Analyzing Market Attraction:
Focus on the Housing Market
Master thesis within Economics
Author:
John Stenson
Tutor:
Mikaela Backman
Pia Nilsson
Jönköping:
2014-05-12
Acknowledgments
In order to finalize this thesis I have received support from many sources. I would
especially like to thank my two supervisors for giving me plenty of guidance and
inspiration. Along with my two seminar colleagues for their useful feedback. Finally, a
big general thank you to friends and family for their support and necessary disruptions
during this semester.
Jönköping, Sweden, May 2014
John Stenson
Master Thesis in Economics
Title:
Analyzing Market Attraction: Focus on the Housing Market
Author:
John Stenson
Tutors:
Mikaela Backman
Pia Nilsson
Date:
May 2014
Keywords:
Regional Housing Market, Market Attraction, Tobin’s Q,
Market Determinants
Abstract
The purpose of this thesis is to analyze the attractiveness of the regional housing market
and the factors effecting it. The analysis focuses on municipalities in Sweden and is based
on the appliance of Tobin’s Q as a measurement of regional attractiveness, inspired by
the previous work of Berg and Berger (2006). Nine market determinants were identified
and analyzed with respect to regional attractiveness. Out of these, a well-educated labor
force, along with the amount of consumer services available within a municipality and its
proximity to the coastline proved to induce the most significant influence. Additionally,
some evidence of varying impact from the factors was found across urban-rural range.
Table of Contents
1. Introduction .............................................................................................................................. 1
2. Theoretical Background ............................................................................................................ 3
2.1 Tobin’s Q and the Housing Market ..................................................................................... 3
2.2 Factors Determining Market Attraction.............................................................................. 8
2.2.1 Population and Congestion .......................................................................................... 8
2.2.2. Accessibility to Consumer Services ............................................................................. 9
2.2.3. Human Capital ........................................................................................................... 10
2.2.4. Natural Amenities and Climate ................................................................................. 12
2.2.5. Crime ......................................................................................................................... 14
2.3 Summary of Effects from Market Determinants............................................................... 15
3. Method.................................................................................................................................... 16
3.1 Dependent Variable .......................................................................................................... 16
3.2 Independent Variables ...................................................................................................... 17
3.3 Empirical Design ................................................................................................................ 19
3.4 Hypotheses........................................................................................................................ 21
4. Results and Analysis ................................................................................................................ 22
4.1 Descriptive Statistics ......................................................................................................... 22
4.2 Regression Models ............................................................................................................ 25
5. Case Study: Jönköping ............................................................................................................. 32
6. Conclusion ............................................................................................................................... 36
7. References............................................................................................................................... 38
8. Appendix ................................................................................................................................. 42
8.1 Appendix 1 ........................................................................................................................ 42
8.2. Appendix 2 ....................................................................................................................... 43
8.3 Appendix 3 ........................................................................................................................ 44
List of Figures
Figure 1: Tobin’s Q Distibution 2000..............................................................................................5
Figure 2: Tobin's Q Disbribution 2010............................................................................................5
Figure 3: Trend of Variables included in Tobin's Q ........................................................................6
Figure 4: Trend of Tobin's Q by Regional Type ..............................................................................7
Figure 5: Distribution of Coastal Region ..................................................................................... 13
Figure 6: Diminishing Returns of Poulation Density ................................................................... 17
Figure 7: Tobin's Q Trend Line for Jönköping and Comparable Municipalites ........................... 32
Figure 8: Map of Southern Sweden ........................................................................................... 33
List of Tables
Table 1: Expected effect from Market Determinants ................................................................. 15
Table 2: Description of Independent Variables .......................................................................... 20
Table 3: Descriptive Statistics for Dependent Variables ............................................................. 22
Table 4: Descriptive Statisicts for Independent Variables .......................................................... 22
Table 5: Bivarite Correlation Matrix for Base Year ..................................................................... 23
Table 6: Mean Values for Sub-Samples....................................................................................... 24
Table 7: Regression Models: Nation-Wide.................................................................................. 26
Table 8: Regression Models: Sub-Samples.................................................................................. 29
Table 9: National Ranking of relevant Market Determinants ..................................................... 34
1. Introduction
Whether to purchase a house or not is often one of the greatest financial decisions a
household has to make and numerous factors influencing social well-being must be
considered (Straszheim, 1975). Households are challenged to match their financial
constraints to preferences of house attributes and location upon making their decision.
Since attributes of one single house can involve endless amounts of potential choices
and individual preferences, it can prove difficult to analyze on a larger scale. An
alternative approach is to analyze the factors affecting the choice of location
concentrating on aggregate measures. The choice for this thesis is therefore to focus on
location determinants. The primary target is to enhance the understanding of the
Swedish regional housing markets by identifying several location determinants and
studying their potential influence on the attractiveness of the housing markets. This
subject of study is of great importance as local governments put great emphasis into
issues regarding regional housing markets.
The market analyzed in this study is defined as the Swedish market of one-dwelling
houses and detached two-dwelling houses. The observed willingness to invest can be
theoretically measured by Tobin’s Q and interpreted as a degree of attractiveness to the
market. Tobin’s Q is a ratio between the cost of existing house stock and cost of new
housing estate (Berg and Berger, 2006). A higher Tobin’s Q value suggests a more
attractive market to investors, since the cost of constructing new housing estate is
comparatively lower than the price of existing house stock. Simply analyzing the
differences in Tobin’s Q between municipalities generates a fair overview of regions’
attractiveness. However, the main focus of the study will be to analyze what different
factors affect the attraction level (i.e. the Tobin’s Q-values). To perform such analysis a
string of regression tests will be performed.
Thus, the questions raised are how attractive the regional housing market is and above all
what factors affect the level of attractiveness. The thesis differ from previous research in
the manner it applies the Tobin’s Q to a housing market analysis. While previous research
has, for instance, analyzed the housing market in regards to house prices (Johnes and
Hyclak, 1994; Quigley, 1999), this thesis will construct an analysis with the observed
Tobin’s Q-values as a dependent variable in the regression models, in order to identify
the factors that influence regional attractiveness. Furthermore, the regressions will be
1
analyzed across two regions types in hope of generating a comprehensive understanding
of the effects.
Additionally, a case study regarding the municipality of Jönköping will be included. This
region, located far from the attractive coastline and Sweden’s larger metropolitan areas,
has still experienced a rising housing market ever since the beginning of the 21st century,
making it an interesting matter of study. The case study helps confirm the rapid rise on
the housing market through a comparison of similar municipalities. Furthermore, the
findings suggests that the observed Tobin’s Q of Jönköping is higher than what can be
expected from the regression results.
The empirical analysis found evidence that the majority of the identified market
determinants influenced regional attractiveness in line with the expectations gathered
from theory. Furthermore, some empirical evidence of differences between region types
was found after analyzing the municipalities across urban-rural range. Primarily, the
analysis has confirmed the important influence of human capital, accessibility to
consumer services and the proximity to shore on regional attractiveness. Suggesting local
governments may gain from expanding their supply of consumer services and focus on
educational institutions.
Initially, the thesis will discuss the theoretical background of the Tobin’s Q and how it is
applied to the housing market, followed by a presentation of the identified market
determinants and their effect on regions. This will all be included in Section 2, which for
convenience will be shortly summarized in section 2.3. The methodology will be
presented in section 3, including the method of application for all variables, the empirical
design and hypotheses. Section 4 will present the results and analysis from the regression
models. And a case study of the municipality of Jönköping will be presented in section 5,
followed by a conclusion of the thesis in section 6.
2
2. Theoretical Background
There are few previous studies focusing solely on factors affecting the Tobin’s Q in the
housing market. Most previous research on the housing market is based purely on
variables affecting house prices. However, as the price level is incorporated in the theory
of Tobin’s Q (Berg and Berger, 2006), several of the factors affecting the supply, demand
and consequently the price levels of the housing market, will be implemented in this
study.
2.1 Tobin’s Q and the Housing Market
The use of Tobin’s Q is most common when discussing financial assets and can be defined
as a ratio of a firm’s market value in relation to the cost of replacing its assets (Chung and
Pruitt, 1994). However, ever since the original theory was carried forward by James Tobin
in 1969 the technique has been considered transparent and can be applied to several
markets, including the housing market (Tobin, 1969). Since Tobin’s Q is first and
foremost an investment measurement the majority of previous studies relating it to the
housing market prefer to analyze the relationship between Tobin’s Q and housing
investment (Jaffe, 1994; Jud and Winkler, 2003; Berg and Berger, 2006). Their results
verified the utilization of Tobin’s Q when analyzing the housing market since a positive
correlation between increasing tendencies in the Q-values and the willingness to invest in
new housing construction was found, indicating that markets showing signs of increasing
Q-values is attractive to investors. Another common use of Tobin’s Q is in regional
development reports created by public organizations. For instance, in a report by the
Regional Development Council in Sörmland (2012) an experimental study was performed
on how the Q-value of a region is affected by different traffic network scenarios.
The theory is applied to the housing market by relating the marginal price of the house to
the marginal production cost, generating a marginal Q-value. Several authors, namely
Hayashi (1982) and Berg and Berger (2006), discuss the implication of a marginal Qvalue stating that it is difficult to empirically observe and the only form of observable Qvalues is an average Q-value. Hayashi’s (1982) demonstrated that under the assumption
that firms are price takers and experience constant return to scale for production and
installation the marginal Q-value will equal the average. Berg and Berger (2006) accepted
these assumptions and applied the theory to the housing market.
