# RECORD de48aa9a311d709ef0a33453d7042001c2e721a6
# TITLE: Public transport, noise complaints, and housing: Evidence from sentiment analysis in Singapore
# YEAR: 2021.0  VENUE: Journal of Regional Science  DOI: 10.1111/jors.12524
# AUTHORS: Yi Fan; Ho-Pin Teo; Wayne Xinwei Wan
# PAGES: 27  CANDIDATE TABLE PAGES (1-based): [12, 14, 15, 19, 20, 21]
# ABSTRACT: This paper investigates the effect of a new bus route on noise complaints of residents and the influence of noise on housing price. To overcome the challenge of mapping noise data with subjective emotion, we use a novel data source — text ‐ based noise complaint records from residents in a town in Singapore — and apply natural language processing tools to conduct sentiment analysis. To address the endogeneity concern regarding the bus route, we use a hypothetical least cost path as an instrument for the existing bus route. We find that living closer to the bus route for every 100 m increases noise complaints by around 10 percentage points, and the effect is more severe on medium floor levels (5th – 8th floors) and near bus stops (within 100 m). We further link noise with housing price and discover a price reduction of 3% with a 1 ‐ scale ‐ point increase in noise complaints. This implies that bus noise offsets 17.8% of the benefit from convenience, which sheds light on the importance of noise insulation in transit ‐ oriented developments. propose a novel methodology to measure the quasi real ‐ time noise sentiment at finer detailed level using the noise complaints data, which naturally exists with the


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J Regional Sci. 2021;61:570–596.
570 |
wileyonlinelibrary.com/journal/jors
Received: 22 January 2020 | Revised: 25 November 2020 | Accepted: 19 December 2020
DOI: 10.1111/jors.12524
R E S E A R C H A R T I C L E
Public transport, noise complaints, and housing:
Evidence from sentiment analysis in Singapore
Yi Fan1
|
Ho Pin Teo2
|
Wayne X. Wan3
1Department of Real Estate, National
University of Singapore, Singapore, Singapore
2Department of Building, National University
of Singapore, Singapore, Singapore
3Department of Land Economy, The
University of Cambridge, Cambridge, UK
Correspondence
Wayne X. Wan, Department of Land
Economy, The University of Cambridge.
19 Silver St, Cambridge CB3 9EP, UK.
Email: xw357@cam.ac.uk
Funding information
National University of Singapore,
Grant/Award Number: R‐297‐000‐134‐133
Abstract
This paper investigates the effect of a new bus route on
noise complaints of residents and the influence of noise
on housing price. To overcome the challenge of mapping
noise data with subjective emotion, we use a novel data
source—text‐based noise complaint records from re-
sidents in a town in Singapore—and apply natural
language processing tools to conduct sentiment analysis.
To address the endogeneity concern regarding the bus
route, we use a hypothetical least cost path as an in-
strument for the existing bus route. We find that living
closer to the bus route for every 100 m increases noise
complaints by around 10 percentage points, and the
effect
is
more
severe
on
medium
floor
levels
(5th–8th floors) and near bus stops (within 100 m).
We further link noise with housing price and discover a
price reduction of 3% with a 1‐scale‐point increase in
noise complaints. This implies that bus noise offsets
17.8% of the benefit from convenience, which sheds
light on the importance of noise insulation in transit‐
oriented developments.
K E Y W O R D S
housing, noise, public transport, sentiment analysis
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
© 2021 The Authors. Journal of Regional Science published by Wiley Periodicals LLC.


