The 26th AsRES Annual Conference 2022
4-7 August 2022, Tokyo - Hybrid
Wet Market –
Convenience or Health Hazard?
Ervi Liusman (School of Hotel & Tourism Management, The Chinese University of Hong Kong)
K.W. Chau (Ronald Coase Centre for Property Rights Research, HKUrban Lab, The University of Hong Kong)
Y.L. Wong (Ronald Coase Centre for Property Rights Research, HKUrban Lab, The University of Hong Kong)
Wet market has both positive & negative externalities
Positive Externalities
Negative Externalities
Convenience
Health & Hygiene problem
Image source: Wikipedia
Image source: Sassy Group Media Limited
Noise pollution
2
Literature review
HOUSING EXTERNALITIES
HEALTH HAZARD OF WET MARKET
Air pollution (Ricker and Henning, 1967; Nourse, 1967)
Pathogen dissemination due to live poultry market (Gao et al.
2016))
Railway operation (Chau and Ng, 1998; Bowes and Ihlanfeldt,
2001; Jayanta et al., 2015; Diao et al., 2016)
Traffic and highway development (Hughes Jr and Sirmans,
1992; Ossokina and Verweij, 2015; Levkovich et al., 2016)
Urban renewal programs (Yau et al., 2008; Chau and Wong,
2015; Rossi-Hansberg et al., 2010; Zheng et al., 2020;
Chareyron et al, 2022)
Heritage conservation (Koster and Rouwendal, 2017; Kee and
Chau, 2020)
Exposure to wet market poultry and influenza A H7N9
infection (Chen et al., 2013; Fournié and Pfeiffer, 2013)
Effect of wet market on early transmission of COVID-19
(Mizumoto et al., 2020)
Pathogen on wet markets cutting board (Lo et l., 2019)
Enhancement of hygienic practice of wet markets (Rao et al.,
2021)
Industrial land use (Burnell, 1985)
Commercial development (Kholdy et al., 2014; Pope and Pope,
2015; Yang et al., 2016; Kurniven and Wiley, 2019)
3
Objectives
▷ Examine combined effect of positive and negative externalities on nearby
housing prices
▷ Examine how the above effect changes over time and across different wet
markets
4
Hypotheses
H1
Despite the preference of using fresh food in cooking, frozen food has gained wider acceptance in Asian countries,
particularly among younger generations. This trend makes proximity to wet markets less desirable over time. Besides, Hong
Kong people becomes increasingly concern about health and hygiene environment after SARS in 2003. This will reinforce
the diminishing positive externality of wet market over time.
The net positive externality of wet markets diminishes over time.
H2
Larger wet markets offer more variety of foods. The shoppers can have more choices and can complete all shopping goals in
one single trip. This implies the scale effect in its positive externalities.
Larger wet markets have stronger net positive external effect on nearby housing prices than smaller wet markets do.
H3
Privately owned properties are more efficiently managed. The private owners have more incentive to increase the
attractiveness of the wet market since it will increase their rent and value. The profit maximizing motive of private wet
market will also enhance the penetrating power to its positive externality to nearby residents.
The net positive external effect of privately-owned wet markets is stronger than that of the publicly-owned wet markets.
H4
Older wet markets are less attractive due to physical deterioration of the building. An old and depleted wet market creates a
negative visual environment in its neighbourhood.
The strength of the net positive external effects of older wet markets in weaker than those newer ones.
5
Empirical test procedures
First
Collect 2nd hand housing
price transaction data in
the vicinity of 9 wet
markets over the period
1991-2020 (30 years)
Second
Estimate hedonic price
models for the 9
markets over 5-yearsub-periods
Third
Estimate relative
strength of the net
positive externalities in
each market sub-period
model (SNE)
Last
Test the hypotheses by
regressing the SNE on
time (end year of each
sub-period) and the
characteristics of the
wet markets.
6
Hedonic price model specification
EQUATION 1:
(
)=
_
+
_
+
+
+
_
+
_
+
∗
+
LOG(PRICE)
Natural logarithm of housing transaction price
U_AGE
Age of the housing unit at the time of transaction
FL
Floor level of the housing unit
U_SIZE
Gross floor area of housing unit
DIST
Linear distance between wet market and housing unit
SUBPERIOD
5-year sub-period time dummy
TIME
Time dummy
∗
+
+
7
Estimate SNE from the hedonic price model
EQUATION 2:
=
+
+
_
+
_
+
+
+
EQUATION 3:
=
+
+
_
+
_
+
+
+
SNE
Relative strength of net externality
SUBPERIOD
5-year sub-period dummy
M_SIZE
Gross floor area of wet market
M_AGE
Average age of wet market
CFC
Cooked food centre dummy
OWNERSHIP
Wet market ownership dummy
AC
Air conditioning system dummy
STOREY
Number of storeys of wet market
+
8
Changes in property value due external effects
Pure positive externalities
Coefficient of the distance from the Wet Market <0
Distance from the wet market
9
Changes in property value due external effects
Pure negative externalities
Coefficient of the distance from the Wet Market >0
Distance from the wet market
10
Changes in property value due external effects
Positive externalities > negative externalities
Positive externality
weakened as distance
increase.
