Does Infrastructure Pave the Way for Higher Property Demand?

DEPARTMENT OF ECONOMICS Uppsala University Bachelor thesis (C level) Author: Isak Myrestam Supervisor: Mattias Öhman Spring Term 2020

Does infrastructure pave the way for higher property demand?

A difference-in-differences analysis of the effect of the Bothnia Line on real estate prices in Västerbotten

Abstract

This study explores the concept of improved train infrastructure in Sweden and how it affects the attractiveness of cities. The research uses a difference-in-differences model to determine whether the construction of the Bothnia Line in northern Sweden has had an impact on real estate prices in the municipalities Nordmaling and Robertsfors between 2008-2016. By employing the hedonic price model, the study finds evidence that house-specific factors such total house size in square meters, location near water and size of backyard all play a role in determining the final purchase price of houses in the two municipalities. However, the findings of this study do not indicate that the launch or the subsequent investments on the Bothnia Line has had any measurable impact on the real estate prices in the region. This is in line with previous research on the project.

Keywords: Train infrastructure. Commuting. Real estate prices. Hedonic price model.

Table of contents

1. Introduction ...... 4

2. Background: The Bothnia Line ...... 7

3. Theoretical framework ...... 10

3.1. Investments and expectations ...... 10

3.2. Previous studies: Commuting and regional growth ...... 10

3.3. Previous studies: The hedonic price model ...... 12

4. Methodology ...... 14

4.1. Data and variables ...... 14

4.2. Descriptive statistics ...... 18

4.3. Empirical Strategy ...... 20

4.4. Model specifications: Difference-in-differences ...... 24

5. Results ...... 28

5.1. Effects on house prices with a pre- and post-treatment variable ...... 28

5.2. Effects on house prices with yearly interaction terms ...... 32

5.3. Effects on house prices at inner-city and non-central locations ...... 33

6. Discussion ...... 35

7. Conclusion ...... 39

References ...... 40

Appendix ...... 44

1. Introduction

In light of the Paris agreement, Sweden faces many challenges in readjusting society to meet the goals of zero carbon emission by 2045 (Miljödepartementet 2017). Infrastructure in general, and fossil-fuel-free modes of transportation in particular, are seen by many as the backbone of such a transition. As a result, there has been significant debate in recent years whether Sweden should invest in high-speed railways. Current plans suggest connecting Stockholm with Malmö and Gothenburg respectively, which the Swedish National Audit Office1 (Riksrevisionen) published a report about as recently as in November last year (Riksrevisionen 2019).

When weighing the pros and cons of expanding the Swedish train network, a noteworthy aspect of this discussion is the effect on communities along the way. Specifically, whether it could increase daily commuting from smaller cities in the vicinity of new stops or improve the attractiveness of previously inaccessible areas. This study will provide commentary on the effects of improving Swedish infrastructure by examining whether one specific project, namely the Bothnia Line in Västerbotten, has had an impact on the regional property demand.

The Bothnia Line (in Swedish Botniabanan) is a railway situated along the coast of northern Sweden that began operating in 2010. It connects Umeå in Västerbotten with a band of coastal cities (Hörnefors, Nordmaling, Husum, Örnsköldsvik, and Nyland) down to Kramfors municipality for a total of 190 km (Larsson 2013). However, the construction and planning process of the railway was subject to several complications and delays. The increased cost of the total investment has weighed heavily to the negative side in previous reports (Riksrevisionen 2011). Considering that the railway did not operate on full capacity until more than a year later, in 2012, these reports are now reasonably outdated.

As previously stated, the purpose of the thesis is to expand upon the literature on the effect of improved train infrastructure on the attractiveness of local communities. While several studies have evaluated the Bothnia Line from a cost-efficiency; commuting behaviour; or expectations perspective (Brandt 2005; Riksrevisionen 2011; Larsson 2013; Uneklint 2016), a comparative study on the real estate prices in Västerbotten before and after the completion of the railway signifies an unexplored research area. This study aims to make unique contributions to the

1 All translations relating to governance in the text body, including myndigheter (government agencies), län (county) and kommun (municipality) are based on the Swedish Parliament Official Wordlist (Sveriges Riksdag 2015). References will still use the Swedish name to more accurately reflect the source (such as “Riksrevisionen 2019”). 4 research by investigating if the railway has increased the attractiveness (proxied by prices in the housing market) of cities along the track. With this explicit purpose in mind, the study will aim to answer the following research question:

Has improved accessibility through the Bothnia Line led to a significant increase in house prices in Nordmaling, compared to Robertsfors, between 2008-2016?

Previous studies have reported high costs and low capacity as some of the struggles that the Bothnia Line faced during its launch (Riksrevisionen 2011). An investigation into the impact on real estate prices before the completion of the railway showed no effects on the market at the time (Brandt 2005). However, several empirical reports have found a positive correlation between proximity to train stops and rising housing prices in other regions of Sweden. Many of these studies use hedonic price models, which theorise that house prices are a sum of implicitly priced characteristics. These can, in turn, be estimated through different econometrical methods (Rosen 1974; Bohman and Nilsson 2016). A prominent example of this is a difference-in- differences study that investigated the introduction of a new railway close to Landskrona in 2000 (Jonsson 2007). Through the looking glass of these previous studies, the thesis establishes three probable hypotheses:

Hypothesis A: The Bothnia Line has had no measurable impact on real estate prices, because of many delays and adaptive expectations.

Hypothesis B: The Bothnia Line has had a positive impact on cities south of Umeå after the renovation of neighbouring train lines, compared with similar cities north of Umeå.

Hypothesis C: The Bothnia Line has had a positive impact on house prices for property closest to the railway but has not influenced the whole municipality.

To explore the concept of regional attractiveness, the study will perform a difference-in- differences study of the real estate prices before and after the launch of the new railway. The subjects of the study are Nordmaling and Robertsfors municipality. They are both located in Västerbotten but only Nordmaling, located south of Umeå, was affected by the launch of the railway. Real estate prices are then examined through the hedonic price model. In order to examine both the effect of the launch of the railway, as well as the effect of improvements on neighbouring lines, two variations of the method are constructed that defines post-treatment period as 2010 or 2012 respectively. These choices are accompanied by a discussion on three

5 necessary requirements for using the difference-in-differences model: Stable unit value assumption, exogeneity and parallel trend assumption.

Several different regression models are constructed in order to test the hypotheses. In line with previous studies, the results of the regressions confirm that several factors such as the size of floor space, location and size of the backyard all play a role in determining the final purchase price of houses in Västerbotten. The study finds inconclusive evidence on whether the launch of the Bothnia Line itself has impacted the real estate price trend in Nordmaling. Simultaneously, the study cannot rule out that the railway has not affected prices. This culminates in a discussion about the nature of underlying assumptions about consumer expectations.

The disposition for the rest of the thesis is as follows. Section 2 gives a historical overview of the Bothnia Line project. Section 3 establishes the theoretical framework, as well as introduces previous studies on regional growth and models to estimate real estate prices. Section 4 describes the methodology of this study; the econometrical intuition of the difference-in- differences model and the choice of data and variables. Section 5 presents the results of the regressions performed on the case study of Nordmaling and Robertsfors. Section 6 analyses the empirical findings from the previous section, discusses the limitations of the study and answers the research question that was presented in the introduction. Finally, section 7 summarises the findings of the study into a concise conclusion.

2. Background: The Bothnia Line

This section is dedicated to an overview of the Bothnia Line project. First, the historical context and the construction process of the railway is described. Second, previous academic studies on the railway are presented alongside an expanded look at how this thesis fits into the bigger picture.

The Bothnia Line is a railway in northern Sweden that stretches 190 km between Kramfors municipality and Umeå municipality (Larsson 2013). Since the older existing railway network is located further inland, the Bothnia Line incorporated several new cities that had been thereto inaccessible by train (Riksrevisionen 2011). See table 1 for more details.

Table 1. Train stops on the Bothnia Line. County Municipality Stops Train access prior to 2010 Västernorrland Kramfors Nyland No Örnsköldsvik Örnsköldsvik C Yes Örnsköldsvik N No Husum No Västerbotten Nordmaling Nordmaling No Umeå Hörnefors No Umeå Ö No Umeå C Yes

An official investigation about the construction of a coast side railway in Northern Sweden was completed in 1996 (SOU 1996). The report predicted construction costs of roughly 10 billion SEK to build the track and make improvements to connecting tracks, such as the Ådal Line to the south. The report also stated highly uncertain net societal benefits. This statement was contested by the Swedish Rail Administration (Banverket), whose own cost-benefit analysis of the project showed greater promise (Riksrevisionen 2011).