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TABLE 1
Summary statistics of noise complaints and housing transactions
Total
Near = 0 (100–200 m)
Near = 1 (within 100 m)
Diff ((5)–(8))
N
Mean
SD
N
Mean
SD
N
Mean
SD
t‐test
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Noise sentiment
593
0.409
0.165
328
0.416
0.170
265
0.399
0.159
0.017
Distance (100 m)
593
1.111
0.493
328
1.483
0.317
265
0.650
0.187
0.833***
LCP (100 m)
593
1.737
1.323
328
2.136
1.349
265
1.243
1.108
0.893***
ECP (100 m)
593
1.068
0.794
328
1.516
0.649
265
0.514
0.578
1.001***
Launch (after = 1)
593
0.575
0.495
328
0.512
0.501
265
0.653
0.477
−0.141***
Near (within 100 m = 1)
593
0.447
0.498
RC center (with = 1)
593
0.069
0.254
328
0.0671
0.251
265
0.072
0.258
−0.005
Building age
593
29.022
2.267
328
28.902
2.584
265
29.170
1.794
−0.267
Floor level
593
6.408
3.960
328
6.479
3.976
265
6.321
3.946
0.158
Morphology
593
2.926
0.293
328
2.878
0.371
265
2.985
0.122
−0.107***
Gender (Male = 1)
527
0.467
0.499
300
0.503
0.501
227
0.419
0.494
0.085*
To train station (100 m)
593
2.125
1.155
328
2.435
1.224
265
1.741
0.933
0.693***
To LRT line (100 m)
593
1.531
1.154
328
1.884
1.155
265
1.093
0.994
0.790***
To bus stop (100 m)
593
1.202
0.501
328
1.335
0.548
265
1.038
0.377
0.233***
To expressway (100 m)
593
4.281
2.030
328
4.553
2.357
265
3.945
1.468
0.608***
To major road (100 m)
593
1.217
0.816
328
1.359
0.758
265
1.041
0.851
0.318***
To shopping center (100 m)
593
5.302
2.863
328
5.181
3,154
265
5.452
2.467
−0.271
Price (thousand SGD)
1450
375.207
73.564
1003
364.924
69.114
447
398.282
77.968
−33.359***
(Continues)
FAN ET AL.
|
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Total
Near = 0 (100–200 m)
Near = 1 (within 100 m)
Diff ((5)–(8))
N
Mean
SD
N
Mean
SD
N
Mean
SD
t‐test
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
Floor
1450
6.710
3.630
1003
6.671
3.588
447
6.799
3.724
−0.128
Area (m2)
1450
103.595
20.399
1003
99.984
19.56
447
111.696
19.928
−11.712***
Note: Morphology equals to 1 if the block follows the new generation building layout, 2 if it follows the point block layout, or 3 if it follows the slab block layout.
Abbreviations: ECP, Euclidean cost path; LCP, least cost path; LRT, light rail train.
*p < 0.1.
**p < 0.05.
***p < 0.01.
582
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FAN ET AL.


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TABLE 2
First‐stage IV estimation results of the effects of bus route on noise complaints at individual level
(1)
(2)
(3)
(4)
Launch
× Near
Launch
× Near
Launch ×
Distance
Launch ×
Distance
Launch × ECP
−0.3653***
−0.3306***
0.4184***
0.4025***
(0.0546)
(0.0615)
(0.0520)
(0.0602)
ECP
0.0190
0.0367
−0.0070
−0.0491
(0.0419)
(0.0451)
(0.0364)
(0.0395)
Launch × LCP
−0.0787**
0.0491
(0.0360)
(0.0352)
LCP
−0.0790
0.1558**
(0.0651)
(0.0644)
Launch
0.7781***
0.8410***
0.8278***
0.7932***
(0.2049)
(0.2067)
(0.2051)
(0.2057)
Building age
−0.0040
−0.0023
−0.0117
−0.0158
(0.0119)
(0.0119)
(0.0104)
(0.0102)
Floor
0.0007
−0.0000
0.0029
0.0033
(0.0123)
(0.0126)
(0.0102)
(0.0105)
Floor squared
0.0005
0.0005
−0.0008
−0.0008
(0.0007)
(0.0007)
(0.0006)
(0.0006)
Point block
0.0401
0.0605
0.3776**
0.3339**
(0.1951)
(0.1908)
(0.1789)
(0.1692)
Slab block
0.0943
0.1315
0.1492
0.0789
(0.1448)
(0.1457)
(0.1397)
(0.1330)
Male
−0.0402
−0.0405
0.0156
0.0149
(0.0299)
(0.0286)
(0.0239)
(0.0228)
RC center
−0.0094
−0.0105
−0.0135
−0.0083
(0.0796)
(0.0803)
(0.0684)
(0.0692)
Block and time fixed effect
Y
Y
Y
Y
First‐stage F‐Stats
22.69
26.81
32.67
38.07
J‐Statistics (p value)
0.2787
0.2235
Observations
527
527
527
527
Note: Columns (1) and (2) report results for the binary closeness indicator. Columns (3) and (4) report results for the
continuous distance variable. Columns (1) and (3) use the ECP, the Euclidean straight lines connecting bus stops, as the
instrument. Columns (2) and (4) use both ECP and LCP, the least travel time path, as the instrument. Unreported control
variables include distance to train stations, LRT viaduct line, bus stops, expressways, major roads, and shopping centers.
