An Empirical Study on the Complexity of China's Real Estate Market

Abstract

Abstract: The abnormal fluctuations of the real estate market and the uncertainty of the impact of economic policy adjustments on the real estate market have drawn great attention of the government and scholars. Based on the theory of complexity science, this paper uses complexity methods to study the complexity characteristics of China's real estate market by correlation dimension tests, Lyapunov index tests, dissipative entropy method and R/S analysis method. The results imply that China's real estate system has the non-linear characteristics of "spikes and thick tails" and the chaotic characteristics of inherent randomness and sensitivity to initial values. Moreover, under the monetary policy, the real estate system is in an orderly state, with the characteristics of dissipative structure, long-term memory and self-similar fractal structure. Therefore, China's real estate system cannot achieve an orderly situation by itself, and it is necessary to combine with the continuous monetary policy regulation to achieve a healthy and orderly development of the real estate market.

Key words: real estate system, nonlinearity, complexity, monetary policy

Zhang Pinyi, Yang Juanni. An Empirical Study on the Complexity of China's Real Estate Market[J]. Management Review, 2022, 34(7): 47-56.

References

[1] Kettani O., Oral M.Designing and Implementing a Real Estate Appraisal System:The Case of Quebec Province, Canada[J].Socio-Economic Planning Sciences, 2015,49(3):1-9
[2] Bernake B., Gertler M.Should Central Banks Respond to Movements in Asset Prices[J].American Economic Review, 2001,91(2):253-257
[3] Omid P., Tomáš M., Serge B.Vision-based Real Estate Price Estimation[J].Machine Vision and Applications, 2018,29(4):667-676
[4] 湛东升,吴倩倩,余建辉,等.中国资源型城市房价时空变化与影响因素分析[J].自然资源学报, 2020,35(12):2888-2900
[5] Farhi E., Tirole J.Bubbly Liquidity[J].The Review of Economic Studies, 2012,79(2):678-706
[6] 张品一.货币政策与房地产周期性波动[J].现代经济探讨, 2019,(5):48-54
[7] 郭菲,冯长春.宏观调控政策下我国住房市场周期变化的影响分析[J].地域研究与开发, 2020,39(6):18-23
[8] 唐小飞,刘伯强,王春国,等.我国房地产行业发展趋势影响因素研究[J].宏观经济研究, 2014,(12):59-66
[9] Shahid S., Pour S.H., Wang X., et al.Impacts and Adaptation to Climate Change in Malaysian Real Estate[J].International Journal of Climate Change Strategies and Management, 2017,9(1):87-103
[10] Pandya J.V., Patel V.M.Evaluating Critical Factors Impacting Real Estate Development Decisions[J].Real Estate Management and Valuation, 2020,28(1):112-126
[11] Rahal C.Housing Markets and Unconventional Monetary Policy[J].Journal of Housing Economics, 2016,32(6):67-80
[12] 常飞,李秀婷,郭琨,等.货币政策对区域房地产住宅市场的差异化影响——基于国内十城市房地产住宅市场的实证研究[J].管理评论, 2013,25(10):3-9
[13] 李志锋.房地产金融理论研究的方法论创新——复杂性科学的方法论对房地产金融理论研究的影响[J].经济问题, 2014,(5):12-15
