On the Influence Factors of Second-hand House Prices in First-tier Cities——Empirical Analysis Based on Panel Data

With the arrival of the ＂housing stock＂ in first - tier cities, the second - handhousing^market will become the dominant property market. This ardcle aim to the first - tiercities of second - hand housing prices and new home price index for the empirical analysis, thedata related to the cointegration analysis found that the result of the first -tier cities real estatemarket in China, the new home price index is the significant factors influencing the second -hand house price indexi For Beijing, Shanghai second - hand housing and new home price in-dex time series johans test, found that there exists cointegration relationship between two varia-bles,the new city real estate market prices out of a line on the secondary market have clearguide. Therefore, the real estate market regulation aiming at the first -tier cities and the＂housing stock＂ should take the second - hand housing market as the main direction, startingwith the sale price and influencing factors of new houses. At the same time, in different cities,we should adhere to the city＇ s policies, reflect the policy differentiation, promote the reformof the real estate supply side, and promote the return of housing properties.

Inner-City Neighbourhood Changes Predicted from House Prices in Windsor, Ontario, since the Early- or Mid-1980s

Changes in prices of homes are hypothesized as correlated with the times of their sale and resale and the attributes of their dwelling unit and neighbourhood and those of neighbouring homes. They may also be correlated with the occurrences of events inside the neighbourhoods caused by the activities of individuals and organizations outside the neighbourhoods, such as whether the local economy is in a recession or has a high unemployment rate. Calibrated hybrid housing price models predict precipitous decreases in house prices of approximately 2900 sold and resold homes in two inner-city neighbourhoods in Windsor, Ontario, during those events since 1981 or 1986. Overall modest predicted percentage increases in houses’ prices during more than 30 years therefore subsumed periods of inner-city neighbourhood deterioration in dispersed locations of unimproved and disimproved homes. Compensatory predictions however are of increasing prices for minorities of homes with improvements to several attributes of the dwelling unit and neighbourhood.

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan(RMB)/m^2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

A Study on Spatial Distribution of Commercial Housing Prices in Xiangtan City

ArcGIS technology is used to study the spatial pattern of housing prices in Xiangtan City,and it is found that the spatial pattern of housing prices shows primary and secondary two-center rings. In Hedong Jianshe Road and near Hexi Jijianying,there are primary and secondary polar nuclei,respectively; the secondary housing price area is located near the east-west and south-north trunk road in the urban area; there are significant regional differences in housing price changes( fastest reduction of prices in the Hedong main center-southwest direction; slow reduction of prices in the main center-northwest,southeast direction; slowest reduction of prices in the main center-northeast direction). In Hexi sub-center,except slow reduction of prices in the Xiangjiang River direction,the prices decline rapidly in other directions. The housing prices exhibit an obvious overall decreasing trend from primary and secondary centers to the suburbs,but there are also exceptions. On this basis,this paper analyzes the driving factors for spatial pattern of housing prices in Xiangtan City,and finds that the spatial pattern of housing prices is mainly influenced by commercial centers,residential environmental conditions,traffic conditions,and urban land layout differences.

A GIS-based Spatial Analysis of Housing Price and Road Density in Proximity to Urban Lakes in Wuhan City, China

As one of the essential urban open spaces, lakes usually contribute immensely to the quality of residents′ daily lives. Different from hedonic approach employed in existing researches on urban open spaces in China, this paper integrates housing price surface with road density to analyze the spatial characteristics in proximity to urban lakes in Wuhan City, China. With the expansion of Wuhan City, urban lakes became polluted, they shrunk or even disappeared, leading to unfavorable conditions for sustainable development of the city. To better understand the spatial relationship between the city and lakes, we classify the urban lakes in Wuhan central area into ′lakes in the urban center′ and ′lakes in urban fringe′. Based on housing price surface we explore the spatial characteristics in proximity to different lakes and differences between the lakes. We also use Geographic Information System(GIS) tool to calculate road density as a supplementary indicator to reflect the accessibility in proximity to urban lakes. The results indicate that relative independence exists between different towns, and the spatial characteristics are different depending on scales and locations. In most of cases, the road density is lower where closer to the lakeshore while the housing price exhibits an opposite pattern. We conclude that city governments and urban planners should give more considerations to these spatial differences, somewhere should be better planned and protected as an important waterfront and somewhere the control of unreasonable real estate development nearby should be strengthened.

