中国住宅地价房价比的空间格局、演变特征及影响因素——基于35个大中城市的空间计量分析
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摘要
研究目的:探索35个大中城市住宅地价房价比的空间格局、演变特征及影响因素,为房地产市场调控政策提供参考。研究方法:基于中国国土勘测规划院中国城市地价动态监测系统中35个大中城市2010—2017年住宅地价房价比的监测数据,运用描述性分析、探索性空间数据分析和面板空间杜宾模型进行研究。研究结果:(1)2010—2017年,35个大中城市地价房价比的平均值呈现先降后增再降的趋势,区域间绝对值和变化趋势的差异较为明显;(2)35个大中城市的地价房价比大致呈东南高、西北低的阶梯状格局,大部分城市的地价房价比分位数在研究段内未发生明显变化,仅北京、广州和呼和浩特明显上升,长春和合肥明显下降;(3)从关联特征来看,35个大中城市的地价房价比存在显著的空间聚集性,其呈现先升后降的趋势,在2013年达到最大;(4)通过面板空间杜宾模型研究发现,住宅用地供给对本地地价房价比有显著负向影响,房地产开发投资额和人均可支配收入对本地地价房价比有显著正向影响。空间效应方面,本地住宅用地供给和人口密度对邻近城市地价房价比有负向溢出效应,房地产开发投资额和人均可支配收入对邻近城市地价房价比有正向溢出效应。研究结论:住宅地价房价比不仅受本地土地供给、房地产开发规模和人均可支配收入影响,还受到邻近城市住宅用地供给和房地产开发规模的影响,因此房地产市场调控应该同时注重"城市联动"和"因城施策"。
The purpose of the paper is to explore the spatial pattern, evolution characteristics and influencing factors of land share in housing price in 35 large and medium-size cities, and to provide reference for real estate market regulation policies. The methods used include descriptive analysis, exploratory spatial data analysis, and spatial Durbin model based on monitoring data of land share in housing price from 2010 to 2017 in China's urban land price dynamic monitoring system. The results show that from 2010 to 2017, the average value of land share in housing price in 35 large and medium-sized cities showed a trend of decreasing first, then increasing, and then decreasing. The differences of absolute values and change trends among sub-regions were obvious. Land share in housing price were generally in a stair-like pattern that was high in the Southeast and low in the Northwest. The quantile of land share in housing price in most cities didn't changed significantly in the study period. The quantile of Beijing, Guangzhou, and Hohhot rose significantly,but that of Changchun and Hefei declined obviously. There was significant spatial aggregation in land share in housing price of 35 large and medium-sized cities, showing a tendency of rising first and then decreasing, with the maximum in2013. Through spatial Durbin model, it is found that the supply of residential land had a significant negative impact on local land share in housing price, while investment in real estate development and per capita disposable income had a significant positive impact on local land share in housing price. In terms of spatial effects, the supply of residential land and population density had a negative spillover effect on land share in housing price of neighboring cities. Investment in real estate development and per capita disposable income had a positive spillover effect on land share in housing price of neighboring cities. In conclusion, land share in housing price is not only affected by local land supply, investment in real estate development and per capita disposable income, but also affected by the supply of residential land and investment in real estate development of neighboring cities. Therefore, real estate market regulation should focus on "City-Cooperative" and "City-Specific" policies.
