Impact of government intervention on housing market dynamics : a study of Seoul and its surrounding cities

다국어 초록 (Multilingual Abstract)

This study investigates the impact of government intervention on housing market dynamics in the
Seoul metropolitan area, focusing on the decoupling between Seoul and its surrounding cities after
a political shift. By applying the Multivariate Granger Causality (MVGC) test, we construct a
network of housing markets and analyze the flow of market information between 30 cities. The study
covers two distinct subperiods: the weak intervention period under the Park administration (2013
2017) and the strong intervention period under the Moon administration (2017–2022). Our findings
reveal a significant decoupling of Seoul from other cities, with speculative trading in Seoul
increasing as government regulations intensified. Meanwhile, industrial hubs such as Suwon and
Hwaseong emerged as key information transmitters, driven by genuine residential demand rather
than speculative behavior. The results suggest that stringent regulations may have unintentionally
promoted speculative activities, exacerbating market inefficiencies in Seoul. We conclude that
regional differences should be considered in policy design to avoid distorting housing markets and
ensure affordability.

국문 초록 (Abstract)

본 연구는 정치적 변동 이후 서울과 주변 도시들 간의 주택 시장 분리 현상에 초
점을 맞추어, 정부 개입이 서울 대도시권 주택 시장 역학에 미치는 영향을 조사하였
다. 다변량 그랜저 인과성(MVGC) 검정을 적용하여 30개 도시 간 시장 정보의 흐름
을 분석하고 주택 시장 네트워크를 구성하였다. 연구는 박근혜 정부(2013–2017)의
약한 개입 기간과 문재인 정부(2017–2022)의 강한 개입 기간이라는 두 개의 명확한
시기로 나누어 진행되었다. 분석 결과, 정부 규제가 강화됨에 따라 서울은 다른 도시
들과의 연결성이 크게 약화되었으며, 서울 내 투기적 거래가 증가한 것으로 나타났다.
반면, 수원과 화성 같은 산업 중심지는 투기적 거래가 아닌 실수요에 의해 주요 정보
전달자로 부상하였다. 연구 결과는 엄격한 규제가 의도치 않게 투기적 활동을 촉진하
여 서울의 시장 비효율성을 심화시킬 수 있음을 시사한다. 따라서, 지역별 차이를 고
려한 정책 설계가 주택 시장 왜곡을 방지하고 주택의 적정성을 확보하기 위해 필요하
다는 결론을 내린다.

목차 (Table of Contents)

• ABSTRACT
• 1. INTRODUCTION
• 1.1. RESEARCH BACKGROUND
• 2. LITERATURE REVIEW
• 2.1. HOUSING MARKET
• 2.1.1. GOVERNMENT INTERVENTION
• 2.1.2. SPECULATIVE BEHAVIOR IN HOUSING MARKETS
• 2.1.3. INFORMATION FLOW BETWEEN REGIONAL HOUSING MARKETS
• 2.1.4. INDUSTRIAL DEVELOPMENT AND HOUSING MARKETS
• 2.1.5. RIPPLE EFFECTS AND REGIONAL HOUSING MARKETS
• 2.2. KOREAN HOUSING MARKET
• 2.2.1. GOVERNMENT INTERVENTION IN THE KOREAN HOUSING MARKET
• 2.2.2. SPECULATIVE BEHAVIOR AND PRICE VOLATILITY
• 2.2.3. HOUSING AFFORDABILITY AND SUPPLY CONSTRAINTS
• 2.3. NETWORK THEORY
• 3. METHODOLOGY
• 3.1. DATA
• 3.2. GRANGER CAUSALITY
• 3.3. MULTIVARIATE GRANGER CAUSALITY
• 3.4. NETWORK ANALYSIS AND CENTRALITY ANALYSIS
• 3.4.1. DEGREE CENTRALITY
• 3.4.2. EIGENVECTOR CENTRALITY
• 3.4.3. HUB AND AUTHORITY SCORES
• 4. APPLICATION OF GRANGER CAUSALITY TO THE REAL ESTATE MARKET
• 5. RESULTS
• 5.1. OVERALL SAMPLE
• 5.2. PHASE TRANSITION AFTER POLITICAL SHIFT
• 5.3. HETEROGENITY IN TRADING MOTIVATION
• 6. DISCUSSION
• 6.1. OVERALL SAMPLE
• 6.2. PHASE TRANSITION AFTER POLITICAL SHIFT
• 6.3. HETEROGENITY IN TRADING MOTIVATION
• 7. CONCLUSION
• 7.1. SUMMARY
• 7.2. THEORETICAL CONTRIBUTION
• 7.3. PRACTICAL IMPLICATION
• 7.4. LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
• REFERENCES
• ABSTRACT IN KOREAN

