# RECORD e8f48e7e91bb043c222f2cfbe026c67e0e422a5f
# TITLE: Valuing Neighbourhood Soundscapes in Urban Housing Markets: Evidence from Karachi
# YEAR: 2026.0  VENUE: Pakistan Development Review  DOI: 10.30541/v64i3pp.241-261
# AUTHORS: Ghamz E. Ali Siyal; Heman Das Lohano; Johane Dikgang; Ayesha Zia
# PAGES: 21  CANDIDATE TABLE PAGES (1-based): [11, 12, 16, 17]
# ABSTRACT: Noise pollution in Karachi has already exceeded WHO-recommended environmental thresholds. It causes sleep disturbances, hearing impairment, stress, and anxiety. However, there is a lack of evidence on how these effects transferred into housing market. This study extends a conceptual framework and empirically explores household data on perceived neighbourhood soundscapes and self-reported housing values. We estimate hedonic pricing models to examine how the presence of natural and non-natural soundscapes around respondent’s residence affects housing values.  This study conducted a primary data survey of 507 respondents affiliated with the Institute of Business Administration (IBA), Karachi. Using hedonic regression results, this study is the first to confirm the effect of natural and non-natural soundscapes on housing values, along with structural, locational, and neighbourhood attributes. The results recommend designing and installing soundproof windows in homes and developing better urban planning and management policies to ensure a good quality of life in Karachi. 


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© The Pakistan Development Review 
64:3 (2025) pp. 241–261 
DOI: 10.30541/v64i3pp.241-261 
Published online: 24 July,2026 
 
Valuing Neighbourhood Soundscapes in Urban Housing 
Markets: Evidence from Karachi 
GHAMZ E ALI SIYAL1, HEMAN DAS LOHANO, 
 JOHANE DIKGANG, and AYESHA ZIA 
Received: 16 October 2025/ Accepted: 23 June 2026 
 
 
Noise pollution in Karachi has already exceeded WHO-recommended environmental 
thresholds. It causes sleep disturbances, hearing impairment, stress, and anxiety. However, there is a 
lack of evidence on how these effects transferred into housing market. This study extends a 
conceptual framework and empirically explores household data on perceived neighbourhood 
soundscapes and self-reported housing values. We estimate hedonic pricing models to examine how 
the presence of natural and non-natural soundscapes around respondent’s residence affects housing 
values.  This study conducted a primary data survey of 507 respondents affiliated with the Institute 
of Business Administration (IBA), Karachi. Using hedonic regression results, this study is the first 
to confirm the effect of natural and non-natural soundscapes on housing values, along with structural, 
locational, and neighbourhood attributes. The results recommend designing and installing 
soundproof windows in homes and developing better urban planning and management policies to 
ensure a good quality of life in Karachi. 
JEL Classification: R21, R31, R23, Q53, I18, Q51 
Keywords: Natural Soundscapes, Non-natural Soundscapes, Noise Pollution, 
Proximity to Amenities, Neighbourhood Characteristics, Housing 
Market Values. 
1. INTRODUCTION 
Megacities in Pakistan, for instance Karachi, have witnessed environmental 
degradation such as reduction of green spaces as a negative externality due to unplanned 
and unmanaged urbanisation (Asian Development Bank, 2024 Mehdi, et al. 2011, 
Qureshi, et al. 2010). The presence of green spaces is well-documented to provide benefits 
to human health and mental well-being via interaction with nature (Alvarsson, et al. 2010, 
Hedblom, et al. 2014, Uebel, et al. 2022). The possible pathways of such interactions are 
based on visual stimuli and soundscapes that are responsible for improving human health 
and mental well-being (Ratcliffe 2013, Smalley, et al. 2022). In particular, bird sounds are 
 
