IN DEGREE PROJECT ENGINEERING AND ECONOMICS,SECOND CYCLE, 30 CREDITS
, STOCKHOLM SWEDEN 2020
Impacts of shopping malls on the housing priceEvidence from Stockholm
RUNFENG LONG
KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT
i
Master of Science thesis
Title
Author Department Master thesis number Supervisor
Keywords
Impacts of shopping malls on the housing price - Evidence from Stockholm Runfeng Long Real Estate and Construction Management
TRITA-ABE-MBT-20592Mats Wilhelmsson
Housing price, hedonic price model, shopping mall
Abstract
Shopping malls, as an important type of commercial facilities, are growing dramatically.
They have gradually become one of the most dominant factors that can influence
people's daily life as well as a city's economic development. People's willingness to pay
for dwellings is also primarily associated with the surrounding commercial layout.
Hence, it is of interest to find out more from a quantitative perspective on the
relationship between shopping malls and housing prices. This study aims to analyze
how the prices of condominiums will be affected by the proximity of shopping malls.
Two aspects are considered and examined in the empirical study, namely a proximity
to the shopping mall, and the number of shopping malls within 3 kilometers radius. We
try to examine if there is any price premium for those apartments near the shopping
mall or with more shopping malls in the neighborhood. In this empirical study, 36
shopping malls in different locations in the county of Stockholm, Sweden, is utilized.
The sample of transactions consists of 336,914 apartments. By using regression
analysis, based on the traditional hedonic model, the results show that there is an inverse
relationship between the apartment prices and its distance from the shopping mall while
the number of shopping malls is positively correlated with apartment prices.
ii
Acknowledgement
Looking back on my last two years of master study, I have learned a lot and always
been delighted and grateful. It has always been a wonderful journey at KTH Royal
Institute of Technology for the last two years. It is a great pleasure to study and work
with all the lovely professors, students and staff here at KTH. Without their help and
encouragement, it would be impossible for me to accomplish my study. I would like to
give my deepest sincere thanks to the ABE School, for those professional and
supporting teachers within the Department of Real Estate and Construction
Management. Their profound knowledge and inspiring ideas have given me a lifetime
fortune. Especially, I would like to thank Professor Mats Wilhelmsson as my thesis
supervisor. Along the way, we are always keeping in touch and having inspiring
discussions. His advice, patience and kindness have given me the confidence and
strength to finish my thesis work.
Regarding my lifestyle, I truly enjoy the pace of living in Stockholm. The beautiful,
friendly environment would always add extra interest to my daily life. I appreciate that
my parents have given me the opportunity to achieve my dreams and always stand
behind my back. Also, I would like to thank my boyfriend’s companion and support.
Because of all the love, I know that I will be able to conquer all the upcoming problems.
I feel truly grateful for what I have received from all the people I love.
This is an end but also a new start for me. I will keep on going with all the lessons
learned from this valuable period. Hope everyone to achieve their dreams and live a
wonderful life.
Runfeng Long
May 24, 2020
Stockholm, Sweden
iii
Examensarbete
Titel Köpcentra påverkar bostadspriset - Bevis från Stockholm
Författarer Runfeng Long Institution Institutionen för Fastigheter och Byggande Examensarbete Master nummer TRITA-ABE-MBT-20592 Handledare Mats Wilhelmsson
Nyckelord Bostadspris, hedonisk prismodell, köpcentrum
Abstrakt
Köpcentra som en viktig typ av kommersiella anläggningar växer dramatiskt dessa år.
De har gradvis blivit en av de mest dominerande faktorerna som kan påverka
människors vardag och en stads ekonomiska utveckling. Människors villighet att betala
för husen på marknaden är också till stor del kopplad till den omgivande kommersiella
utformningen. Därför är det nödvändigt för oss att ta reda på mer i ett mer kvantitativt
perspektiv om förhållandet mellan köpcentra och bostadspriset. Denna studie syftar till
att diskutera hur priset på bostäder kommer att påverkas av köpcentra. Två aspekter
beaktas och undersöks under en empirisk studie, som är närheten till köpcentret och
den andra är antalet köpcentra inom 3 km avstånd. Målet är att avslöja om det finns
något prispremie för dessa fastigheter nära köpcentret eller med fler köpcentra i
närheten. I denna empiriska studie tas 36 köpcentra på olika platser som prov i
Stockholms län, Sverige. Sedan kommer transaktionsdata där proverna består av 336,
914 lägenheter behandlas och analyseras i Stata. Genom att använda giltiga
transaktionsdata, kombinera med matematiska ekvationer och obligatorisk statistisk
kunskap, är syftet med denna studie att beskriva och sammanfatta data för att sedan
genomföra regressionsanalys baserat på fyra hedoniska modeller, inklusive både linjär
och log-linjär form. Regressionsresultatet är signifikant vid 1% konfidensnivå, vilket
innebär att de förklarande variablerna verkligen har betydande effekter på de beroende
variablerna. Resultaten visar att det finns en omvänd relation mellan bostadspriset och
dess avstånd från köpcentret. Medan antalet köpcentra är positivt korrelerat med
bostadspriset.
iv
Bekräftelse
När jag tittar tillbaka på mina två senaste år av masterstudier har jag lärt mig mycket
och alltid varit glad och tacksam. Det har alltid varit en underbar resa på KTHs
Kungliga Tekniska Högskola de senaste två åren. Det är ett stort nöje att studera och
arbeta med alla de härliga professorerna, studenterna och personalen här på KTH. Utan
deras hjälp och uppmuntran skulle det vara omöjligt för mig att genomföra min studie.
Jag vill tacka ABE-skolan för de professionella och stödjande lärarna inom avdelningen
för fastighets- och konstruktionshantering. Deras djupa kunskap och inspirerande idéer
har gett mig en livstid förmögenhet. Speciellt vill jag tacka professor Mats Wilhelmsson
som min examenshandledare. På vägen håller vi alltid kontakten och har inspirerande
diskussioner. Hans råd, tålamod och vänlighet har gett mig självförtroende och styrka
att avsluta mitt avhandlingsarbete.
