兰州市主城区房价分异及驱动因素研究

doi:10.12118/j.issn.1000-6060.2022.124

摘要/Abstract

摘要：

以2021年兰州市主城区678个居住小区房价数据为基础，引入地理场模型量化影响房价的外部因素，通过空间自相关分析、多尺度地理加权回归等模型对房价分异的空间格局及驱动因素的作用机理、带宽差异展开研究，以期为推动河谷型城市房产市场的公平发展提供参考。结果表明：（1）兰州市主城区平均房价为13739元·m^-2，空间上呈现“一主三副”的带状多中心组团式分布格局，房价由多中心向四周递减，价格相似的小区在地理空间上邻近分布，具有“小集中、大分散”的局部空间特征。（2）房价分异是多种驱动因素共同作用的结果，区位特征中的主商圈对房价的影响居于首位，建筑特征中的房龄、容积率和邻里特征中的中学数量、绿化率等对房价的影响较大，城市地理特征对房价具有显著影响，愈靠近黄河的小区、房价越高。（3）各驱动因素的带宽差异明显，主商圈、医院等小尺度变量存在高度空间异质性，而容积率、黄河等全局变量基本不存在空间异质性。

关键词: 河谷型城市, 房价分异, 地理场模型, 多尺度地理加权回归, 兰州市主城区

Abstract:

Based on the price data from 678 residential communities in the central urban area of Lanzhou City, Gansu Province, China in 2021, a geographical field model was introduced to quantify the external factors affecting the housing price. Spatial autocorrelation analysis and multi-scale geographically weighted regression were used to study the spatial pattern of housing price differentiation, the action mechanism of driving factors, and bandwidth differences. This can provide a reference for promoting the fair development of the real estate market in a valley city. The results show that: (1) the average housing price in the central urban area of Lanzhou City is 13739 yuan·m⁻², with a multi-center group distribution pattern of “one principal, three secondaries”, and the housing price decreases from the multi-center to the surrounding areas. Residential communities with similar prices are geographically close together, indicating local spatial characteristics of “small concentration and large dispersion”. (2) Housing price is the result of the combined action of various factors: locational variables such as central business district (CBD) have the highest impact on the housing price; structural variables such as building age and floor area ratio (FAR), and neighborhood variables such as middle schools and green rate have a greater impact on the housing price; urban geographical variables have a significant impact on the housing prices, and the closer the residential community is to the Yellow River, the higher the housing price. (3) There were significant differences in the bandwidth scales of different variables; small-scale variables such as CBD and hospitals have high spatial heterogeneity, whereas global variables such as FAR and the Yellow River have little spatial heterogeneity.

Key words: valley-city, housing price differentiation, geographic field model, multiscale geographic weighted regression, central urban area of Lanzhou City

晁勐, 张俊, 刘翔. 兰州市主城区房价分异及驱动因素研究[J]. 干旱区地理, 2022, 45(6): 2004-2012.

CHAO Meng, ZHANG Jun, LIU Xiang. Research on housing price differentiation and driving factors in central urban area of Lanzhou City[J]. Arid Land Geography, 2022, 45(6): 2004-2012.

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外部变量	影响范围/m	场强划分依据	场强数据来源
公交	500	站点公里格网人口≤4847人的赋值为1，4847人< 站点公里格网人口≤10102人的赋值为2，其余赋值为3	经七次人口普查数据校正后的WorldPop公里格网人口栅格数据集
小学	800	一般小学赋值为1，区属、市属小学赋值为2，省属重点小学赋值为3	兰州市主城四区教育局发布的《小学招生意见》
中学	2000	一般中学赋值为1，区属、市属中学赋值为2，省属重点中学赋值为3	兰州市主城四区教育局发布的《初高中招生计划》
医院	1000	卫生院、一级医院赋值为1，二级医院赋值为2，三级医院赋值为3	国家医保服务平台(https://fuwu.nhsa.gov.cn/)
商超	500	便利店赋值为1，超市赋值为2，大型连锁超市赋值为3	高德地图POI分类编码(https://lbs.amap.com/)
公园广场	1500	热力级别≤2的赋值1，2<热力级别≤4的赋值2，其余赋值为3	城市等级下的百度热力图
主商圈	23800	东方红广场赋值1	-
次商圈	9800	金城中心、兰州西站、金牛街、南关十字赋值1	-
黄河	5100	黄河赋值1	-

回归模型	AIC	RSS	R²	R²_adj
G-HPM	1599.054	402.808	0.406	0.394
D-MGWR	1186.433	133.328	0.803	0.731
G-MGWR	962.614	126.749	0.893	0.832

变量	均值	标准差	最小值	中值	最大值
物业费	0.043	0.160	-0.450	0.033	0.519
容积率	0.102	0.002	0.100	0.101	0.106
绿化率	0.080	0.154	-0.362	0.118	0.351
房龄	-0.106	0.107	-0.321	-0.103	0.150
黄河	0.059	0.002	0.053	0.059	0.065
主商圈	0.152	1.343	-1.729	-0.407	3.072
次商圈	0.019	0.827	-1.584	0.109	1.275
公园广场	0.021	0.103	-0.193	0.021	0.282
公交	0.019	0.087	-0.193	0.000	0.254
商超	-0.037	0.005	-0.040	-0.040	-0.025
小学	0.022	0.096	-0.182	0.024	0.200
医院	-0.044	0.238	-1.087	-0.004	0.832
中学	0.090	0.175	-0.398	0.035	0.444