Combined +ve and
–ve externalities
Distance from the wet market
At the turning point
(minimum): positive
externality diminishes to zero
11
Changes in property value due external effects
Relative strength of positive externalities
weaker positive externality
Relatively weaker
positive externality
Distance from the wet market
Turning point closer
to the wet market
12
Changes in property value due external effects
Negative externalities > positive externalities
Negative externality
weakened as distance
increase.
Distance from the wet market
At the turning point (Maximum):
negative externality diminishes to zero
Combined +ve and
–ve externalities
Changes in property value due external effects
Relative strength of negative externalities
Distance from
the wet market
Turning point further away
from the wet market
Relatively stronger
negative externality
Stronger negative externality
Measured of combined externalities
Distance of turning point as measure of the relative strength of net positive /
negative externality (SNE)
Net positive externality
Distance from of turning point of the
Minimum from the wet market
SNE > 0
Net negative externality
Distance from of turning point of the
Maximum from the wet market
SNE < 0
Decrease (increase) in the relative strength of net positive (negative) externality
Larger (smaller) value of SNE => stronger relative net positive (negative) externality
15
Data of 9 wet markets in Hong Kong
Name
District
Region
Ownership
Year
No. of
Opened storeys
Distance
to MTR
(in m)
230
Remarks
1
Total
area
(in m2)
1,000
Mei Foo Sun
Chun Market
Pei Ho Street
Market
Sheung Wan
Market
Smithfield Market
Sham Shui Po
KLN
Private
1982
Sham Shui Po
KLN
FEHD
1995
3
9,975
170
With CFC
C&W
HKI
FEHD
1989
2
5,460
300
With CFC
C&W
HKI
FEHD
1996
3
6,150
71
Tai Po
Shatin
Kowloon City
NT
NT
KLN
FEHD
FEHD
FEHD
2004
1985
1984
3
1
1
12,000
4,200
1,100
450
180
1,000
With AC
and CFC
With CFC
With CFC
Tai Po Hui Market
Tai Wai Market
To Kwan Wan
Market
Wanchai Market
Yan Oi Market
Wanchai
Tuen Mun
HKI
NT
FEHD
FEHD
2008
1981
1
1
4,800
1,580
300
1,500
With AC
With AC
With AC
Image source: Adapted from Google Map
16
Summary statistics
Variables
First stage regression
PRICE
U_AGE
U_SIZE
FL
DIST
Second stage regression
SNE
M_SIZE
U_AGE
STOREY
N
Mean
Minimum
Maximum
Std. Dev
47,147
47,147
46,794
47,147
47,147
2.99
20.91
661.63
12.94
356.68
.02
1.0
173.0
1.0
17.0
26.49
53.0
3138.0
62.0
899.0
2.17
12.02
221.99
9.39
220.87
35
35
35
35
-137.24
4123.57
24.16
1.43
-696.53
1000.0
0.50
1.0
704.0
12000.0
42.5
3.0
441.93
3552.24
11.01
0.74
17
Results: First stage regression
Variables
DIST*SUBPERIOD1995
DIST2*SUBPERIOD1995
DIST*SUBPERIOD2000
DIST2*SUBPERIOD2000
DIST*SUBPERIOD2005
DIST2*SUBPERIOD2005
DIST*SUBPERIOD2010
DIST2*SUBPERIOD2010
DIST*SUBPERIOD2015
DIST2*SUBPERIOD2015
DIST*SUBPERIOD2020
DIST2*SUBPERIOD2020
AGE effect
Floor effect
Size effect
Time fixed effect
R2
Adjusted R2
Observations
Mei Foo Sun Chuen
-0.0007***
(0.0000)
1.72E-06***
(0.0000)
-0.008***
(0.0000)
2.01E-06***
(0.0000)
-0.0009***
(0.0001)
1.98E-06***
(0.0000)
-0.0003***
(0.0000)
8.66E-07***
(0.0000)
-0.0006***
(0.0001)
1.51E-06***
(0.0000)
-0.0011***
(0.0002)
2.47E-06***
(0.0000)
Yes
Yes
Yes
Yes
0.9125
0.9119
19,950
Pei Ho Street
-0.0001
