When positive externalities of better view meet noisy and polluted negative
externalities: which factors prevail in property prices?
Abstract
Previous research showed that positive externalities of better view lead to higher
property prices where negative externalities such as noise and air pollution lead to
lower property prices. Nevertheless, few studies have been conducted on the impact
of better views, air and noise pollution on housing prices. This paper studied the
positive and negative externalities’ impact on property values in Hong Kong Amog
Garden from 1994 to 2013. It mainly focuses on the effect of better views, noise
pollution and air pollution on the property values. All the hedonic models show that
flats on lower floor were sold at lower property prices but flats which faced the noisy
road were even sold at higher property prices. Besides, property values of flats in
lower floors dropped greater than that those of higher floors if air pollution problem
became worse. This implied that the positive externalities of view outweigh the
problems of negative impact of air and noise pollution.
Keywords: externalities, property prices, housing, pollution
1. Introduction
Previous literature shows that structural, locational and neighborhood attributes
(Tekel & Akbarishahabi 2013) affect housing prices. Sirmans et al. (2005) develop 8
basic categories for consideration on factors in hedonic price model. Environment (or
landscape) factor generally refers to how many green space and landscape can the
property provide. Throughout the world, a property with ocean-view and
mountain-at-the-back is more preferred to those property which does not have.
1 Typically, this factor is established as binary with positive effect and it contributes
more to property price in luxurious building. In Chinese society, there is a possible
factor on property price which is called Feng Shui. This factor is a belief on good
fortune to offspring given by land with better fortune. The environmental factors
affect the quality of environment such as air pollution, noise pollution and water
pollution. The noise mission from rail or airport, water pollution and air pollution, for
example, can have negative effect on property price (Anstine 2003; Nelson 2004;
David 2006). Anstine (2003) examines the impact on property values when two
factories are located near each other in the semi-rural area. It is supposed that noxious
facilities would affect house values significantly. However, if the information cost is
so high that residents near the area cannot get information through the usual sense
channel such as sight and smell, the environmental pollutants may not be capitalized
into house values. Nelson (2004) applies meta-analysis to study the impact on noise
pollution to residential property values in the US and Canada. The weighted-mean
noise discount is 0.58% per decibel around the 20 hedonic property values, covering
33 estimates included 23 airports in Canada and the United States. Furthermore, the
cumulative noise discount in the United States is about 0.5% to 0.5% per decibel at
noise exposure levels of 75 dB or less while the discount is 0.8% to 0.9% per decibel
in Canada. Clark (2006) finds that there are consistently negative impact on houses
2 proximity to rail lines with statistically important influence on residential property
values. It gives out evidence on the price drop on the real sale price of houses if the
houses are located proximity to rail. The above literatures indicate that environmental
externalities should affect property price.
Further, price can be relative high if there are more public facilities around the
property. These include park and walking path in foreign countries and public
transport station, restaurant and shopping mall in Hong Kong (Limsombunchai et al.
2004; Kong et al. 2007; Cebula 2009) . Therefore, there are 2 sector of surrounding
facilities can be estimated which are facilities for leisure entertainment and facilities
for convenience life. For property size, it has different method to measure while it can
measure lot size, square feet of the flat or square meter of the flat. It is not common to
use lot size as measurement in Hong Kong while there is insufficient land for
constructing houses. Therefore, building with more than 20 floors is relative familiar
to Hong Kong. Meanwhile, the measurement method based on British style that
square feet in Hong Kong is a common unit for measuring size. With different
countries and different style of measurement, there are different methods as
intermediate to obtain property size. Apart from the measurement of size, there are
some operations in modeling would like to transform the size into different functional
3 form such as logarithm or squared (Laurice & Bhattacharya 2005; Cebula 2009; Tekel
& Akbarishahabi 2013). Obviously, the property size carries positive relationship
towards the property price. There is a factor that is possible for Hong Kong which is
the existence of windowsills. It is because the windowsills lead to spread between
construction area and saleable area. Concerning the property age, it is directly
installed in the model that carrying negative relationship with property price.
However, there are some occasions that lead to positive relationship. Cebula (2009)
estimates that the property price can be positively correlated with the property age if
that property has been significantly designated as a National Historic Landmark.
