基于空间计量模型的商品住宅开发商定价互动影响及其空间分异研究——以济南市为例
|
摘要
研究目的:检验并测度商品住宅开发商定价的互动影响,分析商品住宅开发商定价互动影响在空间上的分布格局和变异规律。研究方法:空间计量方法和空间插值方法。研究结果:(1)济南市商品住宅开发商定价存在非常显著的正向互动影响。(2)互动影响对各楼盘开发商定价的平均贡献率为30.68%;在商品住宅价格形成过程中,将近三分之一的价格变动来源于开发商之间的互动影响。(3)济南城市建成区内互动影响贡献率整体表现为中心低、周边高,中心—外围递增的空间分异规律。研究结论:地方政府应将开发商定价互动影响纳入房地产调控措施,利用互动影响作用,更有针对性地实现调控目标。
The purpose of the paper is to investigate and measure the interaction of developers to determine the price of commodity housing, and to analyze the spatial distribution and variation pattern of this interaction. The research methods include spatial econometrics and spatial interpolation. The results show that: 1)there is a significant positive interaction effect on the developers of Ji'nan City determining the price of commodity housing. 2)The average contribution rate of interaction effect on the commodity housing pricing is 30.68%, which means nearly one-third of the changes in commodity housing prices originate from the interaction effect between developers. 3)The contribution rate of interaction effect in the built-up area of Ji'nan City is low in the center but high in the periphery, and gradually increases from the center to the periphery. In conclusion, to make use of the interactive effect, the local government should integrate the interaction effect of developers to determine the price of commodity housing into regulation measures, and then achieve the objective of regulation.
The purpose of the paper is to investigate and measure the interaction of developers to determine the price of commodity housing, and to analyze the spatial distribution and variation pattern of this interaction. The research methods include spatial econometrics and spatial interpolation. The results show that: 1)there is a significant positive interaction effect on the developers of Ji'nan City determining the price of commodity housing. 2)The average contribution rate of interaction effect on the commodity housing pricing is 30.68%, which means nearly one-third of the changes in commodity housing prices originate from the interaction effect between developers. 3)The contribution rate of interaction effect in the built-up area of Ji'nan City is low in the center but high in the periphery, and gradually increases from the center to the periphery. In conclusion, to make use of the interactive effect, the local government should integrate the interaction effect of developers to determine the price of commodity housing into regulation measures, and then achieve the objective of regulation.
引文
[1]文乐,彭代彦.土地供给错配、房价上涨与半城镇化研究[J].中国土地科学,2016,30(12):18-27.
[2]李文斌,杨春志.住房价格指数以及区位对住房价格的影响--北京市住房价格实证分析[J].城市问题,2007(8):26-31.
[3]孙玉环.基于海量交易数据的房地产特征价格模型的构建[J].统计与决策,2011(2):9-13.
[4]董志勇,官皓,明艳.房地产价格影响因素分析:基于中国各省市的面板数据的实证研究[J].中国地质大学学报(社会科学版),2010,10(2):98-103.
[5]SEAN HOLLY M.Hashem Pesaran,Takashi Yamagata.Aspatio-temporal model of house prices in the USA[J].Journal of Econometrics,2010,158(1):160-173.
[6]HOLLY S,PESARAN M H,YAMAGATA T.The spatial and temporal diffusion of house prices in the UK[J].Journal of Urban Economics,2011,69(1):2-23.
[7]ALEXANDER C,BARROW M.Seasonality and cointegration of regional house prices in the UK[J].Urban Studies,1994,31(10):1667-1689.
[8]洪涛,西宝,高波.房地产价格区域间联动与泡沫的空间扩散--基于2000-2005年中国35个大中城市面板数据的实证检验[J].统计研究,2007,24(8):64-67.
[9]洪涛,于明超.专业化与城市经济增长--基于中国地级以上城市数据的实证分析[J].中国软科学,2009(5):73-81.
[10]王锦阳,刘锡良.住宅基本价值、泡沫成分与区域溢出效应[J].经济学(季刊),2014,13(4):1283-1302.
[11]张谦,王成璋,王章名.中国城市住房价格的空间效应与滞后效应研究[J].统计研究,2016,33(7):38-45.
[12]王松涛,杨赞,刘洪玉.我国区域市场城市房价互动关系的实证研究[J].财经问题研究,2008(6):122-128.
[13]陈浪南,王鹤.我国房地产价格区域互动的实证研究[J].统计研究,2012,29(7):37-43.