An average Q-value of one suggest that the investment will be repaid without any profit.
Any value above one implies that the market is in fact attractive and encourages suppliers
3
to continue investing in the market, hence the market experience excess demand. On the
contrary, an average Q-value below one indicates an excess supply (Berg and Berger,
2006). The long-run equilibrium for the Q-value will be one since in a situation with
excess supply (Q<1) the need for new construction will be discarded, which triggers asset
prices to increase and eventually return to long-rung equilibrium (Q=1). On the other
hand an excess demand (Q>1) will lead to more construction, leading to a decrease in
asset price, bringing the market back to long-run equilibrium (Jaffee, 1994). In the report
by Regional Development Council in Sörmland (2012) the Q-values are interpreted as
measures of performance and attractiveness of a housing market. However, the report
suggests that investors in practice should only consider Q-values marginally higher than
one in order to expect a profitable investment.
The main advantage of using Tobin’s Q in this kind of analysis is to exploit the convenient
interpretation of the ratio. Since the technique is adaptable to different markets this allows
the thesis to interpret the Q-value as a measurement of attractiveness towards a regional
housing market. As an alternative choice simply implementing actual house prices as a
form of regional attraction measure have sufficed in many previous studies. The argument
for using house prices is often that the construction cost is integrated in the dynamics of
house prices thus suggesting a ratio, such as Tobin’s Q, is needless and the change in
existing house prices is enough to recognize the changing market environment (Takala
and Tuomala, 1990). However, opting to use Tobin’s Q protects the measurement from
misleading results that may occur when ignoring the variance of construction cost across
space (Öner, 2014). The construction cost of houses should, like house prices, vary across
regions since consumption patterns and economic structure differ between municipalities
(Öner, 2014). Furthermore, applying the ratio generates a clear market equilibrium as a
point of reference (Q=1), hence simplifying comparisons between regions or regional
types. Berg and Berger (2006) claim that one reason for the scarcity of previous studies
applying Tobin’s Q to the housing market is due to lack of data. In the case of Sweden,
both national and regional data is available to calculate Q-values. The municipal
distribution of these Q-values are presented figure 1 and 2 below. In order to establish a
general idea of the positioning of the more attractive municipalities and the overall market
changes during the past decade.
4
Figure 2: Tobin’s Q Distribution 2000
Figure 1: Tobin’s Q Distribution 2010
Figure 1 demonstrates the Q-values for 2000 while Figure 2 represents 2010 values. The
darker colors indicate higher observed Q-values. Approximately 80 percent of the
municipalities generated a positive change since the beginning of the 21st century and in
particular, as the figures indicate, in the southern part of Sweden. Furthermore, clear
tendencies of clustering around the larger metropolitan areas of Stockholm, Göteborg and
Malmö can be observed. The municipalities of Jönköping, Norrköping and Helsingborg
are also included along with its respective Q-value. These municipalities will later be a
part of the thesis case study (section 5) and are therefore included.
Figure 3 below, demonstrates the separate historical trends for the variables used to
calculate the set of Tobin’s Q that will be used in this study1. The three variables are
presented in the form of the national average of 289 out of the country’s 290
municipalities2.
1
Variables in equation:
Average Q = Average unit cost of existing stock (CE) / Average unit cost of new housing estate (CN)
2
One current municipality (Knivsta) is disregarded throughout the study due to its late establishment
(2003).
5
18000
16000
Unit Cost (SEK)
14000
12000
10000
8000
6000
4000
2000
0
1980
1985
1990
Average CE
1995
Average CN
2000
2005
2
1,8
1,6
1,4
1,2
1
0,8
0,6
0,4
0,2
0
2010
Tobin's Q
Tobin's Q Variables: National Average
Average Q
Figure 3: Trend of Variables included in Tobin’s Q
Source: Data extracted from Berg and Berger (2006), chart assembled by author.
Since the average unit cost of existing houses (CE) is observed underneath the average
cost of new housing estate (CN) all through the period the general notion of the Swedish
housing market is that it is weak on average, especially from 1990 and onwards, until
2005 when the market improved. Prior to 1990 however the unit cost of existing and new
houses were relatively aligned implying a higher average Q-value during this time.
As mentioned the thesis will consider the difference in regional attractiveness amongst
different regional types. Two region types will be analyzed separately to demonstrate the
difference between municipalities recognized as central and peripheral regions3.
Distributing the municipalities in this manner will generate an overview of variations in
individual preferences and an indication of which factors are more important in each
respective area. An initial view of the differences is presented below in Figure 4 in order
to demonstrate the assumption that a housing market’s behavior is different across urbanrural range.
3
Central municipalities are municipalities with a self-serving central district in a functional region.
Smaller municipalities closely located to one out of the three metropolitan regions (Stockholm, Göteborg,
Malmö) are also considered as central regions. While, peripheral municipalities are the remaining
municipalities recognized as rural.
See appendix 8.1 for map.
6
Tobin's Q:
Trend by Regional Type
1,2
1,1
Tobin's Q
1
0,9
0,8
0,7
0,6
0,5
1980
1985
1990
National Av Q
1995
Central Av Q
2000
2005
2010
Peripheral Av Q
Figure 4: Trend of Tobin’s Q by Regional Type
Source: Data extracted from Berg and Berger (2006), chart assembled by author.
The figure describes the separate trends of the two regional types with the national
average included as a point of reference. Clear signals of an uneven market can be
observed as the peripheral municipalities are considered less attractive across the entire
period. As assumed the two sub-samples experience similar trends since they correspond
to overall market fluctuations. However, the gap between the two shows increasing
tendencies from 1995 and onward suggesting that the importance of the contrasting
characteristics between the regional types are increasing.
7
2.2 Factors Determining Market Attraction
Previous research has discussed several different factors influencing the attraction of a
housing market. For structural reasons, Table 1 (section 2.3) will summaries and divide
the determinants into positive and negative externalities on the basis of their expected
market influence found in previous research.
2.2.1 Population and Congestion
The market size is considered as one of the key variables to determine the attractiveness
of a housing market (Rivera-Batiz, 1988; Quigley, 1999). The size of the housing market
can be expressed as the population of a particular area and a positive population growth
rate leads to increasing demand for houses, suggesting that the area is attractive.
Population density has also been introduced as an indicator of region attractiveness on
the basis that population tend to cluster around popular regions (Rappaport and Sachs,
2003). When discussing the influences of regional population size on markets, authors
point to external economies of scale and the potential benefits for firms operating around
large business areas (Henderson, 1972; Quigley, 1998). However, limits to population
growth has led to theories discussing optimum city sizes. Henderson (1972) explains the
hypothetical optimal city size by weighing the benefits of economies of scale for firms
against it disadvantages. The benefits are described as the increasing return to scale
attainable for firms entering the market. On the contrary, the disadvantages is expressed
as the increased commuting time that occurs when cities expand. The additional
commuting time increase the resource cost and as the population continues to grow the
industry will eventually experience decreasing return to scale (Henderson, 1972).
Brueckner (2000) identified three market failures caused by an over-crowded area. First,
as also suggested by Henderson (1972), increased traffic congestion leads to increased
commuting time for each additional vehicle. Furthermore, a growing region in size and
population will demand a larger and more comprehensive traffic system, inducing large
costs (Emmerink et al., 1998). Secondly, the social value of open space and nature is often
overlooked in the chase for financial benefits, as constructing an excessive amount of new
infrastructure or houses may harm the attractiveness and demand for houses in a region
(Nilsson, 2014). In such a case the market failure is identified as market forces causing
damage to the region by compromising its open space. The third market failure discussed
by Brueckner (2000) is the fact that the additional infrastructure associated with new
8
house construction (e.g. new roads and sewers) increases governmental costs and leads
to increased tax rates. The tax increase affects the entire region, generating an unbalanced
market since the new homeowners do not pay the full price for the additional
infrastructure but are instead charged more for the house itself. Thus, the new profitmaking prices attracts new construction leading to an increasingly congested area
(Brueckner, 2000). Subsequently, demand on the regional housing market may decrease
if the benefits of population size are outweighed by congestion.
2.2.2. Accessibility to Consumer Services
Glaeser et al. (2001) identified several different factors explaining the degree of
attractiveness of cities, the first suggesting that a larger selection of goods and services
will enhance a city’s appeal. Additionally, Krugman (1980) was early in embracing the
concept of “love for variety” based on the acknowledged Dixit-Stiglitz model, which
highlights the importance of variety of good and services. Rivera-Batiz (1988) explains
the effects of services to regional development by identifying two types of services;
producer services and consumer services, and discusses how the variety of services can
improve the attractiveness of regions. Producer services are used by industries in need of
certain assistance (e.g. repairs, transportation and legal advice) and a rise in population
will increase the specialization of these services (Rivera-Batiz, 1988). Consumer services
are services directed towards individual consumers (e.g., restaurant, theaters and barber
shops) and they follow a different trend from producer services by increasing utility
through greater variety. For both types, a population increase will enhance the markets’
ability to satisfy the wide range of services demanded, leading to increased utility (RiveraBatiz, 1988). A valid approach for this thesis is to solely focus on consumer services since
the aim is to analyze region attractiveness from the viewpoint of households.