När det gäller min livsstil tycker jag verkligen om att bo i Stockholm. Den vackra,
vänliga miljön skulle alltid ge extra intresse för mitt dagliga liv. Jag uppskattar att mina
föräldrar har gett mig möjligheten att uppnå mina drömmar och alltid stå bakom min
rygg. Jag vill också tacka min pojkvännas följeslagare och stöd. På grund av all kärlek
vet jag att jag kommer att kunna erövra alla kommande problem. Jag känner mig
verkligen tacksam för det jag har fått från alla människor jag älskar.
Detta är ett slut men också en ny start för mig. Jag kommer att fortsätta med alla
lärdomar från denna värdefulla period. Hoppas att alla ska uppnå sina drömmar och
leva ett underbart liv.
Runfeng Long
24 maj 2020
Stockholm, Sverige
v
Contents
1. Introduction ....................................................................................................... - 1 -
2. Literature review ................................................................................................ - 2 -
3. Methodology ...................................................................................................... - 4 -
3.1. The hedonic price method .......................................................................... - 4 -
3.2. Specification of the price equation ............................................................. - 5 -
4. Data and the study area ...................................................................................... - 6 -
5. Descriptive statistics .......................................................................................... - 9 -
5.1. Dependent variables ................................................................................... - 9 -
5.2. Independent variables ................................................................................. - 9 -
5.3. Descriptive analysis of variables .............................................................. - 11 -
6. Regression results ............................................................................................ - 12 -
7. Discussions ...................................................................................................... - 17 -
7.1. The effect of shop_dist based on different sizes of apartments ............... - 17 -
7.2. The effect of shop_dist on based on the orientation to CBD ................... - 19 -
7.3. The effect of shop_dist based on the distance to CBD ............................ - 20 -
7.4. Non-linear relationship between shop_dist and housing price................. - 20 -
8. Conclusions ..................................................................................................... - 21 -
References ............................................................................................................... - 23 -
Appendix ................................................................................................................. - 27 -
vi
List of Tables
Table 1 Included shopping malls in the county of Stockholm. ................................. - 8 -
Table 2 The explanation of dependent variables ...................................................... - 9 -
Table 3 The explanation and expected sign of independent variables.................... - 10 -
Table 4 Summary of descriptive statistics .............................................................. - 12 -
Table 5 Result of the basic regression .................................................................... - 13 -
Table 6 The effect of shop_dist on housing price – three size groups .................... - 18 -
Table 7 The effect of shop_dist on housing price based on the orientation ........... - 19 -
Table 8 The effect of shop_dist on housing price based on the distance to CBD .. - 20 -
Table 9 Summary of raw data ................................................................................. - 27 -
Table of Figures Figure 1 The county of Stockholm comprises 26 political municipalities ............... - 7 -
Figure 2 Scatter plot of shop_dist and housing price .............................................. - 14 -
Figure 3 Scatter plot of shop_num3 and housing price .......................................... - 15 -
Figure 4 Scatter plot (prediction) of the proximity to shopping mall and housing price
................................................................................................................................. - 21 -
- 1 -
1. Introduction
The concept of a shopping mall is that one or more buildings composed of a complex
of shops or other facilities. Shopping malls can exist as the hub of urban structure and
the foundation of retail economies. It originated in the U.S. and now have become a
modern retail form. During recent years, there has been a quite rapid increase in the
development of shopping malls worldwide, shown in numbers, sizes as well as their
complicities.
However, shopping malls have been challenged by online shopping in recent years. The
form and content of shopping malls are supposed to change in the future. Hence, the
global trend has caused malls to change the role they play in people's daily lives. To
subject to all these changes and meet the needs, they are no longer just focus on
shopping. The idea of shopping has gradually evolved from being purely unavoidable
errands to becoming the main segment of the urban recreational lifestyle (Fasli et al.,
2016). Now when people choose to pay a visit to the shopping malls, they are expecting
experiences that are way more than just taking away the goods they need and then just
go back. Leisure or purchasing activities have cost consumers a fortune. Thus, those
developers behind shopping malls are desperately seeking ways to make shopping and
purchasing more of a leisure pursuit (Howard, 2007). Accordingly, recently developed
shopping centers try to satisfy these new demands in a variety of methods. Those
shopping complexes are viewed as facilities that can provide public citizens with both
convenience and amusement. Therefore, it is reasonable to assume that living closer to
a shopping mall provides people with better flexibility as well as enjoyment. Thus,
theoretically, a positive effect on nearby housing prices is supposed to be generated.
This study aims to investigate how the prices of condominiums will be affected by the
proximity of shopping malls. Two aspects are considered and examined in the empirical
study, namely the proximity to a shopping mall, and the number of shopping malls. We
try to reveal if there is any price premium for those apartments near the shopping mall
or with more shopping malls in the neighborhood, which is within 3 kilometers radius.
There has been some existing paper that reveals the reverse relationship between
housing prices and distance to the shopping mall. We can compare the result and take
some discussions further.
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This study contributes to some of the related studies in the field. A precise valuation of
shopping malls on the apartment values will assist the authority and developers in
making better decisions. Schulz (2004) stated that housing information could be
significantly beneficial for real estate developers, banks, and policymakers. For
instance, this would give the policymakers a clear insight when they are designing the
urban structure. On the other hand, it would also be of great benefit for real estate
developers to examine their developing strategies, if they are going to make a fortune
by diving into the trendy commercial real estate market. Both the private and
institutional investors may also be interested in this potential finding since these
purchasers can compare their potential targets more efficiently.
The impacts of shopping malls on property prices have not been well-examined yet.