Sirmans et al. (2005) find that number of rooms is also an important factor to property
price. However, there are various kinds of rooms existing inside a property. Therefore,
different types of room carry out different intensity to the price relationship such that
the measurement of rooms must be separated in detail as number of bedrooms and
number of bathrooms. Furthermore, in bathrooms, there are some literatures had
further estimated the effect of half bathroom and full bathroom. The number of
bedrooms has positive relationship with the property price. Meanwhile, the half
bathroom has much less impact on price than the full bathroom although both of them
carry positive effect. Second, the internal features of the property give out huge
influence on property price in foreign countries. However, it effect in Hong Kong
4 should be much less that most of the factors are not available in Hong Kong such as
fireplace, sprinkler system, installed air-condition system and basement. Alternatively,
there is another factor that could contribute more in Hong Kong than foreign countries
which is the floor of property. Kryvobokov (2013) indicates binary variable on
different floor to estimate the relationship between floors to price. The result is
positively correlated that higher floor have greater influence on property price.
Neighborhood and location is also critical factor to the property price. While
neighborhood includes the crime rate, average education level in district, average
income group in district, available of landmark in district and majority race in district,
the location includes another factors as distance from main facilities (shopping mall)
in region, distance from leisure place (beach and countryside) and type of location
(urban, rural, industrial zone or popular school zone).1 The factor of neighborhood
and location consist so many possible variables that each effect should not be the
same. Indeed, some of the factors above affect more on the property price. Goodman
(1978) states that black people zone and poverty population are structure of
neighborhood, thus, factor to hedonic price model. Sirmans et al. (2005) indicate that
crime rate as structure of neighborhood. In Hong Kong, there is a factor should be
1
The landmark initially refers to historic landmark. Further detail on: Cebula, R.J. (2009) ‘The
Hedonic Pricing Model Applied to the Housing Market of the City of Savannah and its Savannah
Historic Landmark District’, The Review of Regional Studies, vol. 39, no. 1, pp. 12.
5 dominated in the model which is the school zone that the property in popular school
zone is more expensive than the property out of the popular school zone. Apart from
above, the magnitude of earthquake record is also an environment factor that it is
almost impossible valid in Hong Kong but it is also significant around countries on
earthquake zone.
The transaction related factors are property quality at sold, owner occupation,
time trend, transaction month and holding period before sold. The property quality at
sold can affect the selling price. If the property quality is poor, the selling will be
dropped. Also, the first hand property can have high price in transaction. Laurice and
Bhattacharya (2005) estimate time trends on housing market and month of transaction
in order to determine the effect on seasonal factor. Witte et al. (1979) focuses on the
consumer and supplier characteristics that annual income of household, owner
occupation (blue collar or white collar) and education level as consumer factors and
length of ownership, ownership form and owner’s race as supplier factors to price
model.
Sirmans et. al. (2005) indicates that those tax and financial factors have negative
or no impact on property price. In Hong Kong, the property tax was not a heavy
burden. However, the effect from the property tax will be increased as the tax rate has
been increased. While the investors and speculators around the world have large
6 incentive to real estate market in Hong Kong. Therefore, the property owners, as
investors in Hong Kong, have their own expectation on real estate market. For
example, they tends to hold their property rather than putting it into the market if the
expectation is positive in the incoming months.
Table. 2. List of detail possible factors in hedonic price model2
Category
Construction
and Structure
Factors
Detail
Property size
+
Property age
-
Number of Rooms
Bedrooms, Half
Bathrooms, Full
Bathrooms…
*Windowsills
+
Air-condition system
+
Basement
+
Floor
The floor of property
located
+
Garage size
+
Carport
+
Courtyard
+
Pool
+
Porch
+
*clubhouse
Environment
+
-
Internal features Fireplace
External
features
Expected
Relationship
Clubhouse entertainment
room and pool
+
Good Landscape3
+
Ocean-view
+
*Mountain-at-the-back
+
*Feng Shui
+
2
* refers to factor should fit Hong Kong.
Although good landscape is relative subjective concept, Sirmans, G.S., Macpherson, D.A. & Zietz,
E.N. (2005) has used this as variable for estimation.
