Antoniucci Valentina, Marella Giuliano
Department of Civil, Environmental and Architectural Engineering, University of Padova, via Venezia 1, 35131 Padua, Italy.
Data Brief. 2017 Dec 15;16:794-798. doi: 10.1016/j.dib.2017.12.018. eCollection 2018 Feb.
The database presented here was collected by Antoniucci and Marella to analyze the correlation between the housing price gradient and the immigrant population in Italy during 2016. It may also be useful in other statistical analyses, be they on the real estate market or in another branches of social science. The data sample relates to 112 Italian provincial capitals. It provides accurate information on urban structure, and specifically on urban density. The two most significant variables are original indicators constructed from official data sources: the housing price gradient, or the ratio between average prices in the center and suburbs by city; and building density, which is the average number of housing units per residential building. The housing price gradient is calculated for the two residential sub-markets, new-build and existing units, providing an original and detailed sample of the Italian residential market. Rather than average prices, the housing price gradient helps to identify potential divergences in residential market trends. As well as house prices, two other data clusters are considered: socio-economic variables, which provide a framework of each city, in terms of demographic and economic information; and various data on urban structure, which are rarely included in the same database.
此处展示的数据库由安东纽奇和马雷拉收集,用于分析2016年意大利房价梯度与移民人口之间的相关性。它在其他统计分析中也可能有用,无论是房地产市场分析还是社会科学的其他分支。数据样本涉及112个意大利省会城市。它提供了关于城市结构,特别是城市密度的准确信息。两个最重要的变量是根据官方数据源构建的原始指标:房价梯度,即每个城市中心和郊区平均价格之比;以及建筑密度,即每栋住宅楼的住房单元平均数量。房价梯度是针对新建和现有住房这两个住宅子市场计算的,提供了意大利住宅市场的原始且详细的样本。房价梯度有助于识别住宅市场趋势中的潜在差异,而不是平均价格。除房价外,还考虑了另外两个数据组:社会经济变量,它根据人口和经济信息提供每个城市的框架;以及各种关于城市结构的数据,这些数据很少包含在同一个数据库中。