Furthermore, consumer services are non-tradable, hence only dependent on regional
demand which simplifies the data sampling (Rivera-Batiz, 1988).
In light of the Dixit-Stiglitz model a utility function for consumer services helps explain
the influence of a diversified service stock, demonstrated below:
𝑍𝑗 = 𝑁
(1−𝜃)
𝜃
𝐶𝑗𝑚
The model highlights the effect on utility gained from consuming local services, 𝑍𝑗 , based
on the amount of local services offered, N, in regards to consumer preferences,
9
(1−𝜃)
𝜃
and
household demand. A low value of 𝜃 implies a greater variety between the services (N)
which increases utility.
Accessibility to markets, for instance markets for consumer services, is an important
factor of economic progress as discussed in the early studies of Marshall (1920). He
discusses the simple fact that even locations blessed with abundant resources cannot profit
from these if the market for the finalized good is too distant, and thereby explains how
firms’ geographical position reflect their productivity4. Furthermore, in a study regarding
market accessibility and market influence Verburg et al. (2011) find clear patterns
showing that the influence on regions is most significant around large cities. Thus, theory
underlines the importance of accessibility to certain regions (often larger cities) and its
markets, as the potential productivity growth will attract firms. And an increase in firms,
connected with population growth, will consequently affect the housing market.
Johansson et al. (2002) presents a comprehensive understanding of relevant accessibility
measurements, including two variables expected to enhance the attraction of a region in
the eyes of households. The two “attraction variables” considered are accessibility to
number of jobs and supply of consumer services. The variables are analyzed on three
different accessibility levels; (i) accessibility within the municipality, (ii) within a closely
related sub-region (FR-region5) and (iii) external accessibility. Both internal accessibility
to jobs and consumer services generated positive influences, validating the inclusion of
the variables when estimating region attractiveness.
2.2.3. Human Capital
Human capital is considered a positive element for regions. Schultz (1961) was among
the first to discuss the importance of the difference in “quality of human effort” and how
it, if invested in, could improve firm productivity and hence affect the housing market as
previously discussed. Lucas (1988) provides a comprehensive production function6 in
which both the individual rate of human capital accumulation and the external effect of
human capital are theoretically observed. Initially, by extracting the theory of human
4
Marshall (1920) discussed three effects of firm clustering causing increased productivity; (i) clustering
of downstream and upstream firms, (ii) labor pooling and (iii) knowledge spillovers (Marshall, 1920).
5
Sweden’s 290 municipalities are divided into 81 regions recognized as local labor markets or functional
regions (FR-regions). A functional region usually consists of 4-5 (usually more around the larger
municipalities) closely located municipalities. The distribution is based on the intensiveness of
commuting between the municipalities (Johansson et al, 2002).
6
Lucas (1988) production function: 𝑁(𝑡)𝑐(𝑡) + 𝐾̇ (𝑡) = 𝐴𝐾(𝑡)𝛽 [𝑢(𝑡)ℎ(𝑡)𝑁(𝑡)]1−𝛽 ℎ𝑎 (𝑡)𝛾
10
capital accumulation from the complete production function the time spent on
accumulating human capital (e.g. attending school) is expressed as:
ℎ̇ = ℎ(𝑡)𝛿[1 − 𝑢(𝑡)]
Implying that the change in human capital, ℎ̇, is dependent upon the already attained stock
of human capital, ℎ(𝑡), and the share of time spent on accumulating new human capital
instead of producing goods, [1 − 𝑢(𝑡)]. If all time is dedicated towards accumulating
human capital (i.e. 𝑢(𝑡) = 0) the stock of human capital increases at a maximum speed
(and vice versa). Secondly, Lucas (1988) include the external effect of human capital in
his production function to highlight the influence of an educated society. This effect is
expressed as the average human capital stock of a population and an increasing stock is
assumed to be beneficial for all the production factors (refer to ℎ𝑎 (𝑡)𝛾 in footnote 6).
Building on this theory Mankiw et al. (1995) distinguish the difference between
knowledge and human capital to emphasize the importance of analyzing the accumulation
of human capital. The difference is defined as knowledge being a base of understanding
how the world works, expressed in text books or by academics, while human capital is
the way the population absorbs that knowledge, for example by attending school. Mankiw
(1995) recognize knowledge as a public good that can be easily traded and spread across
large areas. Human capital, however tends to have more regional traits since it is partly
dependent on the quality of education centers in the region, but more notably due to
regional knowledge spillovers. The spillovers are most commonly observed in the form
of new innovations and its tendencies to cluster around areas with an extensive knowledge
base. This is explained on the basis that skilled people, employed in cutting-edge
industries have a greater chance of coming up with new ideas and take advantage of the
comparative advantages that comes with innovations (Glaeser 2000; Audretsch 2000).
Furthermore, as previously mentioned Lucas (1988) capture the influence of regional
knowledge spillovers in his discussion regarding external effects of human capital. Rauch
(1993) adopts the approach by using average level of human capital when providing
evidence of regional differences in both wages and rental rates, affected by higher average
human capital levels. Likewise, Farnham et al. (2011) argue that highly educated people
are more likely to act on the housing market, based on the premise that people with higher
education are expected to receive higher salaries. Therefore, the thesis expect a positive
influence on the housing market from human capital.
11
Additionally, Black and Henderson (1999a) performed a study on the regional impact of
population with a four-year college degree and people with a high-school degree. They
found a significant positive relationship between the proportion of college educated
people and the city size, implying that the more educated people the larger the city size.
Shapiro (2006) discuss the fact that areas with more highly educated people will
encourage more diversity in consumer goods and services which, as previously
mentioned, have proved to attract people. Moreover, highly educated people are more
likely to positively affect their surroundings and in different ways improve the area they
live in, for instance through participating in local organizations or political systems
(Shapiro, 2006).
2.2.4. Natural Amenities and Climate
Glaeser et al. (2001) discuss location amenities by mentioning the attraction generated
from the natural beauty and scenery of cities. Brueckner et al. (1999) presents a
comprehensive comparison of differences in natural amenities and historical
infrastructure effects between cities in the U.S. and Europe. They found that cities in
Europe, with Paris as their prime example, seem to use more governmental resources to
maintain the historical infrastructure compared to cities in the U.S. Due to these political
incitements central Paris has turned into a very attractive area, crowded with high-income
earners. While in many of the American cities the suburbs have proved to be more
attractive (Brueckner et al., 1999). Moreover, the influence of proximity to water on cities
has been discussed by several authors (Brueckner et al., 1999; Koster and Rouwendal,
2013). They discuss the influence in terms of both enhancing natural beauty but also for
convenience purposes as cities often originate along the coastline to decrease
transportation cost. Koster and Rouwendal (2013) found evidence of higher house prices,
as an indicator of a more attractive market, closer to the waterfront. While, Glaeser et al.
(2001) found similar trends when examining the effect of access to water with respect to
population growth.
Figure 5 below demonstrates the Q-value distribution in 2000 for Sweden’s 289
municipalities with regards to the identified coastline regions7. By analyzing the chart we
7
A coastline region is a municipality with direct contact to the seashore. 87 coastline regions were
identified.
12
can see tendencies of correlation between high Q-values and location near the shore, thus
implying an attraction towards living in coastline regions.
Tobin's Q Distribution:
Coastal Regions (2000)
2,5
Tobin's Q
2
Stockholm
1,5
Göteborg
Uppsala
1
Linköping
Jönköping
Helsingborg
Örebro
Umeå
0,5
0
Municipalites
No Coastline
Coastline
Figure 5: Distribution of Coastal Regions
Regional differences in climate have also been analyzed and proven to have a relativity
strong effect on location decisions (Black and Henderson, 1999b; Hanson, 2001).
Especially, for studies conducted on larger areas with greater regional differences, for
example globally (Mellinger et al. 2000) and in the U.S (Roback, 1982). Providing
evidence of higher income per capita and population density in areas with a more pleasant
climate. However, on a regional basis the importance of climate is harder to prove, which
can be logically explained by assuming smaller climate variances. For example, in
Mellinger et al. (2000) global study they differentiate regions between six main climate
categories, stretching from tropical to polar. A similar study in Sweden would only apply
a fraction of these categories. Niedomysl (2008) conducted a questionnaire on the
importance of different regional attributes in Sweden, with the climate attribute divided
into “southern” and “northern” weather based on the duration of seasons, temperature and
precipitation. The findings showed that the majority preferred the weather of their current
region, implying either some influence upon deciding where to live or that inhabitants
tend to adapt to the regional climate.
Additionally, it has proved difficult to find the most usable way to measure climate
differences since climate can be interpreted in several ways. Roback’s (1982) study offers
a very broad interpretation of the term by using four different climate variables; (i)
13
snowfall, (ii) heating degree days8, (iii) number of cloudy and (iv) clear days. The study
suggests that the first three variables have a positive correlation with wage rates,
indicating that people living in snowier, colder and cloudier regions demand higher wages
to endure the poor climate, aligning with previous mentioned studies.