[14]曾祥渭,刘志东,刘雯宇.我国城市群商品住宅价格传导与波动性外溢研究[J].管理评论,2015,27(9):3-13.
[15]张炜,景维民.房地产价格空间溢出效应测度与趋势解析[J].现代财经(天津财经大学学报),2017,37(5):60-73.
[16]彭山桂,程道平,张勇.地方政府土地出让策略互动行为的检验及其影响分析[J].中国人口·资源与环境,2017,27(7):111-119.
[17]ELHORST J P.Spatial econometrics:from cross-sectional data to spatial panels[M].Springer Science&Business Media,2014:10-12.
[18]陈安明.基于主成分分析的住宅项目特征定价模型[J].重庆大学学报(自然科学版),2006,29(6):144-148.
[19]MOK H M K.A Hedonic approach to pricing of residential properties in Hong Kong[J].Hong Kong Journal of Business Management,1995(8):1-15.
[20]温海珍,贾生华.住宅的特征与特征的价格--基于特征价格模型的分析[J].浙江大学学报(工学版),2004(10):101-105,112.
[21]王来健,张绍良,尹鹏程,等.采煤沉陷湖人工湿地对周边土地价格的溢出效应研究--以徐州市九里湖湿地为例[J].地理科学,2017,37(8):1234-1242.
[2]李文斌,杨春志.住房价格指数以及区位对住房价格的影响--北京市住房价格实证分析[J].城市问题,2007(8):26-31.
[3]孙玉环.基于海量交易数据的房地产特征价格模型的构建[J].统计与决策,2011(2):9-13.
[4]董志勇,官皓,明艳.房地产价格影响因素分析:基于中国各省市的面板数据的实证研究[J].中国地质大学学报(社会科学版),2010,10(2):98-103.
[5]SEAN HOLLY M.Hashem Pesaran,Takashi Yamagata.Aspatio-temporal model of house prices in the USA[J].Journal of Econometrics,2010,158(1):160-173.
[6]HOLLY S,PESARAN M H,YAMAGATA T.The spatial and temporal diffusion of house prices in the UK[J].Journal of Urban Economics,2011,69(1):2-23.
[7]ALEXANDER C,BARROW M.Seasonality and cointegration of regional house prices in the UK[J].Urban Studies,1994,31(10):1667-1689.
[8]洪涛,西宝,高波.房地产价格区域间联动与泡沫的空间扩散--基于2000-2005年中国35个大中城市面板数据的实证检验[J].统计研究,2007,24(8):64-67.
[9]洪涛,于明超.专业化与城市经济增长--基于中国地级以上城市数据的实证分析[J].中国软科学,2009(5):73-81.
[10]王锦阳,刘锡良.住宅基本价值、泡沫成分与区域溢出效应[J].经济学(季刊),2014,13(4):1283-1302.
[11]张谦,王成璋,王章名.中国城市住房价格的空间效应与滞后效应研究[J].统计研究,2016,33(7):38-45.
[12]王松涛,杨赞,刘洪玉.我国区域市场城市房价互动关系的实证研究[J].财经问题研究,2008(6):122-128.
[13]陈浪南,王鹤.我国房地产价格区域互动的实证研究[J].统计研究,2012,29(7):37-43.
[14]曾祥渭,刘志东,刘雯宇.我国城市群商品住宅价格传导与波动性外溢研究[J].管理评论,2015,27(9):3-13.
[15]张炜,景维民.房地产价格空间溢出效应测度与趋势解析[J].现代财经(天津财经大学学报),2017,37(5):60-73.
[16]彭山桂,程道平,张勇.地方政府土地出让策略互动行为的检验及其影响分析[J].中国人口·资源与环境,2017,27(7):111-119.
[17]ELHORST J P.Spatial econometrics:from cross-sectional data to spatial panels[M].Springer Science&Business Media,2014:10-12.
[18]陈安明.基于主成分分析的住宅项目特征定价模型[J].重庆大学学报(自然科学版),2006,29(6):144-148.
[19]MOK H M K.A Hedonic approach to pricing of residential properties in Hong Kong[J].Hong Kong Journal of Business Management,1995(8):1-15.
[20]温海珍,贾生华.住宅的特征与特征的价格--基于特征价格模型的分析[J].浙江大学学报(工学版),2004(10):101-105,112.
[21]王来健,张绍良,尹鹏程,等.采煤沉陷湖人工湿地对周边土地价格的溢出效应研究--以徐州市九里湖湿地为例[J].地理科学,2017,37(8):1234-1242.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |