政策动态 | 十省市开展要素市场化配置综合改革试点，赋予地方土地市场更大自主管理权（9.1-9.14）

Core Viewpoint - The article discusses the ongoing reforms in the market-oriented allocation of factors, particularly focusing on land use efficiency and the promotion of REITs projects, with various local governments implementing supportive policies to stimulate housing consumption and optimize land management [1][3][6]. Group 1: Central Government Initiatives - The State Council approved a pilot program for market-oriented allocation of factors in ten regions, including major urban areas, to enhance land use efficiency and promote reforms in land management [3][4]. - The Ministry of Development issued a notice to accelerate the application of mature asset REITs projects, with a focus on affordable rental housing as a key project type [1][6]. Group 2: Local Government Policies - Six provinces and cities released market stabilization policies, with Henan Province introducing twelve new measures to support housing consumption, including increased subsidies and adjustments to housing loan limits [6][9]. - Local governments are encouraged to revitalize idle land through market-oriented methods, with ten provinces and cities leading the pilot implementation [2][4]. Group 3: Regional Characteristics and Innovations - Each pilot region is exploring unique measures based on their geographical and economic characteristics, such as land transfer methods linked to industrial contributions and mixed-use land supply [4][5]. - Notable initiatives include the "limited land price competition" method in Hangzhou, which emphasizes post-sale land productivity rather than just bidding prices [4][5]. Group 4: Policy Trends and Market Response - The frequency of policy releases aimed at stabilizing the market has significantly decreased, reaching a new low since the second quarter, indicating a potential shift in strategy [8][9]. - The article highlights that the most common policies released recently are related to subsidies and tax incentives, with a notable decline in public fund optimization policies [11][14].