2.2.5. Crime
Crime rates are expected to affect the attractiveness of regions in a negative way since
safety issues are often highly prioritized when deciding on residential location (Wang and
Li, 2004). Cullen and Levitt (1999) found strong evidence pointing towards negative
influences of crime rates on population size. Their study show that a ten percent increase
in crime rates leads to a one percent decrease in population size in U.S cities, and that the
population decrease is mainly due to people leaving the region and not a decrease in
immigration. This emphasizes the importance of safety since households are willing to
change their initial choice of location and go through another major transition if they feel
unsafe in their current region. Additionally, Ceccato and Wilhelmsson (2013) discuss the
influence of crime rates on Swedish property prices. Their latest research is based on a
case study similar to this thesis by analyzing the municipality of Jönköping. The most
robust test of their study (using data from 2011) indicated that increasing crime rates had
a significant negative effect on property prices. This observation agrees with several
previous studies implying crime rates interfere with the housing market in a negative way
(Hellman and Naroff, 1979; Gibbons, 2004).
Crime rates have been interpreted in several ways in attempts to prove the expected
negative influence. As mentioned, some previous research have provided evidence
agreeing with the general conception that crime rates acts as a negative externality.
However, the general opinion is that the effect is difficult to statistically prove. For
instance, Gibbons (2004) use data on five crime categories in his analysis on the effect
on the London property market. Out of these, burglary, initially expected to have the
largest negative effect on house prices, proved insignificant. While criminal damage
(graffiti, vandalism etc.) also included in the model proved very significant. Gibbons and
Machin (2008) argue that some of the complication is due to the fact that crime rates are
dependent on other regional factors, for example linked with regional income since it may
be assumed that low-income areas experience a higher frequency of crimes. Or increasing
8
Amount of days below 65 °F (Roback, 1982).
14
with the rate of business activities in an area (Bowes and Ihlanfeldt, 2001). It is also
assumed that the impact of crime rates is difficult for households to realize the effect
from, particularly at the time when they decide to purchase or not (Gibbons and Machin,
2008). Generally, for regions to be rejected due to high crime rates the region has
established a bad reputation over several years.
2.3 Summary of Effects from Market Determinants
Table 1 summaries the expected effect of the identified market determinants. Indicating
whether an increase in each respective determinant will effect regional attractiveness in a
positive or negative way.
Table 1: Expected effect from Market Determinants
Market Determinants
Effect
References
Population Density
Positive
Rappaport and Sachs, (2003)
Congestion
Negative
Henderson (1972); Brueckner
(2000)
Accessibility to Consumer
Positive
Rivera-Batiz (1988)
Positive
Lucas (1988);
Services
Human Capital
Rauch (1993)
Natural Amenities and
Positive and Negative
Koster and Rouwendal (2013)
Climate
Crime
Roback (1982);
Negative
Ceccato and Wilhelmsson
(2013)
15
3. Method
This section will demonstrate the method of application for the variables and regression
results.
3.1 Dependent Variable
James Tobin’s classical theoretical structure of Q-values has been decoded onto the
housing market in different ways. Initially, Berg and Berger (2006) explains the
calculation of the Q-values as marginal price in relation to marginal production cost.
However, in terms of extending theory into practice, data of owner-occupied houses in
Sweden was gathered and later calculated in the following manner:
𝑇𝑜𝑏𝑖𝑛′𝑠 𝑄𝑎
9
𝑃𝑟𝑖𝑐𝑒 (𝑚2 ) 𝑜𝑓 𝑜𝑤𝑛𝑒𝑟 𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑 ℎ𝑜𝑢𝑠𝑒𝑠
=
𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡 (𝑚2 )𝑜𝑓 𝑜𝑤𝑛𝑒𝑟 𝑜𝑐𝑐𝑢𝑝𝑖𝑒𝑑 ℎ𝑜𝑢𝑠𝑒𝑠
Jud and Winkler (2003) interpreted the theory differently by merely examining the market
price levels and the potential arbitrage opportunities consumers may realize when
choosing between new or existing houses. They gathered data from American price
indexes of existing and new houses to develop the following equation:
𝑇𝑜𝑏𝑖𝑛′ 𝑠 𝑄𝑏 =
𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔 ℎ𝑜𝑢𝑠𝑒𝑠
𝑃𝑟𝑖𝑐𝑒 𝑜𝑓 𝑛𝑒𝑤 ℎ𝑜𝑢𝑠𝑒𝑠
Hence, if the price of houses offered to consumers on the existing market exceeds prices
for new houses, demand for new houses will increase and Tobin’s Q will be higher than
one, hence indicate an attractive housing market.
This thesis will however use a third approach also initiated by Berg and Berger (2006).
Unlike the previous suggestion using price levels, this approach measures the unit cost of
existing and new housing and derives the Q-values as follows:
𝑇𝑜𝑏𝑖𝑛′ 𝑠 𝑄𝑐 =
𝑈𝑛𝑖𝑡 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑒𝑥𝑖𝑠𝑡𝑖𝑛𝑔 𝑠𝑡𝑜𝑐𝑘
𝑈𝑛𝑖𝑡 𝑐𝑜𝑠𝑡 𝑜𝑓 𝑛𝑒𝑤 ℎ𝑜𝑢𝑠𝑖𝑛𝑔 𝑒𝑠𝑡𝑎𝑡𝑒
Implying that if cost of building new houses is lower than cost of existing housing stock,
the market is attractive (i.e. Tobin’s Q > 1).
9
Numerator and the denominator are adjusted for net depreciation and production subsidies, respectively
(Berg and Berger, 2006).
16
The three methods demonstrated of measuring Tobin’s Q are only marginally different
and all apply to the same theoretical structure discussed in previous sections.
Consequently, the reason for applying this particular approach is partly due to availability
of data.
3.2 Independent Variables
The first independent variable is the population variable. A region’s attractiveness is
dependent on the density of the population as the majority prefer to live in denser areas
until a certain point (Henderson, 1972). Therefore, the variable representing population
density is followed by a squared version of itself, specifying the congestion variable. This
quadratic test helps to identify the eventual diminishing returns from population growth
caused by congestion (Gujarati and Porter, 2009). Figure 6 below demonstrates the nonlinear relationship between municipal population and its second order term to highlight
the eventual diminishing return from regional population growth.
Population per Municipality
Diminishing Returns of Population
Population2
10
Figure 6: Diminishing Returns of Population Density
The inverted U-shaped form of the line suggests that after certain point, population
density may become unbearable for a region’s inhabitants (e.g. due to traffic congestion)
and the positive effects from population growth will cease.
The most common way to estimate human capital is to identify the proportion of people
with a certain academic background. Although, this approach has been adopted in
different ways. For instance Barro and Lee (2013) produce a very complete examination
by using four different levels of education when analyzing a population sample, the levels
being; no formal education, primary, secondary and tertiary education. However, the
10
The four largest municipalities (Stockholm, Göteborg, Malmö and Uppsala) are discarded to avoid
biasness and to generate a clearer demonstration.
17
thesis will adopt a more common and narrow approach by only consider groups with
higher education, for instance like previous studies using the share of people with a
college degree and/or high-school degree (Black and Henderson, 1999a; Farnham et al.,
2011). This thesis will interpret human capital as the share of the labor force with a threeyear-bachelor degree or higher.
The accessibility to consumer services will be represented by two separate variables. One
denoting the amount of consumer services offered within the municipality. While the
second denotes the amount of consumer services accessible within the FR-region of the
municipality. Since these services are non-tradable commodities, quantifying the amount
of local consumer services available is relatively straightforward if we assume employed
labor within the relevant service industries equals the supply (Johansson et al., 2002). The
second variable regarding consumer services is included since the intensive commuting
across municipalities sharing FR-region will generate some degree of access to the
consumer services offered within the entire FR-region (Johansson et al., 2002).
A dummy variable will be included to represent the identified coastal regions in Sweden.
The coastal regions are defined as municipalities with at least one building located within
100 meters from the coastline11. This variable will determine whether the proximity to
water positively affect the Q-values. Additionally, two separate climate variables,
temperature and precipitation will be included. The two climate variables are based on
the average regional temperature and precipitation over three decades (1961-1990). The
thesis optioned to settle for two climate variables due to the relatively similar climate
conditions within the country and also due to the comprehensive data available via the
Swedish Meteorological and Hydrological Institute (SMHI). However, SMHI measure
climate by using weather station spread across the country and since the municipalities
differ in geographic appearance some quantities will unfortunately be localized.
Additionally, some of the weather stations represents two or more municipalities (ex: the
weather station located at Arlanda represents five different municipalities) which may,
even though the weather conditions are not assumed to vary significantly, lead to some
bias in the regression.
Crime is as discussed in the theory difficult to measure in an efficient way. For a regional
analysis two main approaches are found to be appropriate; crime rates and crime density.
11
see appendix 8.1 for map of municipal distribution
18
Crime rates is simply expressed as reported crimes in relation to the population size, while
crime density is the ratio between reported crimes and region size. Bowes and Ihlanfeldt
(2001) argue that crime density is a more appropriate measurement for studies concerning
smaller neighborhoods, due to the higher probability of crimes occurring around business
activities which may vary across areas. However, for this thesis using crime rates is a
valid approach since the analysis is based on larger areas all including some business
activity.
3.3 Empirical Design
The thesis will use an ordinary least square (OLS) regression model as demonstrated
below, to describe the relationship between the attractiveness of a region expressed as
Tobin’s Q-values, and nine market determinants. The data sample concern 289 out of
Sweden’s municipalities and is stretching from 2000-2010. Most data is gathered from
Swedish Statistics, for opposing cases the data source will be mentioned.