The purpose of this paper is to shed light on that, by conducting different kinds of
regression analyses.
The structure of the rest of the paper is as follows. Chapter 2 outlined the relevant
literature. Chapter 3 elaborates on the methodology and the model used in this study.
Chapter 4 presents the data and the study area. Chapter 5, 6 and 7 presents the
descriptive statistics, empirical analysis, as well as more interesting discussions.
Conclusions are summarized in the last Chapter 8.
2. Literature review
Shopping malls are now playing an increasingly important role in people’s daily lives
as well as urban development. They can offer residents with huge conveniences. As a
result, people are willing to consume more money in dwellings with good accessibility
to shopping malls (Zhang, L. et al., 2020). Previously, there have been varieties of
academic or practical research paper about how these different kinds of facilities would
affect the housing price of adjacent residential properties. For example, the most
common ones are how schools, subway stations, stadiums or common green areas
would affect the housing price in their surrounding neighborhood. However, the
catalogue of shopping malls is much less mentioned and investigated. Many different
aspects of real estate will be considered while buyers are determining the price that they
are willing to pay for their new houses. In the past decades, a number of research paper
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have been done by economists and scholars, focusing on various factors that would
affect housing prices. Some main determinants of residential property price include the
physical characteristics of the property, the environmental and amenity attributes, the
financial status of the sale and, most importantly, the location. Certain facilities such as
schools (Bae & Chung, 2013; Clark & Herrin, 2000; Sedgley, Williams, & Derrick,
2008), greenery areas (Wu, J. et al., 2015; Cho, S. et al., 2006), and landscape (Cassel
& Mendelsohn, 1985; Hui, Chau, Pun, & Law, 2007; Jim & Chen, 2010) are widely
discussed. More scholars have studied the impacts of locations involving transportation
transit (So, Tse, & Ganesan, 1997; Golub, Guhathakurta, & Sollapuram, 2012; Yang,
Zhou, & Shyr, 2019) as well as transport accessibility (McMillan, Jarmin, & Thorsnes,
1992; Henneberry, 1998).
Seago (2013) presents that when it comes to the effects of commercial amenities, such
as shopping malls, the relationship can still be unclear. Some previous studies had tried
to investigate this topic. However, most of the previous findings focus mainly on other
aspects. For example, (Carter, 2009) had discussed the rents, and location, while other
studies pay most of the attention to the role that the shopping mall plays in the whole
society as well as urban development (Ozuduru, 2013; Fasli et al., 2016). Moreover,
how it has become the catalyst of the urban lifestyle (Erkip, 2005). There is no doubt
that shopping malls could generate externalities. However, there are only limited
studies on how externalities of a shopping mall would influence the housing market
nearby. Some researchers have found both the positive and negative effects of
proximity to a shopping mall (Sirpar, 1994; Des Rosiers et al., 1996).
The effect of shopping malls on surrounding house values was examined by Des
Rosiers et al. (1996), which mainly put emphasis on the proximity and the side effects.
This study analyzed the impact of 87 shopping malls of different size levels on
approximately 4000 residential property prices. The outcome had indicated a positive
relationship between the size of a shopping mall and residential housing price. However,
the limitation is that there is still a lack of agreement on how the externalities caused
by commercial development would affect surrounding housing values. Colwell et al.
(1985) first investigated the effects of distances to shopping centers on housing prices.
There are several other studies about this topic. Zhang, L. et al. (2019) found that with
the price gradient method and hedonic price theory, it reveals that West Intime shopping
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mall has a significant impact on housing prices, which decays with distance. Then, with
the price gradient method, it is indicated that majorities of these areas are influenced
with the existence of a shopping mall.
According to the above information, the impacts of shopping malls on property prices
have not been well examined yet. This paper sheds light on this specific topic by
conducting different kinds of regression analysis to examine the relationship, while two
different aspects will be taken into consideration, including the distance and the
quantity.
3. Methodology
3.1. The hedonic price method
Accommodation is one of the most important parts of human lives. Thus, the housing
sector is essential for the stability of our society as well as for economic development.
Therefore, it is of interest to analyze the dominant factors that can affect it. One method
to analyze the relationship between housing values and amenities is the hedonic price
method. The hedonic price model is widely used in the housing market to analyze the
property value (Brunes et al., 2020; Walsh et al., 2012; Zhang et al., 2019; Bayer et al.,
2009; Palmquist, 2006; Deaton and Hoehn, 2004).
The idea is to investigate the relationship between housing prices and their
characteristics at a micro-level. Monson (2009) states that buildings are comparable to
a collection of goods sold in the market, where each character of the building is
considered equally when the overall transaction price is determined. Regression
analysis and hedonic modelling are valuable for real estate professionals to determine
that correlation and as well as to predict future transaction prices (Ceccato and
Wilhelmsson, 2011).
According to Rosen (1974), the principle is that goods are different in attributes, which
can be confirmed by the observed differences in their prices. The expected value is
investigated by the characteristics of the structure, neighborhood, and location (Chau
& Chin, 2003). The hedonic price model is applied as the empirical analysis method to
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understand the differences in the housing price caused by the existence of shopping
malls. Price = f (apartment attributes, distance to shopping mall, the number of
shopping malls, a dummy for a municipality). There are different forms can be applied,
such as linear models, semi-log models, and double-log models (Morancho, 2003).
3.2. Specification of the price equation
According to Ceccato & Wilhelmsson (2011), the hedonic price model regress housing
price (Y) to a set of observable property characteristics (Xs), which can be expressed
as Y = βX+α, where y is a vector of observations on the apartment price, x is matrix
observations on the property attributes. β is a vector of parameters concerning the
explanatory variables (coefficients, the implicit marginal price of each attribute), and α
are random error terms, reflecting unobserved changes in housing prices.