1 Ghamz E Ali Siyal < gasiyal@iba.edu.pk> is Assistant Professor, Institute of Business Administration 
(IBA), Karachi, and Former Postdoctoral Researcher, Department of Economics and Finance/The Water School, 
Florida Gulf Coast University (FGCU), Fort Myers, Zip code 33967, Florida, USA. Heman Das Lohano  
< hlohano@iba.edu.pk> is Professor, School of Economics and Social Sciences (SESS), Institute of Business 
Administration (IBA), Karachi and Visiting Professor, Department of Applied Economics, University of 
Minnesota. Johane Dikgang < jdikgang@fgcu.edu> is Associate Professor, Department of Economics and 
Finance/The Water School, Florida Gulf Coast University (FGCU), Fort Myers, Zip code 33967, Florida, USA. 
Ayesha Zia < ayeshazia@iba.edu.pk> is Assistant Professor, School of Economics and Social Sciences (SESS), 
Institute of Business Administration (IBA), Karachi. 
Authors’ Note: We are grateful to acknowledge the financial support provided by the Competitive 
Research Grant (CRG) of the Institute of Business Administration (IBA), Karachi. The authors also sincerely 
thank Mr. Muhammad Nawaz, Mr. Abdul Qadeer, Mr. Dolat Kumar, and Mr. Muhammad Saad for volunteering 
their assistance in supervising data collection while serving as the graduate students at the IBA, Karachi. 


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Valuing Soundscapes and Neighborhood Quality in Urban Housing Markets          
251 
The identification strategy used in this study is a regression model. For robustness 
of results, we used two models which have only slight difference in structural attributes. 
Both models follow empirical Equation 2, but only the number of restrooms is added in 
place of the number of bedrooms. The details of results from both models are given in the 
next section. 
6. RESULTS 
In this section, the regression model includes a large number of explanatory 
variables compared to the sample size for capturing a wide range of housing, 
neighbourhood, and environment characteristics given in Table 1. Despite rich number of 
variables, it is possible that our analysis may have potential omitted variables and other 
sources of bias in our estimated coefficients. Therefore, results analysis should be 
interpreted as associations rather than causal effects. Secondly, these models’ results are 
based on housing value perceptions among the respondents affiliated with the Institute of 
Business Administration (IBA) that may not accurately reflect urban population of 
Karachi. For multicollinearity, we found moderate or low multicollinearity among 
variables for both models. Results of Variance Inflation Factor (VIF) for both models are 
given in the Appendix 2. Full regression results of both models are given in the Appendix 
1 and abridged results are given in the following Table 2. 
Table 2 
Regression Results for Hedonic Model 
 
(1) 
(2) 
Variables 
Log (house value) 
Log (house value) 
Structural Attributes 
Number of Bedrooms 
0.19084*** 
 
 
(0.01905) 
 
Number of Restrooms 
 
0.22406*** 
 
 
(0.01556) 
Solar 
0.19034** 
0.15393* 
 
(0.08700) 
(0.07881) 
Gas 
0.27690*** 
0.25384*** 
 
(0.10152) 
(0.09281) 
Water Supply 
0.30605*** 
0.21211*** 
 
(0.08358) 
(0.07702) 
Locational Attributes 
Commercial 
-0.32337*** 
-0.20353*** 
 
(0.07240) 
(0.06739) 
Grocery Store within 1 km 
0.17134** 
0.12619* 
 
(0.07043) 
(0.06453) 
School within 1 km 
-0.19937*** 
-0.22155*** 
 
(0.07551) 
(0.06892) 
Police Station within 1 km 
0.02213 
0.14729* 
 
(0.09772) 
(0.08870) 
Environmental Attributes 
Availability of Park within 1 km 
0.34378*** 
0.29760*** 
 
(0.06762) 
(0.06208) 
Neighbourhood & Soundscape Attributes 
Friendly Neighbourhood 
-0.14998* 
-0.10355 
 
(0.08965) 
(0.08213) 
Hear birds often 
-0.22090** 
-0.16602** 


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252 
  
 
Siyal, Lohano, Dikgang & Zia 
 
(0.08997) 
(0.08251) 
Chowks 
-0.12687* 
-0.11883* 
 
(0.06910) 
(0.06324) 
Constant 
15.71325*** 
15.64518*** 
 
(0.18442) 
(0.16732) 
Observations 
484 
484 
R-squared 
0.4572 
0.5498 
Note: Full regression results with insignificant variables of both models are available in Appendix 1. 
 