The regression model used is demonstrated below followed by an explanation of the
variables in Table 2.
2
𝑇𝑜𝑏𝑖𝑛′ 𝑠 𝑄𝑖,𝑛−𝑡 = 𝛽1 + 𝛽2 𝑃𝑜𝑝𝐷𝑒𝑛𝑖𝑡 + 𝛽3 𝑃𝑜𝑝𝐷𝑒𝑛𝑖𝑡
+ 𝛽4 𝐻𝑢𝑚𝐶𝑎𝑝𝑖𝑡 +
𝛽5 𝑀𝑢𝑛𝐴𝑐𝑐𝑖𝑡 + 𝛽6 𝐸𝑥𝑡𝐴𝑐𝑐𝑖𝑡 + 𝛽7 𝐷𝑐𝑜𝑎𝑠𝑡𝑎𝑙𝑖𝑡 + 𝛽8 𝑇𝑒𝑚𝑝𝑖𝑡 + 𝛽9 𝑃𝑒𝑟𝑐𝑖𝑝𝑖𝑡 +
𝛽10 𝐶𝑟𝑖𝑚𝑒𝑖𝑡 + 𝜀𝑖𝑡
With i and t representing the considered municipalities and base year 2000, respectively.
And n is year 2005 and 2010.
19
Table 2: Description of Independent Variables
Variables
Functional Form
Description
Tobin’s Q (Y)
Unit cost of existing stock
Unit cost of new housing estate
PopDen (X1)
Municipal Population
Area (𝑘𝑚2 )
PopDen2 (X2)
Municipal Population 2
[
]
Area (𝑘𝑚2 )
Educated people
Total employment in muncipality
Population density squared indicates
the effect of a congested area
MunAcc (X4)
Employment CS12
Total employment in municiaplity
ExtAcc (X5)
(Empl CS FR − Region − Empl CS Mun)
Total employment in FR − region
Share of the municipal work-force
employed in consumer service
businesses accounts for the amount
of consumer services available
Share of work-force employed in
consumer service businesses within
FR-region
HumCap (X3)
Dependent variable representing
regional attraction measurement
Regional population density
Share of work force with a 3-yeardegree or higher
Dcoastal (X6)
1: coastal region
0: non-costal region
Dummy variable for coastal
municipalities
Temp (X7)
Average temperature 1961-1990
Average temperature measured in
Celsius from 1961-1990.13
Percip (X8)
Average precipitation 1961-1990
Average precipitation measured in
millimeters from 1961-1990.
Crime (X9)
Reported crimes
Population
Reported crimes per capita14
The variables are all expressed as a share of a relevant sample, thus allowing the variables
to be kept in in normal percentage form when running the regressions. A series of
regression test will be completed in order to generate a comprehensive discussion of the
effects of the market determinants and how the observed effect differ across urban-rural
range. Initially, a base year is selected (year 2000) from which the independent variables
will be gathered. The first regression test will be a nationwide test analyzed solely on data
from base year 2000 in. This regression will be followed by a robustness test on a five
and ten year time interval, engineered by changing the dependent variable to the observed
change in Tobin’s Q within the two intervals (i.e. Δ 2000-05/10). If the coefficients
12
Consumer service industries
SIC codes:
SIC (92): 55111-55529, 92310-92792
13
Data on climate variables are gathered from the Swedish Meteorological and Hydrological Institute
(SMHI).
14
Data on crime rates are gathered from The Swedish National Council for Crime Prevention (BRÅ,
2014).
20
change sign or indicate different levels of significance for the models with the observed
change as dependent variable, the variable effect cannot be considered robust and fully
reliable. These regression are performed to test the first hypothesis (refer to section 3.4).
In order to test the second hypothesis (refer to section 3.4) the municipalities has to be
divided into groups representing different region types. The region types compared are
central regions and peripheral regions. The central regions amounts to 139 municipalities
while the remaining 150 are considered peripheral municipalities. The test for robustness
constructed by changing the dependent variable will be repeated here.
3.4 Hypotheses
In light of the discussed theory two hypothesis can be generated:
-
Hypothesis 1:
The influence of the identified market attractors (detractors) is positive
(negative)
As part of Hypothesis 1 the market determinants will be analyzed in light of expectations
developed from theory to evaluate each respective effect. Refer to Table 1 (section 2.3)
for a summary of the expected influence. A market attractor is interpreted as a market
determinant inducing a positive effect, while a market detractor negatively affect regional
attractiveness.
-
Hypothesis 2:
The influence of the identified market determinants vary across urban-rural
range
As part of Hypothesis 2 the analysis will concentrate on the differences in behavior among
the coefficients in order to evaluate the assumed variances across urban-rural range.
21
4. Results and Analysis
This section will begin with a scrutiny of the variables descriptive statistics and
correlation matrix in order to generate an overview of the variable behavior. Followed by
a comprehensive analysis of the regression models. The analysis will be conducted in
light of the two hypothesis shaped from the discussed theory.
4.1 Descriptive Statistics
An examination of the statistics for the dependent variables for the considered years can
be seen in Table 3.
Table 3: Descriptive Statisics for Dependent Variables
Standard
Tobin’s Q
Minimum
Maximum
Mean
Median
Deviation
0.23
2.41
0.701
0.590
0.349
Δ 2000-05
-0.11
0.67
0.079
0.050
0.120
Δ 2000-10
-0.14
0.93
0.152
0.120
0.1872
2000
The mean value has increased over the period indicating an overall rising market. Since
the mean values are marginally higher than the median a slight positively skewed
distribution can be detected. The Tobin’s Q rule of thumb (Q<1 = unattractive market) is
also worth taking into account, and by comparing it to the mean value of 2000 it suggests
that the national average housing market was unattractive at the time.
Table 4 below demonstrates the descriptive statistics for independent variables of base
year 2000.
Table 4: Descriptive Statistics for Independent Variables
Variables
Minimum
Maximum
Mean
Median
(2000)
Standard
Deviation
PopDen
0.270
4008.915
124.490
26.530
417.120
PopDen2
0.070
16071372.90
188887.94
703.65
1411163.92
HumCap
0.060
0.840
0.1863
1.156
0.111
MunAcc
0.01
0.180
0.046
0.041
0.022
ExtAcc
0
0.011
0.043
0.045
0.017
DCoastal
0
1
0.3010
0.00
0.459
-1.90
8.40
5.126
5.80
2.134
344
1076.70
637.443
605.600
132.458
0.002
0.205
0.095
0.096
0.033
Temp (°C)
Precip (mm)
Crime
22
PopDen and PopDen2 generates large standard deviations and is positively skewed,
evidently due to the large variances between the dense metropolitan regions and the sparse
rural regions. The mean of DCoastal implies that 30 percent of the municipalities in Sweden
are coastal regions. The remaining variables seem to be fairly normally distributed.
Table 5 presents the bivariate correlation between all the included variables at base year
2000.
Table 5: Bivarite Correlation Matrix for Base Year
Tobin
Q
Pop
Den
Pop
Den2
Hum
Cap
Mun
Acc
Reg
Acc
Dcoastal
Temp
PopDen
0.608*
PopDen2
0.429*
0.931*
HumCap
0.660*
0.311*
0.123*
MunAcc
0.237*
0.066
0.070
0.194*
ExtAcc
0.346*
0.197*
0.155
0.310*
-0.020
DCoastal
0.481*
0.198*
0.106
0.279*
0.116*
0.149*
Temp
0.414*
0.167*
0.071
0.202*
-0.185*
0.287*
0.175*
Precip
-0.045
-0.128*
-0.108
-0.127*
-0.173*
0.066
-0.069
0.189*
Crime
0.337*
0.325*
0.243*
0.102
0.124*
0.142*
0.165*
0.263*
Precip
-0.023
*Pearson Correlation is significant at the 5% level (2-tailed)
Two or more variables experiencing a combined perfect linear relationship will cause
multicollinearity and harm the regression model (Gujarati and Porter, 2009). The
threateningly high correlation between Pop and congestion PopDen2 is due the fact that
the latter is the quadratic form of the first. Both variables will later be mean centered in
order to reduce some of the correlation. Mean centering is implemented by subtracting
the overall mean of all observations from each individual observation. This method is
common when dealing with polynomial regressions. Each correlation value is tested for
significance as demonstrated in the table.
23
Lastly, prior to running the regressions, Table 6 presents the mean values of each
variable for the two sub-samples; central and peripheral municipalities15
Table 6: Mean Values for Sub-Samples
Variable Mean Values
Central
Peripheral
139
150
Tobin’s Q (2000)
0.8553
0.5583
Tobin’s Q (Δ 2000-05)
0.1253
0.0351
Tobin’s Q (Δ 2000-10)
0.1945
0.1145
PopDen
224.9278
31.430
PopDen2
389647.5
2850.6
HumCap
0.2899
0.1468
MunAcc
0.0517
0.0404
ExtAcc
0.0407
0.0446
DCoastal
0.4029 (55*)
0.2067(31*)
Temp (°C)
5.146
5.1080
Precip (mm)
623.8
650.0
0.1073
0.0835
(n)
Crime
*number of coast regions
The average Tobin’s Q for base year 2000 suggests that central regions are more attractive
than peripheral regions as well as the national average (refer to Figure 3, section 2.1).