Standard errors are clustered by building blocks. Robust standard errors in parentheses.
Abbreviations: ECP, Euclidean cost path; LCP, least cost path.
*p < 0.1.
**p < 0.05.
***p < 0.01.
FAN ET AL.
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TABLE 3
OLS and second‐stage IV estimation results of the effects of bus route on noise complaints at
individual level
Noise sentiment
(1)
(2)
(3)
(4)
(5)
(6)
OLS
ECP IV
Both IVs
OLS
ECP IV
Both IVs
Launch × Near
0.0496
0.1090**
0.1118**
(0.0324)
(0.0459)
(0.0437)
Near
−0.0136
−0.0105
−0.0300
(0.0303)
(0.0529)
(0.0476)
Launch × Distance
−0.0405
−0.0953**
−0.0965**
(0.0338)
(0.0430)
(0.0422)
Distance
0.0074
0.0125
0.0301
(0.0328)
(0.0462)
(0.0423)
Launch
−0.1123
−0.1330
−0.1365
−0.0400
0.0319
0.0333
(0.1217)
(0.1069)
(0.1054)
(0.1167)
(0.1140)
(0.1114)
Building age
−0.0016
−0.0011
−0.0015
−0.0019
−0.0024
−0.0022
(0.0048)
(0.0044)
(0.0044)
(0.0048)
(0.0044)
(0.0043)
Floor
−0.0130*
−0.0126**
−0.0128**
−0.0128*
−0.0122*
−0.0125**
(0.0070)
(0.0063)
(0.0063)
(0.0071)
(0.0063)
(0.0063)
Floor squared
0.0008*
0.0007*
0.0007*
0.0008*
0.0007*
0.0007*
(0.0004)
(0.0004)
(0.0004)
(0.0004)
(0.0004)
(0.0004)
Point block
−0.0487
−0.0764
−0.0593
−0.0423
−0.0457
−0.0415
(0.0811)
(0.0786)
(0.0786)
(0.0792)
(0.0739)
(0.0728)
Slab block
−0.0429
−0.0675
−0.0505
−0.0409
−0.0484
−0.0393
(0.0543)
(0.0549)
(0.0554)
(0.0508)
(0.0475)
(0.0480)
Male
0.0181
0.0195
0.0186
0.0171
0.0166
0.0156
(0.0201)
(0.0188)
(0.0186)
(0.0199)
(0.0187)
(0.0186)
RC center
0.0484*
0.0448*
0.0501**
0.0453*
0.0420*
0.0463*
(0.0269)
(0.0251)
(0.0251)
(0.0260)
(0.0236)
(0.0239)
Block and time fixed effect
Y
Y
Y
Y
Y
Y
Observations
527
527
527
527
527
527
R2
0.214
0.203
0.207
0.213
0.206
0.208
Note: Columns (1)–(3) report results for the binary closeness indicator and Columns (4)–(6) report results for the
continuous distance variable. Columns (1) and (4) are OLS estimation results. Columns (2) and (5) use the ECP, the
Euclidean straight lines connecting bus stops, as the instrument. Columns (3) and (6) use both ECP and LCP, the least travel
time path, as the instrument. Unreported control variables include distance to train stations, LRT viaduct line, bus stops,
expressways, major roads, and shopping centers. Standard errors are clustered by building blocks. Robust standard errors
in parentheses.
*p < 0.1.
**p < 0.05.
***p < 0.01.
584
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instrument(s), respectively. First‐stage results reveal that both ECP and the combination of ECP and LCP are strongly and
statistically significantly correlated with the actual bus service route, controlling for the physical features, gender of the
complainants, and the fixed effect from time and building. The F‐statistics in all specifications are around 20–30, miti-
gating concerns about weak instruments. For estimations with more instrumental variables than endogenous variables
(Columns (2) and (4)), the J‐statistics for over‐identification test are 0.2787 and 0.2235, respectively.
Table 3 presents OLS and second‐stage IV estimation results. Column (1) presents OLS estimates from
Equation (1), while Columns (2) and (3) display IV estimates using ECP and the combination of ECP and LCP,
respectively. Columns (4)–(6) show corresponding estimates from Equation (2). The OLS estimate is positive,
though with no statistical significance, in Column (1), and it is likely contaminated by unobserved factors. For
example, residents who are less sensitive to noise will choose to live in units closer to roads and will also make less
noise complaints. Therefore, the OLS estimate of the noise effect will be downward biased in magnitude. Using ECP
as an instrument, being close to the bus service route (within 100 m) results in a higher negative sentiment score of
0.109 (Column (2)) and 0.112 (Column (3)) using both ECP and LCP as IVs. Both of the two IV estimates are
statistically significant at the 5% level. Since the sentiment score is normalized to the range of 0 to 1, this indicates
that intensification of public transport services worsens the noise sentiment in surrounding public housing by
approximately 11 percentage points. The continuous distance effect is estimated to be −0.095 (Column (5)) using
ECP as the IV, or −0.097 (Column (6)) using the combined IVs. Both estimates are statistically significant at the
5% level. Consistent with previous findings using a binary indicator of closeness, the distance estimates reveal that
by living closer to road traffic by 100 m, the negative noise sentiment will increase by around 10 percentage points
on average.
One assumption in the interpretations above is that the intensification of the bus service is the only source of
changing noise levels along the bus route. A possible concern about the validity of this assumption is that with the
newly opened bus route, increased bus traffic will change the traffic patterns of other vehicles on the route. For
instance, commuters using private vehicles instead of public buses may choose other routes to avoid congestion.
However, under this scenario, the traffic volume from other vehicles is expected to decrease after the treatment,
and the measured increase in noise sentiment in buildings near the bus route will be smaller than the real increase
due to the new bus. In other words, the real impact of closeness to the new bus service on noise sentiment is likely
larger than our estimates.
6.2
|
Robustness checks
We first examine the robustness of our baseline results by setting different cut‐off distances for the treatment and
control groups in the DID estimations. Since the maximum distance to the bus route for buildings in our sample is
198.5 m, we set the cut‐off distance to be 100 m in the baseline estimation to balance sample size in each group. This
100 m boundary for road traffic noise insulation is also supported by the acoustics literature (Avsar & Gonullu, 2005;
Martin & Hothersall, 2002). As robustness checks, we follow the same empirical strategy as Equation (1) but change the
cut‐off distances to 110 or 90 m. The corresponding estimation results are reported in Appendix Table C1. The
estimates are very close to those in our baseline results, for both the magnitude and statistical significance.
Another possible concern about the baseline estimation result is that an individual's sentiment about a new
noise incident is dependent on previous exposure to noise. If the surrounding environment has been noisy for
years, residents may not notice additional noise incidents, while residents living in quiet housing units may be less
tolerant of a sudden increase in the noise level. To alleviate this concern, the average sentiment score of noise
complaints made in the same building with a 1‐year lag is included as an additional control. Results are reported in
Appendix Table C2. The estimates remain robust in magnitude and level of significance.
In addition, the occurrence time of noise incidents may impact the noise sentiment, because residents may
be disrupted more easily at night. Therefore, we use the report time of the noise complaints as a proxy for the occurrence
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estimations, using ECP as IV. Results are presented in Panel B of Table 4. Columns (1) and (2) show estimates using
the dummy variable measuring closeness, while Columns (3) and (4) present estimates using distance variations.
The effects at buildings within 100 m of a bus stop show larger magnitude (0.511 for the binary closeness variable
and −0.288 for the continuous distance variable), with statistical significance at the 1% level. This indicates that the
noise effect from bus service is more severe around bus drop‐off points. A possible explanation is the influx of
visitors around bus stops. Also, it is possible that buses generate more noise when they stop at or leave from the
TABLE 4
Heterogeneous effects of bus route on noise complaints
Panel A: Noise sentiment across floor levels
(1)
(2)
(3)
(4)
(5)
(6)
1–4 Storey
5–8 Storey
Above 8
Storey
1–4 Storey
5–8 Storey
Above 8
Storey
Launch × Near
0.0738
0.2141***
−0.0290
(0.0674)
(0.0799)
(0.0802)
Launch × Distance
−0.0552
−0.2428***
0.0282
(0.0527)
(0.0820)
(0.0608)
Block and time fixed
effect
Y
Y
Y
Y
Y
Y
First‐stage F‐stats
14.83
18.11
7.85
32.74
20.66
11.65
Observations
221
144
162
221
144
162
R2
0.477
0.605
0.544
0.479
0.598
0.564
Panel B: Noise sentiment with different distance to bus stops
(1)
(2)
(3)
(4)
Within 100 m
Out of 100 m
Within 100 m
Out of 100 m
Launch × Near
0.5114***
0.1315*
(0.1924)
(0.0739)
Launch × Distance
−0.2875***
−0.1330**
(0.0853)
(0.0607)
Block and time fixed effect
Y
Y
Y
Y
First‐stage F‐stats
17.24
21.35
38.59
26.27
Observations
179
348
179
348
R2
0.447
0.274
0.590
0.314
Note: Columns (1)–(3) of Panel A report results for the binary closeness indicator. Columns (4)–(6) of Panel A report results
for the continuous distance variable. Columns (1) and (2) of Panel B report results for the binary closeness indicator.
