
0.5864 
ELECHEAT 
-0.17724 
-0.05087 
-0.01808 
0.01086 
0.11 
GASHEAT 
-0.08838 
-0.02672 
-0.00578 
0.015605 
0.066 
SOLIDHEAT 
-0.13769 
-0.01143 
0.013233 
0.035798 
0.1434 
SOC 
-0.45753 
-0.17667 
-0.11152 
-0.03822 
0.1695 
NOGAR 
-0.12862 
-0.0356 
-0.01238 
0.008318 
0.0686 
Air Low 
-0.51179 
-0.1294 
-0.05916 
0.068024 
0.2743 
Air High 
-0.36426 
-0.0184 
0.028329 
0.094529 
0.3003 
Air Very High 
-0.39895 
-0.03821 
0.091154 
0.210515 
0.4235 
Part_mat8_9 
-0.30017 
0.146077 
0.366976 
0.573741 
0.9877 
Part_mat9_10 
-0.30611 
0.073342 
0.244157 
0.406436 
0.6016 
Part_mat10_11 
-0.4055 
0.083145 
0.218267 
0.409633 
0.7065 
Part_mat11_12 
-0.5988 
-0.06218 
0.069224 
0.236632 
0.4633 
Part_mat12_13 
-0.74736 
-0.06276 
0.152049 
0.310833 
1.0128 
AQMA_Within300 
-0.73802 
-0.23896 
-0.065 
0.085322 
0.2262 
AQMA Inside 
-0.41201 
-0.10247 
0.031926 
0.137125 
0.3339 
Road Noise V. Low 
-0.39784 
-0.10161 
-0.05768 
-0.0047 
0.1417 
Road Noise Low 
-0.34726 
-0.13104 
-0.04677 
0.046925 
0.3026 
Road Noise Moderate 
-0.33628 
-0.12076 
-0.05448 
0.004413 
0.2293 
Road Noise High 
-0.93399 
-0.21449 
-0.11814 
-0.04939 
0.1945 
Road Noise V. High 
-0.17279 
-0.01498 
0.055416 
0.11827 
0.3699 
Road Noise Extreme 
-0.55339 
-0.19485 
-0.1067 
0.069955 
0.495 
Road_25 
-0.1841 
-0.08559 
-0.04993 
-0.01087 
0.2707 
Road_50 
-0.3119 
-0.04885 
-0.00306 
0.03743 
0.239 
Road_75 
-0.3353 
-0.07272 
-0.02958 
0.01605 
0.3162 
Road_100 
-0.1128 
-0.03881 
-0.00493 
0.06042 
0.3409 
Road_125 
-0.1767 
-0.05847 
-0.01031 
0.06016 
0.1858 
Road_150 
-0.0998 
-0.01556 
0.02136 
0.06593 
0.2071 
Road_175 
-0.1526 
-0.01055 
0.04263 
0.08869 
0.2883 
Road_200 
-0.1705 
-0.07126 
-0.0403 
-0.01196 
0.1788 
Road_225 
-0.1429 
-0.03221 
0.02923 
0.1596 
0.3939 
Road_250 
-0.09446 
-0.02659 
0.01818 
0.05744 
0.1492 
Rail Noise Low 
-0.6667 
-0.34071 
-0.21947 
-0.11381 
0.1385 
Rail Noise Moderate 
-3.42566 
-0.04185 
0.04535 
0.153624 
8.9443 
Rail Noise High 
-0.78497 
-0.49088 
-0.29307 
-0.23933 
0.423 
Rail Noise V. High 
-0.28753 
0.080428 
0.154869 
0.235694 
0.7482 
Rail_25 
0.003949 
0.1248 
0.1808 
0.2556 
0.4809 
Rail_50 
-0.3125 
-0.03044 
0.07784 
0.2147 
0.5109 
Rail_75 
-0.1334 
-0.01932 
0.07497 
0.2035 
0.3566 
Rail_100 
-0.206 
0.006768 
0.07622 
0.189 
0.3428 
Rail_125 
-0.2445 
0.0732 
0.1344 
0.2063 
0.4515 
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Rail_150 
-0.1155 
0.02697 
0.1178 
0.2083 
0.3777 
Rail_175 
-0.1372 
0.04042 
0.1331 
0.2068 
0.4379 
Rail_200 
-0.2876 
-0.1492 
-0.08597 
-0.00163 
0.1592 
Rail_225 
-0.3425 
-0.1442 
-0.00627 
0.0814 
0.1989 
Rail_250 
-0.2802 
-0.01162 
0.0412 
0.1152 
0.229 
Airport Noise Low 
-0.19238 
-0.06323 
0.04587 
0.172265 
0.5697 
Airport Noise Mod. 
-0.53043 
-0.34411 
-0.20467 
-0.09965 
0.2168 
Airport Noise High 
-0.67079 
-0.42741 
-0.28136 
-0.10115 
0.4266 
Airport400_1000 
-0.2111 
-0.1073 
-0.0475 
0.04688 
0.1988 
Airport1100_1500 
-0.1007 
-0.01716 
0.0502 
0.09886 
0.7588 
Airport1600_2000 
-0.4609 
-0.2464 
-0.1051 
-0.02342 
0.0312 
Airport2100_2500 
-0.3426 
-0.2032 
-0.125 
-0.05381 
0.0099 
Industry Noise Base 
-0.30042 
-0.13967 
-0.01387 
0.089622 
0.4612 
Industry Distance400 
-0.2464 
-0.1052 
-0.05878 
-0.01716 
0.0796 
Industry Distance200 
-0.2371 
-0.128 
-0.08142 
-0.00702 
0.1248 
Linear 
Log-linear 
R2 
0.9026 
.8993 
Adjusted. R2 
0.8553 
.8447 
AIC 
56574.29 
-2367.07 
AICc 
56573.29 
-1451.03 
RSS 
53.24 
45.05 
n 
2,501 
2,501 
    Kernel function: exponential. Adaptive bandwidth: 40 (number of nearest neighbours) 
    Regression points: the same locations as observations are used.  
    Distance metric: Great Circle distance metric is used 
<<<Table VII Flight Path effect on house prices>>> 
 
