ABSTRACT There are many drivers of house prices. In urban locations, public infrastructure is considered one of the leading determinants. To explore this relationship, an Hedonic house pricing model was applied to an established Birmingham, the UK working class residential area which is serviced by the Metro Midland tram line. The selected Wednesbury Great Western Street station provides ease of access for the local residents to Birmingham and Wolverhampton City Centres plus there has been recent sale evidence of 100 properties within close proximity to the station. Independent variables consisted of a range of typical physical, locational and neighbourhood attributes for the Hedonic house pricing model input data. Based on the preferred linear Hedonic model, the findings from the recent 100 Wednesbury house sales showed property type, bedrooms, floor area, ambience and distance to the tram station were key explanatory factors in relation to pricing levels. On the evidence provided, property price increased by £16,878 for every km closer to the tram stop. However, when examining properties within defined distance bands with smaller datasets, the location to the tram station was not a significant statistical driver. This suggests that proximity to a public transport access point alongside key housing characteristics should form part of the housing decision making process as they are significant predictors of local house prices.

中文翻译：

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电车车站对当地房价的价值：享乐建模方法

摘要有许多房价驱动因素。在城市地区，公共基础设施被认为是主要决定因素之一。为了探讨这种关系，Hedonic房屋定价模型应用于已建立的英国伯明翰工人阶级居住区，该区由Metro Midland电车线路提供服务。选定的Wednesbury Great Western街车站为当地居民提供前往伯明翰和Wolverhampton市中心的便捷通道，此外，最近有出售迹象表明，车站附近有100处房屋。自变量由Hedonic房屋定价模型输入数据的一系列典型物理，位置和邻域属性组成。根据首选的线性Hedonic模型，最近100宗Wednesbury房屋销售的调查结果显示，房屋类型，建筑面积，环境和距电车站的距离是与价格水平相关的关键解释因素。根据提供的证据，离电车站较近的每公里物业价格增加了£16,878。但是，在使用较小的数据集检查定义的距离范围内的属性时，电车站的位置并不是重要的统计驱动因素。这表明，与关键住房特征一起靠近公共交通出入口应成为住房决策过程的一部分，因为它们是当地房价的重要预测指标。在使用较小的数据集检查定义的距离范围内的属性时，电车站的位置并不是重要的统计驱动因素。这表明，与关键住房特征一起靠近公共交通出入口应成为住房决策过程的一部分，因为它们是当地房价的重要预测指标。在使用较小的数据集检查定义的距离范围内的属性时，电车站的位置并不是重要的统计驱动因素。这表明，与关键住房特征一起靠近公共交通出入口应成为住房决策过程的一部分，因为它们是当地房价的重要预测指标。