intuition behind the cross-country differences. Comparing Pakistan and Costa Rica at opposite ends of the spectrum, we observe the availability of apartments in Pakistan concentrated at the lower end of the price spectrum, while a much more even distribution in Costa Rica (Figure 1). Moreover, while the price-size relationship (Figure 2) clearly points to apartments being listed cheaper than similar size houses in Pakistan, the opposite is true in Costa Rica. As the BHP represents the typical estimated market price for a standard 50 sqm apartment, the prevalence of more luxurious/expensive apartments in Costa Rica is expected to drive up the benchmark price.

Within countries, there are also significant differences across regions and cities. In South Africa, for instance, price differences of the BHP between cities are stark. House prices in Cape Town are at the higher income spectrum, where even a basic house is priced at US$ 106,536. Cape Town is one of the most popular tourist destinations in Africa and its property market is known to be tailored to more affluent retirees and foreign property buyers. In Morocco, Casablanca is the most expensive city followed by Marrakesh. In addition to consistently high prevalence of European buyers, many wealthy Moroccans families live in the suburbs of these cities including Palmeraie in Marrakesh and Bouskoura in Casablanca where prices usually start around US$ 700,000. In Pakistan, while nationally at the lowest end of the price spectrum of the five economies covered in this study, there are also significant intra-country differences. Being a fast-growing emerging economy, the capital, Islamabad, is a thriving real estate market. While there are many houses at the lower end of the price spectrum, with the cheapest house advertised at roughly US$ 9,000, property prices in Islamabad can go as high as US$ 6 million.

# 5.2 Cross-Validation

The predicted _BHP_ of the apartments obtained by both models were compared with the observed apartment prices in order to determine the predictive power of the different models. One standard measure often used in price estimation models is the quotient between the predicted price and the observed price for the property. The acceptable median ratio between predicted and observed price is 0.9-1.1 (cf. International Association of Assessing Officers 2014; Čeh et al. 2018).

Additionally, we evaluate the performance of the different models with the mean absolute percentage error (MAPE), which measures the average percentage deviation of predicted prices from actual property prices expressed as where p̂ is the predicted property price and _pi_ the actual property price of property _i_ . To understand differences in predictions for the BHP, we estimate a localized MAPE for apartments between 50 and 60 square meters. Comparing the predictive performances of the models based on the performance measures (ratio of predicted vs. actual value, MAPE, localized MAPE), we obtain more precise results in the RF models for all countries. All RF models are within the suggested range of the predicted/observed price ratio of 0.9-1.1, also for the estimations where all potential duplicates are rigorously removed (Annex 2) and where different splits are applied (Annex 5). In the main OLS estimation, South Africa exceeds the acceptable median ratio between predicted and observed price range. The MAPEs indicates that the percent deviation of the RF prediction from the actual property price ranges between 27 percent for Costa Rica and 31 percent for Pakistan. Across the board, while MAPE is relatively high in both RF-based and OLS-based 30