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Will Dockless Bike Sharing System Alter the Subway Price Premium in Rental Market? Evidence from Beijing

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Will Dockless Bike Sharing System Alter the Subway Price Premium in Rental Market? Evidence from Beijing Will Dockless Bike Sharing System Alter the Subway Price Premium in Rental Market? Evidence from Beijing By Jinghong Lyu MSc. in Smart Cities and Urban Analytics, University College London (2016) B.S. in Environmental Sciences, University of East Anglia (2015) B.S. in Environmental Sciences, Fudan University (2015) Submitted to the Department of Urban Studies and Planning in partial fulfillment of the requirements for the degree of Master in City Planning at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2019 © 2019 Jinghong Lyu. All Rights Reserved The author hereby grants to MIT the permission to reproduce and to distribute publicly paper and electronic copies of the thesis document in whole or in part in any medium now known or hereafter created. Author_________________________________________________________________ Department of Urban Studies and Planning May 20, 2019 Certified by _____________________________________________________________ Professor Siqi Zheng Department of Urban Studies and Planning Thesis Supervisor Accepted by______________________________________________________________ Professor of the Practice, Ceasar McDowell Co-Chair, MCP Committee Department of Urban Studies and Planning 2 Will Dockless Bike Sharing System Alter the Subway Price Premium in Rental Market? Evidence from Beijing by Jinghong Lyu Submitted to the Department of Urban Studies and Planning on May 20, 2019, in partial fulfillment of the requirements for the degree of Master in City Planning Abstract The dockless bike-sharing system emerged in 2016 in China. It achieves door-to-door connection and is thus regarded as an efficient solution to the urban last mile problem. Em- pirical studies have found that the entry of dockless bikes can flatten the rent price gradient for subway accessibility for 10 Chinese cities on average, yet haven’t discussed if this ef- fect also exists in a single city where subway accessibility varies across locations. This study uses difference-in-differences (DID) empirical design to analyze the impact of dock- less bike-sharing on Beijing’s rental market, and further explores the spatial heterogeneity of such impact. Results show that the rental price gradient flattens slightly for apartments within the 3km radius from a subway station in Beijing. Taking the heterogeneity of de- velopment in Beijing into account, the study further finds that the rental price gradient be- comes slightly steeper in more developed areas such as North Beijing or Beijing within the 4th Ring Road, and flattens by up to 16% in less developed areas such as South Beijing and Beijing outside the 4th Ring Road. Such impact on rental price gradient is not linear within the 3km radius, where in more developed areas the largest reduction in gradient happens at 1000-2000m, and in less developed areas at 0-500m. The entry of dockless bike-sharing system can generate great social benefits. Switching from walking to biking from homes to subway stations saves about 8.3 minutes of commuting time per trip on average in Bei- jing, where the most developed area saves 7.24 minutes and the least developed saves 11.8 minutes. Allowing for a 10-min commuting time from homes to subway stations, a tenant would be able to choose from apartments within 800m to subway stations to up to 3km to subway stations. This study contributes to the nascent literature on dockless bike-sharing systems and its impacts on housing rental market, and also yields policy implications for better integrating the bike-sharing system and the existing public transit systems, and the resulted benefit of enhancing housing supply in public transit accessible locations. Thesis Supervisor: Siqi Zheng Title: Department of Urban Studies and Planning 3 4 Acknowledgments This study would not have been possible without the help and support from the people I met in Cambridge and in my life. I would like to thank my thesis advisor Prof Siqi Zheng, who brought me to the field of Econometrics and shared great insights in urban economics with me. Through Siqi’s class, I have learned the empirical design methods that are used in this study. Through bi-weekly thesis advising meetings, Siqi provided me with extremely helpful suggestions and introduced me to her connections who also contributed greatly to this study. Siqi not only helped me with the thesis but also strongly encouraged me to present this study at the AsRES international conference. I am grateful for all the supports. I would also like to thank those that helped me with the thesis. Thank you Shengjie Lu, Dr. Lei Dong and Dr. Yongping Zhang for helping me with data collection. Thank you Dr. Rui Du, Yichun