Regression results from Hedonic models given in Table 2 are not causal effects but 
show positive or negative association with reported housing values by respondents. Table 
2 provide insightful findings related to structural attributes, locational attributes, 
neighbourhood attributes, environmental attributes, soundscape attributes. Our results 
from both models particularly agree with most of previous literature such as Ahmed, et 
al. (2020), Wahid, et al. (2023) and other prior hedonic studies conducted in other cities 
of Pakistan confirming structural, locational, neighbourhood attributes effects on housing 
values. The important contribution of this study lies in investigating the relationship of 
new attributes from environmental, neighbourhood, and soundscape attributes to housing 
values. However, the coefficients are found to be higher than previous studies. The R-
square from second models is higher than model 1. It means that explanatory variable 
“number of restrooms” in second model explain variation in housing values better than 
“number of bedrooms” in model 1. Presence of more restrooms in a house increases 
convenience for joint families that could be a possible reason for higher R-square values.  
 
6.1. Structural Attributes 
Structural attributes increase housing prices significantly. They directly affect a 
property’s functionality, convenience, and appeal to potential buyers. For instance, access 
to utilities of gas and water significantly increases house values by 25-27 percent and 21-
30 percent respectively. Considering other structural attributes, the number of bedrooms 
and restrooms in a house increases house size which positively correlates with house 
values. We considered both separately because conceptual relationship between both 
could potentially bias the results. Results describe that with the increase of one additional 
bedroom and restroom housing values increase by 19 percent and 22 percent, respectively.  
Comparing model 1 and 2, the key difference is number of bedroom variables in 
model 1 and number of restrooms is added in model 2. The model 2 has higher R-squared 
value (0.54) than model 1 (0.46) which suggests that the higher number of restrooms 
explains a greater proportion in housing values within the sampled households. One of 
potential reasons behind this is number of bedrooms usually varies less across houses of 
similar size, but the presence of additional restrooms may indicate higher household 
convenience especially in joint family system. 
This makes quite sense as there are differences in rent and housing values in the 
actual market if we compare, for instance, two bedrooms with a three-bedroom house or 
an apartment with an additional restroom.  Along with that, houses which have access to 
solar systems have 15 to 19 percent higher property values than who don’t have it. In this 
case, solar acts as an asset and it reflects some income effect. People with solar systems 
are living in houses with significant differences in property values as compared to people 
who don’t have solar supported electricity supply. Therefore, having a solar system in a 
house has higher value compared to those who don’t have it. 
 
 


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Valuing Soundscapes and Neighborhood Quality in Urban Housing Markets          
253 
6.2. Locational Attributes 
Locational attributes are also related to housing values as mentioned in the literature. 
We found only three important factors from locational attributes which affect housing values. 
Firstly, residential type affects housing values. For example, housing values decrease by 20 to 
33 percent if these are in a commercial area as compared to residential areas. Homes located 
in commercial areas are generally less desirable for residential purposes due to noise, pollution, 
and a lack of privacy. In a city like Karachi, a house situated in a busy commercial area like 
Saddar might be less appealing to families looking for a quiet residential environment. In 
contrast, homes in purely residential areas like Gulshan-e-Iqbal or DHA tend to be more 
valuable, as they offer a more peaceful and family-friendly atmosphere.Secondly, the presence 
of a school near to respondent’s house reduces its value.  Results show that houses that have 
access to schools within 1 km have 19 to 22 percent lower housing values as compared to 
houses far from schools. It is a surprising result but a potential reason for that can be associated 
with traffic jams that increase delays while commuting. Similar results are found in Ahmed, 
et al. 2020, where presence of higher number of schools and hospitals reduces housing values 
in nearby surroundings by 9 percent and 13 percent, respectively. The potential reason 
provided by Ahmed, et al. 2020, is congestion and overcrowded areas. Therefore, our results 
show negative school proximity effect, ranging from –19 to –22 percent, is significantly larger 
than most other proximity variables in the model. This indicates that the congestion externality 
has a substantial economic impact rather than being marginal. Thirdly, unlike schools, 
locational amenities such as grocery stores and police station have longer and flexible 
operating hours which can be potential reason for less traffic congestion and positive effects 
on housing values.  
Similarly, access to grocery stores is also an important factor that affects housing 
values. Results found that people who live near grocery stores, i.e., within 1 Km, have 12 to 
17 percent higher housing values than their counterparts, i.e., who live more than 1 Km away. 
Its potential reason can be people consider access to nearby grocery stores so that it saves the 
time required in fulfilling daily necessities. In addition, access to police station within 1 km 
increases house values by 14 percent as compared to houses away from 1 km in model 2.  
6.3. Environmental Attributes 
Results from the environmental attributes also describe unique relationship with 
housing values. Results show that housing values increase by 29 to 34 percent if people 
have access to a park within 1 km of their house. Many people love to stay in a peaceful 
environment, and parks are the best place for this. Living closer to a park enhances the 
quality of life by offering residents a place to relax, exercise, and enjoy nature, making 
nearby properties more desirable. However, environmental pollution related to frequent 
choking of sewage, open air dumping, and open air burning of trash does not have 
significant effect on housing values. One potential reason for insignificant results is that 
the sample of respondents does not find such problems in their locality. Secondly, we 
have a sample of respondents that are affiliated with IBA institution that may not fully 
represent households across Karachi. The sample may also have similar income levels. 
Thirdly, the effect of these disamenities such as open-air dumping or burning of trash may 
affect housing values if respondent is living next to it. Respondents living very close to 
these sites may have higher exposure to negative visual stimuli by watching such sites or 
odor smells have effect housing values. However, our sample may not have respondents 
very close or next to these sites. 


