Purpose

This paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).

Design/methodology/approach

The authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.

Findings

The results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.

Practical implications

Forecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.

Originality/value

To the best of the authors knowledge, this is the first study to develop and test a real estate price equilibrium model at several different geographical levels of the same political space.

中文翻译：

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欧洲背景下房地产价格的决定因素：四级分析

目的

本文探讨了四个地理层面的房地产价格决定因素：整个欧盟、28 个欧盟国家、一个欧盟国家（葡萄牙）和 25 个葡萄牙大都市统计区 (MSA)。

设计/方法/方法

作者运行两个时间序列回归模型和两个面板数据回归模型，观察潜在的房地产价格决定因素和从欧盟统计局收集的房价指数。

发现

结果表明，价格决定因素，例如国内生产总值 (GDP)、利率、房屋开工率和旅游业，在统计上是显着的，但并非在所有四个地理分析水平中都具有显着性。结果还证实了房地产价格的自回归特征，上一期价格变化是当期房地产价格变化的最重要决定因素。

实际影响

通过了解每个地理分析级别意味着不同的价格决定因素，并且动量是房地产回报的重要决定因素，可以更有效地预测房地产价格。

原创性/价值

据作者所知，这是第一项在同一政治空间的几个不同地理层面上开发和测试房地产价格均衡模型的研究。