基于统计学的北京城市居住用地价格驱动力分析

使用统计学方法从微观上研究了北京市城市居住用地价格的驱动力因子,包括可达性、土地开发强度、文体基础设施和新型交通方式等.利用T检验以及Pearson相关分析法确定驱动力因子为与市中心、与火车站的距离、容积率、1000 m以内的公交路线数、1000 m内是否存在轨道交通和文化设施.并用因子分析将6个因子分为四类.岭回归方法表明这6种因子贡献了自变量Y变化的73.2%.其中与市中心的距离是影响居住用地价格的最重要因素,距离越大居住用地价格越低:容积率与居住用地旱明显的正相关,容积率越高,地价越高;与火车站的距离、1000 m以内的公交路线数等可达性因素对居住用地价格也有影响.1000 m文化设施与轨道交通对周边的土地价格存在明显的增值作用.基十此,本文提出发展轨道交通、改造低效率土地利用方式和加强小区文体设施建设等建议促进城市地价空间分布的优化,提高城市的整体价值.

Driving Force Analysis of Residential Land Price in Beijing Based on Statistical Methods

Statistical methods were employed in this paper to explore the driving forces of residential land prices in Beijing, including accessibility, land intensity, cultural and sport infrastructure and new transport methods. Box-Cox transformations, T-test, Pearson correlation, factor reduce and ridge regression were carried out to identify the key factors that influence the residential land price. Distances to the nearest CBD (P = 0.265), to the nearest road (P = 0.529), to the nearest schools (P = 0.202), to the nearest parks (P = 0.105) and to the nearest hospitals (P = 0.706), which had a low correlation with residential land price, were excluded by Pearson correlation test. Independent samples T-test showed that cultural and sport infrastructure (P = 0.003) and urban subways (P = 0.000) had statistical significant influence on residential land price. Thus, factors including distances to the central area and railway stations, plot ratio, public bus lines within 1 km, urban subways as well as cultural and sport infrastructure were studied in this paper. Factor reduce found that all the remaining factors could be divided into 4 groups. This result was used as one piece of judgment for the regression results, which should use at least one factor of each group. Ridge regression is one of the least-squares refinement methods. In this method, a biased constant is employed to find out a biased estimator, which helps to enhance the precision compared with least-squares methods. It has been proven that the ridge regression method is stable and valid when independent variables are highly correlated. Thus, the multicollinarity among the independent variables in this paper could be resolved by ridge regression analysis. Results of ridge regression indicated that the effects of the studied factors mentioned above accounted for 73.2% change of the independent variable Y in Beijing, and among which, the distance to the central area was the primary factor influencing the price of residential land, followed by the plot ratio. A negative correlation between distance and land price and a positive correlation between plot ratio and land price appeared respectively. Accessibility factors such as bus lines within 1 km had considerable effects on residential land price. Besides, urban subways and cultural and sport infrastructure had a significant value added function to residential land around. Based on statistical analysis, suggestions were proposed in this paper: (1) Land use rate could be improved by enhancing the accessibility and value of suburban areas via land use pattern change and urban subway construction to maximize the land use value. (2) Land use pattern of low efficiency such as 'urban village' could be presented to raise the intensification level of land use to optimize the urban function, thus the urban entity value increased. (3) Cultural and sport infrastructures could help to enhance the additional value of residential land price.