System dynamics simulation demonstrates that regionally differentiated dynamic pricing optimizes sustainability and fairness in commercial housing conversion, highlighting a scalable policy mechanism.
To address unreasonable pricing, insufficient policy sustainability, and regional differences in converting existing commercial housing to affordable housing in Liaoning Province, this study constructs a system dynamics model centered on the combined objectives of cost, fairness, and efficiency. Urban renewal and housing security increasingly require quantitative policy simulation comparable to engineering system optimization, particularly in smart-city contexts where housing distribution, industrial space, infrastructure, and electromagnetic environment constraints need coordinated planning. Based on the research achievements of the Liaoning Provincial Social Science Fund Project, the study identifies key pricing factors, including renovation cost, property-right compensation, government subsidy intensity, regional housing stock distribution, residents’ income level, and regional economic variance. A system flow model is then developed to simulate the effects of fixed pricing, cost-plus pricing, and regionally differentiated dynamic pricing across multiple time horizons. The results show that housing renovation cost, subsidy intensity, and spatial distribution of housing stock have the most significant effects on pricing outcomes. Different pricing policies differ markedly in balancing fairness and efficiency, while regionally differentiated dynamic adjustment improves pricing rationality and long-term sustainability. The study proposes a mechanism including dynamic cost monitoring, fairness evaluation, and policy feedback adjustment, providing a quantitative basis for affordable-housing policy design in Liaoning Province.
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Wu et al. (2026) studied this question.
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