(0.0002)
1.36E-07
(0.0000)
0.0002
(0.0002)
-3.90E-07
(0.0000)
-0.0006***
(0.0001)
9.72E-07***
(0.0000)
0.0004
(0.0002)
-1.32E-07
(0.0000)
4.80E-05
(0.0003)
9.11E-08
(0.0000)
Yes
Yes
Yes
Yes
0.8740
0.8677
2357
Sheung Wan
-0.0006***
(0.0002)
1.38E-07
(0.0000)
0.0004*
(0.0002)
-1.33E-06***
(0.0000)
0.0015***
(0.0003)
-3.33E-06***
(0.0000)
0.0012***
(0.0002)
-2.91E-06***
(0.0000)
0.0009***
(0.0003)
-1.94E-06***
(0.0000)
0.0016***
(0.0004)
-2.85E-06***
(0.0000)
Yes
Yes
Yes
Yes
0.9301
0.9285
5825
Smithfield
Tai Po Hui
6.92E-05
(0.0001)
-2.88E-08
(0.0000)
0.0003
(0.0002)
-1.13E-07
(0.0000)
0.0003**
(0.0001)
-7.38E-08
(0.0000)
1.51E-05
(0.0002)
-1.88E-08
(0.0000)
-0.0002
(0.0003)
-6.49E-08
(0.0000)
Yes
Yes
Yes
Yes
0.9432
0.9415
3682
-0.0016***
(0.0003)
1.17E-06***
(0.0000)
0.0029***
(0.0002)
-2.74E-06***
(0.0000)
0.0031***
(0.0002)
-2.83E-06***
(0.0000)
0.0033***
(0.0003)
-2.99E-06***
(0.000)
Yes
Yes
Yes
Yes
0.8827
0.8808
4751
Tai Wai
-0.0014***
(0.0004)
1.81E-06***
(0.0000)
-0.0027***
(0.0002)
3.07E-06***
(0.0000)
-0.0025***
(0.0002)
2.79E-06***
(0.0000)
-0.0016***
(0.0002)
1.81E-06***
(0.0000)
-0.0006**
(0.0002)
4.94E-07*
(0.0000)
-0.0012***
(0.0003)
1.21E-06***
(0.0000)
Yes
Yes
Yes
Yes
0.9156
0.9117
3095
To Kwa Wan
7.15E-05
(0.0006)
3.16E-06***
(0.0000)
0.0068***
(0.0008)
-6.16E-06***
(0.0000)
0.0114***
(0.0003)
-1.23E-05***
(0.0000)
0.0056***
(0.0003)
-4.75E-06***
(0.0000)
0.0029***
(0.0003)
-2.87E-06***
(0.0000)
0.0026***
(0.0003)
-2.91E-06***
(0.0000)
Yes
Yes
Yes
Yes
0.9455
0.9445
7161
Wan Chai
0.0001
(0.0000)
-2.24E-08
(0.0000)
0.0009***
(0.0000)
-6.94E-07***
(0.0000)
0.0011***
(0.0000)
-1.02E-06***
(0.0000)
0.0008***
(0.0000)
-7.50E-07***
(0.0000)
0.0008***
(0.0000)
-7.23E-07***
(0.0000)
0.0003**
(0.0001)
-1.70E-07
(0.0000)
Yes
Yes
Yes
Yes
0.9226
0.9214
9069
Yan Oi
8.40E-05
(0.0000)
-2.56E-07***
(0.0000)
0.0002***
(0.0000)
-3.33E-07***
(0.0000)
0.0004***
(0.0000)
-2.51E-07***
(0.0000)
0.0005***
(0.0000)
-3.73E-07***
(0.0000)
0.0004***
(0.0000)
-3.07E-07***
(0.0000)
0.0004***
(0.0000)
-3.17E-07***
(0.0000)
Yes
Yes
Yes
Yes
0.9412
0.9408
18,298
18
Results: Second stage regression
Variables
SUBPERIOD
M_SIZE
M_AGE
CFC
OWNERSHIP
AC
STOREY
Constant
R2
Adjusted R2
Observations
I
-19.1203**
(8.76211)
0.1190***
(0.0183)
13.4271*
(7.1020)
-637.8942***
(99.1177)
827.1769***
(128.8405)
-718.1073***
(105.1743)
37969.71**
(17408.27)
0.8001
0.7573
35
II
-27.7571**
(11.9219)
0.1925**
(0.0714)
22.0639**
(10.7667)
-410.2210*
(235.4370)
902.0232***
(146.4790)
-613.2872***
(143.8379)
-363.4620
(341.1689)
55167.88
(23710.77)
0.8082
0.7584
35
19
Conclusions
▷ The combined effect of positive and negative externalities on nearby housing prices
varies among different wet markets.
▷ Our study confirms the relative strength of positive externalities diminishes over time.
▷ Larger wet markets and privately-operated wet markets impose stronger positive
externalities to the surrounding housing prices. However, AC system and cooked food
centre weaken positive externalities.
▷ Older wet markets exert stronger positive external effects, while number of storeys
has no effect on relative strength of net externality.
20
THANK YOU!
For enquiries, please contact:
K.W. Chau (hrrbckw@hku.hk) / Ervi Liusman (ervi@cuhk.edu.hk)
21