7 3
Environmental
Surrounding
Facilities
Neighborhood
and Location
Noise pollution
-
Water pollution
-
Air pollution
-
facilities for leisure
entertainment
Walking path, park
+
facilities for
convenience life
Restaurant, shopping
mall, public transport,
convenience store
+
Crime rate
Majority race
Percentage of minority
race group in that country
*School Zone
Transaction
related
Financial Issues
+
Type of location
Rural, Industrial, Urban…
?
Distance
Distance to city center,
Distance to countryside
-
Danger Zone
Earthquake, Flood
-
Property quality
Need decoration
?
Time trends
?
Transaction month
?
Property tax
?
Future Expectation
?
In fact, noise pollution adversely affects our daily activities or health. Amoy garden in
Kowloon Bay is choosing in the model since a part of the flats face a main road
closely. Ngau Tau Kok Road has four traffics line and there are heavy traffic flows
every day. It is expected there will be many noise generated by those motor vehicles.
High noise levels can contribute to heart diseases in humans. It can cause rising of
blood pressure, and an increase in stress, and an increased incidence of heart disease.
The price of flats facing road are expected to be lower than others since people don’t
favor noise pollutions. This paper studies externalities’ impact on property values in
Hong Kong from 1994to July, 2013. It mainly focuses on the effect of noise pollution
and air pollution on the property value.
8 4. Methodology
Due to the difference of residents’ preference, there is heterogeneity of hedonic
prices (Goodman & Thibodeau 2003) . Leung et al. (2002) shows that a simple linear
hedonic pricing equation can consistently explain about 80% of the cross-house
differences in transaction prices when it is applied to the Hong Kong data. Although it
is widely used, there are some difficulties which may affect the accuracy of it.
Pennington et al. (1990) point out the challenge of predicting the effect of aircraft
noise on residential property values when using it.
Scholars have tried to improve the model by using different statistical methods.
For example, Can (1990) have used Moran's I to test for autocorrelation and Bowen et
al. (2001) advocate the use of spatial diagnostics in hedonic house price estimation.
Techniques, such as maximum likelihood (Brassington 1999), mixed
regressive-autoregressive (Simons 2001), Q-factor analysis (Dale-Johnson 1982),
principal component analysis (PCA) (Maclennan & Tu 1996; Bourassa et al. 2003)
have been employed to adjust for the spatial autocorrelation. However, a popular and
commonly used method is Rosen’s hedonic model (Rosen 1974).
Rosen’s hedonic equation “represents a joint envelope of a family of value
functions and another family of offer functions (Rosen 1974)”. Therefore, the
equation contains two sides: consumers and suppliers. For function of consumers,
Rosen defines a value or bid function for each household which indicates the
maximum amount consumers are willing to pay for alternative housing bundles, C’s.
These housing bundles contain different amounts of attribute
( C = c1 , c 2 , c3 ......c n ) from which indicates the consumers’ utility. In addition to the
features of the housing bundle, the consumers’ bid, β, for a given bundle is affected
also by their income level (y) and tastes (π). Thus, the formula for housing consumers
is: β = β (c1 , c 2 ,..., c n , y, π ) where π is a vector of taste.For function of suppliers, Ω,
represents the minimum unit price the producers are willing to accept for the housing.
Assuming that firms are rationally and profit maximized, its function depends on the
characteristics of the bundle ( C = c1 , c 2 , c3 ......c n ) , factor prices (f) and production
parameters (p) if it is sold in the first market. Therefore, the formula
is: Ω = Ω(c1 , c 2, ..., c n , f , p) where f and p is a vector containing measures of factor
prices and production parameters. In market equilibrium, it is attained when
9 Q S (C ) = Q D (C ) , Q S (c i ) = Q D (ci ) or that β=Ω, for all i.
4.1 Data
4.1.1 Data description table
Variable names
Price
Description
Transaction price (in million)
Type
-
Area
Area of the flat(in
-
ex
Exchanbe rate between RMB & Daily
HKD
)
Data source
Hong
Kong
CENTADATA4
Hong
Kong
CENTADATA5
Yahoo! Hong
Kong -Finance6
=
GDP
Hong Kong GDP at current Quarterly Hong
Kong
market prices (in million)
Cenus
and
Statistics
Department7
CPI
Composite
Consumer
Price Monthly Hong
Kong
Index
Cenus
and
Statistics
Department8
FLOOR_DUMMY Flats in the lowest 10 floors: Hong
Kong
FLOOR_DUMMY=1, otherwise
CENTADATA
=0.