Additionally, the average increase in Tobin’s Q values is significantly higher for the
central regions during both periods, suggesting faster growing and more attractive
markets within these regions. The mean values of the two population variables (PopDen
and PopDen2) are understandably greater for the central regions. The average human
capital (HumCap) is also higher in the central regions agreeing with theory regarding
human capital and its tendencies to cluster around well-endowed regions. The mean
values for the two consumer services variables (MunAcc and ExtAcc) suggests an inverse
relationship with the central regions experiencing higher accessibility within the
municipality, while the peripheral regions tend to be more dependent on supply from
adjacent regions. This effect is most probably due the distribution of the FR-regions as
15
see appendix 8.2 for complete descriptive statistics table
24
most include one larger central municipality (associated with more consumer services)
surrounded by smaller peripheral municipalities. Crime show higher values for the central
regions presumed to be because the higher population density and greater concentration
of businesses in these regions. The mean values for the dummy variable representing
coastal regions (Dcoastal) shows the distribution of coastal access between the two region
types, with approximately 40 percent of the central municipalities and 20 percent of the
peripheral municipalities are aligned along the coast.
4.2 Regression Models
The two tables in this section presents the coefficients of a total of nine regression tests.
Table 7 includes three regressions on nation-wide data with the first based on data from
base year 2000, followed by two representing the change of each respective time interval.
Table 8 presents three regressions for each sub-groups (total of six regressions) running
the same estimations. To remediate for multicollinearity, the two population variables
(PopDen and PopDen2) with observed high correlation have been mean centered. This
method lowered their variance inflation factor (VIF). However, as previously discussed
the high correlation between the two variables is understandable due their quadratic
relationship, hence some multicollinearity is present16. All regressions are tested with
Newey-West standard errors (HAC) to minimize the potential threat of both
heteroscedasticity and autocorrelation (Gujarati and Porter, 2009). The Newey-West
standard errors suffices to this study since the number of observations are large enough
and signs of, first and foremost, heteroscedasticity is present17 (Gujarati and Porter, 2009).
Heteroscedasticity is a common occurrence when analyzing cross-sectional data. Crosssectional data concern members of a population at a certain time and these members are
often divided into sub-populations (e.g. members of a certain municipality). Since these
sub-populations experience varying surroundings the disturbance term in a crosssectional regression is most likely non-constant, which suggests heteroscedasticity is
present (Gujarati and Porter, 2009).
16
17
See appendix 8.3 for VIF test.
See appendix 8.3 for heteroscedasticity test.
25
Table 7 below presents the coefficient for the initial nation-wide regression tests. The
Newey-West standard errors are displayed in parentheses and each significant coefficient
is marked with asterisks. The R2 and F-value are also included in order to highlight the
validity of each regression.
Table 7: Regression Models: Nation-Wide
Variable
Tobin’s Q
2000 (base year)
Tobin’s Q
Δ 2000-2005
Tobin’s Q
Δ 2000-2010
Constant
0.0105
(0.066)
0.0006**
(0.0002)
-8.28E-08**
(3.53E-08)
1.0325**
(0.1784)
2.6542**
(0.5557)
0.7556
(0.5218)
0.1670**
(0.0271)
0.0346**
(0.0057)
0.0002*
(8.04E-05)
0.3484
(0.4175)
0.77
102.74
-0.2578**
(0.0399)
2.74E-05
(5.43E-05)
1.20E-08
(1.23E-08)
0.2322**
(0.1087)
1.2032**
(0.3230)
0.3183
(0.2954)
0.0469**
(0.0130)
0.0173**
(0.0031)
8.91E-05*
(3.32E-05)
0.6585**
(0.1596)
0.53
35.22
-0.4054**
(0.0602)
-0.0001*
(5.81E-05)
1.60E-08
(1.59E-08)
0.2451**
(0.0884)
2.6539**
(0.4875)
-0.3965
(0.6037)
0.1384**
(0.0253)
0.0419**
(0.0052)
0.0002**
(6.50E-05)
0.4719
(0.2944)
0.54
37.38
PopDen
PopDen2
HumCap
MunAcc
ExtAcc
Dcoastal
Temp
Percip
Crime
R2
F
**significant coefficients at the 5% level
* significant coefficient at the 10% level
(Newey-West Standard Errors in parentheses)
Beginning by analyzing the R2 values, each test indicates a fairly good model fit. Adding
more variables may enhance the R2 but brings a risk of increasing forecast errors (Gujarati
and Porter, 2009). The high F-values show signs of an overall significance for the
regressions since the general hypothesis, stating that together all the independent
variables have zero effect on the dependent variable, can be rejected (Gujarati and Porter,
2009).
Initially, analyzing the base year regression confirms that seven out of the total nine
explanatory variables indicate significant values. Population density (PopDen) suggests
a small positive impact, while congestion (PopDen2) suggests a small negative impact
confirming the theory of diminishing positive effects from regional population growth
26
(Henderson, 1972; Brueckner, 2000). Neither variable manage to stay constant in sign or
significance across the robustness test. Hence, conclusions regarding these variables
should be made with caution. Human capital (HumCap) remain significant and positive
through each tests implying that a higher educated people labor force is likely to
positively effect a region, for instance by increasing productivity rates as suggested by
Lucas (1988), which subsequently leads to increased demand on the housing market.
Additionally, it is believed that isolated influence caused by the attraction of popular
academic institutions may increase regions both in terms of population density and human
capital, as suggested by the relatively high correlation (0.31) between the two independent
variables (refer to Table 5).
Out of the two variables measuring accessibility to consumer services, the internal
accessibility (MunAcc) estimates high and positively significant coefficients over all three
tests, while the external accessibility (ExtAcc) fail to impact the dependent variable in a
significant way. From a statistical perspective the large impact of the internal accessibility
to consumer services can be partly explained by assuming that the variable absorb effects
originated from other factors, for instance from population density since the consumer
services are associated with the amount of people working in the relevant industries.
Nevertheless, the significance results suggests that a greater variety of consumer services
offered within a region will enhance regional attractiveness and increase demand for
houses, pushing the market towards an excess demand for houses (Q>1). This aligns with
Rivera-Batiz (1988) theory concerning utility and its tendencies to increase if the
widespread demand of consumer preferences can be satisfied. Johansson et al. (2002) also
found internal accessibility to consumer services to positively affect their chosen
measurement of region attractiveness (using population growth rates). However, likewise
to this thesis their variable for external accessibility to consumer services in adjacent
regions (using a comparable FR-region approach) failed to show a significant impact.
The interpretation of Dcoastal implies that since the coefficient remain positive and
significant the regions with a coastline tend to have on average higher Q-values. This
agrees with previous research such as; Koster and Rouwendal (2013) findings of higher
house prices closer to shore, and Glaeser et al. (2001) verifying the appeal of living near
the coastline based on findings of higher population growth around these areas. The two
climate variables (Temp and Percip) indicate low but significant coefficients for each
regression. Previous studies performed on large areas suggest that people prefer warmer
27
climate with low precipitation (Mellinger et al 2000; Roback, 1982). For this test the
temperature variable supports this theory while the effect from an increased precipitation
is instead expected to be negative. The positive effect from temperature can be explained
by the higher Q-values associated to the southern part of the county (refer to Figure 1 and
2, in section 2.1). The influence can however be questioned since weather conditions in
Sweden tend not to differ excessively as insinuated by the observed intervals in Table 4.
Additionally, previous theory suggest no major variance in influence within the country
(Niedomysl, 2008). The Crime variable representing regional crime rates contradicts the
expected influence of regional attractiveness since it display positive coefficients. One
reason for this can be, as briefly suggested in theory, that crime rates are dependent on
other regional factors (Bowes and Ihlanfeldt, 2001). For instance as crime tend to group
around business intensive areas it is more likely to occur in central regions often
associated with higher Q-values. Furthermore, since the crime variable is based on the
total amount of crimes and not focused on the severity behind it, the effect caused by an
unsafe environment may be outdone by the fact that regions with a larger population will
be punished for more crimes (including lenient crimes, e.g. parking tickets). Nonetheless,
since only one out three test show significant coefficients the results cannot be fully
reliable.
In light of the regression results the first hypothesis stated in section 3.4 can be answered.
Hypothesis 1 can be fully accepted for variables if they remain significant and consistent
in signs through all tests in Table 7. Hence, the hypothesis test cannot be fairly judged for
either ExtAcc or Crime as they fail to impact the Q-values significantly in most tests,
therefore the hypothesis is rejected for these variables. Even as both population variables
(PopDen and PopDen2) demonstrates the suggested signs for the base year test, neither
manage to produce robust coefficients and should, as previously mentioned, be handled
with caution. With this in mind the hypothesis can only be partly accepted for both
variables. Percip should according to theory be considered a regional detractor leading to
a rejection of the hypothesis since the coefficients produce positive signs. For the
remaining variables (HumCap, MunAcc, DCoastal and Temp), hypothesis 1 can be fully
accepted since they stay significant and positive for all tests and should therefore be
considered regional attractors.
28
Table 8 below demonstrates a total of six regression tests performed on the two subgroups. The same procedures for dealing with potential statistical violations applied to
previous regression models have been repeated here.
