predictions, the RF estimation consistently performs better than the OLS prediction. While the quality and quantity of information used in both estimation techniques were identical, since the predictor in the RF model explores the hierarchical structure of features, it can more sensitively track the possibility that the effect of each attribute on price varies by context (Hong 2020).

The limited coverage of observable property features within EMDEs could be one explanation for the acceptable accuracy of the estimation. In addition, while most studies applying the RF model focus on a very narrow housing market (e.g., Čeh et al. 2018; Levantesi & Piscopo 2020) our data expands to the entire housing market of the five emerging economies covering heterogeneous properties with varying amenities including interior decorations, building age, or other features that are not captured in the model as these property features were not consistently available on listing websites. Equally, we are jointly estimating property prices for a large swathe of locations within an economy – beyond just different neighborhoods within a city but aggregating both rural and urban areas. This deviates from the use of RF models in the literature to predict property prices for a well-defined narrow set of locations, typically a city or a province/state. The limited property features and available explanatory variables pose limitations to the use of the model to predict individual property prices across the spectrum. However, the purpose of the prediction in our case is to arrive at a typical price for a standard property that can be compared across countries and contexts. The RF and OLS-based approach, notwithstanding the relatively high MAPE, can be considered improvements over the alternative of only considering one-dimensional summary statistics of price.

# 6. Conclusion

Given the difficulty of obtaining reliable private property price data in emerging economies, most analyses of house prices or affordability assessments are constrained to developed economies and limited in scope. Most cross-country analyses that assess trends in house prices are based on available indices, which often aggregate to the national level, masking important within-country dynamics and regional differences. To overcome this flaw and provide more in-depth insights into housing markets in emerging economies, we demonstrate how to collect a large range of localized data through web scraping of property listing websites. Further, to compare property prices across countries, we introduce the concept of _the Basic House Price_ – which constitutes the average price of a basic one-bedroom apartment of 50 square meters in an urban area – that allows for comparability across countries.

By way of demonstrating the methodology and data processing for five EMDEs, we show that web scraping offers a cost-effective way to obtain a large amount of price data for countries where official data is absent and where alternate data sources on prices are not available. The main constraint to this approach remains the unorganized structure of listing websites and the limited information available on property features and attributes. This approach will only improve over time as the capabilities of listing websites improve and become the preferred method of listing. There is also room to improve the web scraping on several fronts. For instance, image recognition software can extract information that is not supplied systematically in listing websites and could improve model precision. In addition, addresses could be geotagged to incorporate crucial details about location and to differentiate within-city variation.

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