哈尔滨市住房需求量预测研究

发布时间：2018-03-01 03:09

本文关键词： 哈尔滨市住房需求多元线性回归预测灰色GM（1 1）预测　出处：《哈尔滨工业大学》2013年硕士论文　论文类型：学位论文

【摘要】：房地产业是关乎民生的重要行业，房地产市场健康有序地发展更是政府经济工作的重中之重。近年来哈尔滨市的房地产市场也随着经济得快速发展呈现出了一片大好的态势。房地产投资额持续增长，住宅施工面积、竣工面积、销售面积逐年递增，人均住房使用面积在国家的政策下也正逐步达到小康水平。研究住宅市场的需求量，从而对住宅市场的整体走势进行把握，同时也为政府制定相应的政策措施提供一定的理论和数据依据。运用M-W模型研究住房需求量问题，从人均住房使用面积和人口两个因素出发。通过分析哈尔滨市的住房现状，参考相关文献对人均住房使用面积影响因素的研究成果，借助统计学软件SPSS，定量地分析出对人均住房使用面积影响较大的四项影响因素，，即为人均可支配收入、人均居住消费性支出、平均住宅价格、住宅施工面积，对这四项影响因素进行灰色GM（1,1）预测得出预测模型。运用该模型得到2000年到2011年这十二年间的预测值，并带入到人均住房使用面积的多元线性回归方程得出人均住房使用面积的多元回归预测值。将人均住房使用面积的多元回归预测值、人均住房使用面积的灰色GM（1,1）预测值与2000年到2011年间的真实值进行拟合，得出最终的人均住房使用面积的修正模型。运用灰色GM（1,1）预测出2013年到2018年这六年间总人口数。将人均住房使用面积2012到2018年间的预测值与总人口2012到2018年间的预测值数带入到住房需求量的M—W模型中，进而预测出2013年到2017年这五年内的住房需求量。分析构成住房需求量的三个部分，可以看出以改善型需求为主的现有人口对未来的住房需求量占整个住房需求量的比重最大大约占到70%，其次未来期内现有住房折旧在一定程度上也对住房需求量产生着影响。针对以上预测结果为政府工作提出五条政策建议，控制住宅市场投资额及住宅开发总量；加强政府住宅体系的宏观调控；采取相应的政策措施从需求入手而不只是强调供应；加强政策指导作用；完善住宅市场的信息流通渠道；完善住宅建设监管体系及加强住宅周边配套建设，使更多的空置房屋可以流入到住宅市场。
[Abstract]:The real estate industry is an important industry related to people's livelihood. The healthy and orderly development of the real estate market is the top priority of the government's economic work. In recent years, the real estate market in Harbin has also shown a good trend with the rapid economic development. The real estate investment continues to grow. The housing construction area, the completed area and the sales area are increasing year by year, and the per capita housing use area is gradually reaching the well-off level under the policy of the country. Study the demand of the housing market, so as to grasp the overall trend of the housing market. At the same time, it also provides some theoretical and data basis for the government to formulate corresponding policies and measures. Using M-W model to study the housing demand problem, starting from the two factors of per capita housing area and population. Through the analysis of the housing situation in Harbin, referring to the relevant literature on the per capita housing use area factors research results, With the help of statistical software SPSS, the paper quantitatively analyzes the four factors that have a great influence on the per capita housing use area, namely, per capita disposable income, per capita residential consumption expenditure, average housing price, housing construction area, etc. The prediction model was obtained by grey GM1 / 1) prediction of the four influencing factors. The predicted values for the 12 years from 2000 to 2011 were obtained by using the model. The multivariate regression prediction value of per capita housing use area is obtained by introducing the multivariate linear regression equation into the per capita housing use area, and the multivariate regression prediction value of per capita housing use area is obtained. The grey GM-1) predicted value of per capita housing use area was fitted to the real value from 2000 to 2011. The final revised model of per capita housing use area is obtained. The total population for the six years from 2013 to 2018 is predicted by using the grey GM1). The forecast value of the per capita housing use area from 2012 to 2018 is compared with the forecast of the total population from 2012 to 2018. The measured values are brought into the M-W model of housing demand. Then the housing demand for the five years from 2013 to 2017 is predicted. The three components of housing demand are analyzed. It can be seen that the proportion of future housing demand of the present population, which is dominated by improved demand, accounts for about 70 percent of the total housing demand, followed by the depreciation of existing housing in the next period, and to a certain extent, the production of housing demand. In view of the above forecast results, five policy recommendations for the work of the government are put forward. To control the investment in housing market and the total amount of residential development; to strengthen the macro-control of the government housing system; to take appropriate policy measures starting from demand rather than just emphasizing supply; to strengthen the role of policy guidance; to improve the housing market information circulation channel; Improve the housing construction supervision system and strengthen the supporting construction around the housing so that more vacant houses can flow into the housing market.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F299.23

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