ROAD_DUMMY
Flats facing Ngau Tau Kok Road Hong
Kong
:
ROAD_DUMMY
=
1,
CENTADATA
otherwise=0
SO2
Sulphur Dioxide (inµg/m3)
Daily
Hong
Kong
Environmental
Protection
Department9
NO2
Nitrogen Dioxide(inµg/m3)
Daily
Hong
Kong
Environmental
Protection
Department
4
Hong Kong CENTADATA http://hk.centadata.com/tfs_centadata/Pih2Sln/TransactionHistory.aspx?type=3&code=EWKSBPYOPS&info=basicinfo 5
Hong Kong CENTADATA http://hk.centadata.com/tfs_centadata/Pih2Sln/TransactionHistory.aspx?type=3&code=EWKSBPYOPS&info=basicinfo 6
Yahoo! Hong Kong -­‐Finance http://hk.finance.yahoo.com/q?s=CNYHKD=X 7
Hong Kong Cenus and Statistics Department Table 030 http://www.censtatd.gov.hk/showtableexcel2.jsp?tableID=030&charsetID=2 8
Hong Kong Cenus and Statistics Department Table 052 http://www.censtatd.gov.hk/showtableexcel2.jsp?tableID=052 9
Hong Kong Environmental Protection Department http://epic.epd.gov.hk/EPICDI/air/station/?lang=zh 10 NOX
Nitrogen Oxides(inµg/m3)
Daily
O3
Ozone(inµg/m3)
Daily
RSP
Respirable
Suspended Daily
Particulates(inµg/m3)
4.1.2
Data summary table
Variables
Mean
Price
Area
ex
GDP
CPI
FLOOR_DUMMY
ROAD_DUMMY
SO2
NO2
NOX
O3
RSP
2.525519759
470.1334
1.183648189
469382.8
104.6597146
0.355104281
0.061470911
10.30016
60.74625
118.2945
34.62436
46.01813
4.1.3
Hong
Kong
Environmental
Protection
Department
Hong
Kong
Environmental
Protection
Department
Hong
Kong
Environmental
Protection
Department
Standard
Deviation
0.585242542
47.83749
0.039496307
38401.73
4.614529679
0.478676288
0.240258028
5.055576
20.5026
49.71141
23.96145
22.53059
Coefficient of Deviation
Variation
0.231731524
0.101753
0.033368282
0.081813
0.044090792
1.347987939
3.90848327
0.490825
0.337512
0.420234
0.69204
0.48960
Air pollutants’ data
The air pollution data is collected from HKSAR Environmental Protection
Department. This research use air pollutants’ data from Kwun Tong Monitoring
Station. The Address of the monitoring station is ‘Yue Wah Mansion, 407-431 Kwun
Tong Road, Kwun Tong, Kowloon’. Its height above ground is 25 metres. It is in a
Urban land use zone (HKEPD - Kwun Tong Monitoring Station 2013). Figure 1 show
that Kwun Tong Monitoring Station, which is in point A, is close to the Amoy
Gardens (point B). So that the data collected in it is accuracy and suitable for
represent the degrees of air pollution. The distance between A and B is about 2
kilometers. Since Environmental Protection Department do not provide pollutants’
11 daily data directly. The daily data used in the model is calculated by the mean of
24hours hourly data.
= Hourly pollutants’ data.
= Daily pollutants’ data.
Figure 1
Source: Google Map
4.1.4
Property values data
All of the property values data from Jan, 2010 to June, 2013 is collected from
CENTADATA (Centaline Property Agency 2013). The floor, area and transaction
12 price of flats are included in the dataset. There are total 1822 data (n=1822) in the
data set.
Amoy Gardens is in Kowloon Bay. There are 19 towers in total. They were built
between 1980 and 1987. The tallest blocks have 33 floors and the shortest blocks have
26 floors. It was chosen since the properties density is high so that the result will be
more accuracy and processing time can be reduced. Also, some variables that may
affect the price can be ignoring in the model. For example, distance between the flat
and MTR station, the time that it need to travel to CBD. These variables can be
ignored since the whole estate has the same figure in these variables. The transaction
number of Amoy Gardens is large enough to allow us have enough sample size to do
a scientific research.