참고문헌 (Reference)

1. The world trade network, De Benedictis, L., & Tajoli, L., 34(8), 1417-1454, , 2011

2. Housing and the Korean economy, Kim, K.-H., 13(4), 321-341, , 2004

3. Centrality measures in networks, Bloch, F., Tebaldi, P., Jackson, M. O., 61(2), 413-453, , 2023

4. Complex systems: Network thinking, Mitchell, M., 170(18), 1194-1212, , 2006

5. Demystifying the Chinese housing boom, Fang, H., Gu, Q., Xiong, W., & Zhou, L.-A, 30(1), 105-166, , 2016

6. Internet of things as complex networks, Yang, Y., Luo, X., Wu, X., Li, P., Wang, J., IEEE Network, 35(3), 238-245, , 2021

7. Complex networks: Structure and dynamics, Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D.-U, 424(4-5), 175-308, , 2006

8. Epidemic spreading in scale-free networks., Pastor-Satorras, R., & Vespignani, A., 86(14), 3200., , 2001

9. Economic Consequences of Housing Speculation, Xiong, W., Gao, Z., Sockin, M., The Review of Financial Studies, 33(11), 5248-5287. https://doi. org/10.1093/rfs/hhaa030, , 2020

10. A change of perspective in network centrality, Ridolfi, L., Sciarra, C. Chiarotti, G. Laio, F., 8(1), 15269, , 2018

11. Monetary policy and the global housing bubble, Dokko, J., Doyle, B. M., Kiley, M. T., Kim, J., Sherlund, S., Sim, J., & Van Den Heuvel, S., 26(66), 237-287, , 2011

12. A real estate boom with Chinese characteristics, Glaeser, E., Huang, W., Ma, Y., & Shleifer, A, 31(1), 93-116, , 2017

13. Seoul housing prices and the role of speculation, Xiao, Q., & Park, D., 38(3), 619-644. https://doi. org/10.1007/s00181-009-0282-x, , 2010

14. Authoritative sources in a hyperlinked environment, Kleinberg, J. M., 46(5), 604-632, , 1999

15. Politics, Financial Regulation and Housing Bubbles, Sorge, M. M., 1-27, , 2023

16. Urban housing reform and state capacity in Vietnam, Yip, N.-m., & Tran, H.-a, 21(2), 189-210, , 2008

17. Social network analysis and complex dynamic systems, Mercer, S., 73-82, , 2015

18. Is the Korean housing market following Gangnam style?, Caporin, M., Mensi, W., Ko, H.-U., Kang, S. H., Al-Yahyaee, K. H., 61, 2041-2072, , 2021

19. Regulation and the high cost of housing in California, Quigley, J. M., & Raphael, S., 95(2), 323-328, , 2005

20. Volatility decomposition of Australian housing prices, Lee, C. L., & Reed, R., 23(1), 21-43, , 2014

21. Amsterdam house price ripple effects in The Netherlands, Teye, A. L., Knoppel, M., de Haan, J., & Elsinga, M. G., 10(3), 331-345, , 2017

22. Cross-regional connectedness in the Korean housing market, Lee, W. S., Lee, H. S., 46, 101654, , 2019

23. A critical review of centrality measures in social networks, Heidemann, J., Friedl, B., Landherr, A., 2, 371-385, , 2010

