Finally, in recent years, crowd-sourced platforms such as Numbeo,<sup>9</sup> which rely on user inputs on property prices in various locations around the world, have added their own house price index along with publicly available per square foot price ranges for properties within the city center and outside the city center. These platforms add more distributional aspects to the average house prices and point predictions published by other indices, but suffer from the reliability of the self-reported data.

Despite the apparent issues in comparability across countries, the listed sources are the most comprehensive databases on property prices currently available for a larger set of countries. Therefore, many papers draw on these indices to conduct country or region-specific analyses on property price developments over time (e.g., Girouard et al. 2006; Igan & Loungani 2012; Yoshino & Helble 2016). One of the earliest systematic presentations of house prices is a historical time series data set of nominal residential property prices in 13 advanced economies by Borio et al. (1994). Some studies that provide comparative assessments combine the data sources outlined above or enhance them with some primary data collection on some additional countries that are not yet covered by the indices (e.g., Deghi et al. 2020).

# 2.2 Determinants of House Prices

The volume of research on the housing market, particularly estimating its impact on real economic activity, has experienced a steep influx after the global financial crisis in 2008–2009. Most studies in this realm investigate the various channels through which housing and house prices affect macroeconomic and financial outcomes, particularly as housing bubbles are associated with significant output losses (e.g., Catte et al. 2004; IMF 2008; Jordà et al. 2015). Single-country studies on house prices and house price developments mainly focus on developed economies, particularly OECD countries, EU countries, and the United States or Canada (e.g., Alter & Mahoney 2021; Davis & Heathcote 2005; Knoll et al. 2017; Philiponnet & Turrini 2017).

Most studies investigate determinants of house prices over time. Jordà et al. (2016), for instance, have gathered time series data on disaggregated bank credit for 17 advanced economies since 1870. With this historical data for the total value of the residential housing stock (structures and land), the authors relate household mortgage debt to asset values, showing that the rise in mortgage credit has financed a substantial expansion of home ownership from about 40 percent in 1950 to 60 percent in the 2000s. Similarly, Knoll et al. (2017) assess how house prices have evolved over time for 14 advanced economies, gathering historical house price data to estimate what drives changes in house prices. The authors show that changes in house prices are largely attributed to changes in land prices. This finding is corroborated by others who also attribute rising property prices to sharp increases in residential land prices, while construction costs have remained relatively stable over time (e.g., Glaeser & Ward 2009; Gyourko et al. 2013). In major metropolitan areas, it is not uncommon for the cost of land to exceed 40 percent of total property price; in extreme cases, like San Francisco, the share can stretch to as much as 80 percent (McKinsey Global Institute 2014).

Gao et al. (2019) dissect property features into two groups when predicting house prices: non-geographical features, such as the number of bedrooms and floor space area, and geographical features, such as the distance to the city center and the quality of nearby schools. This is also documented by Gröbel and

> 9 <u>https://www.numbeo.com/property-investment/</u>

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