There is nothing, in theory, to suggest which specification form of the hedonic price
equation that is preferable. Usually, it is an empirical question which function form you
choose to use. For the dependent variable, we test whether we can exclude not
transforming the variable with a natural logarithm transformation. We do the same for
the independent variables. This means that we basically test four different functional
forms, namely a linear relation, log-linear, inverted log-linear, and a log-log relation.
It is not only the form of function that is important when specifying the hedonic price
equation. Of course, at least as important is the choice of dependent and explanatory
variables. As the dependent variable will transaction price be used, that is, we are using
prices set on the market and not valuations.
The central research question is to estimate the relationship between proximity to the
shopping mall and housing values. To be able to isolate this effect, it is important that
all relevant variables are included in the hedonic price equation. Three types of
independent variables are grouped into structural characteristics, locational
characteristics as well as neighborhood characteristics. Together they will have impacts
on the dependent variables.
The question of causality, or the absence of causality, is, of course, always an issue that
is important to consider and to discuss possible solutions. If we omit important variables
in the hedonic price equation, it can create omitted variable bias that makes the model
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not exogenously given (Wooldridge, 2006). We have solved this by including the most
important explanatory variables both in terms of characteristics in the property and the
apartment but also in the geographical location by including distance to CBD, dummy
variables for the municipality, and that the coordinates are included as explanatory
variables. Our assessment is that this has reduced the risk of omitted variable bias and
spatial dependency in the form of spatial autocorrelation and spatial heterogeneity
(Wilhelmsson, 2002). The latter, we have also tried to control by including different
forms of interaction variables. That is, we test if there exist parameter heterogeneity.
We analyze whether the estimates are constant north and south of the CBD and if the
impact is affected by different segments of the housing market, such as the size and the
value of the apartment. We have also tested whether proximity to a shopping mall has
a greater significance near the shopping mall and whether this value has changed over
time.
There may also be a simultaneity problem. Have you located a shopping mall where
the home values are higher, and thus high potential consumer demand, or are the high
housing values a consequence of the proximity to the shopping mall? Here we argue
for the latter as most of the shopping malls were established a long time ago. Some
more newly established shopping malls also have a non-central location, which would
contradict the hypothesis of reverse causality.
4. Data and the study area
We are using Stockholm as a case study to estimate the relationship between housing
values and proximity to shopping malls. Stockholm County (Swedish: Stockholms län)
is a county (län in Swedish) on the Baltic Sea coast of Sweden, which has 26
municipalities (kommun in Swedish)1. Its location is shown in Figure 1 below. In this
study, all the data is limited to this specific area, which has a total population of
2,377,0812. The population density is 360/km2, which makes Stockholm county the
most populous one in Sweden.
1 The description comes from Wikipedia, https://en.wikipedia.org/wiki/Stockholm. 2 The population data come from Statistics Sweden (statistikmyndigheten SCB), which is responsible for official statistics and for other government statistics.
- 7 -
In the estimation of the hedonic price equation, it is important to have a large number
of the historical cross-sectional transactions of dwellings with actual transactional
prices. The data in this study comes from Svensk Mäklarstatistik AB and covers a
period from 2006 to 2019. This transactional database contains information on
apartments, including size, floor level, the height of the property, number of rooms,
municipality codes, and their latitude as well longitude (coordinates). In total, there are
336,914 observations.
Figure 1 The county of Stockholm comprises 26 political municipalities
(Source: from www.scb.se)
In terms of the shopping malls, we have included 36 shopping malls all across the county to get a reliable and convincing result. All these malls scatter in different zones or regions in our target area. Table 1 below is a summary table of these malls, which include information that are needed later, such as their region in the county and their coordinates.
- 8 -
Table 1 Included shopping malls in the county of Stockholm.
Region Mall name Latitude Longitude
Stockholm Municipality
1 Bromma Blocks 59.3555818 17.9530637 2 Farsta Shopping Centre 59.2430898 18.088431 3 Fältöversten 59.3396091 18.0892044 4 Gallerian 59.3308348 18.0653858 5 Globen Shopping 59.2932719 18.0789308 6 Ringen Centrum 59.3082909 18.0732146 7 Vällingby Centrum 59.3462651 17.8644459 8 Kista Galleria 59.4023124 17.9435451 9 Liljeholmstorget 59.3098222 18.0195201 10 MOOD Stockholm 59.3343282 18.0670737 11 Nordiska Kompaniet 59.333155 18.066982 12 Skrapan 59.31239 18.0717117 13 Skärholmen Centrum (SKHLM) 59.2756756 17.9057188 14 Sturegallerian 59.3360588 18.0711886 15 Västermalmsgallerian 59.3346509 18.0301484
South 1 Haninge Centrum, Handen 59.2005286 17.9839337 2 Lidingö Centrum, Lidingö 59.3665407 18.131575 3 Nacka Forum, Nacka 59.3100188 18.1625852 4 Sickla Köpkvarter, Nacka 59.3040395 18.1227579 5 Tyresö Centrum, Tyresö 59.243833 18.2246802
Huddinge 1 Heron City 59.2671217 17.908082 2 Huddinge Centrum 59.2358312 17.9795052 3 Länna Shopping Centre 59.1978627 18.1230504
Södertälje 1 Kringlan, Södertälje 59.1957882 17.6265479 2 Moraberg 59.2021377 17.6619741 3 Weda Shopping Centre 59.2161037 17.6452652
North
1 Arninge Centrum, Täby 59.4620823 18.1320292
2 Barkarby Shopping Centre,
Jakobsberg 59.4236317 17.8323419
3 Sollentuna Centrum, Sollentuna 59.4985575 17.7859228 4 Solna Centrum, Solna 59.3609725 17.9971 5 Stinsen Shopping center, Häggvik 59.4370869 17.9349316 6 Mall of Scandinavia, Solna 59.3691707 18.0031763 7 Mörby Centrum, Danderyd 59.3988886 18.0332915 8 Täby Centrum, Täby 59.4451126 18.0587862
9 Veddesta Shopping Centre,
Jakobsberg 59.4235298 17.7669121
10 Väsby Centrum, Upplands Väsby 59.5185284 17.9104879
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5. Descriptive statistics
5.1. Dependent variables
As discussed before, hedonic price model will be used in this study and there will be
four forms, which has involved both linear ordinary least squares (OLS) model and log
- linear OLS model. The changes in absolute value also the percentage to the total value
can both be showed. Therefore, there will be four types of dependent variables,
including variable price, lnprice, pprice and lnpprice. Their meanings will be explained
in Table 2 below.