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Siyal, Lohano, Dikgang & Zia 
APPENDIX 
 
Table 3 
Appendix 1: Full Regression Results Model 1 and 2 
Variables 
(1) 
(2) 
Log (house value) 
Log (house value) 
Structural Attributes 
Number of Bedrooms 
0.19084*** 
 
 
(0.01905) 
 
Number of Restrooms 
 
0.22406*** 
 
 
(0.01556) 
Solar 
0.19034** 
0.15393* 
 
(0.08700) 
(0.07881) 
Gas 
0.27690*** 
0.25384*** 
 
(0.10152) 
(0.09281) 
Water Supply 
0.30605*** 
0.21211*** 
 
(0.08358) 
(0.07702) 
Locational Attributes 
Commercial 
-0.32337*** 
-0.20353*** 
 
(0.07240) 
(0.06739) 
Grocery Store within 1 km 
0.17134** 
0.12619* 
 
(0.07043) 
(0.06453) 
School within 1 km 
-0.19937*** 
-0.22155*** 
 
(0.07551) 
(0.06892) 
College within 1 km 
0.17704 
0.10853 
 
(0.11147) 
(0.10220) 
University within 1 km 
-0.06739 
-0.04698 
 
(0.13293) 
(0.12162) 
Hospital within 1 km 
0.08957 
0.04538 
 
(0.08688) 
(0.07950) 
Bus stop within1 km 
-0.06862 
-0.03853 
 
(0.07352) 
(0.06703) 
Police Station within 1 km 
0.02213 
0.14729* 
 
(0.09772) 
(0.08870) 
Railway Station within 1 km 
-0.22920 
-0.28402 
 
(0.27549) 
(0.25189) 
Airport within 1 km 
-0.08662 
-0.17339 
 
(0.36110) 
(0.33029) 
Post office within 1 km 
0.04187 
0.02894 
 
(0.11913) 
(0.10902) 
Bank within 1 km 
0.04841 
0.02741 
 
(0.06972) 
(0.06369) 
Environmental Attributes 
Trees 
0.09482 
0.06448 
 
(0.07637) 
(0.06997) 
Availability of Park within 1 km 
0.34378*** 
0.29760*** 
 
(0.06762) 
(0.06208) 
Open Air Waste Dump within 1 km 
0.02968 
0.03656 
 
(0.07530) 
(0.06888) 
Open Air Burning of Waste 
-0.10674 
-0.07552 


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Valuing Soundscapes and Neighborhood Quality in Urban Housing Markets          
257 
 
(0.06896) 
(0.06306) 
Choked Sewer pipes 
-0.04820 
-0.05673 
 
(0.06954) 
(0.06356) 
Neighbourhood & Soundscape Attributes 
Poor roads 
0.02837 
0.02430 
 