Figure2
Source: Google Map
13 Figure3
Source: CentaMap
4.1.5
Data handling
To improve the result of the models, some outliers are deleted from the data set.
To find out the effect of facing a traffic road, a dummy variable is set up. If the flats
are facing the road closely, the dummy variable will be equal to one, otherwise equals
to zero. In the model, Flat 1, 2 of Block A, B, I, J and K are defined as facing road
closely. A dummy variable (ROAD_DUMMY) equals to one was added to these flats.
Since the transaction price, GDP and exchange rate has a unit root. We take the first
order differential to these three variables.
Figure5
14 Source: Google map
4.2 The Models
Model 1 mainly focuses on the effect of air pollutants on the property value.
As the coefficient of SO2, NO2, O3 and RSP are all not significant. We can’t
conclude that the property values are affected by the air pollutants. Also, this model
has a low adjusted
. It means regressors X have low explanatory power on the Y.
But this model shows that area and the floor of the flat will affect the change in
property value. There are positive relationship between them, if the area is larger, it
will cause a positively change in property value. On the other hand, the change of
transaction price will decrease by 0.17 million if the flats are on the lowest ten floors.
Model 2 mainly focuses on the effect of lower floor and air pollutants on
property price. There are four interaction variables; they help us to find out the
interaction effect between flats in lower floor and air pollutants. The interaction
between SO2, NO2, O3 and floor dummy is negative. It show that lower floors
property values will be decrease greater than that in higher floors if SO2,NO2 and O3
15 increase. It may because air pollutants are mainly appear in lower height of the urban
area. The higher flats are affected by air pollutants less since the stronger wind can
improve the air quality.
In model 3, floor dummy is added. The significant level of interaction term change
when floor dummy is added in the model. The variable: NO2*FLOOR_DUMMY
become positive and not significant. SO2* FLOOR_DUMMY become more
significant and the coefficient is negative as model 2. O3*FLOOR_DUMMY is
become less significant. The variable significance at 90% confidence level only. And
the coefficient is negative, which is same as model 2. The adjusted R square of this
model is the largest. The adjusted R square 0.515626. It means the model explain
51.56% of Y (change in property price).
Model 4 excludes some insignificant variables from the previous models.
As
the previous models show, the variable related to NOX and NO2 are always
insignificant, so model 4 has drop out those variables. It show that the floor of flats
and whether it is facing a road closely or not do affect the property values. But the
coefficients of SO2 and O3 are not as my expected. The coefficients are positive, it
means the relationship between air pollutants and property value is positive, the
coefficients are significant at 90% confidence level. The result of RSP is as expected;
it has negative effect on property value at 95% confidence level.
For the interaction variables, it finds that SO2* FLOOR_DUMMY and
O3*FLOOR_DUMMY have negative coefficient which is as expected. As mentioned
before, compare to higher floors, the negative effect of air pollutants on lower floors
16 is expected to be greater so all of the interaction variables are expected to have
negative coefficient.
Dependent variable: d(price)
Regressor
(1)
d(price)(-1)
Area
0.004769***
(2)
(3)
-0.4922***
-0.49369*** -0.49349***
0.004757**
0.004778**
0.004737**
*
*
*
d(ex)
2.22737
0.27596
0.209142
d(gdp)
-1.76E-06
-8.93E-07
-1.02E-06
FLOOR_DUMMY
-0.169833**
(4)
-0.19321*** -0.16005***
*
ROAD_DUMMY
0.067334*
0.089678**
0.089188**
0.088556**
*
*
*
SO2
-0.000587
NO2
0.000224
NOX
0.000127
O3
0.000259
0.000815*
RSP
-0.000365
-0.00148**
SO2*
0.004022*
-0.00732*
-0.00801**
-0.00133*
0.001257
-0.00839**
FLOOR_DUMMY
NO2*FLOOR_DUMM
Y
O3*FLOOR_DUMMY
-0.00218*** -0.00122*
-0.00201**
RSP*FLOOR_DUMM
0.001833*
0.00323***
17 0.001545
Dependent variable: d(price)
Regressor
(1)
(2)
(3)
d(ex)(-1)
5.493084**
5.255006*
d(gdp)(-1)
1.76E-07
1.22E-07
(4)
Y
Intercept
-2.2002***
-2.1894***
-2.19048*** -2.17377***
Adjusted
0.268884
0.510888
0.515626
0.512707
***=1%, **=5%,*=10%
To have a review of all four models, we can find that the variables- Area,
FLOOR_DUMMY and ROAD_DUMMY are significant in all the four models. Area
is positive with property values and flats in lower floors have relatively low price.