The Swedish Parliament granted funding for the construction of the Bothnia Line in 1997 (Riksrevisionen 2011). The government at the time argued that investing in infrastructure in northern regions was needed for developing the private sector and promoting regional growth as a whole, regardless of potential low profitability (Riksrevisionen 2011). Three main aspirations of the Bothnia Line were laid out: First, improve the capabilities of long-distance travel to and from Stockholm. Second, establish access to daily commuting by train between coastal cities along the track. Third, make transportation of goods between the north and the south more cost and time-efficient (Brandt 2005; Riksrevisionen 2011).

In 1999, the finalised construction plan was adopted (Brandt 2005). However, several complications marred the planning and construction process, which led to significant delays, renegotiations with local partners and increased construction costs (Brandt 2005; Riksrevisionen 2011; Uneklint 2016). One major factor to the delay was that the railway was to cut through a natural reserve in Umeå. The dispute was not resolved until 2003 (Brandt 2005).

At its launch in August 2010, the departures on the Bothnia Line were limited in frequency and scope, pending construction work being done on the Ådal Line to accommodate the increased traffic (Riksrevisionen 2011; Larsson 2013). Only daytime commuter trains between Umeå- Örnsköldsvik were running at first, while in December 2011, the whole stretch Umeå- Örnsköldsvik became operational. In August of 2012, the Bothnia Line and Ådal Line were officially integrated, which marked the start for the promised long-distance train departures Umeå-Stockholm (Larsson 2013).

The Bothnia Line has been the subject of several studies. Brandt (2005) investigated local expectations on the new railway through quantitative and qualitative measures. Brandt performed a regression analysis on the real estate prices on houses sold 1994-2001. The data were divided into a treatment group from 1998 to 2001, and a control group of 1994-1997 – before the announcement of the railway had been made. The analysis is based on the logarithmic purchase price, controlled for house specific factors (such as square meters, proximity to water, and distance to the proposed station).2 The results showed no effect on the prices in the municipalities (Brandt 2005). The author points out that because of the long construction time, people in the region have plenty of time to adapt to the situation. Simultaneously, the many delays could bring doubts about whether the project will come to fruition, or even spark unrealistic expectations when it is finally completed (Brandt 2005). According to Brandt, if the expectations are not met, it could have led to stagnant or even lowered house prices. This gives us the first hypothesis of the thesis:

Hypothesis A: The Bothnia Line has had no measurable impact on real estate prices between 2008-2016, because of many delays and adaptive expectations.

This study has the added benefit of examining the situation before and after the railway was completed. Furthermore, the choice of control groups at different time intervals in Brandt’s

2 This approach to examine real estate prices is explained in more detail in subsection 3.3 and section 4. 8 study does not account for time-dependent factors, which will be rectified by the model in this thesis.

The National Audit Office published an evaluation of the Bothnia Line project in 2011, which was before complementary construction work on the Ådal Line had been finished. The report estimated that the total cost of the Bothnia Line, alongside the all necessary adjacent projects, would be at least 26 billion SEK. The report also stated that neither the traffic capacity targets nor the promised travel times (Umeå-Stockholm in 5 hours) had been reached, and that the net value of the railway depends on the further investment of neighbouring lines. One of these projects mentioned in the evaluation is a North Bothnia Line between Umeå and Luleå, that at the time of the report had not been greenlit (Riksrevisionen 2011). The Swedish Transport Administration (Trafikverket) first earmarked funding for the North Bothnia Line in 2018 (Trafikverket 2018). The juxtaposition of the two railway projects provides an excellent opportunity for this thesis to revisit the Bothnia Line project after the capacity south of Umeå have been increased (2010-2012) but before the announcement and construction of the northern extension (2018). The previous information leads us to the second hypothesis of the study:

Hypothesis B: The Bothnia Line has had a positive impact on cities south of Umeå after the renovation of neighbouring train lines, compared with similar cities north of Umeå.

As explained in section 4.2., the time frame for the data will 2008-2016 and comparable municipalities to the north and south of Umeå have been chosen.

To summarise, previous studies have evaluated that the Bothnia Line has not been cost-efficient or to have had an effect on house prices (Brandt 2005; Riksrevisionen 2011; Uneklint 2016). However, an updated view of the situation after the renovation of adjacent projects is required to make a fair assessment. Issues with time-dependent factors present in previous empirical studies can be solved by adding a control group that does not yet have access to railways in the research model of this thesis.

3. Theoretical framework

This section presents the theoretical framework and explores previous qualitative and empirical research on the core themes of this study. Namely, the effect of railway infrastructure on regional growth and an empirical approach to analysing real estate price development. The first subsection presents theoretical assumptions about investments and expectations. Section 3.2. discusses how the previous research in the field of economics has examined the effect of regional and/or commuter trains on local growth and labour markets. Finally, section 3.3. delves into empirical analyses of the real estate market and introduces the hedonic price model. This price model returns in the methodology in section 4, where the method is tailored to the municipalities affected by the Bothnia Line.

3.1. Investments and expectations In his study that was described in section 2, Brandt (2005) discussed the nature of expectations and at what stage in the construction process an infrastructural project would influence real estate prices. According to Jonsson (2007), purchasing a house can be seen as both an investment and consumption. Assuming that house ownership is viewed as an investment, rational individuals would take the increased accessibility into account as soon as a project is announced. On the other hand, if the house is seen as a good that can be consumed then it would be more likely, according to the author, that the lion’s share of the effect would rather be seen when the project has been completed. Ultimately, the effect most probably differs from project to project as there is no definitive answer to how people view accommodation (Jonsson 2007).

In a study on a new transit line in Chicago, McMillen and McDonald (2004) found evidence that house prices in the area close to the tracks started to rise after the project was announced, but before the construction process was completed. Furthermore, the effect was more noticeable the closer to the train tracks the houses were (McMillen and McDonald 2004).

3.2. Previous studies: Commuting and regional growth There are other important aspects of infrastructure investments. Stjernberg and Mattisson (2016) expressed that “public transport is regarded as an important factor towards achieving other goals and other public values, particularly those related to economic and environmental issues” (Stjernborg and Mattisson 2016). Commuting behaviour is a well-documented topic in economics. Johansson et al. have explored how the integration of regional labour markets depend on commuting distance through random choice preference functions (Johansson,

Klaesson and Olsson 2002, 2003). Their findings suggest substantial differences in the willingness to commute over large, medium and short distances. In their “Commuters' non- linear response to time distances” (2003), the authors describe the relationship as an S-curve. Over short distances, additional commuting time does not influence the number of commuters, whereas a steep drop occurs around the 30-40 minute mark. The remaining people who still commute after that point are again relatively indifferent to additional commuting time (Johansson, Klaesson and Olsson 2003).

As this study examines the effect of an infrastructural change rather than plotting commuting behaviours dependent on distance, the random choice model is not as relevant a method as other alternatives. Nevertheless, the results of Johansson et al. (2003) can still be integrated into the model in two ways.

Firstly, based on the assumption that the appeal of improved train infrastructure is most noticeable over short distance travel, it would make sense to examine small municipalities within a short distance of major labour markets. Therefore, the subject of empirical study has been narrowed down to the municipalities of Nordmaling and Robertsfors. They are within less than 50 km on either side of Umeå, as well as located at similar distances to comparably bigger cities to the north and south, Örnsköldsvik and Skellefteå respectively. See figure D in the appendix for more details.

Secondly, if the hypothesis that the Bothnia Line has affected house prices is correct, it would have the largest impact on house prices closest to the train station. After all, it is the total commuting time and not the physical distance that matters. Under this assumption, people living outside the inner city would be less inclined to take advantage of the train, if the total commuting time falls outside the threshold. Accordingly, the model will include a control variable for whether the sold house is located within the inner-city borders. For small cities such as Nordmaling and Robertsfors, this cut-off would still include all households within 2,5 km of the train station (Lantmäteriet 2020b). From the aforementioned studies, the following hypothesis can be deduced:

Hypothesis C: The Bothnia Line has had a positive impact on house prices for property closest to the railway, but has not influenced the whole municipality.

3.3. Previous studies: The hedonic price model One hurdle for this thesis is to find a feasible and accurate way to measure changes in the housing market. There are a multitude of factors that influence house prices and several ways to estimate them. One possible route is to explicitly ask respondents what they would prefer and how they would rank a particular property trait in hypothetical scenarios. This method is also known as stated preferences. An alternative method, revealed preferences, instead tries to infer what factors matter most by observing people’s actual financial decisions (Bohman and Nilsson 2017). The hedonic price model is a popular example of the latter (Rosen 1974).