(1) 
Linear 
(2) 
Log-linear 
(3) 
GWR 
 
β 
β 
%effect 
min 
1st Qu. 
Median 
3rd Qu. 
Max 
Mod. flight noise 
-1215.8** 
-.040** 
-3.95% 
-0.3615 -0.1945 
-0.1331 
-0.0683 
0.0413 
High flight noise 
-3854.2** 
-.075** 
-7.26% 
-0.9453 -0.3511 
-0.1426 
-0.0195 
0.0903 
Under flight path 
-6560.8** 
-.056** 
-5.45% 
-0.1837 -0.0572 
-0.0168 
0.0248 
0.1344 
 Denotes: ***99% level; **95% level; *90% level 
 
<<<Table VIII Spatial Lag Models>>> 
 
 
 
Air Pollution Models 
Noise Pollution 
 
(1) 
(Base No Lag) 
(2) 
(Base +Lag) 
(3) 
(NO2) 
(4) 
(PM2.5) 
(5) 
Rail 
(6) 
Airport 
(7) 
Road 
C 
10.408 
(204.587)*** 
0.1259 
(0.967) 
2.8883 
(5.814)*** 
6.8997 
(6.247)*** 
1.5883 
(4.930)*** 
0.0142 
(0.041) 
0.0862 
(0.398) 
  W, = 1/	
, 
NA 
0.958673 
(80.659)*** 
0.706914 
(16.211)*** 
0.331722 
(3.414)*** 
0.827571 
(29.155)*** 
0.967677 
(31.560)*** 
0.9621 
(50.103)*** 
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===== PAGE 49 (score 117, numeric-paren 107, table-words 3, var-words 4) =====

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P ∗NO 
NA 
NA 
0.017422 
(6.439)*** 
NA 
NA 
NA 
NA 
NO2 
NA 
NA 
-0.19054 
(-6.170)*** 
NA 
NA 
NA 
NA 

 ∗ 2.5/ 
NA 
NA 
NA 
0.064866 
(6.812)*** 
NA 
NA 
NA 
2.5/ 
NA 
NA 
NA 
-0.70096 
(-6.478)*** 
NA 
NA 
NA 

 ∗  !"# 
NA 
NA 
NA 
NA 
0.081707 
(5.131)*** 
NA 
NA 
Rail Noise 
NA 
NA 
NA 
NA 
-0.9084 
(-4.973)*** 
NA 
NA 

 ∗ $"%&'%( 
NA 
NA 
NA 
NA 
NA 
-0.0033 
(-0.2737) 
NA 
Airport Noise 
NA 
NA 
NA 
NA 
NA 
0.04471 
(0.326) 
NA 