The Influence of Built Environment on the Spatial Distribution of Housing Price:Based on Multiple Big Data and Hedonic Model

In recent years,more and more researches focus on the self characteristics and spatial location of housing,and explore the influencing factors of urban housing price from the micro perspective.As representative of big cities,spatial distribution pattern of housing price in national central cities has attracted much attention.In order to return the spatial distribution pattern of housing price to the research on influencing factors of housing price,the reasons behind the spatial distribution pattern of housing price in three national central cities:Beijing,Wuhan and Chongqing are explored.The results show that①urban housing price is affected by many factors.Due to different social and economic conditions in each city,there are differences in the influence direction of the proximity to expressways,city squares,universities and living facilities,characteristics of companies and enterprises on Beijing,Wuhan and Chongqing.②Various factors have different value-added effects on housing price in different cities.The location of ring line in Beijing and Wuhan has the greatest increase effect on housing price,while metro station of Chongqing has the greatest increase effect on housing price.

城市间房价分化的影响因素研究

面对一二三线城市房价增速的分化走势,“因城施策”逐渐成为主基调,为此需要准确识别不同城市房价分化的主要因素。本文综合使用XGBoost等机器学习方法以及SHAP值解释性方法,基于2009—2019年全国70个大中城市面板数据,测算并分析一二三线城市房价分化的主要因素。结果表明:(1)预期因素在多轮房价分化期间都起到重要驱动作用;(2)货币政策本身并不是房价分化的主要因素,但可以通过影响预期导致房价分化;(3)需求因素和供给因素本身也不是房价分化的主要因素,但也可以通过影响预期导致房价分化。有鉴于此,“因城施策”的关键在于稳定公众对房价的预期:一线城市应在控制土地成本的前提下适当增加住房供给,削弱由于供不应求所带来的房价上涨预期;二三线城市需进一步完善基础设施,提高教育、医疗和环境质量,改善居民购房需求,防范人口外流带来的房价下跌预期。

中心城市科技创新能促进城市群协调发展吗?——兼论收入房价比的调节作用

利用2007—2019年127个地级市的数据,系统检验了中心城市科技创新对城市群协调发展的影响机制,并讨论了收入房价比对二者关系的调节作用。研究发现:第一,中心城市科技创新水平的提高不仅显著促进了城市群协调发展,还加快了外围城市经济增长收敛,并在不同地区和不同发展阶段的城市群表现出明显的异质性。第二,中心城市科技创新通过深化产业分工促进城市群协调发展。第三,收入房价比能够调节中心城市科技创新与城市群协调发展之间的关系,但调节效果存在区域异质性,仅在中部地区显著增强了中心城市科技创新对城市群协调发展的促进作用,对东部和西部地区没有明显的调节作用。研究结论不仅丰富了中心城市科技创新影响城市群协调发展的理论研究,且在提高收入房价比方面提供了政策启示。

基于多尺度地理加权回归模型的城市住宅价格影响因素空间异质性——以桂林市主城区为例

基于桂林市2022年10月二手房价格数据,结合多尺度地理加权回归(multi-scale geographically weighted regression,MGWR)模型,分析不同尺度下城市住宅价格影响因素的空间异质性。结果表明:(1)桂林市住宅价格呈显著空间自相关,局部空间自相关结果表明,桂林市住宅价格空间呈显著的集聚分布,以“高-高”和“低-低”集聚为主,“高-低”和“低-高”集聚范围较小;(2)相较于普通最小二乘法(ordinary least squares,OLS)和地理加权回归(geographically weighted regression,GWR)模型,MGWR模型的R2更高,能更准确地测度变量对住宅价格的影响程度和空间差异性;(3)住宅房龄、教育医疗和商务设施为全局变量,绿化率、公共交通设施、铁路设施配套、生活配套和旅游景点的空间异质性处于中等水平,对外交通设施的空间异质性较大,在局部范围内对住宅价格有显著影响且差异明显;(4)住宅房龄、生活配套和旅游景点对住宅价格具有负向影响,教育医疗和商务配套对住宅价格具有正向影响,其余变量在不同空间区位上对住宅价格表现出抑制或增值作用。

住房价格、预期收支与劳动力城市选择

引导人口有序分流是推进全面市民化并构建城市群协调发展格局的关键所在。住房是流动劳动力在城市生活的基础,预期收入和支出是劳动力预期收益的核心组成,直接影响其流动城市选择。研究表明:第一,在综合考虑流入地城市特征和预期收支情况下,城市房价上涨将降低劳动力选择该城市的概率,但这一挤出效应会随时间推移而减弱;第二,城市规模越大、劳动力技能水平越高,房价所产生的拉动效应越能超过挤出效应;第三,城市房价上涨既能通过提高收入财富吸引劳动力流入,也会通过提升生活成本和减少就业机会阻碍其流入;第四,预期收入有助于缓解房价对劳动力城市选择的负向影响,而预期支出则相反。基于此,坚持“房住不炒”基本定位、提升市场信息透明度及解决劳动力生活和就业问题将对引导人口有序分流产生重要的助推作用。