These two variables are as expected and consist with the literatures. But the variableROAD_DUMMY always has positive coefficients which contradict with expectation.
As mentioned in introduction, the price of flats facing road are expected to be lower
than others since people don’t favor noise pollutions and air pollutions. The models
may omit some important variables.
5. Conclusion
The models show that flats facing road have higher price. To find out why
ROAD_DUMMY has positive coefficient, the effect of views of flat should be
examined. As the figure10 in Appendix show, different blocks in Amoy Gardens are
close together. It affect the views of flat greatly. Below are two examples to illustrate
the difference between different flats. Figure 6 and figure 7 shows the views of Flat 2,
Block A of Amoy Gardens. The views of flats facing road are not blocked by nearby
18 buildings. They enjoy the view of victoria harbor. The field of view is wide and they
can see the buildings far away from their flats. The view of flats in higher floors is
better than that in lower floors. It is another reason that why dummy variable of lower
floor in the models has negative coefficient.
Figure 6
Source: CENTALINE PROPERTY
Figure 7
19 Source: CENTALINE PROPERTY
Compare to flats facing road, other flats may have worse views. Figure 8 and 9
show the views of other flats. Since different blocks are close together in Amoy
Gardens, the views are easily blocked by other blocks. Figure 8 shows the view of a
lower floor flat view. The field of view is not wide, most of view is blocked. In
general speaking, views of flats facing road are more valuable than that of other flats.
People are willing to pay more to have a better view.
To conclude, this paper mainly focuses on the effect of noise pollution and air
pollution on the property value. Amoy Gardens was chose to be the study site.
Different models find that flats in lower floor will have lower property price, flats
facing road have higher property price. Also, property values of flats in lower floors
will be decrease greater than that in higher floors if air pollution becomes worse.
Four models in this paper have the same limitation- effect of view is not included.
Since the effect of view is hard to quantify. It is very subjective. Different people
have different favor. So it is hard to quantify that.
The result of flats facing road have higher property price in the models may due
to the positive externalities of view. When the positive effect of view is greater than
the negative effect of air and noise pollution, flats facing road will have higher price.
Figure 8
20 Source: CENTALINE PROPERTY
Figure 9
Source: CENTALINE PROPERTY
Appendix
21 Figure 10
Model 1
Dependent Variable: TRAN_D
Method: Least Squares
Sample (adjusted): 2 1822
Included observations: 1718 after adjustments
Variable
C
AREA
EX_D
GDP_D
ROAD_DUMMY_1
FLOOR_DUMMY_1_LOW_0_HIGH
SO2
NO2
NOX
O3
RSP
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
Coefficient Std. Error
-2.200197
0.004769
2.22737
-1.76E-06
0.067334
-0.169833
-0.000587
0.000224
0.000127
0.000259
-0.000365
0.273142
0.268884
0.396237
268.0058
-841.794
64.14646
0
22 0.101111
0.000199
3.371313
3.51E-06
0.040056
0.020146
0.002816
0.001085
0.000443
0.000672
0.000696
t-Statistic
-21.7602
23.93732
0.660683
-0.50047
1.680975
-8.43001
-0.20847
0.206273
0.287866
0.384686
-0.52441
Prob.
