房价收入比影响因素研究.pdf

房价收入比影响因素研究房价收入比影响因素研究 文/文/孟燕青孟燕青住房是人民的基本生活需求，因此住房问题也是重要的民生问题。我国住房发展取得了巨大的成就，但在发展中也产生了诸多问题：供给侧，开发商暴雷、项目烂尾的事件频出；需求侧，由于部分城市的房价上涨速度过快，中低收入家庭面临买不起房、甚至租不起房的问题。房价收入比作为衡量房地产业发展情况的重要指标，对其进行研究具有多方面的价值和意义。本文收集了中国部分地级及以上城市的经济数据，通过回归分析法，分析和论证影响房价收入比的因素。房价收入比是指房屋价格与居民收入之间的比例关系。它是衡量住房可负担性的一个重要指标，该指标能够评估住房市场的整体运行情况和居民购房承受能力。政府在进行房地产产业研究和制定相关政策时，房价收入比常被用来判断房价的合理性和市场稳定性。比较早提出房价收入比概念的是美国学者 Weicher，他计算了美国 1949-1975 年新建住房的房价收入比，并使用这一指标分析美国人对新建住房的支付能力。因其计算方式简单，并且能够真实反映住房负担，得到了世界银行和联合国的广泛应用。以 2010-2020 年中国 296 个地级及以上城市的平均房价收入比作为统计对象，将其进行归类做图如下：2010-2020 年中国 296 个地级及以上城市平均房价收入比分类图从图中可以看出，经济发达城市的房价收入比普遍偏高，具体到城市，像北京、上海、广州、深圳等城市的房价收入比远高于其他城市，这些城市经济发展迅速，其城市吸引力也相应较强，大量人口流入，进一步推动了房地产市场的繁荣。2010-2020 年中国 296 个地级及以上城市房价收入比箱线图从图中可以看出，2010-2020 年城市的房价收入比分布未发生较大变化，历年的数据分布都比较集中，波动性小且稳定，大部分城市的房价收入比相对较低，较少的城市拥有非常高的房价收入比，从而拉高了整体的平均水平。以房价收入（pir）比为因变量，从经济、城镇化、人口、教育、医疗、文化等可能影响房价收入比的因素中选取了几个代表性的变量作为自变量进行研究，经济方面选取了取对数后的地区生产总值（gdp），城镇化方面选取了城镇化率（ur），人口及教育方面以普通小学在校学生数占户籍人口比重（edu），结合两者共同对因变量进行解释，医疗方面选取了每万人拥有医院床位数（med），文化方面采用了公共图书馆数（cul），以下是变量的主要情况：中国经贸CHINA GLOBAL BUSINESS16 中国经贸 2024年4月中主要变量的描述性统计变量样本量平均值标准差最小值最大值pir32566.6272.7082.12037.570edu32567.0912.7521.78636.885ur325655.19415.47912.970100.00gdp32567.2680.9973.93510.564med325644.44312.55112.35094.300cul32569.0224.8491.00043.00局部莫兰指数2020 年地级及以上城市房价收入比局部莫兰散点图局部莫兰指数可以分析识别某城市及其邻近城市都有高房价收入比的“热点”和某城市及其邻近城市都有低房价收入比的“冷点”以及某城市的房价收入比与其邻近城市显著不同的“异常点”。从图所示的局部莫兰散点图来看，大部分城市位于第一、第三象限中，呈现区域自相关的现状，可以通过空间计量模型去进一步验证城市间的空间相关性。构建空间回归模型首先构建普通最小二乘法模型（OLS），并对其进行LM 检验，结果为：空间滞后方面：LM-Lag 检验的统计量为 146.289，p 值小于 5%，显示了空间滞后效应的显著性，稳健性LM-Lag检验的统计量为19.341，p值小于5%，这表明在控制其他形式的空间依赖性后，空间滞后效应仍然显著。考虑到 LM-Lag 和 LM-Error 的统计量都显著，因此拒绝 OLS 回归模型，选择空间杜宾模型来分析城市房价收入比的空间关系。对模型进行稳健性检验，对数似然比检验(LRtest)和沃尔德检验(Waldtest)的 p 值均小于 5%、均显著，于是得出结论，空间杜宾模型提供了比空间误差模型和空间之后模型更丰富的信息。固定效应模型存在三种效应，时间固定效应、个体固定效应、时间个体双固定效应，分别作出三种效应的回归模型对比，时间固定模型的为 0.31，个体固定效应模型的为 0.091，时间个体双固定效应模型的为 0.018，综合对比后选择时间固定效应模型为最终模型。空间杜宾模型溢出效应分解空间杜宾模型效应分解变量直接效应间接效应总效应edu0.180*0.0200.200*(9.47)(0.81)(8.16)ur0.049*0.0030.052*(13.96)(0.61)(8.95)gdp0.130*0.651*0.781*(2.03)(10.94)(9.38)med-0.020*-0.037*-0.057*(-4.65)(-7.38)(-8.87)cul0.096*-0.174*-0.079*(9.37)(-9.94)(-3.71)*p0.01,*p0.05,*p0.1从表中可以比较全面的理解变量之间的相互关系以及它们如何在空间上相互作用。直接效应表示一个城市的影响因素对本城市房价收入比的影响程度。间接效应可以反映空间溢出效应的效果,表示本城市的影响因素对邻接地区房价收入比的影响。总效应则反映一个城市的房价收入比变化由自身驱动因素和驱动因素的空间互动，共同作用下的影响效果。从回归分析的结果来看，GDP 作为衡量城市经济发展水平的重要指标，在很大程度上与房价收入比呈现正相关关系，这意味着经济发展水平越高的城市，其房价相对于居民收入的比例也越高，而且 GDP 的发展不仅可以拉动本城市房价收入比的升高，同时也会带动周边邻近地区的房价收入比升高。城镇化率也是与房价收入比正相关的因素，城镇化速度的加快带动了城市房地产需求的增长，尤其是在资源有限的情况下，会导致本城市的房价上涨，但是城市的城镇化情况在邻近城市间不具有显著的溢出特性。人口教育综合指标亦与房价收入比呈正相关，反映出教育资源需求量大的城市往往具有更高的房价水平，与城镇化率类似，城市的教育资源需求在邻近城市间不具有显著的溢出特性，这有可能与我国的学籍、户籍制度以及学区划分有关，在小学阶段就读的学生需要居住在规定的学区内，教育资源并不能辐射周边城市，也不会带动周边城市房价的增长。医疗和文化资源的丰富程度与房价收入比之间存在负相关关系，这可能表明，医疗和文化资源的充足能在一定程度上缓和房价上涨的速度，而且，医疗和文化资源都存在显著的溢出特性，这可能表明优质的医疗和文化资源不仅能够服务于本城市的居民，还能够辐射周边邻近城市，使得居民不必过分集中于经济最繁华的地区，进而减少对于中心城市房产的过度竞争。（作者单位：中国人民大学）Moran scatterplot(Morans I=0.316)y2020Wzz-2-10123456789-2-1012345石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石石宏观经济Apr 4 2024CHINA GLOBAL BUSINESSAPR.2024 CHINA GLOBAL BUSINESS 17