基于数据挖掘的房地产价格分析预测研究

发布时间：2018-08-07 09:47

【摘要】：随着我国市场经济快速的发展,房地产行业的不断崛起，目前，房地产行业已成为了国民经济的支柱产业中最重要的产业之一，因此房地产行业发展的兴衰直接关系到个人、集体以至于国家利益。因此保证房地产行业的健康稳定的发展是一个至关重要的任务。那么我们应该怎么样去促使该行业健康稳定的发展？除了国家必要的一些政策和措施外，还应对房地产价格进行合理的预测和分析，一来可以为政府相关部门对土地拍卖价格的定位；其次还可以有效的帮助房地产开发商预算出该房地产的利润收益；最后还能帮助房屋购买者了解正常的房地产市场价格。近年来数据挖掘技术的不断发展，并渗入房地产行业。由于各类数据信息日积月累不断的增加累积，从而逐渐的构成了一个房地产数据海洋。如果只是通过观测或者归纳的方法来对房地产价格进行预测，那么将是一项工作量非常大的任务。而数据挖掘技术则正是从大量信息中挖取未知的却又对我们的每一个决策有用的知识和信息的过程。这将对房地产价格的预测提供了有力的技术支持。本文研究了从现有的房地产交易数据中挖掘得到关联规则，，并从中分析，得到一个比较精确的模型。首先是使用SQL2005进行相关房地产属性的关联分析，从影响房地产价格的众多因素中提出关联度比较高的因素作为参数。其次，结合了实际实例对本文选择的模型进行了验证。
[Abstract]:With the rapid development of China's market economy and the rising of real estate industry, the real estate industry has become one of the most important industries in the pillar industries of the national economy, so the rise and fall of the real estate industry is directly related to the individual. Collective and even national interests. Therefore, to ensure the healthy and stable development of the real estate industry is a crucial task. So what should we do to promote the healthy and stable development of the industry? Besides some necessary policies and measures, we should make reasonable forecast and analysis on the real estate price, so as to position the land auction price for the relevant government departments. Second, it can effectively help real estate developers budget the profits of the real estate. Finally, it can help buyers to understand the normal real estate market prices. In recent years, the continuous development of data mining technology, and infiltration of the real estate industry. As a result of various kinds of data information accumulation, gradually constitute a real estate data ocean. If only through observation or inductive method to predict the real estate prices, it will be a very heavy workload. Data mining technology is the process of extracting the unknown knowledge and information from a large amount of information, which is useful for every decision. This will provide strong technical support for real estate price forecasting. In this paper, we study the mining of association rules from the existing real estate transaction data, and get a more accurate model. Firstly, the correlation analysis of the property of real estate is carried out by using SQL2005, and the factors with high correlation degree are put forward as parameters from the many factors that affect the real estate price. Secondly, the model selected in this paper is verified by a practical example.
【学位授予单位】：南昌大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP311.13

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