[14] 朱少平.参数未知的房地产投资系统混沌同步[J].统计与信息论坛, 2011,26(3):42-44
[15] Horino H., Nonaka H., Carreón E.C.A., et al.Development of an Entropy-based Feature Selection Method and Analysis of Online Reviews on Real Estate[C].2017 IEEE International Conference on Industrial Engineering and Engineering Management, 2017
[16] 刘水.基于分形视角的中国房地产市场有效性研究[J].宏观经济研究, 2019,(9):102-114
[17] Jirasakuldech B., Emekter R.Nonlinear Dynamics and Chaos Behaviors in the REIT Industry:A Pre-and Post-1993 Comparison[J].Journal of Real Estate Portfolio Management, 2012,18(1):57-77
[18] Radzewicz A.Real Estate Market System-phase Space Theory Approach[J].Real Estate Management and Valuation, 2013,21(4):87-95
[19] Li H.Chaos Synchronization Modle of Complex Networks on Real Estate Investment Systems[J].Applied Mechanics and Materials, 2014,2974(501-504):2715-2718
[20] 周雷,张建坤.房地产业系统的耗散结构研究[J].东南大学学报(哲学社会科学版), 2008,(S1):76-79
[21] 王正明,温桂梅,路正南.基于耗散结构系统熵模型的产业有序发展研究[J].中国人口·资源与环境, 2012,22(12):54-59
[22] Cajueiro D.O., Tabak B.M.Testing for Time-varying Long-range Dependence in Real Estate Equity Returns[J].Chaos, Solitons&Fractals, 2008,38(1):293-307
[23] Argyroudis G.S., Siokis F.M.Spillover Effects of Great Recession on Hong-Kong's Real Estate Market:An Analysis Based on Causality Plane and Tsallis Curves of Complexity-Entropy[J].Physica A:Statistical Mechanics and its Applications, 2019,524:576-586
[24] 刘超,马文腾,马玉洁,等.人民币汇率市场分形特性研究[J].经济问题探索, 2014,(4):117-126
[25] 王晋忠,梁贺,李俊霖.沪深300指数期货市场的分形特征研究[J].经济学家, 2015,(12):60-67
[26] 葛永波,曹婷婷,朱蓉.中国黑色金属期货市场分形特征的研究[J].价格理论与实践, 2019,(1):101-104
[27] 何恺,程道平.我国城市房地产市场风险测度研究——基于综合赋权评价方法对济南市的测算[J].价格理论与实践, 2016,(10):148-151
[28] 王天雨.货币政策对住宅商品房价格影响的区域差异——来自我国31省份的证据[J].经济理论与经济管理, 2018,(8):75-90
[29] 涂红,徐春发,余子良.货币政策对房价影响的区域差异:来自多层混合效应模型的新证据[J].南开经济研究, 2018,(5):41-57
[30] 陈创练,戴明晓.货币政策、杠杆周期与房地产市场价格波动[J].经济研究, 2018,53(9):52-67
[31] 郑挺国,赵丽娟,宋涛.房地产价格失调与时变货币政策立场识别[J].金融研究, 2018,(9):1-18
[32] 张辉.我国货币政策传导变量对产业结构影响的实证研究[J].经济科学, 2013,(1):22-35
[33] 潘海英,史梦,汪欣.货币政策的城市房价传导异质性及房价溢出效应研究[J].河海大学学报(哲学社会科学版), 2021,23(1):80-88
[34] 宋勃,雷红.银行信贷冲击与房地产价格波动:1999-2014——基于我国一、二线城市面板数据分析[J].财经科学, 2016,(5):12-19
[35] 高波,樊学瑞,赵奉军.金融冲击与房地产市场波动——一个宏观分析框架及中国的经验证据[J].经济理论与经济管理, 2017,(6):45-56
[36] 张能,蒋云峰,程成.参数约束下永磁同步电机混沌动力学特性仿真[J].计算机仿真, 2019,36(10):223-226
[37] 徐昌彪,钟德,夏诚,等.一个新的超大范围混沌系统及其自适应滑模控制[J].振动与冲击, 2019,38(3):125-130
[38] 龚云帆,徐健学.混沌信号与噪声[J].信号处理, 1997,(2):112-118
[39] 尹海员,王盼盼.非线性视角下证券市场波动及危机监管研究——本土与跨国途径两个维度的视角[J].上海经济研究, 2015,(7):9-15
[40] Belaire F.J., Opong K.K.A Time Series Analysis of U.K.Construction and Real Estate Indices[J].The Journal of Real Estate Finance and Economics, 2013,46(3):516-542
[41] 李士勇.非线性科学与复杂性科学[M].哈尔滨:哈尔滨工业大学出版社, 2006
[42] 刘胜群,肖祥颜,谢姗姗.论房地产业的耗散结构特征[J].商场现代化, 2007,(32):247-249
[43] 王维,傅毓维,张铁男.基于熵的企业创新管理理念与机制[J].现代管理科学, 2008,(6):25-26
[44] 汪振双,张家楠,赵宁.低碳建筑合同能源管理系统风险稳定性研究[J].系统科学学报, 2020,28(1):49-54