The hedonic price theory is built upon the assumption that people estimates the value of a specific good as the sum of a set of utility-bearing traits that make up the good. The more of these characteristics can be found in a good, the higher the price tag (Rosen 1974). To estimate these hidden marginal utilities, Rosen differentiated them through indexing first-step regressions together. When applied to the housing market, the dependent variable (price) is examined through the all of these individual attributes; by controlling for variables such as the size in square meters, the location, or whether it has a backyard or not (Bohman and Nilsson 2017). This method has been utilised in several empirical studies about the impact of public transportation on real estate prices (Jonsson 2007; Blind, Dahlberg and Engström 2016; Bohman and Nilsson 2016, 2017)

In their study “Effekter av tågtrafik i Västra Götaland”, Bohman and Nilsson (2017) found that the proximity to train stations had a positive impact on house prices in western Sweden. The number of departures per hour also had a positive effect. The authors divided the relevant variables in their hedonistic price model into three categories:

1. Accommodation specific variables. 2. Geographical and socioeconomic variables. 3. Infrastructural variables.

Accommodation specific variables are factors such as total living space (floor space) in square meters, construction year, access to and size of the backyard (Bohman and Nilsson 2017). These factors differ on an entity level and will, therefore, be included in the model of the thesis. Geographical variables refer to aspects such as distance from the closest major city and access/proximity to water. These also differ between entities or, at the very least between communities. The discussion about proximity to water as an entity or region-based geographical

12 variable is expanded upon in section 4.1. Finally, infrastructural variables are assumed to be aspects such as access to railways and highways, distance to public transportation stops, and commuting distance (Bohman and Nilsson 2017).

Another hedonic price model study performs a difference-in-difference (DiD) analysis on a new railway between Landskrona and Helsingborg in 2000, and its effect on real estate prices (Jonsson 2007). Jonsson constructs a DiD model that: First, separates the sold houses into a treatment group, with entities within 2 km of the railway, and a control group; second, adds a binary before and after variable; third, includes interaction terms for the treatment group and each year. The study only finds significant effects on the housing market in one out of four cases but provides insight that is valuable for this study. For instance, the author uses the natural logarithmic purchase price. In many circumstances, the natural logarithmic purchase price allows the coefficient of the independent variables to be interpreted as the percentage increase of the price (Jonsson 2007). Moreover, two areas with similar price development but different nominal prices would have the same percentage increase over time. (Jonsson 2007). Consequently, the logarithmic purchase price will be incorporated into the thesis, coupled with the information about the flaws of the nominal purchase price as a price development indicator explained in section 4.1.

To summarise, the hedonic price model allows empirical analysis of the different components that, according to theory, influence real estate prices. The estimation can be done as a panel data study with fixed effects as well as a DiD regression. The latter method hinges on an exogenous effect that changes one group but not the other, to allow observation of the effects before and after the event. The introduction of a railway falls under this category, and difference-in-differences will be the thesis’ model of choice.

4. Methodology

The following section explains the empirical model that the thesis will use to answer the research question. The first section (4.1.) discusses the data and variables, as well as the credibility of its sources. Section 4.2 presents descriptive statistics of the variables, to ease the understanding of the regressions in later parts of the study. Section 4.3. gives an overview of the empirical strategy by reviewing potential econometric models and their respective strengths and limitations. Lastly, the chosen model is specified and expanded upon in section 4.4.

4.1. Data and variables According to Statistics Sweden (SCB), there are essentially three main ways of comparing real estate prices over time: the real estate price index (fastighetsprisindex), the average purchase price (medelskilling), and the purchase price coefficient (köpeskillingskoefficient) (SCB 2020c). The real estate price index is a weighted index based on the taxation value3 that the Swedish Tax Agency (Skatteverket) ascribes to properties roughly every three years. As a result, the taxation value does not change yearly. Fortunately, the index takes this into account and allows one to follow and calculate changes in real estate prices similar to CPI (SCB 2020c). However, the index demands a large number of purchases and is only available on the national level, or for large city regions such as Stockholm.

Instead, to find information on the municipal level, one is left with the average purchase price and the purchase price coefficient. The average purchase price on its own is a poor estimator for general trends, as the price does not reflect any major differences in size, quality or location of the houses sold (SCB 2020c). A municipality where many large properties were sold in one specific period could therefore incorrectly show a steep increase in house prices, even if the price per square meter is in line with the overall trend.

The purchase price coefficient (PPC) is better at taking individualities into account because it measures how much more than the taxation value, which is entity-based, the property was sold for (SCB 2020c):

푃푢푟푐ℎ푎푠푒 푝푟𝑖푐푒 푃푃퐶 = 푇푎푥푎푡𝑖표푛 푣푎푙푢푒

3 Taxation value is the aggregated estimated value of a property, based on the land size/quality, the state of the building and location. It is the basis for determining things like property tax, insurance coverage, and loans (Skatteverket 2020). 14

The coefficient is still calculated from a nominal value that only changes every three years, so ultimately, it becomes a deceiving measurement when performing regressions on overlapping taxation periods (SCB 2020c). Thus, what options are there left? As mentioned in section 3.3., by combining the purchase price with details about the floor space, location, and other house- specific factors it is possible to create a somewhat robust picture of what each house is worth at a given moment. For this reason, the model specification will use the natural logarithm of the purchase price, ln (푃푟𝑖푐푒), as its main dependent variable.

Moreover, the purchase price will be adjusted using CPI to the price level of 2008. While this calculation is not strictly necessary as the difference-in-difference model removes the effect of inflation, it enables a direct comparison of the house price levels before and after the launch of the railway. Additionally, adjusting the price levels for inflation has only a minor impact on the results of the other variables in the regression. See table C in the appendix for a comparison. Finally, the hedonic price model is incorporated by controlling for available, and relevant, characteristics. These control variables are:

• Size of floor space (in square meters) • Size of ancillary area (such as storage rooms, barns and garages) • Size of backyard • Inner-city location (as a binary variable). • Proximity to water (as a binary variable), measured as house location within 300 meters of a large body of water (lake or sea).

Proximity to water, interpreted as seaside or lakeside location, has been measured through the Lantmäteriet map service (Lantmäteriet 2020b). To consistently decide what constitutes a “large body of water” among the many small creeks dotting the landscape, the study has chosen a minimum width (not length) of at least 150 meters. To mesh with the scope of the study, water access is categorised as a binary variable and is included for several reasons. Previous studies have suggested that lakeside or seaside location is closely correlated with higher purchase prices (Bohman and Nilsson 2017). This observation is strengthened by looking at the 90th percentile of price per square meters in the data, divided into groups depending on if they have access to water or not:

Table 2. Location close to water for most expensive houses (price/sqm) Municipality Water access No water access Nordmaling 48 19 Robertsfors 46 15

A potential argument for excluding water access would be that the physical location of a city and its distance to the sea, are assumed to be constant over time. Granted, both municipalities in the study share the trait of being located along the coast. Thus, when estimating the effect on the price of an average house in the municipality, they could be considered comparable. However, the city of Nordmaling lies considerably closer to the coast than the city of Robertsfors does, so when performing the inner-city versus rural population regressions, this could potentially skew the results. Additionally, the location of each house varies on an entity level.

The study uses repeated cross-sectional data that has primarily been collected from two sources. House prices, title deeds (lagfarter) and accommodation specifics have been pooled from Booli. Demographical and municipal-specific statistics are from Statistics Sweden (SCB). Booli is a database run by SBAB (Statens Bostadsfinansieringsaktiebolag) and contains current house listings and past sales information on the property market; including final sale price; accommodation size, and property location (Booli 2020b). However, it is vital to determine whether the information from this service is not only convenient but accurate as well.

The service claims to have Sweden’s most comprehensive list of real estate purchase (Booli 2020b), but that is not enough evidence on its own. Its credibility as a reliable source has been discussed in other reports, such as a Swedish Debt Office (Riksgälden) report in 2019 (Bjellerup and Majtorp 2019). In this report, Booli is described as a valuable source of statistics for sales of apartments, along with Svensk Mäklarstatistik and Valueguard. Apartments are not actively documented by Swedish governmental agencies such as Statistics Sweden, which highlights the benefits of additional actors that examines this form of property (Bjellerup and Majtorp 2019). However, the price development for apartments is still principally based on self-reported sales numbers from real estate agents around the country (Bjellerup and Majtorp 2019).

To ensure accurate data over time, this study instead focuses on the sales of small houses (i.e. villas). More specifically, the analysis is based on the total number of small houses sold between 2008-2016 that are registered to the Booli website through a title deed. The title deeds, as opposed to apartment sales, are registered to Booli after the information is delivered by

Lantmäteriet (Booli 2020a). This complete register only goes back to 2008, which has been chosen as the measurement starting point accordingly. Moreover, the dataset has been pruned to only show representative sales. The limits for outliers are based on the limits used by Svensk Mäklarstatistik (2020):

10 000 < Purchase price in nominal SEK < 20 000 000 10 < Square meters < 500

These restrictions resulted in one discarded data entry that was bigger than 500 square meters, and one entry sold for less than 10 000 SEK.