 ∗  '!) 
NA 
NA 
NA 
NA 
NA 
NA 
-0.002 
(0.233) 
Road Noise 
NA 
NA 
NA 
NA 
NA 
NA 
0.02389 
(0.234) 
AREA 
0.01009 
(34.729)*** 
0.003986 
(23.380)*** 
0.003905 
(23.431)*** 
0.003931 
(23.758)*** 
0.004007 
(23.735)*** 
0.003974 
(23.299)*** 
0.00398 
(23.342)*** 
APP 
0.366772 
(15.078)*** 
0.051301 
(3.835)*** 
-0.00555 
(-0.392) 
-0.00617 
(-0.447) 
0.037728 
(2.796)*** 
0.04626 
(3.387)*** 
0.05144 
(3.784)*** 
TER 
-0.10434 
(-6.728)*** 
-0.025474 
(-3.101)*** 
-0.03527 
(-4264)*** 
-0.0378 
(-4.617)*** 
-0.03192 
(-3.809)*** 
-0.02886 
(-3.441)*** 
-0.0255 
(-3.110)*** 
DET 
0.204127 
(8.315)*** 
0.034670 
(2.650)*** 
0.048884 
(3.767)*** 
0.058726 
(4.497)*** 
0.03604 
(2.777)*** 
0.033414 
(2.542)** 
0.0344 
(2.629)*** 
PRE1919 
-0.06371 
(-2.222)** 
-0.065664 
(-4.356)*** 
-0.08027 
(-5.421)*** 
-0.08606 
(-5.841)*** 
-0.06855 
(-4.579)*** 
-0.06753 
(-4.472)*** 
-0.0655 
(-4.341)*** 
INTERWAR 
-0.03624 
(-1.355) 
-0.041892 
(-2.978)*** 
-0.05072 
(-3.683)*** 
-0.05496 
(-4.013)*** 
-0.03979 
(-2.854)*** 
-0.04325 
(-3.071)*** 
-0.0419 
(-2.981)*** 
POSTWAR 
-0.03179 
(-1.099) 
-0.078489 
(-5.157)*** 
-0.07516 
(-5.055)*** 
-0.07323 
(-4.956)*** 
-0.07922 
(-5.257)*** 
-0.07649 
(-5.004)*** 
-0.0785 
(-5.154)*** 
EARLYMODER 
-0.00802 
(-0.249) 
-0.068598 
(-4.054)*** 
-0.06558 
(-3.967)*** 
-0.06552 
(-3.990)*** 
-0.0695 
(-4.149)*** 
-0.06687 
(-3.934)*** 
-0.0685 
(-4.051)*** 
ELECHEAT 
0.00069 
(0.022) 
-0.012883 
(-0.794) 
-0.01024 
(-0.646) 
-0.00894 
(-0.567) 
-0.01371 
(-0.854) 
-0.01285 
(-0.792) 
-0.0129 
(-0.778) 
GASHEAT 
-0.01906 
(-0.811) 
-0.002525 
(-0.204) 
-0.00191 
(-0.157) 
-0.00047 
(-0.039) 
-0.00419 
(-0.342) 
-0.0023 
(-0.185) 
-0.0026 
(-0.215) 
OILHEAT 
-0.01857 
(-0.858) 
-0.002954 
(-0.259) 
-0.00241 
(-0.217) 
-0.00062 
(-0.056) 
-0.0038 
(-0.337) 
-0.00291 
(-0.256) 
-0.0029 
(-0.258) 
PRIV 
0.186685 
(8.525)*** 
0.056389 
(4.849)*** 
0.057544 
(5.054)*** 
0.055683 
(4.920)*** 
0.05453 
(4.721)*** 
0.052528 
(4.445)*** 
0.0564 
(4.836)*** 
GAR 
0.003206 
(0.219) 
0.010816 
(1.409) 
0.012548 
(1.673)* 
0.012156 
(1.632) 
0.009612 
(1.264) 
0.011042 
(1.439) 
0.0105 
(1.378) 
R2 
0.5460 
0.8744 
0.8804 
0.8819 
0.8771 
0.8746 
0.8745 
Adjusted R2 
0.5436 
0.8737 
0.8796 
0.8812 
0.8763 
0.8738 
0.8736 
F-statistic 
230.11 
*** 
1237.26 
*** 
1143.09 
*** 
1160.20 
*** 
1108.03 
*** 
1083.68 
*** 
1081.40 
*** 
AIC 
0.297 
-0.987 
-1.034 
-1.047 
-1.006 
-0.987 
-0.986 
Log-likelihood 
-357.83 
1249.87 
1310.42 
1326.78 
1276.18 
1251.84 
1249.06 
Observations 
2,501 
2,501 
2,501 
2,501 
2,501 
2,501 
2,501 
 Denotes: ***p<.001; **p<.05; *p<.10. 
 
 
 
 
Page 47 of 47
Journal of European Real Estate Reserach
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