Table 2 The explanation of dependent variables
Variables Descriptions Form
price The total housing price (SEK) Linear OLS
lnprice Logarithm of the total housing price Log - linear
OLS pprice The average housing price per square meter (m2) Linear OLS
lnpprice Logarithm of the average housing price Log - linear
OLS
5.2. Independent variables
Before presenting the descriptive statistics, we have created two new variables, namely
proximity to the shopping mall and the number of shopping malls within a 3-kilometer
radius. These variables are the main variables that we are analyzing. The proximity to
the shopping mall is constructed using Euclidean distance, which can be used to
calculate the distance between any two points with the information of their coordinates.
The formula is d(q,p) = (𝑞 − 𝑝 ) + (𝑞 − 𝑝 ) , Where q1, q2 are the coordinates
for the shopping malls, and p1, p2 are the coordinates for all the individual properties.
Hence, the distance from each apartment to all the shopping malls can be calculated.
The shortest distance to all those would give us the nearest proximity to a shopping
mall to that specific dwelling. In terms of the number of shopping malls, it is the number
of shopping malls around the apartment within a certain proximity. 3-kilometer radius
is chosen in this case. Here we are assuming that this distance to be the proximity. The
expected impacts are also included in the table, which is explained by mathematical
- 10 -
signs (plus means a positive relationship, minus means a negative relationship). Those
expectations are based on previous theories and findings.
Table 3 The explanation and expected sign of independent variables
Classification Variables Descriptions Expectation
Explanatory characteristics
shop_dist The Euclidean distance from the
apartment to the shopping mall (km) -
shop_num3 The number of shopping malls around
the apartment within a 3-kilometer radius
+
Structural characteristics
size
The construction living area of the apartment (m2)
+
floor Level No. (the ground floor as the first
floor) unknown
storeys Number of floors above ground level -
roomnum The number of rooms in the apartment +
Location characteristics
center If it belongs to the center area in the
municipality (dummy variable) -
cbd_dis The Euclidean distance from the apartment to Sergels torg (km)
-
north The bearing of the apartment to
Sergels torg unknown
There are several other factors that can influence the housing price. As said earlier, we
need to include those variables to get a more accurate analysis. Here we divide the
housing characteristics into three groups, which are respectively structural
characteristics, location characteristics, and neighborhood characteristics. Structural
characteristics are the intrinsic characteristics the property itself owns, such as the size
of the dwelling. Location characteristics measure the accessibility of the location of
properties, such as accessibility to public transportation. Neighborhood characteristics
are equally important in terms of the decision of the housing price. A good
neighborhood can be an absolute price catalyst. For example, surrounding facilities or
the decent view of the house can boost the price.
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Structural characteristics are important since conditions of the properties can have
direct effects on how people would perceive each property and how much they are
willing to pay, for instance, size, floor, room numbers, etc. all these elements are needed
to be controlled in the model. Location characteristics refers to the different locations
of housing within a city or a municipality. Different locations can differ significantly
in the housing price because of different environment and degrees of accessibility.
Stockholm has its particular geographical pattern. The distance to the central locations
– cbd_dist, i.e. Central Business District (CBD), Sergels torg is viewed as the center
point. It is the most central public space in Stockholm, Sweden. Apart from the CBD,
taking the distance to the center of its own municipality into consideration can be a
better control that help to decrease the errors. it is not reasonable to give the same
standard since each municipality are different in sizes and structures. Therefore, we
distract the code for each municipality and calculate the average number. The criterion
is to compare the own code of the apartments to the average number of its municipality.
If it is smaller than the average one, then it will be treated as in the center area, which
is entitled to a value of 1. What is more, north is another control variable, which is also
a dummy variable. The value is determined by its bearing to the Sergels torg. If it is
located in the north, then it gets the value of 1.
5.3. Descriptive analysis of variables
The final database consists of 336,914 apartment transactions, nine independent
variables. Among these nine variables, the distance to a shopping mall and the number
of shopping malls with a 3-kilometers radius will be our main. In other words, shop_dist
and shop_num3 will be our objects since we are interested in how they can explain our
models. The variables size, floor, storeys, roomnum, center, cbd_dist and north will
become our control variables. In Table 4 below, we present descriptive statistics
regarding the variables we use in the analysis.
- 12 -
Table 4 Summary of descriptive statistics
Variable Obs Mean Std. Dev. Min Max
price 336,914 2687743 1638060 595000 9400000
lnprice 336,914 14.64175 0.5714369 13.29632 16.05622 pprice 336,914 43890.33 22866.32 8666.667 110000
lnpprice 336,914 10.54273 0.5663205 9.06724 11.60824 shop_dist 336,914 3.302046 6.788902 0.1623499 45.95251
shop_num3 336,914 2.491384 2.501112 0 9 floor 336,914 2.566285 1.955205 0 10 size 336,914 64.71098 23.69971 24 140
roomnum 336,914 2.449809 0.9950683 1 5 storeys 336,914 4.139585 2.721898 0 15 center 336,914 0.6852342 0.4644232 0 1
cbd_dis 336,914 10.34592 10.55972 0.9377442 59.29909 north 336,914 0.5052981 0.4999727 0 1
The total housing price ranges from 595,000 to 9,400,000 SEK, with a mean of
2,687,743 SEK. The average housing price per square meter is from 8,666 SEK to
110,000 SEK, with a mean of 43,890 SEK. Thus, the variation is relatively high in the
dependent variable. The size also shows a relatively high variation. The average size of
the dwelling is 65 square meters, with a standard deviation of 24 square meters. The
average distance to CBD is 10 kilometers, which is also the standard deviation. The
distance to the nearest shopping mall amounts to about 3.3 kilometers, but the variation
is substantial. The standard deviation is almost 6.8 kilometers. The number of shopping
within a 3-kilometer radius amounts to just under 2.5. Values of these statistics are
relatively reasonable. All the information provides us with a basic understanding of the
market.