Another aspect is that Booli does not weight the purchase price to compensate for the location or other similar qualities, which is why its average purchase price differs slightly from other sources (Bjellerup and Majtorp 2019). Since the same source of data is used for all years and both municipalities, this discrepancy is not a massive issue in a contained context. Furthermore, the study includes control variables for inner-city location and access to water to compensate for this weakness in the data.

The municipal data from Statistics Sweden (SCB) is showcased in the descriptive statistics section below. The tables and statistics are present to strengthen the case that the municipalities are comparable and that the primary difference between the treatment group (Nordmaling) and the control group (Robertsfors) is access to trains. The appendix contains complementary tables for other trends that are closely aligned, such as median income and share of commuters out of the total population. As seen in figure 1 below, population growth also followed the same trend in both Nordmaling and Robertsfors and did not warrant inclusion in the model.

4.2. Descriptive statistics Nordmaling and Robertsfors are both located at comparable distances from Umeå and share many similarities. Figure 1 shows one of these traits: a shrinking population in recent years, up until around the launch of the Bothnia Line.

8200 8000 7800 7600 7400 7200 Nordmaling 7000

Population Robertsfors 6800 6600 6400 6200

6000

2005 2012 1998 1999 2000 2001 2002 2003 2004 2006 2007 2008 2009 2010 2011 2013 2014 2015 2016 2017 2018 1997 Figure 1. Population in Nordmaling and Robertsfors (1997-2018). Source: SCB

The dwindling population growth can have any number of causes, such as urbanisation and reduced labour market prospects in rural areas. However, there does seem to be a positive change in population growth in both municipalities around 2012. How much the Bothnia Line plays into this trend reversal is hard to pinpoint, especially since the change is present over municipal borders. Due to the difficulty of untangling how different factors play into regional growth, this study narrows its scope to one aspect, property demand, to be able to make any valuable contribution to the discussion of attractiveness. See the appendix for more graphical comparisons between the two municipalities.

Most importantly, to perform the regression, there needs to be a parallel trend in the variables of interest before the change takes place.4 Figure 2 showcases the purchase price coefficient for Nordmaling and Robertsfors 1997-2018, with the cut-off point marked as 2010.

4 The parallel trend assumptions and other prerequisites are explained further in section 4.4. The figures are meant to give a cursory overview of the situation. 18

2,5

1,5

Nordmaling 1 Robertsfors

Purchase price coefficientprice Purchase 0,5

2006 2008 1998 1999 2000 2001 2002 2003 2004 2005 2007 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1997

Figure 2. Purchase price coefficient in Nordmaling and Robertsfors (1997-2018)

The purchase price coefficient is an estimation of how much more than the taxation value the property was sold for. The coefficient lends itself nicely for a graphic comparison but, as previously stated, not as a tool for regression analysis as the denominator changes every three years. In the years before 2010, the purchase price coefficient seems to follow a similar pattern in both Nordmaling and Robertsfors. A spike can be seen in 2010, and onwards the trends do not align as closely. This observation is discussed in section 4.4.

Finally, table 2 summarises many of the variables used in the regression analysis.

Table 2. Descriptive statistics5 Variable Obs Mean Std. Dev. Min Max Nordmaling Purchase price (thousand SEK) 672 594.65 458.03 9.65 2975.49 Log Price 672 6.07 0.89 2.27 8.00 Price/sqm 672 5344.38 4796.58 83.18 34293.17 Area (sqm) 672 121.01 41.15 35 360 Ancillary Area (sqm) 672 50.42 84.86 0 1950 Backyard 672 3003.50 6776.42 275 81300 Median Income 672 209.04 9.73 198.00 225.92 Population 672 7098.79 73.33 7006 7276

5 Purchase price, logarithmic purchase price, price/sqm and median income have all been inflation adjusted to the price level of 2008 (CPI). 19

Robertsfors Purchase price (thousand SEK) 602 538.31 437.93 9.56 2640.05 Log Price 602 5.96 0.89 2.26 7.88 Price/sqm 602 5402.18 5768.55 72.62 56315.59 Area (sqm) 602 118.50 43.67 24 368 Ancillary Area (sqm) 602 40.09 42.81 0 373 Backyard 602 2736.05 3828.98 0 32914 Median Income 602 220.19 11.90 204.30 237.70 Population 602 6784.69 60.96 6717 6900 Total 1274

4.3. Empirical Strategy The study will use a difference-in-differences (DiD) model to investigate real estate prices before and after the construction of the Bothnia Line. The primary purpose is to evaluate the effect of a sudden change in circumstance, rather than predicting outcomes by estimating causal relationships of one variable to another. One potential alternative to examine the effect of infrastructure on real estate prices would be an Ordinary Least Squares (OLS) regression model. At its core, OLS is a method that examines how a dependent variable (Y) is affected by one or more independent variables (X). OLS then measures how much, on average, the change of one unit of X affects Y by predicting the outcomes as a linear relationship. Why individual outcomes differ from a perfect, hypothetical, linear relationship is explained by the error term (u), which encompasses all other factors not currently being part of the model. This empirical approach is a staple in economics research (Stock and Watson 2015).

The ideal scenario for empirical analysis would be a completely random distribution of treatment (in this case, the existence of railway), as that makes every independent variable uncorrelated with other variables currently hiding in the error term (u). In other words, in a simple OLS model such as 푌 = 훽0 + 훽1푋 + 푢, the conditional mean assumption 퐸(푋|푢) = 0 is fulfilled (Stock and Watson 2015). In the case of the Bothnia Line, the existence of the railway is not randomly distributed, as “treated” individuals are grouped geographically due to the physical location of the train tracks. There could be a multitude of reasons why an individual lives in a specific place and, consequently, there should be many factors that influence house prices. If these factors are not accounted for, it leads to considerable omitted variable bias in the model (Stock and Watson 2015). This issue could be hypothetically be solved by adding

20 control variables for everything that could influence house prices. Simultaneously, too many control variables tend to inflate the measurement error and lessen the usefulness of the model (Stock and Watson 2015).

Moreover, the data in this study is distributed over time, and as such, each data point has not been collected under identical conditions. This fact severely limits the comparability between data entries, and the only viable option would instead be to examine the houses sold during one particular year. See table B in the appendix for an example of an OLS regression in 2010. A method that does allow regressions of data over time is the fixed effects model. This approach employs panel data, data on the same entities over multiple time periods, to account for unobserved factors that vary over time but not between entities, or vice versa. These added terms are called entity fixed effects and time fixed effects respectively (Stock and Watson 2015). Fixed effect models have been frequently used in studies that examine real estate prices, including Blind et al. (2016) and Bohman and Nilsson (2017). The method can remove a considerable amount of variation due to unobserved variables and is a good fit for the hedonic price model. The different house characteristics are introduced as independent variables along with time and entity fixed effects to examine house prices (Blind, Dahlberg and Engström 2016).

Despite its merits, the model will not be used in this study in its entirety for two reasons. First, the data collected for this study is not strictly in panel data form but rather repeated cross- sectional data, as the houses sold are different each year. This fact renders the model unfit for entity fixed effects, even though time fixed effects can still be mimicked through individual time dummies. Second, both the standard OLS and the fixed effect model can only estimate how a factor, such as the size of living space, directly relates to another, such as the house price. Without an anchored point of comparison, a control group, there is no way feasible way to incorporate the launch of the Bothnia Line into the model. The resulting study would be a legitimate attempt to comment on how attributes such as the living space, size of the garage or the location influences the house price, but nothing more. Therefore, the study turns to the difference-in-differences regression model for its primary research question.

The difference-in-differences method is a quasi-experimental model that is broadly used in studies researching the effect of a particular law or policy change (Lechner 2010). Compared to the OLS and fixed effects-models presented in section 3.1., it is a much more appropriate tool to examine the effect of sudden exogenous treatments (Lechner 2010).

The premise for performing a DiD regression requires access to data over time and in two comparable groups of individuals. These individuals are categorised into one treatment group, that is affected by the change, and one unaffected control group. There also needs to be data available over time before the shock takes place, as well as after, for each group. In the end, this culminates in four formations of data: The pre-treatment control group (1), the pre- treatment treatment group (2), the post-treatment control group (3) and the post-treatment treatment group (Lechner 2010). To illustrate how this information is used, equation 1 shows a baseline version of a DiD regression model.

푌 = 훽0 + 훽1푇푟푒푎푡푚푒푛푡 + 훽2푇𝑖푚푒 + 훽3(푇푟푒푎푡푚푒푛푡 × 푇𝑖푚푒) + ε (1)

Here, Treatment is a dummy variable that equals 1 if the individual belongs to the treatment group, and 0 if belonging to the control group. Time is another dummy that equals 0 for the pre- treatment period, and 1 for the post-treatment period. The coefficients are interpreted as:

6 훽0 = The reference point of the control group (Ct) in the first period, 퐶푡=0.

훽1 = The difference between the treatment group (Tt) and the control group in the first

time period, 푇푡=0 − 퐶푡=0.

훽2 = The difference between the two time periods in the control group, 퐶푡=1 − 퐶푡=0.

훽3 = The interaction effect of the time and treatment variable. Also interpreted as the difference of the difference between the two time periods in the treatment group and

the difference between the two time periods in the control group, (푇푡=1 − 푇푡=0) −

(퐶푡=1 − 퐶푡=0). ε = The error term

The presence of the control group is assumed to mimic the counterfactual scenario where the event never occurred. This assumption allows a comparison between what did happen before and after the change, with what would have happened without it (Lechner 2010). The variable of interest is the interaction term (denoted as 훽3 in the explanation above), as this shows the difference in average outcomes over time between the two groups and is the focal point of the comparison. Consequently, the interaction variable is the centre of attention when discussing the results of the regressions in section 5.

6 To better illustrate the calculation that the coefficients are based on, the control group is denoted (Ct) and the treatment group (Tt). Furthermore, t denotes pre-treatment (t=0) or post-treatment (t=1). 22

When performing a DiD regression, several conditions need to be fulfilled for the method to be valid. The most prominent ones are:

• Stable unit treatment value assumption (SUTVA) • Exogeneity assumption • Parallel trend assumption

SUTVA specifies that treatment needs to be observable for every single entity in the dataset and that the treatment group and the control group do not interact with each other. The distribution of the control group should not be affected by the event taking place (Lechner 2010). In the case of Robertsfors, it could be argued that the city relatively speaking becomes less attractive compared to cities along the new railway. On the other hand, no entity-based factors such as location or proximity to water, are affected by the change. Since the two cities are on opposite sides of Umeå and do not have any overlap in local government, resources or local labour market, this prerequisite is assumed to be met.

Exogeneity refers to the assumption that the distribution of the treatment group is not dictated by the existence or knowledge of the treatment itself. In an ideal scenario, the change comes rapidly and unexpectantly, so that the affected group does not have time to change their behaviour prior to the event (Lechner 2010). This condition cannot be measured but only assumed, and stands out as the most challenging assumption for this study to take for granted. The railway became known to the public in 1997 (Riksrevisionen 2011), and it is possible that people could have planned for this accordingly. In his study on the Bothnia Line, Brandt (2005) could not see any changes in the inhabitants’ behaviour due to the announcement. There is enough ambiguity around the nature of expectations that it is relevant to examine the real estate prices in the region anew. However, the discussion in section 6 elaborates on how the exogeneity relates to this study.

Lastly, the parallel trend assumption reveals the importance of comparability between the treatment group and the control group. The condition specifies that the average outcomes should be identical regardless of which group the individual belongs to – if the exogenous change had not taken place. Granted that the exogenous change is the only reason why two groups differ in the post-treatment period, then they should also share identical trends before the treatment (Lechner 2010). The next section, 4.4, relates this to Nordmaling and Robertsfors. If the condition of parallel trends is fulfilled, then the act of subtracting the changes over time in

23 different groups reduces or removes the issue of omitted variable bias that can plague the regular OLS method. If it is broken, the estimator will be biased (Lechner 2010).

4.4. Model specifications: Difference-in-differences In order to check the parallel trend assumption in the case of the Bothnia Line, the period before the completion of the railway in 2010 is examined. The study has also highlighted 2012, when completed renovations lead to increased train capacity, as another potential event of significance. Both events will be examined to see if the conditions for applying the DiD model are fulfilled. Since the purchase price is the dependent variable, it is the topic of interest.

Figure 2 in section 4.2 shows a similar trend for purchase price coefficients in the years leading up to the completion of the railway. In 2010 and onwards, however, there is an apparent discrepancy in the trend. Since the railway was completed halfway through the year, in August, the spike in 2010 could be attributed to the completion of the railway. Since the data is on a yearly basis, the end of 2009 is chosen as the cut-off point, as 2010 coincides with the start of the Bothnia Line and ensures that the fluctuation in the purchase price belongs to the post- treatment period. 2012, on the other hand, loses some merit as a cut-off point. Graphically, the argument for the existence of parallel purchase price trends after 2010 is not as strong in figure 2. Because of the relatively low number of sales, there is also a risk that the picture painted here by the purchase price coefficient is not representative of the overall trend (SCB 2020c). In conclusion, a more in-depth discussion of how well this case study fulfils the prerequisites of the model is needed. The limitations of the study are examined more closely in section 6.

To examine the effect of the Bothnia Line on housing prices, the study uses the natural logarithmic purchase price of houses sold from 2008 to (and including) 2016. Three different models have been constructed in accordance with the literature and similar previous studies (Jonsson 2007; Lechner 2010; Blind, Dahlberg and Engström 2016; Bohman and Nilsson 2017). The first application of the DiD model is similar to equation 1, as it is based on a single binary variable to differentiate the years before and after the treatment. To incorporate the hedonic price model, equation 2 also includes house specific variables, as was explained in section 4.1. These variables are the area of floor space (Area), size of the ancillary area (AncillaryArea), size of the backyard (Backyard), inner city-location (InnerCity) and access to water (Water).

ln(푃푟𝑖푐푒푖푡) = 훽0 + 훽1퐴푟푒푎푖 + 훽2퐴푛푐𝑖푙푙푎푟푦퐴푟푒푎푖 + 훽3퐵푎푐푘푦푎푟푑푖 (2)

+ 훽4퐼푛푛푒푟퐶𝑖푡푦푖 + 훽5푊푎푡푒푟푖 + 훾1푅푎𝑖푙푤푎푦 + 훾2푇𝑖푚푒

+ 훿1(푅푎𝑖푙푤푎푦 × 푇𝑖푚푒) + ε

Where the coefficients are interpreted as:

훽0 = The intercept for one house in the control group in the first period.

훽1 = The coefficient for floor space. Expressed differently, it symbolises the percentage change in the purchase price by one extra square meter.

훽2 = The coefficient for the ancillary area (such as garages and storage rooms). The percentage change in the purchase price by one extra square meter of ancillary space.

훽3 = The coefficient for the size of the house’s backyard. The percentage change in purchase price per one extra square meter of backyard space.

훽4 = The coefficient for inner-city location (as a binary variable). Interpreted as the average percentage change in purchase price when going from a rural or inner-city location.

훽5 = The coefficient for proximity to water (as a binary variable). Interpreted as the average percentage change in purchase price when going from a house without access to water to a house located less than 300 meters away from a lake or sea.

훾1 = The difference between the treatment group and the control group in the first time period. The binary variable Railway equals 1 if Nordmaling, 0 if Robertsfors.

훾2 = The difference between the two time periods in the control group (Robertsfors).

훿1 = The interaction effect of the time and treatment variable, also interpreted as the difference between the price changes over time in both groups. ε = The error term

Following equation 2 is a model tangent with hypothesis C and examines the effect of the railway in just central Nordmaling and central Robertsfors. To achieve this, equation 3 simply drops all observations where InnerCity = 0. The aforementioned variable is then dropped from the equation as well, as all observations share the same value (1):

ln(푃푟𝑖푐푒푖푡) = 훽0 + 훽1퐴푟푒푎푖 + 훽2퐴푛푐𝑖푙푙푎푟푦퐴푟푒푎푖 + 훽3퐵푎푐푘푦푎푟푑푖 (3)

+ 훽4푊푎푡푒푟푖 + 훾1푅푎𝑖푙푤푎푦 + 훾2푇𝑖푚푒 + 훿1(푅푎𝑖푙푤푎푦 × 푇𝑖푚푒) + ε

The next model, equation 4, does not contain one binary variable for the time, but rather one variable for each year. Interaction terms are also added for each time dummy. The intuition is that one does not have to choose the cut-off point and can examine the treatment-control difference for each individual year (Jonsson 2007).

ln(푃푟𝑖푐푒푖푡 ) = 훽0 + 훽1퐴푟푒푎푖 + 훽2퐴푛푐𝑖푙푙푎푟푦퐴푟푒푎푖 + 훽3퐵푎푐푘푦푎푟푑푖 (4)

+ 훽4퐼푛푛푒푟퐶𝑖푡푦푖 + 훽4푊푎푡푒푟푖 + 훾1푅푎𝑖푙푤푎푦 + 훿12008 …

+ 훿92016 + 휃1(푅푎𝑖푙푤푎푦 × 2008) … + 휃9(푅푎𝑖푙푤푎푦 × 2016) + ε

Unless stated otherwise, the coefficients should be interpreted the same as in equation 2. New coefficients include:

훿푖 = The coefficient for each time dummy, which in practice is identical to the inclusion of time fixed effects. Considers differences that vary across time but not across individual entities.

휃푖 = The interaction effect of each time dummy and treatment variable, also interpreted as the difference between the price changes in both groups for each year between 2008 and 2016.

Finally, equation 5 below also harkens back to equation 2 but only changes the interaction term. In order to investigate how the railway has affected the houses closes to the station, the model focuses on the interaction of time and central house location, as opposed to the rest of the municipality. The treatment variable Railway is dropped since Nordmaling and Robertsfors are compared separately, and entities within each municipality share the same binary value for this variable. Other coefficients stay the same as in equation 2.

ln(푃푟𝑖푐푒푖푡) = 훽0 + 훽1퐴푟푒푎푖 + 훽2퐴푛푐𝑖푙푙푎푟푦퐴푟푒푎푖 + 훽3퐵푎푐푘푦푎푟푑푖 (5)

+ 훽4푊푎푡푒푟푖 + 훾1퐼푛푛푒푟퐶𝑖푡푦푖 + 훾2푇𝑖푚푒

+ 훿1(퐼푛푛푒푟퐶𝑖푡푦푖 × 푇𝑖푚푒) + ε

Additionally, figure C in the appendix is included as for a parallel trend comparison between central Nordmaling/Robertsfors and the rest of the municipality. Due to lack of within- municipality sales data before 2008, it is unclear whether the condition is entirely fulfilled. The trend differs wildly after 2010, so 2012 has been dropped in subsequent regressions entirely. Robertsfors also seems to diverge earlier than Nordmaling, but the regressions of both

26 municipalities will still be presented in section 5.3 for further discussion. Going forward, it is important to keep in mind that the parallel trend assumption is weaker for equation 5 than other specifications.

The four models established in equation 2, 3, 4 and 5 will be the basis for the regressions performed in section 5. With a binary cut-off in equation 2, 3 and 5, and a time fixed effects variation with yearly interaction terms in equation 4, they encompass all necessary information to test the hypotheses of the study.

As a final note, section 3 explained that the natural logarithmic purchase price allows approximation of percentage changes (Blind, Dahlberg and Engström 2016). However, this approximation becomes less accurate as the values of the coefficients grow. As a rule of thumb, for logarithmic coefficients with a value of less than 0.2, the percentage change approximation is valid (Wooldridge, 2003). If any coefficient, such as 훽1, is greater than 0.2, the actual percentage change needs to be calculated as:

∆% = 100 × exp(훽1) − 1

Furthermore, approximating logarithmic prices as percentage changes does not work for negative coefficients (Wooldridge, 2003). These two conditions, 훽1 ≤ 0.2 and 훽1 > 0, need to be considered in the regressions below.

5. Results

In this section, the results of the difference-in-difference regressions are presented. Since the study has presented several hypotheses and two potential cut-off points, the regressions are showcased in five tables for ease of reading. The column labelled “2010” should be interpreted as a cut-off point at the end of 2009 and start of 2010, while “2012” denotes end of 2011 and start of 2012.7 The regressions use robust standard errors to allow for heteroskedasticity, an assumption that the variance of the error term will not be constant as the number of observations rises (Stock and Watson 2015).

5.1. Effects on house prices with a pre- and post-treatment variable The first table shows the regression performed on equation 2, with the cut-off point at the end of 2009 and start of 2010. Nordmaling municipality as a whole is the treatment group, and Robertsfors municipality is the control group.

Table 3. Regression of purchase price on railway access at the municipal level (2010) Purchase price (log) VARIABLES 1 2 3 4 5

Area 0.0024*** 0.0024*** 0.0023*** 0.0018*** 0.0033*** (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) Ancillary Area 0.0002 0.0002 0.0000 0.0001 (0.0004) (0.0004) (0.0005) (0.0003) Backyard 0.0000* 0.0000*** 0.0000*** (0.0000) (0.0000) (0.0000) Inner City 0.3573*** 0.4968*** (0.0469) (0.0461) Water 0.7938*** (0.0687) Railway 0.1654 0.1647 0.1566 0.1390 0.0646 (0.1103) (0.1103) (0.1107) (0.1078) (0.1068) Time 0.1901** 0.1921** 0.1967** 0.2075** 0.1129 (0.0912) (0.0913) (0.0909) (0.0914) (0.0899) Railway × Time -0.0751 -0.0769 -0.0692 -0.0677 0.0023 (0.1235) (0.1237) (0.1240) (0.1211) (0.1188)

Observations 1,274 1,274 1,274 1,274 1,274 Adjusted R-squared 0.0178 0.0173 0.0180 0.0475 0.1464 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

7 Expressed differently, “2010” defines the post-treatment period as 2010 and onwards, where 2012 denotes post-treatment from 2012 and onwards 28

At first glance, the results of the regression have a very low adjusted R2, with the interpretation that only 1,78-14,64% of the variation of house prices can be explained by the model. However, due to the presence of many binary variables when examining an effect, rather than predicting outcomes, the R2 will naturally be much lower. Low R2 is not necessarily an issue for the validity of a difference-in-differences model (Stock and Watson 2015).

The interaction variable Railway × Time, showing the difference-in-differences between Nordmaling and Robertsfors, is not statistically significant in table 3. Without controlling for Water, the interaction is even negative, though with relatively high standard errors.

However, several other variables are significant on the 1% level, such as Area, Backyard, Inner City and Water. According to the regression in column 5, one additional square meter of floor space (Area), leads to an average increase in purchase price of 0.33%. Proximity to water stands out as having a large impact on house prices, even though the coefficient (0.7938) is too large to directly translate to a percentage change. The actual value is 1.2118, meaning that the model suggests that a house located close to water on average raises the house price by 121.18%.8

The variable Time shows the average change in price in the control group (Robertsfors) when going from the period 2008-2009 to 2010-2016. Since the purchase price has been adjusted to price levels of 2008, it is possible to make meaningful comparisons between the two periods. In all both the last configuration (column 5), Time is statistically significant with the interpretation that houses sold in 2010 and onwards are, on average, 19.01-20.75% more expensive than the houses sold before that. When Water is included, the results become statistically insignificant and less conclusive. A possible explanation would be a that higher number of lakeside or seaside houses were sold in the post-treatment period.

The next regression in table 4 is identical to the previous configuration, apart from having the post-treatment defined as greater than or equal to 2012.

8 exp(0.7938) − 1 = 1.2118 29

Table 4. Regression of purchase price on railway access at the municipal level (2012) Purchase price (log) VARIABLES 1 2 3 4 5

Area 0.0026*** 0.0026*** 0.0025*** 0.0020*** 0.0035*** (0.0006) (0.0006) (0.0006) (0.0006) (0.0006) Ancillary Area 0.0002 0.0002 -0.0000 0.0001 (0.0004) (0.0004) (0.0005) (0.0003) Backyard 0.0000* 0.0000*** 0.0000*** (0.0000) (0.0000) (0.0000) Inner City 0.3567*** 0.4932*** (0.0467) (0.0460) Water 0.7744*** (0.0689) Railway 0.1287* 0.1270* 0.1247 0.1052 0.0728 (0.0761) (0.0761) (0.0761) (0.0745) (0.0727) Time 0.2558*** 0.2561*** 0.2588*** 0.2605*** 0.1770** (0.0719) (0.0719) (0.0718) (0.0725) (0.0705) Railway × Time -0.0404 -0.0407 -0.0396 -0.0341 -0.0092 (0.0995) (0.0996) (0.0995) (0.0979) (0.0940)

Observations 1,274 1,274 1,274 1,274 1,274 Adjusted R-squared 0.0297 0.0292 0.0298 0.0592 0.1527 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

In table 4, the coefficient for the interaction effect (Railway × Time) is still statistically insignificant, but now negative (-0.0092). The former means that the regression cannot confidently state that the price development in Nordmaling has been different from Robertsfors after the renovation of the neighbouring train lines were completed in 2012. As mentioned in the methodology section 4.4, the parallel trend assumption for 2012 can be considered compromised, assuming that the purchase price coefficient during 1997-2010 shows the price trend correctly.

In column 1 and 2, the coefficient for Railway is statistically significant at the 10% level. The results give the impression that houses in Nordmaling, on average, sold for 12.70-12.87% more than houses in Robertsfors in the pre-treatment period. As more house characteristics are introduced in the regression, this tendency disappears, which alludes to it being a result of omitted variable bias instead. Contrastingly, Time stays statistically significant in all configurations. With the inflation-adjusted prices in mind, the interpretation would once more be that in Robertsfors, houses sold in the post-treatment period were more expensive than in the previous period.

The next regression utilises equation 3, which drops all observations outside of central Nordmaling and Robertsfors. This regression aims to comment on the hypothesis that the introduction of the railway has impacted house prices in the direct vicinity of the station.

Table 5. Regression of purchase price on railway access at the city level (2010 and 2012) Purchase price (log) VARIABLES 2010 2012

Area 0.0045*** 0.0045*** (0.0006) (0.0006) Ancillary Area 0.0005 0.0005 (0.0007) (0.0007) Backyard 0.0000 0.0000 (0.0000) (0.0000) Water 0.5074*** 0.5139*** (0.1092) (0.1125) Railway 0.5088*** 0.4722*** (0.0957) (0.0800) Time -0.0167 0.0181 (0.1018) (0.0894) Railway × Time 0.0503 0.1262 (0.1141) (0.1069)

Observations 348 348 Adjusted R-squared 0.3404 0.3487 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

The results of the regressions in table 5 paint a slightly different picture than previous configurations. In this model, the coefficients of the interaction effect are positive in both 2010 and 2012. The coefficients for 2010 (0.0503) and 2012 (0.1262) would have allowed the interpretation as percentage changes but remains statistically insignificant.

Backyard is no longer statistically significant, while the effect of one extra square meter of floor space has increased (0.0045). The coefficient for Railway is also larger than in previous models for both 2010 and 2012, implying an average price increase of 66.32% and 60.35% going from a house in Robertsfors to Nordmaling in the pre-treatment period.9 The coefficient is also statistically significant for both periods.

9 For 2010, the real percentage change is exp(0.5088) − 1 = 0.6632. For 2012, exp(0.4722) − 1 = 0.6035. 31

5.2. Effects on house prices with yearly interaction terms This section introduces regressions based on yearly interaction terms rather than a binary pre- and post-treatment variable. This allows a closer look at how the introduction of the railway could have an effect in a specific year, which if neighbouring years differ greatly would get lost with a binary cut-off. The regressions are performed in accordance with equation 4, and they investigate potential effects of the Bothnia Line at both the municipal and the city level. The yearly time dummies are denoted as time fixed effects at the bottom of table 6. The dummy variable for 2008 as well as the interaction term Railway × 2008 have been dropped to avoid issues with multicollinearity; variables that correlate perfectly or almost perfectly with each other (Stock and Watson 2015). In this case, 2008 becomes the point of comparison for interpreting the other time dummies.

Table 6. Regression of purchase price on railway access at municipal and city level (Yearly interaction terms) Purchase price (log) VARIABLES Whole municipality Inner city

Area 0.0035*** 0.0044*** (0.0006) (0.0006) Ancillary Area 0.0001 0.0006 (0.0003) (0.0008) Backyard 0.0000*** 0.0000 (0.0000) (0.0000) Inner City 0.4938*** (0.0463) Water 0.7648*** 0.5868*** (0.0687) (0.1192) Railway 0.2447 0.5133*** (0.1612) (0.1630) Railway × 2009 -0.3844* 0.0217 (0.2076) (0.2126) Railway × 2010 -0.0564 -0.1472 (0.2051) (0.2566) Railway × 2011 -0.2520 -0.1011 (0.2183) (0.2191) Railway × 2012 -0.1836 0.0273 (0.2129) (0.2106) Railway × 2013 -0.0556 0.0735 (0.2131) (0.2754) Railway × 2014 -0.2587 0.2319 (0.2062) (0.2270) Railway × 2015 -0.0754 0.0924 (0.2116) (0.2209)

Railway × 2016 -0.3265 0.1753 (0.2030) (0.1609)

Time fixed effects Yes Yes Observations 1,274 348 Adjusted R-squared 0.1565 0.3474 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

With regressions on the municipal and city level side-by-side, the only interaction effect that stands out is the coefficient for Railway × 2009. The coefficient is statistically significant on the 10% level but is also negative (-0.3844), which renders it impossible to approximate as a percentage change. The behaviour of other coefficients is in line with the binary cut-off models, such as the coefficient for the living area becoming larger and the size of backyard losing significance.

5.3. Effects on house prices at inner-city and non-central locations Section 5.3 is dedicated to regressions comparing central Nordmaling and Robertsfors with the rest of the respective municipality. See table 7 below.

Table 7. Regression of purchase price on railway access at inner-city and non-central locations (2010) Purchase price (log) VARIABLES Nordmaling 2010 Robertsfors 2010

Area 0.0036*** 0.0030*** (0.0008) (0.0008) AncillaryArea 0.0003 -0.0000 (0.0003) (0.0009) Backyard 0.0000** 0.0000*** (0.0000) (0.0000) Water 0.7201*** 0.8618*** (0.0904) (0.1070) InnerCity 0.8330*** 0.3164** (0.1091) (0.1422) Time 0.1464 0.1382 (0.1093) (0.1202) InnerCity × Time -0.1005 -0.1499 (0.1227) (0.1601)

Observations 672 602 Adjusted R-squared 0.2135 0.1181 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

As previously mentioned, this regression is included as a topic of discussion but the conditions supporting it cannot be completely verified. The interaction term InnerCity × Time shows the differences of pre- and post-treatment between the central cities and the rest of the municipalities. The negative coefficients (-0.1005 and -0.1499) cannot be approximated but would, if they were significant, point in the direction that house prices in central Nordmaling and Robertsfors have not seen a higher increase of prices than the rest of the municipality. Inner City is statistically significant and alludes that the house prices in the pre-treatment period were substantially different between the treatment and the control group. Other variables such as Area and Water are all statistically significant at the 1% level, giving credence to their influence on house prices.

To summarise, this chapter has presented the results of regressions performed on the four models established in the methodology. The regressions show statistically significant results for many factors, such as the floor space, but for neither of the interaction terms, Railway × Time or InnerCity × Time.

6. Discussion

This study aimed to update the literature on the effect of the Bothnia Line on property demand in Västerbotten. A difference-in-differences analysis was performed based on the hedonic price model in order to investigate the real estate prices in Nordmaling from 2008-2016. Two variations of the method were used, where one defined post-treatment period as 2010 and one as 2012. By analysing previous studies and the historical context of the railway, the study established several hypotheses, that theorised that the new railway could have affected the house prices differently depending on expectations or proximity to the train station. The primary research question was as follows:

Has improved accessibility through the Bothnia Line led to a significant increase in house prices in Nordmaling, compared to Robertsfors, between 2008-2016?

The results of the regressions do not give any concrete evidence that the construction of the railway has led to real estate prices rising more in Nordmaling compared to Robertsfors. As seen in the regressions in section 5, the primary interaction effect, Railway × Time, was not statistically significant in any of the binary time variable models. The interaction-year model came closest and was able to produce one instance of statistical significance with the interaction term for 2009. However, this observation does not give any more information on the role of the Bothnia Line, as the railway was not completed until a year later. When interpreting the interaction effect before 2010, the variable Railway could only refer to Nordmaling specifically, as opposed to being a stand-in for railway access.

That the trend is not statistically significant for the interaction effect does not mean that prices in Nordmaling have been stagnant or decreased over time. After all, the graph in figure 2 points towards an upwards trend for house prices in Nordmaling. Several regressions from section 5 show significant results for the variable of Time. This means that, even if price levels in Nordmaling have increased, they have also done so in Robertsfors, regardless of inflation. It is still possible that the construction of the Bothnia Line has had an impact on the property market – it just cannot be verified in this study. Hypothesis B was established to discuss whether the Bothnia Line had a positive impact on cities south of Umeå compared with the north, specifically after 2012. In the case of Nordmaling and Robertsfors, and as far as this study has been able to show in regressions with post-treatment defined as 2012, the answer is no.

Hypothesis A, which states that the railway has not had a measurable impact on house prices (due to adaptive expectations), has partly been answered by stating that the regressions cannot confirm a price increase in Nordmaling. See the discussion about the limitations of the study for another look at the adaptive expectations.

To answer hypothesis C, that claimed that the effect would be larger for houses near the new train station, the study primarily turns to equation 3 that compares inner-city Nordmaling and Robertsfors. The regression results for the inner cities in table 5 and 6 do not differ substantially from other models, and the interaction effect of Railway × Time is insignificant. Assuming that the model in this study is correct, neither the launch of the Bothnia Line in 2010 nor the completed renovations in 2012, have substantially affected the house prices in the direct vicinity of the train station.

Nevertheless, the study has produced other noteworthy results. Many variables in the regressions were shown with a high level of confidence to influence house prices. The coefficient for Area, which denotes the size of total floor space, is consistently significant in all regressions. The average marginal effect of one extra square meter of floor space on the purchase price ranged from 0.33-0.45%. For a random house in Nordmaling, that had a median house price of 594 650, this would mean an additional 1963-2675 SEK per extra square meter.10 Proximity to water stands out as another important factor, though with a drastically larger impact on house prices. This is partially attributed to its binary nature. Its inclusion also removed the statistical significance that Time had in other columns in table 3, where a possible explanation would be a that higher number of lakeside or seaside houses were sold in Robertsfors in the post-treatment period.

The impact of these variables, as well as the statistically significant results for inner-city location, is in line with the findings of previous literature. Bohman and Nilsson (2017) and several others have reported a big impact on property prices from house-dependent characteristics. Backyard was statistically significant in all but one model, but with limited influence, as it was consistently rounded off to 0.000. In the regression comparing city-level observations in table 4, Backyard was no longer statistically significant. This could allude to houses in urban areas having smaller backyards in general, so the overall variation from house to house is neither as large nor as important. Lastly, the study did not find evidence that the

10 Adjusted to price levels of 2008. 36 ancillary area had any effect on the final purchase price. A tentative conclusion that the size of storage spaces would matter less for the final purchase price would also have grounds in previous research, as Jonsson (2007) finds insignificant results for the same variable.

There is need for nuance when it comes to discussing statistical significance, as evidenced by the coefficient for Railway. Here, an insignificant result such as the ones seen in table 3, 4 and 6 only means there is no evidence that the prices were different between the municipalities in the pre-treatment period. If anything, the lack of disparity in price levels gives credit to the parallel trend assumption that the situation in both municipalities was similar or identical prior to the exogenous change. Simultaneously, the most important thing in this context is that the trend is similar, not the nominal values.

Lechner (2010) points out in his paper that the parallel trend also assumes that, had the railway never been constructed, the potential outcomes for an observation should be the same regardless of group. The choice of Robertsfors as a control group for Nordmaling is based on the all stated similarities in population growth, median incomes, number of commuters, and purchase prices – but a limitation of the study is that total comparability still cannot be guaranteed. Ultimately, the parallel trend is implied but not confirmed for any of the regressions in the study. Intertwined with this assumption is the issue of finding appropriate comparison methods of house prices at the municipal, and lower, levels. Purchase price does not take other factors into account; price per square meter is not available before 2008, and the purchase price coefficient changes denominator every three years. By looking at the purchase price coefficient, there is insecurity surrounding 2012 as a cut-off point, which could have biased the estimates. 2010, which is more likely to uphold the parallel trend assumption, did not give any results that are substantially different from the 2012 version. In the end, the volatility of the 2012 post- treatment configuration did not substantially impact the other findings in the study.

Lack of available data on regions smaller than municipalities also makes it hard to compare income levels, educational background and other factors between central-city and non-central residents. In light of factors such as urbanisation trends, the assumption that urban and rural areas face the same challenges and trends becomes even more ambiguous. Consequently, equation 5 and the results of its regression cannot be assumed to be unbiased enough or uphold necessary assumptions, as it displays even fewer confirmed parallel trends.

More detailed data, and a larger scope than this thesis allows, would enable a more in-depth look at the Bothnia Line project. With more information, it would be possible to also examine other stops along the tracks, such as Hörnefors in Umeå municipality. With more time, binary variables such as Water and InnerCity could be expanded to continuous variables that use the exact distance from a station or sea.

Finally, another important aspect is the nature of expectations and the risk of not upholding the condition of exogeneity. As mentioned in section 2.1, how the expectations of potential house owners are formed depends on assumptions made about the nature of accommodation. Studies for other projects have shown effects prior to completion, while Brandt (2005) did not find any indication that the announcement itself of the Bothnia Line had affected the real estate prices. This study updates the picture and comes to the same conclusion. Whether the results are valid depends on if the exogeneity assumption is fulfilled, and there are arguments that swing the pendulum in either direction. Did the long construction time of the Bothnia Line lead to people taking the launch into account years in advance? Or did the many delays and periods of stalled construction make people less certain of when the change was going to take place? This study’s adherence to, or violation of, the exogeneity assumption is dependent on how one chooses to answer these questions. As studies in other countries have shown adaptive tendencies in buyers, but Brandt (2005) did not, it could point in the direction that the launch of the Bothnia Line could be viewed as an exogenous, rapid change.

7. Conclusion

This study set out to make valuable contributions to the topic of infrastructure and its effect on real estate prices. In doing so it has both confirmed previous research on important factors for house prices, while also showed that the effect of the Bothnia Line on property demand is a topic for further discussion. The results of a difference-in-differences analysis of Nordmaling and Robertsfors 2008-2016 do not indicate that the launch or the subsequent investments on the Bothnia Line has had any measurable impact on the real estate prices in the region. These findings are based on assumptions about consumer behaviour that cannot be guaranteed, but previous research on the Bothnia Line suggests that the horizon for expectations has been rather short in the past. Lastly, the study has identified factors such as the house size and a location near water as important characteristics that impact real estate prices.

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Appendix

Appendix A: Tables

Table A. Variable list and descriptions VARIABLE DESCRIPTION AncillaryArea Size of the ancillary area (such as storage rooms, barns and garages) in square meters. Area Size of the floor space (i.e. living space) in square meters. Backyard Size of the backyard in square meters. InnerCity A binary variable for inner-city location. InnerCity equals 1 if the house is located in central Nordmaling or Robertsfors, and 0 if in any other place in the municipality. Ln(Price) The natural logarithmic purchase price. The primary dependent variable of the study. Purchase price Final purchase price of a house, measured in thousands of SEK. Railway The dummy variable for treatment. Railway equals 1 if Nordmaling and 0 if Robertsfors. Railway × Time The interaction effect of the time and treatment variable. Also interpreted as the difference of the difference between the two time periods in the treatment group and the difference between the two time periods in the control group. Time Time is a dummy variable that equals 0 for the pre-treatment period (before 2010 or 2012, depending on the model), and 1 for the post- treatment period. Water A binary variable for a house location within 300 meters of a lake or sea.

Table B. OLS-regression of the purchase price in Nordmalng and Robertsfors (2010) VARIABLES Nordmaling Robertsfors Area 0.0076*** 0.0026 (0.0020) (0.0039) Ancillary Area 0.0007 0.0014 (0.0019) (0.0020) Backyard 0.0001*** 0.0000 (0.0000) (0.0000) Inner City 0.7436*** 0.0828 (0.1514) (0.1807) Water 0.5033 0.2904 (0.5047) (0.2846)

Observations 62 59 Adjusted R-squared 0.3675 -0.0425

Table C. Regression comparison between nominal and CPI-adjusted prices (2010) Purchase price (log) VARIABLES Nominal price CPI 2008-adjusted

Area 0.0033*** 0.0033*** (0.0006) (0.0006) Ancillary Area 0.0001 0.0001 (0.0003) (0.0003) Backyard 0.0000*** 0.0000*** (0.0000) (0.0000) Inner City 0.4969*** 0.4968*** (0.0461) (0.0461) Water 0.7964*** 0.7938*** (0.0688) (0.0687) Railway 0.0636 0.0646 (0.1068) (0.1068) Time 0.1539* 0.1129 (0.0899) (0.0899) Railway × Time 0.0036 0.0023 (0.1188) (0.1188)

Observations 1,274 1,274 Adjusted R-squared 0.1486 0.1464

Appendix B: Figures

250

200

150 Nordmaling 100 Robertsfors

50 Median Yearly Income Yearly Median

2006 2007 2008 2009 1998 1999 2000 2001 2002 2003 2004 2005 2010 2011 2012 2013 2014 2015 2016 2017 2018 1997 Figure A. Median yearly income in Nordmaling and Robertsfors (1997-2018). Source: SCB

0,3

0,25

0,2

0,15 Nordmaling

0,1 Robertsfors

0,05

Share of outward commuters outward of Share 0

2002 2010 2018 1998 1999 2000 2001 2003 2004 2005 2006 2007 2008 2009 2011 2012 2013 2014 2015 2016 2017 1997 Figure B. Share of outward commuters by total population (1997-2018). Source: SCB

10000 9000 8000 7000 6000 0 - Nordmaling 5000 0 - Robertsfors 4000 1 - Nordmaling 3000 1 - Robertsfors 2000

Price per sqm (Central vs rural) vs (Central sqm per Price 1000 0 2008 2009 2010 2011 2012 2013 2014 2015 2016

Figure C. Price per sqm between inner-city (1) versus the rest of each municipality (0). Source: Booli