varying across contexts and countries. Despite the immense importance of understanding these challenges and identifying how to mitigate housing market failures, the empirical literature in this area is highly underdeveloped for EMDEs. This can be primarily attributed to a dearth of robust property price data and associated analysis in these markets.

This paper pursues a two-fold approach to address the gap: First, addressing the information vacuum in EMDEs regarding property price data, this paper presents a novel approach to collecting house price data in data-scarce environments common in EMDEs. Instead of relying on survey data or obtaining property price data from various official and unofficial sources, we scrape available price information from EMDEs’ listing websites where private properties are listed for sale. In doing so, we largely constrain our analysis to the formal housing market and disregard informal housing not transacted in online markets, such as the incremental self-building of houses typical in EMDEs. Our approach offers a first foray in providing the distribution of property price data otherwise nonexistent in EMDEs.

In the second part of this paper, we demonstrate the useability of distributional private property price data and present an approach to estimate the price of a “standard house”. We refer to this price as the Basic House Price (BHP). This BHP allows for comparability of prices across EMDEs, standardizing the significant variation in the type of property across markets. Further, the BHP presents a standard measure that can be used in future research to monitor aspects of inclusiveness. When linked to other data such as, for instance, household income or housing finance data, progress on the Sustainable Development Goals (SDGs) may be monitored. The BHP also offers a standard measure to assess and compare housing affordability across population segments if paired with income data.

We present the web scraping approach, descriptive statistics, and the methodology for estimating the BHP exemplarily for five countries: Albania, Costa Rica, Morocco, Pakistan, and South Africa.<sup>2</sup> Overall, we collect residential property price data from over 200,000 online listings and present an approach to clean, analyze, and visualize these data. In this paper, we only demonstrate the applicability of the collected house price data for the price estimation of a defined housing unit. We leave for future research on how a standard house (BHP) price may contribute to understanding affordability in EMDEs.

This paper proceeds as follows: After this introduction, Section 2 discusses previous efforts in collecting and estimating house prices, which are mainly focused on developed economies. This section outlines how – if collected at all – private property prices are mostly available in an indexed format which only allows for monitoring of changes over time. Section 3 describes the data collection process for the five countries and discusses the prospects and pitfalls of a web scraping approach to obtain data for EMEDs. Section 4 defines a standard housing unit, which is the underlying concept for estimating the BHP. This section also rolls out how we apply a Random Forest (RF) model to estimate the BHP. Section 5 presents BHP results for the five markets, including estimations for the largest cities in these countries. Section 6 concludes and outlines how the methodology we propose can be expanded to a more extensive set of EMDEs.

> 2  Our ongoing research extends the collection and estimation of private property prices to over 60 EMDEs and will be presented in future papers.

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