24. The anatomy of a large-scale hypertextual web search engine, Brin, S., & Page, L., 30(1-7), 107-117, , 1998

25. The impact of supply constraints on house prices in England, Hilber, C. A., & Vermeulen, W., 126(591), 358-405, , 2016

26. The time-varying effect of interest rates on housing prices, Lee, C., Park, J., 11(12), 2296, , 2022

27. Risk-dependent centrality in economic and financial networks, Estrada, E., Grassi, R., Clemente, G. P., Benzi, M., Bartesaghi, P., 11(2), 526-565, , 2020

28. An overview of the crisis: Causes, consequences, and solutions, Allen, F., & Carletti, E., 10(1), 1-26, , 2010

29. Unwanted housing: Localism and politics of housing development, Manville, M., Monkkonen, P., 44(2), 685-700, , 2024

30. The science of science: From the perspective of complex systems, Zhou, J., Wu, J., Stanley, H. E., Wang, Y., Zeng, A., Shen, Z., Fan, Y., 714, 1-73, , 2017

31. What is the minimal systemic risk in financial exposure networks?, Pichler, A., Diem, C., Thurner, S., 116, 103900, , 2020

32. Government intervention in the housing market: Who wins, who loses?, Stroebel, J., Floetotto, M., Kirker, M., 80, 106-123. https://doi. org/https://doi. org/10.1016/j. jmoneco.2016.04.005, , 2016

33. Further Evidence on the Integration of REIT, Bond, and Stock Returns, Glascock, J. L., So, R. W., Lu, C., 20(2), 177-194. https://doi. org/10.1023/A:1007877321475, , 2000

34. Urban Middle-Class and the Politics of Home Ownership in South Korea, Kim, M., 47(4), 503-534, , 2018

35. Centrality measure in social networks based on linear threshold model, Molinero, X., Serna, M., Riquelme, F., Gonzalez-Cantergiani, P., Knowledge-Based Systems, 140, 92-102, , 2018

36. Controlling COVID-19 transmission with isolation of influential nodes, Phang, P. S., Chaharborj, S. S., Nabi, K. N., Chaharborj, S. S., Feng, K. L., Chaos, Solitons & Fractals, 159, 112035. https://doi. org/https://doi. org/10.1016/j. chaos.2022.112035, , 2022

37. Systemic risk in financial institutions: A multiplex network approach, Xie, Y., Tse, Y., Li, S., Liu, Q., Jiao, F., 73, 101752, , 2022

38. The world trade network: country centrality and the COVID-19 pandemic, Falbo, P., Rizzini, G., Antonietti, R., Grassi, R., Fontini, F., 7(1), 18. https://doi. org/10.1007/s41109-022-00452-4, , 2022

39. Causality between tourism and housing prices: A wavelet-based approach, Wu, H.-C., Wu, T.-P., Wu, Y.-Y, Wang, C.-M., 15(4), 465-485, , 2023

40. Unintended Consequences of Housing Policies: Evidence from South Korea, Ko, J., Kim, C., 15(4), 3407, , 2023

41. A social network analysis of the citation diffusion of academic articles, Mostafa, M. M., 1-13, , 2024

42. Speculation and the informational efficiency of commodity futures markets, Pütz, A., Bohl, M. T., Sulewski, C., 23, 100159, , 2021

43. Regulation and housing supply. In Handbook of regional and urban economics, Gyourko, J., & Molloy, R., Vol. 5, pp. 1289-1337, , 2015

44. What drives house price cycles? International experience and policy issues, Duca, J. V., Murphy, A., Muellbauer, J., 59(3), 773-864, , 2021

45. Selective housing policy in local housing markets and the supply of housing, Nordvik, V., 15(4), 279-292. https://doi. org/https://doi. org/10.1016/j. jhe.2006.10.002, , 2006

