Abstract:

China’s urban housing system has long focused on improving the self-owned housing of residents, ignoring the important role of leasing in the housing market equilibrium, and failing to effectively play its essential role in the rational allocation and flow of urban housing resources. At present, China’s housing leasing market is at the initial stage of development, whose related theoretical research is also lagging behind. This paper focuses on the design of housing rental market development policy. From the perspective of policy tool combination, the policy design process is abstracted into a high-dimensional multi-objective optimization problem. Based on the ideas of opinion mining, using the co-occurrence network, text mining and other methods, and using authoritative literature and expert opinions as data sources, the objective function and the constraint function coefficients are determined to construct a multi-objective function of housing rental market policy combination in line with China’s reality of development. In addition, this paper proposes a two-stage evolutionary high-dimensional multi-objective optimization algorithm based on Pareto dominance relationship to solve the problem of solving high-dimensional multi-objective functions. Therefore, the design framework, process, and method of housing rental market policy tools are established to provide a methodology for government policy formulation.

Key words: housing rental market, policy design, multi-objective optimization

摘要： 中国城镇住房制度长期以来侧重于提高居民住房自有水平，忽视了租赁方式在住房市场均衡中的重要作用，未能有效发挥其在城市住房资源合理配置及流动中的本质功能。目前，中国住房租赁市场处于初级发展阶段，与其相关的各类理论研究也相应滞后，研究关注住房租赁市场发展政策设计问题。以政策工具组合为视角，将政策设计过程抽象为高维多目标优化问题。基于观点挖掘思想，利用共现网络、文本挖掘等方法，以权威文献和专家观点作为数据来源，确定目标函数及约束函数系数，从而构建符合中国发展实际的住房租赁市场政策组合多目标函数。此外，提出一种基于Pareto支配关系的两阶段进化高维多目标优化算法以解决高维多目标函数的求解问题，从而搭建出住房租赁市场政策工具组合设计框架、流程和方法，以期为政府政策制定提供科学方法。

关键词: 住房租赁市场, 政策设计, 高维多目标优化