Columns (3) and (4) of Panel B report results for the continuous distance variable. All estimations use ECP, the Euclidean
straight lines connecting bus stops, as the instrument. Unreported control variables include building age, floor and floor
squared, gender, morphology, RC center, and distance to train stations, LRT viaduct line, bus stops, expressways, major
roads, and shopping centers. Standard errors are clustered by building blocks. Robust standard errors in parentheses.
*p < 0.1.
**p < 0.05.
***p < 0.01.
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TABLE 5
Net effects of the new bus service on housing price
(1)
(2)
log (price)
log (price)
Launch × Near
0.0129**
(0.0054)
Near
−0.0097**
(0.0038)
Launch × Distance
−0.0134***
(0.0050)
Distance
0.0104***
(0.0037)
Launch
0.0101
0.0299***
(0.0077)
(0.0094)
Prime lending rate
−2.8766***
−2.8815***
(0.3558)
(0.3563)
Floor
0.0070***
0.0070***
(0.0003)
(0.0003)
Area
0.0030***
0.0030***
(0.0008)
(0.0008)
Building age
−0.0009
−0.0009
(0.0006)
(0.0006)
Distance to train station
−0.0035**
−0.0036**
(0.0015)
(0.0015)
Distance to bus stop
−0.0042
−0.0046
(0.0031)
(0.0032)
Distance to expressway
0.0025***
0.0024***
(0.0008)
(0.0008)
Distance to major road
−0.0014
−0.0010
(0.0016)
(0.0017)
Distance to shopping center
0.0037***
0.0037***
(0.0009)
(0.0009)
Block and time fixed effect
Y
Y
Observations
1450
1450
R2
0.941
0.941
Note: Column (1) reports results for the binary closeness indicator and column (2) reports results for the continuous
distance variable. Unreported control variables include building morphology and RC center. Standard errors clustered by
building blocks. Robust standard errors in parentheses.
*p < 0.1.
**p < 0.05
***p < 0.01.
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bus stops. In addition, with frequent visual exposure to noise sources at bus stops, subjective feelings of annoyance
by surrounding residents may be intensified.
7
|
COST‐BENEFIT ANALYSIS: PUBLIC TRANSPORT AND HOUSING
PRICE
Public transport offers convenience to surrounding residents, and thus raises housing price in the neighborhood
(Chalak et al., 2016; Cohen & Paul, 2007; Holmgren, 2014; Monchambert & De Palma, 2014). Nonetheless, it also
generates noise pollution, which exerts a negative impact on the housing price (Diao et al., 2016). To implement a
cost‐benefit analysis, we start with an estimation on the overall impact of the new bus route on housing price.
Specifically, we follow the same specification in Equations (1) and (2), but replace the dependent variable of noise
sentiment with housing price (Diao et al., 2017):
β
β
β
θ
μ
φ
ω
ε
=
+
+
∗
+
+
+
+
+
′
′
Price
Near
Launch
Launch
Near
X
U
log (
)
,
ijt
i
t
t
i
i
j
t
i
ijt
1
2
3
(6)
β
β
β
θ
μ
φ
ω
ε
=
+
+
∗
+
+
+
+
+
′
′
Price
Distance
Launch
Launch
Distance
X
U
log (
)
.
ijt
i
t
t
i
i
j
t
i
ijt
1
2
3
(7)
TABLE 6
Effects of noise complaints on housing price
(1)
(2)
log (price)
log (price)
Noise sentiment in previous 12 months
−0.0300**
−0.0262**
(0.0149)
(0.0132)
Noise sentiment in previous 12 months × Low/high floors
−0.0053
(0.0084)
Distance to nearest bus stop connecting CBD
−0.0027*
−0.0027*
(0.0016)
(0.0016)
Prime lending rate
−3.5344***
−3.5089***
(0.4423)
(0.4638)
Floor
0.0064***
0.0065***
(0.0005)
(0.0006)
Area
0.0075***
0.0075***
(0.0002)
(0.0002)
Building age
−0.0035***
−0.0035***
(0.0009)
(0.0009)
Block and time fixed effect
Y
Y
Observations
893
893
R2
0.919
0.919
Note: Unreported control variables include unit layout, building morphology, RC center, and distance to train stations, LRT
viaduct line, bus stops, expressways, major roads, and shopping centers. Standard errors are clustered by building blocks.