The purpose of the paper is to explore the spatial pattern, evolution characteristics and influencing factors of land share in housing price in 35 large and medium-size cities, and to provide reference for real estate market regulation policies. The methods used include descriptive analysis, exploratory spatial data analysis, and spatial Durbin model based on monitoring data of land share in housing price from 2010 to 2017 in China's urban land price dynamic monitoring system. The results show that from 2010 to 2017, the average value of land share in housing price in 35 large and medium-sized cities showed a trend of decreasing first, then increasing, and then decreasing. The differences of absolute values and change trends among sub-regions were obvious. Land share in housing price were generally in a stair-like pattern that was high in the Southeast and low in the Northwest. The quantile of land share in housing price in most cities didn't changed significantly in the study period. The quantile of Beijing, Guangzhou, and Hohhot rose significantly,but that of Changchun and Hefei declined obviously. There was significant spatial aggregation in land share in housing price of 35 large and medium-sized cities, showing a tendency of rising first and then decreasing, with the maximum in2013. Through spatial Durbin model, it is found that the supply of residential land had a significant negative impact on local land share in housing price, while investment in real estate development and per capita disposable income had a significant positive impact on local land share in housing price. In terms of spatial effects, the supply of residential land and population density had a negative spillover effect on land share in housing price of neighboring cities. Investment in real estate development and per capita disposable income had a positive spillover effect on land share in housing price of neighboring cities. In conclusion, land share in housing price is not only affected by local land supply, investment in real estate development and per capita disposable income, but also affected by the supply of residential land and investment in real estate development of neighboring cities. Therefore, real estate market regulation should focus on "City-Cooperative" and "City-Specific" policies.
引文
[1]谢旦杏,林雄斌.城市住房价格时空间特征及其影响因素研究[J].经济地理,2014,34(4):70-77.
[2]杨奎奇,汪应宏,欧向军.江苏省小城镇地价空间分布与成因研究[J].经济地理,2014,34(2):73-79.
[3]冯波,李淑芹,陈昕.我国城市间土地价格差异的影响因素研究--基于35个大中城市面板数据的实证分析[J].价格理论与实践,2014(6):54-56.
[4]沈昊婧,冯长春,侯懿珊.城市间土地价格及影响因素的空间差异研究[J].城市发展研究,2014,21(3):4-8.
[5]吕萍,甄辉.基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J].经济地理,2010,30(3):472-478.
[6]任辉,吴群.基于ESDA的城市住宅地价时空分异研究--以南京市为例[J].经济地理,2011,31(5):760-765.
[7]张文新,蒋立红.国外地价与房价关系及其启示[J].中国土地科学,2004,18(3):50-54.
[8]张清勇.地价与住房价格比例关系再检视[J].中国土地科学,2007,21(3):74-80.
[9]宋勃,高波.房价与地价关系的国际比较研究[J].亚太经济,2007(3):73-77.
[10]张学良.探索性空间数据分析模型研究[J].当代经济管理,2007,29(2):26-29.
[11]张松林,张昆.全局空间自相关Moran指数和G系数对比研究[J].中山大学学报(自然科学版),2007,46(4):93-97.
[12]张清勇.房价和地价的因果关系--基于文献的讨论[J].中国土地科学,2008,22(12):68-75.
[13]张浩,李仲飞.房价预期、土地价格与房地产商行为[J].管理评论,2016,28(4):52-61.
[14]黄静,王洪卫,柯昇沛.基于行为金融学的房价预期对地价的动态作用机制研究[J].财经研究,2013,39(7):134-144.
[15]王鹤,周少君.城镇化影响房地产价格的“直接效应”与“间接效应”分析--基于我国地级市动态空间杜宾模型[J].南开经济研究,2017(2):3-22.
[16]丁如曦,倪鹏飞.中国城市住房价格波动的区域空间关联与溢出效应--基于2005-2012年全国285个城市空间面板数据的研究[J].财贸经济,2015,36(6):136-150.
[17]J·保罗·埃尔霍斯特.空间计量经济学:从横截面数据到空间面板[M].肖光恩,译.北京:中国人民大学出版社,2015:74-76.
[18]LESAGE J P,PACE R K.Spatial econometric modeling of origin-destination flows[J].Journal of Regional Science,2008,48(5):941-967.
[19]ELHORST J P.Matlab software for spatial panels[J].International Regional Science Review,2014,37(3):389-405.
[20]国土资源部土地利用管理司,中国土地勘测规划院.中国城市地价报告2016[M].北京:地质出版社,2017:171-174.
