preferences from our model.<sup>16</sup> Finally, we are only concerned with home ownership and defer scraping of rental data to further research. The relationship between property prices and rental prices has been discussed in depth elsewhere (e.g., Campbell et al. 2009; Engsted & Pederson 2015; Gallin 2008).

We define a basic house as a _formally supplied 50 square meters (sqm) one-bedroom, one-bathroom apartment located in an urban area within a given country, assumed to provide basic municipal or on-site services including water, sanitation, road access, and an energy source._ While we presented summary statistics for both houses and apartments, the _BHP_ deliberately only includes apartments. We constrain the _BHP_ to apartments for comparability purposes and to avoid potential distortions that can be attributed to the different reporting of size (plot size versus usable surface size) in houses. In addition, by reporting the _BHP_ exclusively for apartments, we also account for the ongoing debate regarding the need to increase the housing density in emerging economies, particularly in cities that experience an influx of migration and growth, through densification of existing settlements or the building of multi-story, complex buildings. This is particularly relevant for Africa, where cities are 20 percent more fragmented compared to cities in Asia, more expensive and less accessible for most (Lall et al. 2017). In some African countries, the densification, which is not served by the market, takes place in the informal realm. In many countries, single family homes built on a plot of land are turned into mini‐compounds where a main house is surrounded by ‘backyard shacks’ that are rented. This phenomenon of _backyarding_ is particularly well documented for South Africa where _backyarding_ increased from 1.1. million in 2011 to about 1.8 million in 2016 providing many families an informal way of overcoming the limitation of housing supply in urban areas (e.g., Brueckner et al. 2018).<sup>17</sup> Densification of houses has many beneficial effects, including a reduction of land use costs as well as cost of connecting to utility infrastructure and services, particularly in areas of accelerated urbanization (e.g., Kurvinen & Saari 2020). While we deliberately apply a narrow definition of the _BHP_ , the presented methodology in this paper allows for easy transferability of other comparative units similar to _BHP_ that might be more suitable for other researchers’ focus.

# 4.2 Estimating the Basic House Price: Random Forest Estimation

To estimate the _BHP_ for each country, we run the following predictive regression specification using houselevel data that we obtain by web scraping online listings: Price,j,= f(Sizei, Typei, Chari, Locationi) where Pricei is the listed price of the property i; Sizei is the size in square meters; Chari is a vector of characteristics of the property i to include the number of bedrooms and bathrooms; Location is a vector to denote the location on property i, and includes, where available, the municipality, county, and/or city.

The predictive framework above is estimated in its linear form using OLS and through non-parametric estimation of the RF model. When it comes to ML approaches to predicting house prices, there is an

> 16 Recent research has applied deep learning models, especially convolutional neural networks (CNN), which allow the incorporation of the surroundings and other preferences into price estimation models (e.g., Law et al. 2019; Poursaeed et al. 2018).

> 17 Backyarding is not just a phenomenon in emerging economies. In Los Angeles or Sydney, for instance, so-called _granny flats_ or _casitas_ , which are essentially small backyard houses, are encouraged by the city administration to combat the growing number of homeless people (e.g., Durst & Wegman 2017; Gurran et al. 2020).

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