Robust standard errors in parentheses.
*p < 0.1.
**p < 0.05.
***p < 0.01.
590
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The definitions for the other variables are the same as in Equations (1) and (2). Table 5 displays the
corresponding results using all of the resale transaction data in our study area from 2010 to 2018. Column
(1) reveals that before the bus starts operation, the housing prices within 100 m of the route are 0.97% lower
than those farther away (100–200 m). This is probably because the route is designed along the community
road, generating traffic noise but offering limited convenience before the new bus service begins. After the
introduction of bus service, the housing price within 100 m of the bus route increases by 1.3% compared
to units within 100–200 m of the bus route. A similar conclusion can be drawn if we use a continuous variable
of distance to estimate the benefit from the convenience of public transport (Column (2)). The prices
for units closer to the new bus route by 100 m increase by 1.34%. The estimate is statistically significant at
the 1% level.
Nevertheless, this explicit benefit is likely to be offset by an implicit decrease in housing price due to the
noise generated by the new bus route. We thus examine the negative effect of noise sentiment on housing
price using the same set of resale transaction records. Column (1) in Table 6 presents the corresponding OLS
regression results of Equation (3). We include all the transaction records in our study area which can be
matched with the lagged noise sentiment. To estimate the exact economic costs of noise pollution on housing
price, we also control for the distance to the nearest bus stop connecting the CBD before and after
the launch of bus no. 972. It reveals that a 1‐scale‐point increase in noise sentiment over the past 12 months
is correlated with a 3% decrease in housing price.9 This estimate is statistically significant at the
5% level.
Finally, we analyze the cost and benefit for intensifying public transport on housing price. Since living closer to the
new bus service route by 100 m has caused the noise sentiment score to increase by around 9.53% (Column (5)
in Table 3), the negative effect of its noise on housing price can be interpolated as approximately 0.29% per
100‐m distance to the bus route. Taking the explicit 1.34% increase in housing price per 100 m closer to the new bus
route, we estimate that the implicit effect of traffic noise on housing value has offset around 17.79% of the economic
benefit brought by the improvement of public transport.
We also investigate the heterogeneous impact of noise from the new bus route on housing price across floor
levels in two steps. First, we examine the heterogeneous impact of the new bus service on noise sentiment on
low, medium, and high floors. We find that the noise nuisance from the new bus service is positive and statis-
tically significant on medium floors only, as reported in Table 4. Second, we estimate the impact of sentiment
from general noise on housing price across floor levels. Specifically, we modify Equation (3) by interacting
−
SIi t,
1
with a dummy variable denoting the units on the low and high floors. The estimation results are reported in
Column (2), Table 6. We find that if the 1‐year lagged noise sentiment increases by 1 scale point, the housing
price on medium floors decreases by 2.62%. This estimate is statistically significant at the 5% level. In addition,
the overall impact of a 1‐scale‐point increase in sentiment from general noise on housing price is similar across
housing units on low/high floors and on medium floors, which is reflected by the statistically insignificant
coefficient for the interaction term between the lagged noise sentiment and the dummy variable for units on
low/high floors.
Combining the results from these two steps, we conclude that for units on medium floors, a decrease of
distance to the bus route by 100 meters increases the noise sentiment by 24.3 percentage points (Column (5) in
Panel A, Table 4), which translates to a 0.64% drop in housing price, or equivalently, a loss of around 2401 SGD
(1766 USD) per housing unit. In comparison, for units on high/low floors, as the impact of the new bus service on
noise sentiment is statistically insignificant (Columns (4) and (6) in Panel A, Table 4), the direct impact of noise from
the new bus on housing price is likely small.
9The results remain robust if we use the noise sentiment over the past 6 months instead. The estimation results are reported in Appendix Table C8.
FAN ET AL.
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