0
0
0.5089
0.6168
0.093
0
0.8349
0.8366
0.7735
0.7005
0.6001
Mean dependent var 0.000729
S.D. dependent var 0.463407
Akaike info criterion 0.992775
Schwarz criterion
1.027664
Hannan-Quinn criter. 1.005684
Durbin-Watson stat 2.598817
Model 2
Dependent Variable: TRAN_D
Method: Least Squares
Sample (adjusted): 3 1822
Included observations: 1716 after adjustments
Variable
Coefficient Std. Error
C
TRAN_D(-1)
AREA
EX_D
GDP_D
ROAD_DUMMY_1
SO2*FLOOR_DUMMY_1_LOW_0_HIGH
NO2*FLOOR_DUMMY_1_LOW_0_HIGH
O3*FLOOR_DUMMY_1_LOW_0_HIGH
RSP*FLOOR_DUMMY_1_LOW_0_HIGH
EX_D(-1)
GDP_D(-1)
-2.1894
-0.4922
0.004757
0.27596
-8.93E-07
0.089678
-0.00732
-0.00133
-0.00218
0.001833
5.493084
1.76E-07
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.514025
0.510888
0.324104
178.9945
-495.479
163.8504
0
0.077045
0.01692
0.000163
2.806626
2.87E-06
0.032791
0.00376
0.000783
0.000707
0.000963
2.802137
2.87E-06
t-Statistic
-28.4172
-29.0893
29.12933
0.098324
-0.31172
2.734872
-1.94634
-1.69653
-3.07876
1.90333
1.96032
0.061384
Prob.
0
0
0
0.9217
0.7553
0.0063
0.0518
0.09
0.0021
0.0572
0.0501
0.9511
Mean dependent var 0.001195
S.D. dependent var 0.463427
Akaike info criterion 0.591467
Schwarz criterion
0.629563
Hannan-Quinn criter. 0.605563
Durbin-Watson stat 1.956388
Model 3
Dependent Variable: TRAN_D
Method: Least Squares
Sample (adjusted): 3 1822
Included observations: 1716 after adjustments
Variable
Coefficient Std. Error
C
TRAN_D(-1)
AREA
EX_D
GDP_D
ROAD_DUMMY_1
FLOOR_DUMMY_1_LOW_0_HIGH
SO2*FLOOR_DUMMY_1_LOW_0_HIGH
NO2*FLOOR_DUMMY_1_LOW_0_HIGH
O3*FLOOR_DUMMY_1_LOW_0_HIGH
RSP*FLOOR_DUMMY_1_LOW_0_HIGH
EX_D(-1)
GDP_D(-1)
-2.19048
-0.49369
0.004778
0.209142
-1.02E-06
0.089188
-0.19321
-0.00801
0.001257
-0.00122
0.001545
5.255006
1.22E-07
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.519015
0.515626
0.322531
177.1567
-486.624
153.1375
0
23 0.076671
0.016842
0.000163
2.793046
2.85E-06
0.032632
0.045968
0.003745
0.000993
0.00074
0.000961
2.789108
2.85E-06
t-Statistic
-28.5699
-29.313
29.38512
0.074879
-0.35814
2.733163
-4.20313
-2.13976
1.265386
-1.64917
1.607782
1.884117
0.042602
Prob.
0
0
0
0.9403
0.7203
0.0063
0
0.0325
0.2059
0.0993
0.1081
0.0597
0.966
Mean dependent var 0.001195
S.D. dependent var 0.463427
Akaike info criterion 0.582312
Schwarz criterion
0.623583
Hannan-Quinn criter. 0.597583
Durbin-Watson stat 1.951517
Model 4
Dependent Variable: TRAN_D
Method: Least Squares
Sample (adjusted): 3 1822
Included observations: 1806 after adjustments
Variable
Coefficient Std. Error
C
TRAN_D(-1)
AREA
FLOOR_DUMMY_1_LOW_0_HIGH
ROAD_DUMMY_1
SO2*FLOOR_DUMMY_1_LOW_0_HIGH
O3*FLOOR_DUMMY_1_LOW_0_HIGH
RSP*FLOOR_DUMMY_1_LOW_0_HIGH
SO2
O3
RSP
-2.17377
-0.49349
0.004737
-0.16005
0.088556
-0.00839
-0.00201
0.00323
0.004022
0.000815
-0.00148
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.515407
0.512707
0.318466
182.0499
-490.589
190.9138
0
24 0.077236
0.016442
0.000157
0.041685
0.031474
0.003789
0.000859
0.001073
0.002341
0.000495
0.000639
t-Statistic
-28.1446
-30.015
30.08828
-3.83959
2.81367
-2.21351
-2.34102
3.01071
1.717783
1.645581
-2.3091
Prob.
0
0
0
0.0001
0.005
0.027
0.0193
0.0026
0.086
0.1
0.0211
Mean dependent var 0.001009
S.D. dependent var 0.456213
Akaike info criterion 0.55547
Schwarz criterion
0.588963
Hannan-Quinn criter. 0.567831
Durbin-Watson stat 1.950827
References
Anstine J. (2003) Property Values in a Low Populated Area When Dual Noxious
Facilities are Present. Growth and Change 34 345--58.
