Abstract
Renting is, like owning a house, a way to realize residence rights, playing an important role in maintaining the equilibrium of the housing market. The lack of attention paid to policy design of the rental housing market causes low effectiveness in the housing resource flow and allocation at both local and national levels. Thus, we propose a novel design framework and process of public policy, in particular the development policy for the rental housing market. This innovative approach abstracts the policy design process into a solution-formation process for a high-dimensional and multi-objective optimization problem. First, based on opinion mining, using co-occurrence networks, text mining and other methods, in addition to authoritative literature and expert opinions from the Chinese Social Sciences Citation Index (CSSCI) as data sources, the objective function and the constraint function coefficients were determined to construct a multi-objective function of rental housing market policy. Second, this paper proposes a two-stage evolutionary high-dimensional multi-objective optimization algorithm based on the Pareto dominance relationship to solve high-dimensional multi-objective functions. Finally, we designed a rental housing policy tool-mix selection system-modeling process and obtained six sets of feasible solutions and objectives after 300,000 simulations. Therefore, the policy tool-mix selection system presented in this study effectively supports the policymaking process.
Highlights
When decision-makers scrutinize reports or learn from other people’s experiences, they often wonder if the findings or conclusions are applicable to their own situation
It indicates that the policy tool combination optimization function and multi-objective optimization (MOO) algorithm proposed in this paper are effective
There are many targets in designing public policy. Policymakers can use this multi-objective-optimization evolutionary algorithm with the Pareto approach (MOEA/PT) model to achieve the optimal combination of various policy tools when multiple targets need to be maximized simultaneously
Summary
When decision-makers scrutinize reports or learn from other people’s experiences, they often wonder if the findings or conclusions are applicable to their own situation. This research has the following objectives: (1) to choose the most effective policy tool for rental housing market, (2) to improve the effectiveness of policy tool mix and (3) to propose a multi-objective optimization algorithm suitable for policy tool mix optimization problem. This research evaluates several practical algorithms of MOO and proposes an integrated content analysis and clustering method to build the quantitative policy-design framework. The literature review still shows that there is a lack of quantitative public policy design methods to calculate the optimal solution sets of policy tools. The design of a rental housing policy needs more efficient policy documents to achieve optimized development in the market
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