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文章以城镇化发展为前提,从房屋需求视角建立了房价预测指标体系,运用遗传算法和最小二乘支持向量机研究以深圳市为例的多因素房价预测模型,通过各因素对住宅、办公楼及商业用房房价的量化影响分析得到几个重要结论:(1)深圳市商业用房存在较大的投机性;(2)深圳居民目前的租房需求远远大于购房需求,政府可适当增加政策性建房满足需求,或增加投机购房成本,平衡深圳市过高的房价;(3)城镇化的发展过程对房价变化有重要的影响,特别是商品房房价对城镇化过程中人才需求和人才比例非常敏感。
Based on the premise of urbanization development, this paper establishes a housing price forecasting index system from the perspective of housing demand, uses the genetic algorithm and least square support vector machine to study the multi-factor house price forecasting model with Shenzhen as an example. Through various factors, (1) there is a large speculative business space in Shenzhen; (2) the current rental demand of Shenzhen residents is far greater than the demand for housing, the government may increase the appropriate policy (3) The development process of urbanization has an important impact on house price changes, especially the demand for housing and the proportion of talent in the process of urbanization Very sensitive.