Bourassa S.C., Hoesli M. & Peng V.S. (2003) Do housing submarkets really matter?
Journal of Housing Economics 12, 12-28.
Bowen W.M., Mikelbank B.A. & Prestegaard D.M. (2001) Theoretical and Empirical
Considerations Regarding Space in Hedonic House Price Estimation. Growth
and Change 32, 466-90.
Brassington D.M. (1999) Which Measures of School Quality Does the Housing
Market Value? Journal of Real Estate Research 18, 395-413.
Can A. (1990) The Measurement of Neighbourhood Dynamics in Urban House Prices.
Economic Geography 66, 254-72.
Cebula R.J. (2009) The Hedonic Pricing Model Applied to the Housing Market of the
City of Savannah and its Savannah Historic Landmark District. The Review of
Regional Studies 39, 9-22.
Centaline
Property
Agency
(2013)
Centadata.
URL
http://hk.centadata.com/eptest.aspx?type=3&code=EWKSBPYOPS&info=fp
&code2=&page=0.
Dale-Johnson D. (1982) An Alternative Approach to Housing Market Segmentation
Using Hedonic Price Data. Journal of Urban Economics 11, 311-32.
David E.C. (2006) Externality Effects on Residential Property Values: The Examples
of Noise Disamenities. Growth and Change 37, 460.
Goodman A.C. (1978) Hedonic Prices, Price Indices and Housing Markets. Journal of
Urban Economics 5, 471-84.
Goodman A.C. & Thibodeau T.G. (2003) Housing market segmentation and hedonic
prediction accuracy. Journal of Housing Economics 12, 181-201.
HKEPD - Kwun Tong Monitoring Station (2013) Kwun Tong Monitoring Station.
URL http://www.epd-asg.gov.hk/english/backgd/Kwun_Tong.html.
Kong F., Yin H. & Nakagoshi N. (2007) Using GIS and Landscape metrics in the
Hedonic Price Modeling of the Amenity Value of Urban Green Space: A Case
Study in Jinan City, China. Landscape and Urban Planning 79, 240-52.
Kryvobokov M. (2013) Hedonic Price Model: Defining Neighborhoods with Thiessen
Polygons. International Journal of Housing Markets and Analysis 6, 79-97.
Laurice J. & Bhattacharya R. (2005) Prediction Performance of a Hedonic Pricing
Model for Housing. The Appraisal Journal 73, 198-209.
Limsombunchai V., Gan C. & Lee M. (2004) House Price Prediction: Hedonic Price
Model vs. Artificial Neural Network. American Journal of Applied Sciences 1,
193-201.
25 Maclennan D. & Tu Y. (1996) Economic Perspectives on the Structure of Local
Housing Systems. Housing Studies 11, 387-406.
Nelson J.P. (2004) Meta-Analysis of Airport Noise and Hedonic Property Values:
Problems and Prospects Journal of Transport Economics and Policy 38, 1-28.
Pennington G., Topham N. & Ward R. (1990) Aircraft Noise and Residential Property
Values Adjacent to Manchester International Airport. Journal of Transport
Economics and Policy 24, 49.
Rosen S. (1974) Hedonic Prices and Implicit Markets: Product Differentiation in Pure
Competition. Journal of Political Economy 82, 34-55.
Simons R.A. (2001) The Effects of an Oil Pipeline Rupture on Single-family House
Prices. The Appraisal Journal, 410-8.
Sirmans G.S., Macpherson D.A. & Zietz E.N. (2005) The Composition of Hedonic
Pricing. Journal of Real Estate Literature 13, 3-43.
Tekel A. & Akbarishahabi L. (2013) Determination of Open-green Space’s Effect on
Around House Prices by Means of Hedonic Price Model; in Example of
Ankara/Botanik Park. Gazi University Journal of Science 26, 347-60.
Witte A.D., Sumka H.J. & Erekson H. (1979) An Estimation of a Structural Hedonic
Price Model of the Housing Market: an Application of Rosen’s Theory of
Implicit Markets. Econometrica 47, 1151-73.
26