Table 8: Regression Models: Sub-Samples
Variable
Tobin’s Q 2000
(base year)
Central Peripheral
Regions
Regions
Tobin’s Q Δ2000-05
Tobin’s Q Δ2000-10
Central
Regions
Peripheral
Regions
Central
Regions
Peripheral
Regions
-0.2270**
(0.0642)
1.44E-05
(5.97E-05)
1.39E-08
(1.41E-08)
0.1231**
(0.0546)
1.1697**
(0.6148)
0.1934
(0.3736)
0.0485**
(0.0166)
0.0227**
(0.0048)
-0.2736**
(0.0706)
0.0003**
(0.0001)
-2.42E06**
(1.18E-06)
0.4512**
(0.1264)
0.6274**
(0.2769)
2.2789**
(0.7138)
0.0198**
(0.0096)
0.0144**
(0.0031)
-0.3390**
(0.0649)
-0.0001*
(5.41E-05)
1.79E-08
(1.55E-08)
0.8537**
(0.2240)
2.7229**
(0.7033)
2.4196**
(0.8778)
0.1599**
(0.0279)
0.0305**
(0.0092)
0.3906**
(0.1595)
0.0014**
(0.0003)
-1.45E05**
(3.66E-06)
0.7877**
(0.1920)
2.1406**
(0.6835)
0.5933
(1.5874)
0.1574**
(0.0425)
0.0210**
(0.0068)
0.2080**
(0.0918)
2.3074**
(0.7080)
-0.9139
(0.8036)
0.1015**
(0.0231)
0.0437**
(0.0076)
-0.4144**
(0.1500)
0.0008**
(0.0002)
-6.73E06**
(2.58E-06)
0.5046**
(0.2579)
2.3105**
(0.6105)
3.0931*
(1.6157)
0.1475**
(0.0368)
0.0375**
(0.0075)
Percip
9.40E-05
(9.25E-05)
0.0002*
(0.0001)
5.72E-05
(4.69E-05)
0.0001**
(4.57E-05)
0.0001*
(7.65E-05)
0.0003**
(8.34E-05
Crime
-0.1571
(0.7742)
-0.7552
(0.4699)
0.7252**
(0.2890)
0.0011
(0.1893)
0.5450
(0.5193)
-0.2630
(0.3682)
0.80
0.60
0.55
R2
55.57
23.97
17.56
F
**significant coefficients at the 5% level
* significant coefficient at the 10% level
(Newey-West Standard Errors in parentheses)
0.45
12.48
0.54
16.84
0.63
26.84
Constant
PopDen
PopDen2
HumCap
MunAcc
ExtAcc
Dcoastal
Temp
0.1267
(0.1033)
0.0005**
(0.0001)
-7.18E-08*
(3.68E-08)
Initially, in regards to the two test statistics included, the R2 shows a similar model fit for
the central regions as for the nation-wide models for base year 2000. Even though R2
values for the peripheral regions indicates a worse fit the levels are still acceptable. All
the F-values show an overall significance.
Similarly to the nation-wide tests, population density (PopDen) and congestion
(PopDen2) show significant and expected signs for base year 2000 for the two region
types. Nevertheless still very small, they indicate a diminishing return of population
growth as previously discussed. As for the difference between the region types, although
29
very small, worth observing is that the peripheral regions experience larger effect from
both variables. Suggesting that peripheral regions are more sensitive to increased
population density. Analyzing the robustness of the two variables, both fail to show
consistency in sign or significance for the central regions. On the other hand they manage
to stay robust for the peripheral municipalities and can, according to these tests, be fully
reliable when solely analyzing these municipalities. This outcome advocates the decision
of dividing the regions into two sub-samples and further emphasizes the differences of
impact across urban-rural range. Human capital (HumCap) is significant and have a
positive effect across all tests. Glaeser (2000) and Audretsch (2000) suggests an increase
in human capital is more influential in regions with a greater knowledge base. In line with
this, Table 6 presents a higher average percentage rate of educated labor in the central
regions. The results for our first regression (base year 2000) aligns with the theory since
the effect is marginally higher for the central regions.
For the first out of the two accessibility variables, the internal accessibility (MunAcc)
remain robust and positively significant for all tests. The coefficients demonstrates a
strong impact on market attraction, particularly for the central regions, again suggesting
that a wider range of consumer services offered will further enhance the region (RiverBatiz, 1988). The external accessibility (ExtAcc) behaves rather ambiguously across the
two region types as it generates a significant coefficient for the central regions during
base year 2000, followed by two insignificant results. Whereas, conversely the
coefficient for the peripheral regions during base year 2000 is insignificant, followed by
two significant results. Evidently, this variable has to be treated with caution if to be
trusted at all. The significant effect on the central regions for the base year model may
be predominantly caused by the broad supply accessible to the small central
municipalities bordering the metropolitan areas18.
The effect from the natural amenities of the shoreline (Dcoastal) show similar behavior to
the nation-wide tests and remain fairly homogenous throughout each tests signifying
robustness but no major difference across urban-rural range. Likewise, the two climate
variables (Temp and Percip) show similar results to the nation-wide test. This is expected
since the sub-sample distribution of the municipalities is not based on geographic
For example, Stockholm’s’ effect on the attractive municipality of Solna (Tobins’s Q of 2.28) is clear,
as Solna records the 9th highest regional accessibility to consumer services in the nation, which is mainly
due to the fact Solna share FR-region with Stockholm.
18
30
location. Additionally, by revising Table 6 no substantial differences in average
temperature or precipitation can be observed. Dividing the sample into two sub-groups
has slightly amplified the influence of crime rates (Crime). Even though none of the
coefficients are significantly different from zero, the signs for the regressions of base year
2000 are now negative as suggested by theory (Hellman and Naroff, 1979; Ceccato and
Wilhelmsson, 2013). The peripheral regions appear to be more negativity influenced by
an increase in crime rates whereas the central regions on average experience more crime
(refer to Table 6). Even so, a lengthier analysis concerning crime rates serves little
purpose since the majority of coefficients fail to show significant results.
In regards to the hypotheses from section 3.4, hypothesis 2 can be fully accepted for
variable coefficients that remain constant in sign and significance level, while varying
across the two sub-samples. Initially, none of the climate variables (Temp nor Percip)
along with the dummy variables for coastal regions generated any significant variance
between the two region types and therefore the hypothesis is rejected. A combined
analysis of the two population variables (PopDen and PopDen2) suggests, as previously
discussed, a more sensitive impact from an increase in population density on peripheral
regions. However, the coefficients are still minor and failed to indicate robustness for the
central regions, hence conclusions should be made carefully and the hypothesis can only
be partly accepted for both variables. HumCap and MunAcc indicate the largest variance
between the two region types and the hypothesis can be accepted for both variables. Once
more, the hypothesis test cannot be fairly judged for either ExtAcc or Crime since for
most tests the coefficients cannot prove to be significantly different from zero.
31
5. Case Study: Jönköping
The municipality of Jönköping is located far from the attractive coastline and Sweden’s
larger metropolitan areas but has seen a recent rising housing market making it an
interesting matter of study. This thesis has been written in accordance with the local
government of Jönköping in order to analyze their current queries and hence a case study
of the municipality of Jönköping has been included.
The municipality is located just south of Sweden’s second largest lake, Lake Vättern.
Jönköping is today Sweden’s 10th largest municipality in terms of population and has
experienced a growing population all through the period of study (2000-2010). To
analyze the municipality in line with the thesis’ approach an initial overview of the
region’s Q-value is presented in Figure 7.
Tobin's Q
Tobin's Q Trend Line
1,8
1,7
1,6
1,5
1,4
1,3
1,2
1,1
1
0,9
0,8
0,7
0,6
0,5
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009
National Av Q
JKP
HEL
NOR
Figure 7: Tobin’s Q Trend Line for Jönköping and Comparable Municipalities
Source: Data extracted from Berg and Berger (2006), chart assembled by author.
For comparative purposes the national average along with two additional municipalities
have been included in Figure 7. The additional municipalities are Norrköping (8th largest),
Helsingborg (9th), both hand-picked based on similarities in population rates. Prior to
1993 Jönköping performed equivalently to the national average and well below the
additional municipalities of Helsingborg and Norrköping. By analyzing Figure 8 below,
the realization of Jönköping’s unfavorable location becomes clear. Jönköping (1) is
located in the center of the figure and is the focal point of its county with the nearest
metropolitan region, Göteborg (8), relatively distant. As repeatedly stressed in this thesis,
32
urban sprawl often originates around established regions located along the coast and
Jönköping has therefore undoubtedly suffered from its location. In the case of
Helsingborg (6) one may argue that its favorable location, situated close to a large
metropolitan region (Malmö, 9) and along the coast, has clearly enhanced its housing
market. The comparison to Norrköping (5) further highlights the recent surge of the
Figure 8: Map of Southern Sweden
attraction level of Jönköping as the two municipalities have comparable distances to
metropolitan regions but historically very different market performances. Norrköping has
throughout the period experienced an attractive housing market, with Tobin’s Q values
well above the national average and in line with Helsingborg during most of the 1980s.
However, without suffering much internally, Norrköping has seen the rapid increase of
Jönköping outperform its own market since the beginning of the 21st century.
One of the main reasons behind the rising market of Jönköping is believed to be due to
the growing university located in Jönköping. As previous research and the results from
the thesis suggests, human capital has a positive impact on regions. The thesis analysis
argue that the isolated influence of a popular academic institution may affect regional
population growth since the two variables shared a relatively high correlation.