46. Co-integration and error correction: representation, estimation, and testing, Engle, R. F., & Granger, C. W., 251-276, , 1987

47. Node centrality in weighted networks: Generalizing degree and shortest paths, Opsahl, T., Agneessens, F., & Skvoretz, J, 32(3), 245-251, , 2010

48. Speculation and price bubbles in the Korean and Japanese real estate markets, Kim, K.-H., & Suh, S. H., 6, 73-87, , 1993

49. Factoring and weighting approaches to status scores and clique identification, Bonacich, P., 2(1), 113-120, , 1972

50. Housing Price Bubbles in Hong Kong, Beijing and Shanghai: A Comparative Study, Hui, E. C. M., Yue, S., 33(4), 299-327. https://doi. org/10.1007/s11146-006-0335-2, , 2006

51. An Economic Study on Estimating the Housing Price Bubble in Seoul Capital Area, Ma, J., Kim, M., Kim, K., Gyeonggi Research Institute Policy Research 2022-14) https://www. gri. re. kr/eng/contents/publications. do?schM=view&page=2&viewCount=10&schProjectNo=20220152&schBookResultNo=15093, , 2022

52. Positive feedback investment strategies and destabilizing rational speculation, De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J, 45(2), 379-395, , 1990

53. A machine learning-based early warning system for the housing and stock markets, Ryu, D., Park, D., IEEE Access, 9, 85566-85572, , 2021

54. Investigating causal relations by econometric models and cross-spectral methods, Granger, C. W., 424-438, , 1969

55. Applying centrality measures to impact analysis: A coauthorship network analysis, Yan, E., & Ding, Y, 60(10), 2107-2118, , 2009

56. International co-movement of housing price cycles in East Asia and Greater China, Kim, K.-H., Park, Y.-J., Asian Economic Papers, 15(1), 78-98, , 2016

57. Urban transformation Korean style: Lessons from property-based urban development, Shin, H. B., In Exporting Urban Korea? Taylor & Francis, , 2020

58. Time lag analysis of real estate supply policies for housing construction projects, Jeong, S., Choi, J., Lee, J. G., 1-12, , 2024

59. Impact of Macroeconomic Variables on Housing Prices in Saudi Arabia: A VAR Approach, Ahmed, D., https://doi. org/10.20944/preprints202012.0715. v1, , 2020

60. Time-varying inter-urban housing price spillovers in China: Causes and consequences, Lu, Y., Li, J., Yang, H., 77, 101396, , 2021

61. An improvement method for degree and its extending centralities in directed networks, Huang, C., Xu, C., Liu, L., Liu, J., Jia, P., Physica A: Statistical Mechanics and its Applications, 532, 121891. https://doi. org/https://doi. org/10.1016/j. physa.2019.121891, , 2019

62. Identifying influential spreaders in complex networks for disease spread and control, Liu, S., Zhao, J., Wang, Y., Wei, X., 12(1), 5550. https://doi. org/10.1038/s41598-022-09341-3, , 2022

63. Lessons from the history of Samsung’s SCM innovations: focus on the TQM perspective, Chung, B. D., Jung, U., Total Quality Management & Business Excellence, 27(7-8), 751-760, , 2016

64. Deregulating a Developmental State: Changes since South Korea's 1998 Regulatory Reform, Son, Y., Park, N., Ham, J., 47(4), 514-545, , 2023

65. Assessing network patterns in illegal firearm markets. In Transnational Organized Crime, Morselli, C., pp. 375-396, , 2017

66. A financial network perspective of financial institutions’ systemic risk contributions, Uryasev, S., Yao, S., Zhuang, X.-T., Huang, W.-Q., 456, 183-196, , 2016

67. Commuting paradox of the technopole Newtown: A case study in the Seoul Metropolitan Area, Dalavong, P., Choi, C. G., 19(7), e0306304, , 2024

68. Structural and Evolutionary Analysis of Coauthorship Network in Scientometrics Information, Jiang, L.,, Liu, P.,, Liu, H., 2021 International Conference on EducationEIMSS, , 2021

