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.

- 2 -

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

- 4 -

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

- 5 -

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

- 6 -

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

- 9 -

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.

- 11 -

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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