33
Additionally, The City Office (2013) reported that in 2010 the share of University of
Jönköping graduates, living in the municipality, had increased by the time they had
graduated. Suggesting that an increasing amount of individuals chose to stay in the region
after graduating. Table 9 below, demonstrates the national ranking of the relevant market
determinants used in this thesis and entails that Jönköping has a higher percentage of
highly educated people in their labor force compared to Norrköping, thus reinforcing the
assumption.
Table 9: National Ranking of relevant Market Determinants
National Ranking (2000): Variables
Jönköping
Helsingborg
Norrköping
Population Density
64th
20th
60th
Human Capital
95th
89th
133rd
Internal accessibility to consumer services
78th
99th
80th
External accessibility to consumer services
214th
141st
244th
57th
6th
10th
Crime rates
The thesis empirical analysis further suggested that the accessibility to consumer services
also serves as a strong regional attractor. In regards to the ranking in Table 9, Jönköping
has the highest percentage of internal accessibility to consumer services and the second
highest for the regional accessibility. The supply of regional consumer services originates
from the three additional municipalities included in the FR-region; Habo (2), Mullsjö (3)
and Vaggeryd (4) (refer to Figure 8). These municipalities are significantly smaller and
rely heavily on supply from Jönköping. In contrast, Helsingborg is one out of 11
municipalities in its respective FR-region, hence absorbing more resources from
neighboring regions as Table 9 demonstrates. Furthermore, the difference in crime rates
between regions is quite substantial. Theory suggests that crime rates often depend on
other regional factors, for instance population density as seem to be the case for
Helsingborg. Jönköping does however indicate low crime rates in portion to it population
size. Out of the 20 largest municipalities in Sweden, only two register lower crime rates
than Jönköping in 2000. Nilsson (2014) states that the central district of Jönköping is
rather small, which may explain part of the low crime rates since theory suggests crime
rates cluster around business intensive areas. As a final point, the natural scenery of
regions have proved to enhance attractiveness. The thesis has, for restrictive purposes,
chosen to express this factor in terms of proximity to the seashore and therefore not
34
included Jönköping in this group of municipalities. However, one may very well argue
that the appeal of Lake Vättern does enhance attraction to the municipality of Jönköping
in similar ways.
Conclusively, in order to relate the thesis approach to the market attraction of Jönköping,
a residual analysis will be performed. Applying the observed values in Jönköping during
base year 2000 to the nation-wide regression model from table 7 predicts Jönköping’s Qvalue to be 0.73. However, our observed Q-value for the same year is 0.83, implying the
residuals measure up to 0.1. The positive residuals suggests that the model underestimate
the performance of Jönköping in 2000. Or in other words, due to unobserved factors
offered within the municipality of Jönköping the region is more attractive than expected.
An emerging university, increasing population rates and the increase in Tobin’s Q
suggests the region is growing. And with an observed Q-value of 1.32 in 2010 the local
housing market is now experiencing an excess demand.
35
6. Conclusion
This thesis has applied a relatively new and experimental approach in its attempt to
analyze factors effecting the Swedish housing market, by using a traditional investment
instrument, the Tobin’s Q, as a housing market attraction measurement. The Tobin’s Q,
when applied to the housing market, is a ratio between the cost of existing house stock
and cost of new housing estate. The ratio signifies whether or not a regional market is
attractive for investors. The main advantage of using Tobin’s Q in this kind of analysis is
the convenient interpretation of the ratio, since it generates a clear market equilibrium as
a point of reference (Q=1) and therefore simplifies comparisons between regions.
Furthermore, the varying price range and construction cost between regions is recognized
by using the ratio, thus making any comparisons unbiased. Nine established regional
market determinants have been tested against the Tobin’s Q in order bring light to their
effect on the local housing market. The determinants have been analyzed nation-wide as
well as across urban-rural range in order to generate a comprehensive understanding of
their market influence. Population density, congestion, human capital, accessibility to
consumer services, proximity to shore, climate and crime rates are the market
determinants analyzed.
Two hypotheses were created to bring structure to the analysis. The empirical findings
could confirm that six out of the nine market determinants significantly influenced the
market in line with expectations. Whereas, four of which showed signs of varying effects
between the two region types. Firstly, suggesting that a higher level of educated people
represented in the labor force of a municipality will enhance the appeal of any region
type. However, the influence proved to be higher within central regions. Likewise, a
greater amount and variety of consumer services proved to positively affect a central
region slightly more than a peripheral region. Population density caused a minor positive
impact on both regions and combined with the variable representing congestion it
confirmed the assumption of diminishing returns from population increased population
density. The influence was also more prominent for the peripheral regions. Natural
amenities such as proximity to the seashore along with higher average temperatures
proved to enhance any region type. However, crime rates and consumer service supply
from adjacent regions did not generate adequate results to be reasonably accepted for
either hypotheses. While precipitation generated minor positive effects, hence
contradicting expectations.
36
As mentioned in the introduction, the large amount of factors to consider for households
before entering the housing market complicates any attempt to analyze the market.
Evidently, identifying the market determinants proved difficult and by applying other
relevant variables the findings would be different. Another approach could be to
recognize some factors as control variables in order to isolate focus and analyze certain
variables in more depth. Furthermore, even as the period of time analyzed was primarily
set in order to control for robustness, more emphasis could be directed towards the results
from the models analyzing the variables relationship to the change in the observed
Tobin’s Q values. Since Tobin’s Q is first and foremost an investment measurement the
change over time is what investors wish to focus on and analyze. Likewise, emerging
rural municipalities in unfavorable locations are also of interest since the source of the
surge may be undisclosed. Future studies should take this into consideration when
attempting to analyze the housing market. Implementing Tobin’s Q as an alternative to
house prices or other regional attractor measurements is encouraged, since the
transparency and convenience of the ratio generates a good proxy for regional
attractiveness. Future academic publications as well as local governments crafting reports
on regional development could gain from considering the use Tobin’s Q. Whether the
question concerns investment possibilities, comparison of markets or effects from market
determinants the use of Tobin’s Q is endorsed.
37
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41
8. Appendix
8.1 Appendix 1
Map of Municipal Distribution
42
8.2. Appendix 2
Standard
Minimum
Maximum
Mean
Median
Deviation
Cen
Per
Cen
Per
Cen
Per
Cen
Per
Cen
Per
0.27
0.23
2.41
1.74
0.855
0.558
0.800
0.520
0.401
0.209
PopDen
0.269
0.86
4008.9
307.04
224.92
31.430
52.710
19.074
584.39
43.30
PopDen2
0.07
0.74
160713
94271.
389647
2850.6
2778.3
363.83
201930
10976.
72.9
0
.5
3.11
5
Tobin’s
Q (2000)
5
0.06
0.07
0.84
0.36
0.228
0.146
0.177
0.133
0.138
0.138
0.01
0.01
0.18
0.14
0.0517
0.0404
0.0476
0.0361
0.0228
0.0199
0.0
0.02
0.11
0.10
0.0407
0.0446
0.0499
0.0443
0.0229
0.0101
0
0
1
1
0.4029
0.2067
0.000
0.000
0.4922
0.4062
Temp
-1.90
-1.40
8.4
7.80
5.1460
5.1080
5.9
5.650
2.3605
1.9089
Precip
344.0
426.30
1076.7
1050.9
623.86
650.02
570.1
617.45
145.56
118.13
0.0018
0.20
0.204
0.1073
0.0835
0.1056
0.0835
0.0319
0.0319
Hum
Cap
MunAcc
ExtAcc
DCoastal
0
Crime
0.04
Complete descriptive statistics for the central (cen) and peripheral (per) municipalities
(excluding the change in Tobin’s Q).
43
8.3 Appendix 3
PopDen
Pop
Den
Pop
Den2
Hum
Cap
Mun
Acc
Ext
Acc
Dcostal
PopDen2
HumCap
MunAcc
ExtAcc
1,083
1,272
1,153
1,191
1,336
1,154
1,109
1,259
Dcostal
Temp
Percip
Crime
1,133
1,309
1,101
1,179
1,194
1,138
1,310
1,105
1,224
1,147
1,135
1,311
1,099
1,229
1,193
1,140
1,257
1,097
1,219
1,149
1,282
1,100
1,261
1,309
1,105
1,259
1,065
1,204
8,183
7,391
10,522
9,055
1,572
10,690
9,210
1,600
1,173
10,565
9,121
1,643
1,164
1,193
Temp
10,611
9,132
1,651
1,116
1,159
1,138
Percip
10,664
9,205
1,654
1,164
1,188
1,148
1,273
Crime
10,011
8,938
1,622
1,134
1,193
1,147
1,261
1,262
1,106
As the table demonstrates the VIF values for PopDen and PopDen2 suggests some
multicollinearity is present since the rule of thumb states that any VIF value above 10
implies multicollinearity (Gujarati and Porter, 2009).
Heteroskedasticity Test: White’s
Prob. ChiSquare
Nation-Wide (2000)
Central (2000)
Peripheral (2000)
H0: Homoscedasticity
HA: Heteroscedasticity
0.0000
0.0000
0.0002
Hence, we rejected the null hypothesis to confirm that heteroscedasticity is present
(Gujarati and Porter, 2009).
44