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首页> 外文期刊>Urban, Planning and Transport Research >Residential housing prices: impact of housing characteristics, accessibility and neighbouring apartments – a case study of Dortmund, Germany
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Residential housing prices: impact of housing characteristics, accessibility and neighbouring apartments – a case study of Dortmund, Germany

机译:住宅价格:住房特征,可及性和邻近公寓的影响–以德国多特蒙德为例

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In this research we analyse the most important factors that determine housing prices. On the one hand, we test whether neighbourhoods with a good accessibility are more attractive and consequently show higher housing prices. For this purpose, we introduce an adapted Walk Score as part of the accessibility indicators. On the other hand, we compare an ordinary-least-squares regression (OLS) and a spatial lag model and test which model better explains residential housing prices. The regression models show the importance of classical factors such as dwelling characteristics or the types of neighbours. In addition, they also reveal that a differentiated approach is needed for analysing the accessibility, the location and the environment of a dwelling. The mere presence of a single amenity, a public transport stop or a motorway access is not a sufficient explanatory factor. Information such as density of supply, walking distances or public transport service quality needs to be taken into account as well as. The test of the spatial lag model reveals that prices of the most proximate dwellings can be taken into account as a relevant factor in explaining residential housing prices and should therefore be included in research on residential housing prices.
机译:在这项研究中,我们分析了决定房价的最重要因素。一方面,我们测试了交通便利的社区是否更具吸引力,从而显示出更高的房价。为此,我们将调整后的步行得分作为辅助功能指标的一部分。另一方面,我们比较了普通最小二乘回归(OLS)和空间滞后模型,并测试了哪个模型可以更好地解释住宅价格。回归模型显示了诸如住宅特征或邻居类型等经典因素的重要性。此外,他们还揭示了需要一种差异化的方法来分析住宅的可及性,位置和环境。仅仅存在一个便利设施,一个公共交通站点或高速公路通道是不足以解释的因素。还需要考虑诸如供应密度,步行距离或公共交通服务质量之类的信息。对空间滞后模型的检验表明,可以将最近住宅的价格作为解释住宅价格的相关因素,因此应将其包括在住宅价格研究中。

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