基于GIS与Hedonic模型的成都市住宅价格空间分布特征及其影响因素研究

结合空间分析方法和Hedonic模型,采用ArcGIS、Surfer、SPSS等软件,以成都市为例,对城市住宅价格空间分布特征及其影响因素进行系统性分析。结果表明:成都市主城区住宅价格总体上以城中和城南为双核心,呈马蹄状向四周逐级递减;成都市住宅价格在空间分布上呈明显的正相关性,具有相似价格的住宅呈现集聚特点;楼盘档次、区位条件、环境因素、交通可达性,历史与人文等因素是影响成都市住宅价格空间分异的主要因素。

西安市主城区居住空间住宅价格分布格局与驱动机制

采用探索性空间数据分析法(ESDA)、地理加权回归(GWR)等手段,定量分析西安市主城区居住空间住宅价格分布格局与驱动机制,并辅以定性探讨。研究结果表明:(1)西安市新房与二手房分布密度呈现城南城北大于城东城西的空间布局形式,新房居住空间呈现出二环以外大明宫—张家堡、曲江—电视塔、高新区南二环—锦业路3个连续组团,二手房居住空间呈现整体南倾的格局。(2)商业中心位置与大部分居住空间布局关系不紧密;随文化景观要素和重点中学要素的距离增加,大部分居住空间住宅价格降低;距离交通干线越近居住空间住宅价格不一定越高。(3)产业布局会影响西安市居住空间布局,城市整体路网密度的提升可能会弱化交通通达因素的影响,学区与景区周边的确出现了较显著的住房需求,而政策引导可以直接形成新的居住空间发展热点。基于先进的空间统计算法,可以有效提升研究结果的空间精度;而将新房与二手房对比出现,实现了空间换时间的数据处理方式,从而可以根据价格格局现状间接推断居住空间的演变。

不同等级城市房价与地价关系的规律初探

研究目的:对房价与地价关系进行因果关系检验,探索其地域等级差异规律,以期为因地制宜地制定房价调控政策提供参考。研究方法:对比分析法,ECM,Granger因果关系检验。研究结果:(1)地价对房价的短期影响表现为:三线城市最强,二线次之,一线最差;房价对地价的短期作用表现为:二线城市强于一线和三线城市;(2)房价与地价的相互关系在中期稳定性差,且不同城市表现出变动的随机性;(3)地价对房价的长期作用表现为一线、二线和三线城市逐渐增强;房价对地价的长期影响则恰好相反。研究结论:(1)房价与地价关系特征相似的城市,在相关政策的制定上可以彼此借鉴;(2)房价调控政策要将时效性考虑在内;(3)房价调控政策的制定要依据城市等级而有所差异。

中国房价上涨对城市扩张的驱动效应研究——来自69个大中城市门限模型的经验证据

我国房价正经历艰难的理性回归之路,城市扩张也正面临着转型。基于非线性视角,运用门限模型对69个大中城市房价与城市扩张的关系进行综合测度,分析不同房价收入比地区的房价波动对城市扩张的非线性影响机制。研究结果表明:房价波动对城市扩张的影响路径存在拐点,当房价处于低增长机制时,房价上涨对城市整体扩张有显著促进作用,反之,其作用逐渐减弱甚至出现挤出效应;房价上涨对城市土地扩张具有持续正向推动效应,而对城市人口扩张具有显著的溢出效应,即仅当房价处在低速增长阶段时,房价上涨才有利于城市人口的集聚;在不同房价收入比城市,房价上涨对城市扩张的非线性影响路径和效果存在异质性。因而政府应以房地产市场调控为抓手,促进房价合理回归,实现城市的有序扩张,推动城市人口和土地的协调增长。

