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The Effect of Transport Innovation on Property Prices: a Study on the New Commuter Line Between Uppsala and Älvsjö

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The Effect of Transport Innovation on Property Prices: a Study on the New Commuter Line Between Uppsala and Älvsjö The effect of transport innovation on property prices: A study on the new commuter line between Uppsala and Älvsjö Student: Brikena Meha Supervisor: Ina Blind Master of Science Programme in Economics Department of Economics Uppsala University, Sweden January 2017 Abstract The aim of this paper is to investigate the impact of transport innovation on housing prices. More precisely, I study the effects on housing prices of the new commuter line (J38) between Uppsala and Älvsjö that started on December 9, 2012. The properties covered in the analysis are located around the 11 station stops between Uppsala and Älvsjö. This transport innovation was initiated to increase integration between Uppsala County and Stockholm County. Using the start of the J38 line as a quasi-experiment in a hedonic price model, I compare the changes in housing prices in treated areas and untreated areas after the introduction of the new line. Separate models are estimated for properties in “Housing Cooperative” (Bostadsrätt), and in “Home Ownership” (Äganderätt). The models include contract prices at which the property is sold, house characteristics, distance to nearest rail station, bus station, water and time-fixed effects. The results suggest that apartment prices (in housing cooperative) were negatively affected by the new line, whereas the effect for house prices (home ownership) is not statistically significant different from zero. Key words: Housing Prices, Transport Innovation, Housing cooperative, Home Ownership, Distance to Commuter Station, Hedonic Price Model, Difference-in-Difference. 2 Table of Contents 1. Introduction ................................................................................................................................. 4 2. Literature review ......................................................................................................................... 6 3. Description on housing types and transportation in Sweden ....................................................... 8 3.1. Housing types traded on the market ..................................................................................... 9 3.2. The transport innovation..................................................................................................... 10 4. Study perimeter ......................................................................................................................... 11 4.1. Data .................................................................................................................................... 11 4.2. Descriptive Statistics .......................................................................................................... 12 5. Econometric specification ......................................................................................................... 15 5.1. The model ........................................................................................................................... 15 5.2. Regression results ............................................................................................................... 16 5.2.1. Baseline results for housing cooperative regression ........................................................ 17 5.2.2. Baseline results for home ownership regression ............................................................. 19 6. Sensitivity Analysis ................................................................................................................... 21 6.1. Anticipation effects ............................................................................................................ 21 6.2. Other thresholds ................................................................................................................. 22 6.3. Measurement error in coordinates ...................................................................................... 22 7. Conclusions ............................................................................................................................... 23 References ..................................................................................................................................... 25 APPENDIX ................................................................................................................................... 27 3 1. Introduction Transport infrastructure plays an important role in urban development since it connects peoples’ residence and work, as well as influences the form, density, and expansion of urban areas. Economically active individuals make decisions that depend on the trade-off between housing and the cost of transportation to work. In big cities, some people mainly rely on rail transportation for such commuting. The number of work commutes via rail transportation within the Stockholm area is about 40% (Transport Analysis, 2011). Demand for good access to rail transportation can thus be thought to have a positive effect on housing prices close to commuter rail stations. However, there can also be negative externalities associated with commuter rail station, e.g. noise, pollution, increase in crime rates (see e.g. Bowes and Ihlanfeldt, 2001; Debrezion et al., 2007). By identifying how new transport lines actually affect property prices, policy makers will be able to support their decisions on whether to invest in transport innovation or to what extent to do so. The aim of this paper is to investigate the effect on housing prices of the new commuter line (J38) that goes between Uppsala and Älvsjö, and started on December 9, 2012. The properties covered in this analysis are located around the 11 station stops of J38 line between Uppsala and Älvsjö. The starting point of this analysis is a simple hedonic price model that relates property prices to the J38 line. This pricing model treats every property as a heterogeneous good that is determined by its features. In housing context, these features determine the price of the house and are categorized into three groups: physical, accessibility, and environmental (Fujita, 1989; Bowes and Ihlanfeldt, 2001). Rosen (1974), who popularized the theory of hedonic price model discusses that, decomposing housing prices into implicit prices is challenging since there are not two identical dwellings, or that they are not in the same location. House features cannot be sold separately, as there is no market for them, thus, it is not possible to observe them independently. 4 Given the information on changes on transport innovation over time, that is the new commuter line (J38) on December 2012, I use a difference-in-difference (DID) approach to examine how property prices respond to this new line. This approach compares housing prices before and after J38 started, for housing that are close to a rail station and those that are farther away. The key identifying assumption is that the trends of housing prices in the areas closer to the rail stations are the same to areas farther from the rail station, in the absence of the new commuter line. This study is important because it is the first of its kind to apply a difference-in-difference approach that addresses the relationship between transport innovation and property prices in Stockholm County. This quasi-experimental approach controls for unobservable location-fixed characteristics that could otherwise bias the estimation of the relation between the J38 line and housing prices. To implement this method, I will use data on housing sales in the county of Stockholm, for the time period from January 1, 2009 to December 31, 2015. The data set is very rich as it contains crucial information on the variables of interest, such as sales prices, housing characteristics, geographical coordinates for each property, accessibility to the nearest rail station (i.e. commuter, subway) and bus station, as well as to nearest water. The results for properties in housing cooperative suggest that there is a decrease in housing prices after the December event for dwellings in the treated areas, and for properties in home ownership are insignificant but positive. Further, I do four different robustness checks to test the specification of the two main regression models. The remainder of the paper is structured as follows. Section 2 provides a review analysis on the existing literature about the relationship of proximity to the station on house prices. Section 3 provides an overview of the housing types that are treated on the Swedish housing market, and a description of the J38 line. Section 4 is the study perimeter, including data, and descriptive statistics. Section 5 is the baseline empirical 5 analysis. Section 6 provides sensitivity analyses and section 7 gives the conclusions of the study. 2. Literature review Theories on land values use accessibility as a determinant factor to explain variation in property prices. A monocentric model developed by Alonso (1964) explains that jobs are located around the central business district (CBD) of the city, where most of the economic agents work. In the means of compensating for long commutes to work, housing prices fall as the distance to CBD increases (see also Muth, 1969). There is thus a trade-off between commuting to work and property prices. Similarly, people that reside in properties located around rail stations can be expected to benefit in terms of cost savings and transport time, which pushes up property prices that are closer to rail station. Furthermore, there is a considerable amount of empirical literature on the relationship between
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