6. Regression results
The estimation of the hedonic price equation has been carried out by Stata version 15.1.
The outcome shows a good result and thus, confirms our hypothesis. Firstly, the
correlation between variables are examined. In fact, shop_dist and cbd_dist are highly
correlated to each other. This has a simple explanation - shopping malls tend to be built
close to the central area. So, in this case cbd_dist is excluded as a control variable in
the basic OLS regression. The remaining variables will not have a high correlation to
each other, which means the multicollinearity problem will be diminished. In the next
- 13 -
step, VIF and heteroscedasticity test will be done after we run the OLS regression
analysis. The VIF value is within an acceptable extent. Then, heteroscedasticity will be
checked. Heteroscedasticity is a problem because OLS regression assumes that all
residuals are derived from a population with a constant variance (homoscedasticity).
To fix this problem, here the Robust Standard Errors is used.
Table 5 Result of the basic regression
1 2 3 4
price_ols lnprice_ols pprice_ols lnpprice_ols
shop_dist -28082.382*** -0.016*** -395.856*** -0.016*** (-21.220) (-29.783) (-22.569) (-28.451)
shop_num3 229929.792*** 0.075*** 3851.493*** 0.081*** -247.931 -260.499 -302.18 -272.693
floor 42296.479*** 0.012*** 601.684*** 0.012*** -44.735 -44.056 -48.271 -43.674
size 43871.983*** 0.011*** -203.001*** -0.004*** -222.166 -223.151 (-86.514) (-81.027)
roomnum -154260.681*** 0.016*** 1100.039*** 0.007*** (-37.606) -14.361 -21.998 -6.001
storeys -8418.819*** -0.004*** -221.732*** -0.005*** (-13.164) (-21.831) (-25.492) (-27.698)
center 483731.956*** 0.155*** 8198.819*** 0.172*** -125.803 -117.657 -149.504 -124.223
north 266039.191*** 0.051*** 4162.253*** 0.061*** -61.447 -41.177 -71.59 -48.142
_cons -3.464e+06*** 12.499*** -
3883.985*** 9.359***
(-222.152) -2694.091 (-19.925) -1883.405
N 336914 336914 336914 336914 r2 0.758 0.815 0.779 0.798
r2_a 0.758 0.815 0.779 0.798 F 10135.178 27293.809 19517.881 25039.705 p 0 0 0 0
t statistics in parentheses
* p<.1, ** p<.05, *** p<.01
The results of four models are shown in the Table 5 above. In the analysis, the outcome
is achieved by using OLS method to make a regression analysis of the independent
variables and dependent variable - housing price. The municipality is controlled mainly
- 14 -
because there is a huge difference regarding the housing price among different
municipalities. What is more, the year and month are controlled. Housing price goes up
with time and at the same time, seasonal effect exists. It is reasonable that a better
weather can contribute to more transactions or a more decent price.
The F value is high in all four models which indicates that the overall models function
well. The hypothesis that coefficient is 0 is rejected. R square (r2) are respectively high
in four models, 0.758, 0.815, 0.779 and 0.798. r2 of Model 1 is 0.758 which means that
the independent variables could explain 75.8% of the dependent variable. The same
rule for Model 2, 3 and 4, the independent variables could explain 81.5%, 77.9% and
79.8% of the dependent variables, respectively. The explanatory power for all these
four models is high at 1% significance level. This means that there is only a low
possibility of making the wrong decision when the null hypothesis is true. It may be
considered as a high degree of explanation and comparable to other studies. The risk of
omitting variables should be negligible. Therefore, the outcome is significant.
Figure 2 Scatter plot of shop_dist and housing price (Source:
outcome from Stata)
The variable of primary interest is, of course, the distance to the nearest shopping mall.
The effect is in line with expectations, i.e., negative. The farther away from the
shopping mall you come, the lower the expected house value, everything else equal.
According to the result, the coefficient of shop_dist are -28082.382, -0.016, -395.856
and -0.016, respectively in these four models. All these minus signs of coefficients
show that the impact of distance on housing price is negative, which means that a higher
- 15 -
distance will lead to a decrease in the housing price. To be more detailed, in Model 1,
every increase of 1km in the distance to the shopping mall is associated with a decrease
of 28082.382 SEK in the total housing price. In Model 2, every increase of 1km in the
distance to the shopping mall is associated with a decrease by 1.6% in the total housing
price. In Model 3, every increase of 1km in the distance to the shopping mall is
associated with a decrease of 395.856 SEK in the average housing price. In Model 4,
every increase of 1km in the distance to the shopping mall is associated with a decrease
by 1.6% in the average housing price. Also, the scatter plot of shop_dist and housing
price above (Figure 2) shows a downward line, which also reveals the negative
relationship between shop_dist and housing price. The interpretation should be made
in the light of the fact that we have included the distance to the CBD in the model
together with fixed municipal effects as well as the coordinates. For all estimates, we
can reject the null hypothesis that the variable does not have an effect on the price.
