# 4. Application: Estimation of the Basic House Price

This section demonstrates how the large volumes of property price data collected for select EMDEs can be used beyond descriptive statistics. To do so, we introduce the notion of the _Basic House Price (BHP)_ , the price of a standard house that is defined identically across all markets. By fixing the type of house to be the constant, _BHP_ aims to provide a data point on price that is independent of the distribution of type/quality of housing that varies widely across markets. The _BHP_ is a key concept that allows for the comparison across and within EMDEs, assessing critical drivers of price and performance of housing markets at the lower end of the price spectrum.

We apply a machine learning technique, Random Forest (RF), to estimate the _BHP_ from the collected web scaped data. Compared to Ordinary Least Squared (OLS) regression, Random Forest has consistently been found to perform better and provide more accurate price predictions (cf. Section 2.3). While the results are presented for five countries as way of application in the next section, the methodology introduced can be rolled out for all emerging economies.

## 4.1 Defining a Basic House

Housing costs reflect the value of the land, the price of the house, the age, condition, and location of the property, as well as the local market. Also, private property prices depend on macroeconomic as well as demographic conditions including migration, urbanization rates, population growth, income growth, a country’s housing finance system including current interest rates and the availability of mortgage lending for all segments of the population. Other aspects relevant to house prices include challenges on the supply side such as a restrictive regulatory environment with lengthy permit granting processes, a shortage of labor and low mobility, as well as high construction costs.

In estimating the _Basic House Price_ , we focus on formal housing built by private developers, through public-private partnerships between developers and governments, or by private individuals. We disregard projects that are purely government-sponsored, or housing that is self-built and probably not transacted in the housing market. Formal housing combines specializations in the housing value chain to deliver titled properties that can be pledged as collateral for a mortgage, that is structurally sound, and that complies with local planning standards and building codes (World Bank 2015). As formal housing often remains unaffordable to low-income HHs in many emerging economies, many families in these economies adhere to incremental self-building. Self-building is particularly common at the outskirts of larger cities or in smaller towns. A recent study in India, for instance, found that 62 percent of newly financed houses are self-built (Das et al. 2018). In self-built environments, the initial house serves as anchor for a multi‐room home that accommodates multiple unrelated people or households (World Bank 2015). While these selfbuilders add to providing shelter to many families where the alternative is often homelessness, we disregard these houses for this project as self-built houses are often highly insecure in terms of tenure and do not necessarily comply with quality housing standards, building codes, or zoning regulations, and are not transacted in formal housing markets.<sup>15</sup> Also, we do not consider endogenous factors such as HH preferences over a set of amenities or locations, that might differ across HHs and countries. Given the limited information available and to maintain comparability across countries, we exclude these exogenous

> 15 In Europe, in contrast, self-building is actively promoted as a means of addressing issues related to housing quality, affordability and sustainability (e.g., Bossuyt et al. 2018; Mullins & Moore 2018).

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