【作者】 杜晓芳；

【导师】 张金锁；

【作者基本信息】 西安科技大学 ， 企业管理， 2006， 硕士

【摘要】 在市场经济体制下，建筑价格预测作为建筑价格管理体系不可缺少的组成部分，不仅是业主和承包商保持竞争优势和取得盈利的关键，也是政府进行宏观调控和建筑市场其他相关利益主体进行投资决策及制定发展战略的重要依据。国外在建筑价格预测方面有较深入的研究，国内由于历史原因，使得这方面的研究尚处于起步阶段，定量研究的文章较少。为此，本文在定性分析建筑价格相关因素的基础上，采用相关分析法及神经网络理论对建筑价格预测进行了研究。主要的研究工作和成果如下： 首先，基于1992-2004年的《中国统计年鉴》所统计的经济指标，从经济学和管理学角度、十四个方面，分析得出了与建筑价格相关的所有宏微观因素44个，这些因素较全面地反映了影响建筑价格的各个方面。 其次，与建筑价格相关的宏微观因素之历史数据是建模预测的基础，其因素个数太多不一定能得出合理的结果，为此，本文应用相关分析法，对44个与建筑价格相关的宏微观因素进行了定量分析，得出了建筑价格的15个强相关因素。 最后，以定量分析出的15个强相关因素的历史数据为基础，建立了BP神经网络预测模型，样本内预测精度达到了要求。用此模型对未来建筑价格进行了预测。预测结果显示，今后五年建筑价格呈上升趋势。 本文的研究工作和成果对我国建筑价格管理具有一定的指导作用。更多还原

【Abstract】 In market economy system, construction cost prediction,which is regarded as an essential component of construction cost administration system, not merely is the key that owners and contractors maintain the competitive advantage and obtain the profits, but also is the basis on which the government carries on the macroeconomic regulation and control and other correlation benefit main bodies in the construction market make investment decision and developmental strategy. Construction cost prediction is studied thoroughly abroad,but it is still at the start stage and the quantitative research articles on it are few in our country as a result of the historical problems. For this reason,this thesis adopts correlation analysis method and neural network theory to study domestic construction cost prediction on the base of the qualitative analysis of the relevant factors which affact it. The main research work and results are as follows:First of all,this thesis analyses all the economic statistic indexes related to construction cost ,which is involved in 《China Statistics Yearbook》 of 1992-2004 , from angles of economics and management and 14 aspects ,and elicits 44 marcro or micro interrelated fators of construction cost. These factors reflects various aspects which influences construction cost comprehensively.Secondly, this thesis carries on quantitative analysis to the 44 marcro or micro correlated factors with correlation analysis method and elicits 15 high correlated factors.Because the historical data of the factors is the foundation of predicting model and too many factors do not always lead a retional result.Finally , this thesis sets up BP nerve network predicting model on the basis of historical data of the 15 high correlated factors. The precision within the sample meets the requirement.The predicting model is empolyed to forecast the construction cost of the future years.The result of it shows that the construction cost in the next 5 years assume the更多还原