(1)根据国家统计局每月全国房地产开发投资和销售情况中关于区域的划分标准,东部地区包括北京、天津、河北、上海、江苏、浙江、福建、山东、广东、海南10个省(市);中部地区包括山西、安徽、江西、河南、湖北、湖南6个省;西部地区包括内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆12个省(市、自治区);东北地区包括辽宁、吉林、黑龙江3省。
(1)其余变量均是相同情况,对单一城市而言,房地产开发投资额、人口密度、城镇人均可支配收入和人均GDP的间接效应平均是0.930(31.625/34)、1.411(-47.977/34)、7.858(267.176/34)、3.554(-120.834/34)。
[2]杨奎奇,汪应宏,欧向军.江苏省小城镇地价空间分布与成因研究[J].经济地理,2014,34(2):73-79.
[3]冯波,李淑芹,陈昕.我国城市间土地价格差异的影响因素研究--基于35个大中城市面板数据的实证分析[J].价格理论与实践,2014(6):54-56.
[4]沈昊婧,冯长春,侯懿珊.城市间土地价格及影响因素的空间差异研究[J].城市发展研究,2014,21(3):4-8.
[5]吕萍,甄辉.基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J].经济地理,2010,30(3):472-478.
[6]任辉,吴群.基于ESDA的城市住宅地价时空分异研究--以南京市为例[J].经济地理,2011,31(5):760-765.
[7]张文新,蒋立红.国外地价与房价关系及其启示[J].中国土地科学,2004,18(3):50-54.
[8]张清勇.地价与住房价格比例关系再检视[J].中国土地科学,2007,21(3):74-80.
[9]宋勃,高波.房价与地价关系的国际比较研究[J].亚太经济,2007(3):73-77.
[10]张学良.探索性空间数据分析模型研究[J].当代经济管理,2007,29(2):26-29.
[11]张松林,张昆.全局空间自相关Moran指数和G系数对比研究[J].中山大学学报(自然科学版),2007,46(4):93-97.
[12]张清勇.房价和地价的因果关系--基于文献的讨论[J].中国土地科学,2008,22(12):68-75.
[13]张浩,李仲飞.房价预期、土地价格与房地产商行为[J].管理评论,2016,28(4):52-61.
[14]黄静,王洪卫,柯昇沛.基于行为金融学的房价预期对地价的动态作用机制研究[J].财经研究,2013,39(7):134-144.
[15]王鹤,周少君.城镇化影响房地产价格的“直接效应”与“间接效应”分析--基于我国地级市动态空间杜宾模型[J].南开经济研究,2017(2):3-22.
[16]丁如曦,倪鹏飞.中国城市住房价格波动的区域空间关联与溢出效应--基于2005-2012年全国285个城市空间面板数据的研究[J].财贸经济,2015,36(6):136-150.
[17]J·保罗·埃尔霍斯特.空间计量经济学:从横截面数据到空间面板[M].肖光恩,译.北京:中国人民大学出版社,2015:74-76.
[18]LESAGE J P,PACE R K.Spatial econometric modeling of origin-destination flows[J].Journal of Regional Science,2008,48(5):941-967.
[19]ELHORST J P.Matlab software for spatial panels[J].International Regional Science Review,2014,37(3):389-405.
[20]国土资源部土地利用管理司,中国土地勘测规划院.中国城市地价报告2016[M].北京:地质出版社,2017:171-174.
(1)根据国家统计局每月全国房地产开发投资和销售情况中关于区域的划分标准,东部地区包括北京、天津、河北、上海、江苏、浙江、福建、山东、广东、海南10个省(市);中部地区包括山西、安徽、江西、河南、湖北、湖南6个省;西部地区包括内蒙古、广西、重庆、四川、贵州、云南、西藏、陕西、甘肃、青海、宁夏、新疆12个省(市、自治区);东北地区包括辽宁、吉林、黑龙江3省。
(1)其余变量均是相同情况,对单一城市而言,房地产开发投资额、人口密度、城镇人均可支配收入和人均GDP的间接效应平均是0.930(31.625/34)、1.411(-47.977/34)、7.858(267.176/34)、3.554(-120.834/34)。