69. The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference, Barnett, L., & Seth, A. K., 223, 50-68, , 2014

70. Unveiling the structure of supply networks: case studies in Honda, Acura, and DaimlerChrysler, Choi, T. Y., & Hong, Y, 20(5), 469-493, , 2002

71. Urban zoning for sustainable tourism: A continuum of accommodation to enhance city resilience, Yiu, C.-Y., Cheung, K.-S., 13(13), 7317, , 2021

72. A multi‐emission‐driven efficient network design for green hub‐and‐spoke airline networks, Li, Z., Zhang, N., Sun, M., Lv, Y., Tian, Y., Li, J., Dong, X., 18(2), 346-376, , 2024

73. Predicting COVID-19 infections using multi-layer centrality measures in population-scale networks, Hedde-von Westernhagen, C., Garcia-Bernardo, J., Bagheri, A., 9(1), 27. https://doi. org/10.1007/s41109-024-00632-4, , 2024

74. Detecting spatial and temporal house price diffusion in the Netherlands: A Bayesian network approach, Teye, A. L., Ahelegbey, D. F., 65, 56-64, , 2017

75. Optimizing the design of an intra-city metro logistics system based on a hub-and-spoke network model, Zhou, L., Yang, P., Li, H., Zhou, J., Zhao, L., 116, 104086, , 2021

76. Understanding real estate price dynamics: The case of housing prices in five major cities of China✰, Fan, Y., Yavas, A., Yang, Z., 43, 37-55, , 2019

77. Research performance and degree centrality in co-authorship networks: The moderating role of homophily, Badar, K., Frantz, T. L., & Jabeen, M., 68(6), 756-771, , 2016

78. Centrality in social networks: Conceptual clarification. Social Network: Critical Concepts in Sociology, Freeman, L. C., 1, 238-263, , 2002

79. The developmental state, speculative urbanisation and the politics of displacement in gentrifying Seoul, Shin, H. B., &, Kim, S.-H., 53(3), 540-559, , 2016

80. The Two Million Housing Drive in Korea: A supply solution for affordable housing in the developing world, Feather, C., 12(5), 906-933, , 2019

81. Centrality in Networks: Finding the Most Important Nodes. In P. Moscato & N. J. de Vries (Eds.) New Ideas, Gómez, S., and Analyticspp. 401-433 Springer International Publishing. https://doi. org/10.1007/978-3-030-06222-4_8, , 2019

82. The nexus between stock market index and apartment and villa prices: Granger causality test of Swedish data, Öhman, P., Yazdanfar, D., 10(3), 450-467, , 2017

83. Housing price and transaction intensity correlation in Hong Kong: Implications for government housing policy, Li, L. H., Cheung, K. S., 32, 269-287, , 2017

84. The connectedness between Hong Kong and China real estate markets: spillover effect and information transmission, Tsai, I.-C., Empirical Economics, 63(1), 287-311, , 2022

85. Integrated aviation model and metaheuristic algorithm for hub-and-spoke network design and airline fleet planning, Shi, J. J., Wu, J., Zhang, P.-w., Wang, Y., 164, 102755, , 2022

86. Forecasting spatially correlated targets: simultaneous prediction of housing market activity across multiple areas, Lee, C., 26(2), 119-126, , 2022

87. Does Proximity to a Power Plant Affect Housing Property Values of a City in South Korea? An Empirical Investigation, Kim, J.-H., Kim, Y.-K., & Yoo, S.-H., 16(4), , 2023

88. Hub-and-spoke network design problem under uncertainty considering financial and service issues: A two-phase approach, Yang, K., Yang, L., Gao, Z., 402, 15-34, , 2017

89. Housing prices and real effective exchange rates in 18 OECD countries: a bootstrap multivariate panel Granger causality, Bahmani-Oskooee, M., & Wu, T.-P., 60, 119-126, , 2018

