al. 2018; Fan et al. 2006; Mullainathan & Spiess, 2017; Pérez-Rave et al. 2019; Truong et al. 2020; Yan & Zong 2020). Within that realm, Fan et al. (2006) constitute one of the earliest contributions that move beyond hedonic price models to predict property prices. Applying a decision tree technique, the authors explore the relationship between house prices and housing characteristics, which aided the determination of the most important variables for price predictions.

While ML techniques are comparatively weak in inference, they have strong predictive power, manage to fit complex data, are very flexible in assumptions on functional form without overfitting, and work well in out-of-sample estimations (e.g., Athey 2018; Mullainathan & Spiess 2017). ML estimations such as random forest have become a suitable, and frequently applied alternative to hedonic price estimates, particularly for property price estimation. While RF and other decision tree-based models also rely on model assumptions, they are better at modeling non-linear relationships compared to simple, multi-linear regression.

Authors applying ML to price estimations mostly focus on narrowly defined housing markets in developed economies such as Ljubljana, Slovenia (e.g., Čeh et al. 2018), Gangnam, Republic of Korea (e.g., Hong et al. 2020), London, Great Britain (e.g., Levantesi & Piscopo 2020), Arlington County, USA (e.g., Wang & Wu 2018) or housing markets in upper-middle income economies such as Mamak District, Ankara, <mark>Türkiye</mark> (Yilmazer & Kocaman 2020), Petaling, Jaya, Selangor, Malaysia (Mohd et al. 2019), or St. Petersburg, Russian Federation (Antipov & Pokryshevskaya 2012). In assessing the housing sector, many of these authors contrast the predictive performance of ML algorithms with standard regression techniques. Across the board, the authors find that RF (significantly) outperforms parametric estimation techniques in terms of accuracy and predictive power.

2.4 Data Gap: EMDEs Are Largely Absent in Property Price Analyses This overview on existing studies and data sources within the realm of house prices points to five major gaps that we try to address with this paper: First, there is a striking data gap in the availability of house prices, particularly for EMDEs. Most existing property price compilation efforts concentrate on developed economies, publish data only in indexed format, and do not report underlying actual house prices. This may be attributed to the fact that national sources, such as central banks or statistical offices on which these indices base their data, do not collect, report, or publish property price data. Further, as underlying data to these indices are very country- and context-specific, they fit the purpose of monitoring price changes over time within a specific country but do not facilitate cross-country assessments. Some countries, for instance, only consider prices for family homes in the capital while other countries use flats in urban areas for the index. The same applies for prices: some report the transaction prices while others draw on listed prices, while yet others average prices (cf. BIS database; Mack & Martínez-García 2011). Mack & Martínez-García (2011), who collate publicly available national sources to build a database of (nominal and real) house prices for developed economies, acknowledge this flaw outlining that the main contribution of their database is “sorting out the existing data by country, selecting the most _similar_ series and documenting the differences across countries to clarify the extent to which international sources can be made comparable for empirical analysis purposes” (Mack & Martínez-García 2011: 3). Achieving comparability across countries with the existing data sources is almost impossible.

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