上海市住房价格梯度及其影响因素分析

选择从上海市中心区至宝山区的一条南北向区段,通过采集沿线内环以内、内环和中环之间、中环和外环之间以及外环以外四个区间内二手房、新房的价格样本,分析其价格梯度差,发现二手房价格一般要高于新房价格,但其价格递减速度比新房更快。根据实际情况,提取繁华程度、市场供求比例、地理区位、交通条件、人口状况、基础设施、环境质量七个影响住房价格的主要因子,运用多元回归分析方法对样本区域的房地产价格进行分析,得出了多元线性回归方程,并进行了回归分析效果检验;最后分别运用偏相关系数分析法和单项因子权重度量法来估算各因子的影响程度。结果表明,二手房市场和新房市场具有明显差异,市场供求是影响二手房价格的最主要因子,而环境质量则是影响新开楼盘价格的首要因子;繁华程度和交通条件的重要影响作用在本次回归模型中没有得到验证。

房价上涨对中国城市创新产生抑制效应了吗?——基于中国35个大中城市面板数据的实证分析

实施创新驱动战略是实现中国经济转型的重要动力,厘清城市创新的影响机制、消除其障碍因素是提高中国城市创新水平的主要思路。本文从城市房价管理的角度分析房价与城市创新的联系机制、效应评估和政策选择,发现当前的房价上涨整体阻碍了中国城市创新水平的提高,一二线城市的负面效应尤其明显;房价上涨主要通过知识型员工流动、企业研发强度以及政府财政中科教支出比重对城市创新水平产生影响,其中介效应呈现出依次递减的态势。地方政府要将房价管理定位为提升城市创新的重要工具:对于房价过度上涨的一线城市应加大对房价的控制力度,对于轻微上涨的二线城市应积极促使房价回归正常水平,对于三线城市则需维持房价在现有合理范围内;一二线城市在具体举措上应着力于促进城市的产业升级,靠中高端产业的发展来消化房价上涨压力,三线城市可承接一二线城市的产业转移,依靠低房价加大人才引进力度,为城市创新提供必要的人才储备。

上海市学区房价格的影响机制

研究目的:探讨上海学区房价格的影响机制。研究方法:特征价格法和多元回归分析法。研究结果:学校因素对房价影响程度之和达20.63%,影响程度居第二位,仅次于建筑面积的影响;具体而言,在其他条件不变的情况下,学校重点等级每下降一级,住宅价格将平均下降8.698%;小学本区排名每降低一位,住宅价格将平均下降0.896%;到重点中学距离每增加100 m,住宅价格将平均下降0.995%;距重点小学距离每增加100 m,住宅价格将平均下降0.499%。研究结论:重点中小学对周边住宅价格存在着比较重要的积极影响,在相同情况下,学校等级越高、学校排名越靠前、距离重点学校越近,住宅价格则越高;重点小学和重点中学相伴的"双学区房"更加受到青睐。

北京市住宅价格的影响因素和轨道交通效应

以北京住宅楼盘样本为例，定性总结了轨道交通对住宅价格的明显增值效应以及距离衰减规律，并且明确了空间影响的距离参数。住宅价格随距离轨道交通站点的远近而变化，最高值出现在离最近轨道交通站点300—600m之间。从建筑结构、地理区位和邻里环境三方面选取23个变量，采用特征价格模型定量分析了不同因素对住宅价格的空间影响效应和规律。其中，11个变量对住宅价格都有一定影响，建筑面积、混合住宅、精装和容积率以及至高速公路、工业区和公园的距离与价格正相关；至天安门和商业设施距离与价格负相关。住宅价格的轨道交通效应凸显，影响程度高于距天安门和高速公路的距离，已建地铁比在建地铁影响程度高。至已建和在建地铁站距离每减少1km，价格分别平均上升1313元／m2和502元／m2。

中国沿海和内陆城市住房价格波动差异与动力因素

研究目的:研究中国城市住房价格短期波动的决定因素,分析均衡因素之外的房价变化自相关和均值回复特点。研究方法:面板误差修正模型。研究结果:沿海和内陆城市房价变化均有回复到均衡价值倾向,但沿海城市的房价变化具有显著自相关特点。研究结论:中国沿海和内陆城市房价波动特点存在明显差异,沿海城市收入高、人口密度大、建设成本增长快等因素促使了房价变化强自相关。

应用改进的时空地理加权模型分析城市住宅价格变化

以深圳市为例对城市住宅价格进行时空地理加权统计回归分析,揭示城市住宅价格在时间与空间方面的变化趋势。研究结果表明,时空地理加权回归模型不仅能够分析相关因素在空间的差异性上对住宅价格具有重要影响,还能够揭示其在时间上的差异性,得出相关因素对住宅价格影响是随时间和空间变化的特点。依据模型的拟合度标准,GTWR能够从OLS的0.617,GWR的0.736提高到0.895。AIC标准的统计信息则表明,GTWR模型明显优于OLS与GWR模型。