Fan, Prof Chun Kuang, Yao Zhao and Rachel Luo for directing me through the modelling process. Thank you Prof Jinhua Zhao (reader of this study), Prof Joseph Ferreira, Dr. Fabio Duarte, Davor Ljubenkov for coming to my defense, giving a lively discussion and providing great suggestions. Lastly, I want to thank for the continuous support from my family, friends, and the DUSP family. Thank you my parents who have always been supportive. Thanks to the friends at DUSP who have supported each other throughout the two years. Thank you to the friends I met at UNESCAP for all the reassuring chats. Thanks to my RA supervisor Prof David Hsu, who has always been respectful and supportive to my decisions in research, during the thesis-writing period, and in my future career. I appreciate all your supports and love, and I will not fear for any future challenges with you by my side. 5 6 Contents 1 Introduction 13 1.1 Introduction . 13 1.2 Research questions and hypotheses . 16 1.3 Thesis structure . 17 2 Literature Review 19 2.1 Bike-sharing: integration of public transportation . 19 2.2 Housing price premium . 21 2.3 Gaps in the literature . 24 3 Methods and Data 27 3.1 Empirical strategy . 27 3.2 Data . 29 3.2.1 Rental transactions . 29 3.2.2 Dockless bike usage . 31 3.2.3 Points of Interest (POIs) . 36 3.2.4 Beijing’s walkable road network and network-based distance to amenities . 42 3.2.5 Time saving . 43 4 Results and Discussion 45 4.1 Results . 45 7 4.1.1 Baseline models - Impact of entry of dockless bike-sharing on rental prices . 45 4.1.2 Heterogeneity of the effect . 46 4.2 Discussion . 61 4.2.1 Benefits of dockless bikes . 61 4.2.2 Limitation of the study . 64 5 Conclusion 67 Bibliography 71 8 List of Figures 3-1 Locations of complexes and subway stations . 33 3-2 Average rental price for a whole apartment in each complex . 34 3-3 Average rental price for a single room in each complex . 35 3-4 Hourly demand of dockless bikes in Beijing from May 10th-16th and 18th- 23rd, 2017 . 36 3-5 Origins of morning rush hours . 37 3-6 Destinations of morning rush hours . 38 3-7 Locations of Grade AAA hospitals and subway stations . 39 3-8 Locations of universities and subway stations . 40 3-9 Locations of big shopping malls and subway stations . 41 3-10 Distribution of POIs with respect to distance to complexes . 42 4-1 Average rental price premium in distance segments pre and post entry of dockless bikes in Beijing. 56 4-2 North/South and Inner/Outer of Beijing . 57 4-3 North/South Beijing within/outside the 4th Ring Road . 59 4-4 Economic benefits for a standardized hypothetical apartment near Xingong subway station . 61 4-5 Time saving by switching from walk-and-ride to bike-and-ride . 62 9 10 List of Tables 3.1 Summary statistics of variables. 32 4.1 Baseline models . 47 4.2 Heterogeneity effect . 50 4.3 Robustness check 1: Impact on rental price gradients using continuous dis- tance. Limited to complexes that are within 2km to their closest subway stations . 53 4.4 Robustness check 2: Impact on rental price gradients using continuous dis- tance. Limited to complexes that are within 1.5km to their closest subway stations . 54 4.5 Impact on rental price gradients using distance dummies . 55 4.6 Time saving by using dockless bikes . 63 11 12 Chapter 1 Introduction 1.1 Introduction The last mile problem in transportation network refers to the difficulty in transferring people from origins (homes, offices, etc.) to public transportation hubs (subway stations, bus stops, etc.), or from public transportation hubs to their final destinations. This adds to substantial commuting costs. Policy makers have initiated many programs to solve this problem; the public bike-sharing system is one among the solutions. The first genera- tion of bike-sharing system was proposed in Amsterdam, Netherlands, in 1965, providing transiting services to citizens free of charge. In the 1970s to 1990s, as technologies be- came more advanced and affordable, many bike-sharing systems were initiated in many European cities (Shaheen et al., 2010). In the early 2000s, China started to support public bike-sharing systems in big cities such as Beijing, Shanghai, Hangzhou and Wuhan. These bike-sharing systems place bike stations in certain areas in cities; users need to pick up the bikes at stations, make payment with coins or smart cards, and return the bikes to any station after the trip. The dock-based bike-sharing system plays an important role in solving urban last mile problem. Until 2016 October, there were up to 1,700 public bike stations and more than 55,000 dock-based bikes serving 380,000 registered users. The daily use of dock-based bikes in Beijing is around 200,000. Studies have found that bus stops and subway stations 13 are popular destinations of public bike trips, indicating that public bike-sharing systems integrate into city’s public transportation infrastructure (Zhao and Li, 2017).
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