90. Twenty years of Wikipedia in scholarly publications: a bibliometric network analysis of the thematic and citation landscape, Mostafa, M. M., 57(6), 5623-5653, , 2023

91. Research on interdisciplinarity of five-metrics in China based on Chinese Citation Data under the background of open science, Wang, X., Zhao, H., 01655515241263286, , 2024

92. A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks?, Laurier, W., Vignery, K., 15(12), e0244377, , 2020

93. Enhancing performability through domestic and international clustering: a case study of Samsung Electronics Corporation (SEC), Moon, H.-C., Parc, J., Yim, S. H., & Yin, W., 9(1), 75, , 2013

94. Getting Housing Incentives Right: A Case Study of the Effects of Regulation, Taxes, and Subsidies on Housing Supply in Malaysia, Malpezzi, S., Mayo, S. K., 73(3), 372-391. https://doi. org/10.2307/3147174, , 1997

95. Investigating the ripple effect through the relationship between housing markets and residential migration in Seoul, South Korea, Kim, L., Seo, W., 12(3), 1225, , 2020

96. Government intervention in land market and its impacts on land supply and new housing supply: Evidence from major Chinese markets, Yan, S., Wu, Q, Ge, X. J., 44, 517-527, , 2014

97. Comparative housing, urban crisis and political economy: An ethnographically based ‘long view’from Auckland, Singapore and Berlin, Wetzstein, S., 34(2), 272-297, , 2019

98. Transformations in the governance of urban and regional planning in Korea: From (neo-) developmentalism to civic democracy, 1965–2020, Douglass, M., In Exporting Urban Korea? Taylor & Francis, , 2020

99. Environmental regulation and corporate tax avoidance：A quasi-natural experiments study based on China's new environmental protection law, Luo, L., Yu, H., Qu, S., Fang, D., Liao, L., Xiong, G., 296, 113160. https://doi. org/https://doi. org/10.1016/j. jenvman.2021.113160, , 2021

100. The causal relationship between house prices and growth in the nine provinces of South Africa: evidence from panel – Granger causality tests, Chang, T., Simo-Kengne, B. D., Gupta, R., 6(4), 345-358. https://doi. org/10.1504/IJSE.2014.065389, , 2014

101. Portfolio diversification possibilities between the stock and housing markets in G7 countries: Evidence from the time-varying Granger causality, Chen, C.-F., Chiang, S.-h., 49, 103124, , 2022

102. The Mathematical Simulation of South Korea’s Financial and Economic Impacts from Real Estate Bubbles: Lessons from the China Evergrande Collapse, He, Y., Wang, D., 12(19), 3058, , 2024

103. A Real Options Analysis on Speculative Bubbles in Housing Market: Considering Seoul Metropolis Case In M. Hosoe, B.-G. Ju, A. Yakita, & K. Hong (Eds.), Park, H., Contemporary Issues in Applied Economics: Ten Years of International Academic Exchanges Between JAAE and KAAE (pp. 327-340). Springer Singapore. https://doi. org/10.1007/978-981-13-7036-6_18, , 2019

104. Critical junctures and path dependence in urban planning and housing policy: A review of greenbelts and New Towns in Korea’s Seoul metropolitan area, Lee, S., Seong, E. Y., Choi, C. G., Kim, H., Land Use Policy, 80, 195-204, , 2019

105. The Effect of Green Characteristics in Reducing the Inventory of Unsold Housing in New Residential Developments—A Case of Gyeonggi Province, in South Korea, Yoon, H., Yoo, H., 10(4), 377, , 2021

106. Government Intervention in Real Estate Market: Is Tax Reform Effective in Seoul Housing Market? In Theory and History in Regional Perspective: Essays in Honor of Yasuhiro Sakai (, K. Kourtit, M. Kawano, Kim, E., Moon, I., Kim, A., Y. HiganoEds, P. Nijkamp, pp. 221-243). Springer Nature Singapore. https://doi. org/10.1007/978-981-16-6695-7_12, , 2022