Figure 3 Scatter plot of shop_num3 and housing price
(Source: outcome from Stata)
Coefficients of shop_num3 are respectively 229929.792, 0.075, 3851.493 and 0.081. In
Model 1, the explanation is that one more shopping mall existing within 3km distance
will lead to an increase of 229929.792 SEK in the total housing price. In Model 2, with
every increase in the shopping mall within 3km distance, the total housing price will
increase by 7.5%. In Model 3, one more mall existing within 3km scope will lead to an
increase of 3851.493 SEK in the average housing price. Lastly, for Model 4, the average
housing price will increase by 8.1% accordingly with one more shopping mall. Figure
3 above also shows the positive relationship between shop_num3 and housing price.
- 16 -
Since the independent variable shop_num3 is discrete, the shape is a bit different from
Figure 2.
After examining our research objects, we take a closer look at the remaining variables.
The result can be reasonable as well, which complies with our common senses. In terms
of floor, all the four models show that higher floor is associated with a higher total or
average housing price. The explanation is that it brings advantages such as less traffic
noise, better view and more privacy for the residents. For variable storeys, the higher
the building are, the lower total or average housing price will be. This also makes sense
since lower building always indicate that the property is more likely to be high-quality
villa instead of high-rise apartment building, which shares less common public space
and is surrounded by a better living environment. This kind of comfort comes with a
higher price. However, the coefficient for roomnum is negative in Model 1 and positive
in other models. We can assume that the correlation between roomnum and sizes can
somehow affect their coefficients for the fact that these two characteristics are to some
extent representing the familiar information.
The coefficient for variable north in Model 1 and Model 2 is 266039.191 and 0.051,
which means that the total housing price for apartments in the north is generally
266039.191 SEK or 5.1% higher than those in the south. In Model 3 and 4, the
coefficient for north is 4162.253 and 0.061, which indicated that the average price for
apartments in the north is in general 4162.253 SEK or 6.1% higher. For variable center,
the explanation behind is the same. Taking Model 1 as the example, the coefficient is
483731.956. This means for the apartments in the center area, the total housing price is
generally 483731.956 SEK higher.
- 17 -
7. Discussions
This study aims to reveal that how the distance to shopping mall as well as the number
of shopping malls would affect the surrounding housing price. Based on the regression
analysis, the results show that there is a negative relationship between distance and
housing price while a positive relationship between quantity and housing price. These
finding are following the existing knowledge. Apart from the above observations, some
interesting discoveries can be discussed further in this following part. This can
enlighten us from several different perspectives on this topic, which are explained in
detail as follows.
7.1. The effect of shop_dist based on different sizes of apartments
The first discussion is about the effects on different sizes. Are the effects the same to
different sizes of the housing? To test the hypothesis that the effects the same to
different sizes of the housing, all the apartment samples are divided to three different
size groups, using quantile (xtile) in Stata. The number of samples are 113685, 113453
and 109776, respectively. Then we run the regression analysis separately and the
outcome is shown below in the Table 7. From lnpprice_1 to lnpprice_3, the size of
apartments becomes larger. We choose to present and explain the logarithm of the
average housing price (lnpprice) in this case.
As it is shown in Table 6, the coefficient for shop_dist is -0.0221, -0.0176, and -0.0048
respectively in these three groups. The explanation follows that for lnpprice_1, every 1
km increase in the distance, the average housing price will go down by 2.21%. For
lnpprice_2, every 1 km increase in the distance, the average housing price will go down
by 1.76% while for lnpprice_3, the average housing price will only decrease by 0.48%.
The marginal effects are becoming less with the increase in the size and which indicates
that the effect of being close to the shopping mall is capitalized primarily on smaller
apartments.
- 18 -
Table 6 The effect of shop_dist on housing price – three size groups
1 2 3
lnpprice_1 lnpprice_2 lnpprice_3
shop_dist -0.0221*** -0.0176*** -0.0048*** (-20.867) (-21.231) (-5.572)
shop_num3 0.0468*** 0.0936*** 0.0989*** -125.1 -197.111 -155.976
size -0.0154*** -0.0103*** 0.0024*** (-175.804) (-67.260) -25.316
floor 0.0098*** 0.0125*** 0.0135*** -28.92 -27.362 -25.528
roomnum 0.0929*** 0.0974*** -0.0304*** -65.475 -51.192 (-14.547)
storeys -0.0055*** -0.0051*** -0.0047*** (-22.494) (-15.987) (-11.997)
center 0.1705*** 0.1384*** 0.1863*** -101.638 -68.094 -63.546
north 0.0369*** 0.0582*** 0.0412*** -27.728 -26.527 -14.519
_cons 9.9401*** 9.5125*** 8.8231*** -1384.608 -930.642 -927.873
N 113685 113453 109776 r2 0.8667 0.8102 0.773
r2_a 0.8667 0.8101 0.7728 F 11709.2025 8777.6534 7687.1091 p 0.0000 0.0000 0.0000
t statistics in parentheses
* p<.1, ** p<.05, *** p<.01
It is reasonable to assume that it is younger people who live in these apartments and
that it is for these households’ proximity to the shopping mall is important. However,
it can be an effect of the fact that small apartments are mainly located in the central
locations in Stockholm and that the result can, therefore, be an effect of it.
- 19 -
7.2. The effect of shop_dist on based on the orientation to CBD
The next discussion is about the orientation to CBD. Is there any difference of the effect
for the apartments north to CBD or south to CBD? All sample apartments are divided
into south and north, given the value of 0 and 1. The Sergels Torg is used as the
reference point. We choose to represent the logarithm of the total housing price (lnprice)
as well as the logarithm of the average housing price (lnpprice). As shown in Table 7
below, in terms of the total price (lnprice), the coefficients for lnprice_0 is -0.0266
while -0.0165 is for lnprice_1. These numbers show that the downward percentage for
south is higher, which is 2.66% to 1.65%. As for the average price (lnpprice), the
decreasing percentage is 2.48% and 1.71%, which gives the same conclusion. Thus, the
result is clear, it can be deduced that the effect of shop_dist on housing price is more
significant for the apartments in the south.