(0.07313) 
(0.06693) 
Streetlight 
-0.02707 
-0.00476 
 
(0.08168) 
(0.07479) 
No garbage Collection 
-0.17426 
-0.17237 
 
(0.23352) 
(0.21370) 
Crime 
-0.11143 
-0.09445 
 
(0.06979) 
(0.06385) 
Respect Neighbourhood 
0.12975 
0.12689 
 
(0.08700) 
(0.07959) 
Trust Neighbourhood 
0.02358 
-0.00760 
 
(0.09925) 
(0.09089) 
Friendly Neighbourhood 
-0.14998* 
-0.10355 
 
(0.08965) 
(0.08213) 
Hear Birds often 
-0.22090** 
-0.16602** 
 
(0.08997) 
(0.08251) 
Hear Traffic often 
0.03630 
0.02107 
 
(0.06815) 
(0.06227) 
Hear Construction often 
0.04938 
0.03640 
 
(0.06699) 
(0.06132) 
Chowks 
-0.12687* 
-0.11883* 
 
(0.06910) 
(0.06324) 
Constant 
15.71325*** 
15.64518*** 
 
(0.18442) 
(0.16732) 
Observations 
484 
484 
R-squared 
0.4625 
0.5498 
 
Table 4 
Appendix 2: Model 1 Variance Inflation Factor (VIF) 
 
VIF 
1/VIF 
Trust Neighbourhood 
1.599 
.625 
Friendly Neighbourhood 
1.576 
.635 
Respect Neighbourhood 
1.524 
.656 
Distance Rail within 1 km 
1.448 
.691 
Distance Airport within 1 km 
1.394 
.717 
Distance School within 1 km 
1.393 
.718 
Water Supply 
1.369 
.73 
Distance College within 1 km 
1.35 
.741 
Gas 
1.349 
.741 
No Sweeping 
1.339 
.747 
Poor Road 
1.331 
.751 
Distance Bus Stop within 1 km 
1.329 
.753 
Distance Post Office within 1 km 
1.326 
.754 
Distance Bank 1 km 
1.27 
.788 
Number of Bedrooms 
1.269 
.788 
Distance Hospital within 1 km 
1.265 
.791 
Distance Police within 1 km 
1.264 
.791 


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Siyal, Lohano, Dikgang & Zia 
Distance Kachra 1 km 
1.263 
.792 
Witness Burning 
1.24 
.807 
Witness Choked pipes 
1.237 
.808 
Distance Grocery within 1 km 
1.234 
.81 
Streetlight 
1.233 
.811 
Hear Traffic Frequently 
1.194 
.837 
Distance Park within 1 km 
1.192 
.839 
Noisy Chowks 
1.188 
.842 
Crime 
1.166 
.857 
Solar 
1.156 
.865 
No Garbage Collection 
1.154 
.867 
Distance University within 1 km 
1.141 
.877 
Hear Construction Frequently 
1.134 
.882 
Trees 
1.131 
.884 
Commercial  
1.127 
.888 
Hear Birds Frequently 
1.096 
.912 
Mean VIF 
1.281 
. 
 
Table 5 
Appendix 2: Model 2 Variance Inflation factor (VIF) 
 
VIF 
1/VIF 
Trust Neighbourhood 
1.601 
.625 
Friendly Neighbourhood 
1.579 
.633 
Respect Neighbourhood 
1.522 
.657 
Distance Rail within 1km 
1.445 
.692 
Distance Airport within 1km 
1.392 
.718 
Water Supply 
1.388 
.72 
Distance School within 1km 
1.385 
.722 
Distance College within 1km 
1.355 
.738 
Gas 
1.346 
.743 
No Sweeping 
1.338 
.747 
Poor Road 
1.331 
.751 
Distance Post Office within 1km 
1.325 
.754 
Distance Bus Stop within 1km 
1.319 
.758 
Number of Rest Room or Washrooms 
1.308 
.764 
Distance Bank within 1km 
1.265 
.79 
Distance Hospital within 1km 
1.264 
.791 
Distance Kachra within 1km 
1.262 
.793 
Distance Police within 1km 
1.244 
.804 
Witness Burning 
1.238 
.808 
Distance Grocery within 1km 
1.237 
.808 
Witness Choked Pipes 
1.234 
.81 
Streetlight 
1.234 
.81 
Distance Park within 1km 
1.2 
.834 
Hear Traffic Frequently 
1.19 
.84 
Noisy Chowks 
1.188 
.842 
Crime 
1.166 
.858 
Commercial 
1.165 
.858 
No Garbage Collection 
1.153 
.867