Table 7 The effect of shop_dist on housing price based on the orientation
lnprice_0 lnpprice_0 lnprice_1 lnpprice_1
shop_dist -0.0266*** -0.0248*** -0.0165*** -0.0171*** (-39.817) (-37.116) (-19.769) (-19.870)
shop_num3 0.0652*** 0.0707*** 0.0729*** 0.0788*** -217.726 -228.54 -96.845 -104.33
size 0.0101*** -0.0053*** 0.0109*** -0.0038*** -141.039 (-66.564) -179.195 (-56.812)
floor 0.0132*** 0.0135*** 0.0124*** 0.0128*** -32.151 -31.995 -36.532 -36.276
roomnum 0.0220*** 0.0168*** 0.0176*** 0.0059*** -13.55 -9.887 -12.28 -3.874
storeys -0.0048*** -0.0059*** -0.0034*** -0.0048*** (-16.351) (-19.707) (-14.687) (-19.901)
center 0.0540*** 0.0671*** 0.3590*** 0.3828*** -36.857 -43.313 -107.531 -114.116
_cons 13.3438*** 10.2307*** 12.3842*** 9.2335*** -1231.021 -929.44 -2078.486 -1479.024
N 166672 166672 170242 170242 r2 0.804 0.796 0.8483 0.8286
r2_a 0.804 0.796 0.8483 0.8286 F . . . . p . . . .
t statistics in parentheses
* p<.1, ** p<.05, *** p<.01
- 20 -
7.3. The effect of shop_dist based on the distance to CBD
For this factor, we take dependent variable pprice into comparison. pprice_1 is defined
as the closet to the CBD. pprice_2 in the middle distance while pprice_3 means the
furthest proximity to the CBD. In the Table 8 below, the coefficients show that the
changes in absolute value are more significant in the apartments that have a shorter
distance to CBD. For instance, for pprice_1, the coefficient for shop_dist is -4098.2727.
for pprice_2, it is -993.6624 while for pprice_3, it is just -199.0167. The same principle
applies to lnpprice, which means the changes in relative value are also more significant
for apartments that are located closer to the CBD. This indicates that shop_dist
influences the housing price more significantly
Table 8 The effect of shop_dist on housing price based on the distance to CBD
pprice_1 pprice_2 pprice_3
shop_dist -4098.2727*** -993.6624*** -199.0167*** (-62.205) (-22.999) (-11.218)
N 112309 112301 112304 r2 0.7779 0.6623 0.7027
r2_a 0.7778 0.6621 0.7025 F - - - p - - -
t statistics in parentheses * p<.1, ** p<.05, *** p<.01
7.4. Non-linear relationship between shop_dist and housing price
As discussed before, a longer distance to the shopping mall can lead to a decrease in
housing values. However, is this relationship linear or non-linear? In other words, the
last discussion is about that, with the increase in distance from a shopping mall, will
the price drop all the time? By analyzing the prediction in a scatter plot, we can discover
the relationship. According to the outcome, presented in Figure 4 below, there is a U-
shape relationship between proximity and housing values.
- 21 -
Figure 4 Scatter plot (prediction) of the proximity to shopping
mall and housing price (Source: outcome from Stata)
With the increase in the distance, the marginal effect is indeed decreasing. This is
consistent with our expectations. However, the attributes of the property will change
when it goes further to the countryside area, which makes the interpretation more
complicated.
8. Conclusions
This paper aims to examine the effects of shopping malls on residential property value,
given samples in the county of Stockholm. By using the hedonic price model, this study
analyzed the influence of shopping malls on the surrounding housing prices from the
perspective of both the distance and the quantity.
It is shown in the results of the regression that the explanatory variables have significant
effects on the dependent variables. Moreover, the results also reveal an inverse
relationship between the housing price and its distance from the shopping mall. The
increase in proximity to the shopping mall is expected to lead to an increase in the
housing price while the number of shopping malls is positively correlated to housing
prices. This is consistent with previous studies. The relationship seems to be non-linear,
which means that with the constant increase in the distance to the shopping mall, the
housing price is going down. The effects the distance has on the housing price are more
significant for smaller apartments. Also, the effects are stronger for the apartments in
the south to CBD. Moreover, effects are stronger if the property is located closer to
CBD.
- 22 -
These findings are original and valuable for related parties. There are many policy
implications based on empirical results. Amenities and disamenities have an impact on
housing values. Knowledge about, for example, the impact of shopping malls on
housing values is important while valuing apartments. This may apply, for example, to
the taxation of housing, to loan applications and, of course, to the sale of housing.
Compared to previous studies, it extends the investigation about different aspects of the
effects of shopping malls on housing prices.
- 23 -
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Appendix
Table 9 Summary of raw data
Variable Obs Mean Std. Dev. Min Max
n 336,914 1 0 1 1
id 336,914 168458 97258.84 1 336914
municipali~f 336,914 169.926 21.5046 114 192
congregati~f 336,914 17006.1 2155.769 11401 19204
apartment_~r 336,914 2.63258 12.96732 -11 7063
building_s~s 336,914 4.21354 26.02809 0 15014
living_area 336,914 65.0557 33.1632 20 8765
number_of_~s 336,914 2.45847 1.068487 1 120
monthly_fee 336,914 3464.31 2474.982 1 848480
longitude 336,914 18.0292 0.153966 14.1 22.20443
latitude 336,914 59.3404 0.107731 56.9157 65.44
price 336,914 2717369 1825156 500000 6.00E+07
location_r~t 336,914 1622580 13928.19 1411545 3846041
location_r~h 336,914 6584879 13313.27 6321934 7404896
year 336,914 2013.04 3.984802 2005 2107
month 336,914 6.24713 3.376399 1 12
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