Second, while price indices present equilibrium outcomes of housing markets, they do not cover details about, broadly, the quantity of housing. They often only include a particular type of housing for which prices are tracked. Whereas in high-income economies, the latter may remain relatively stable in the short term, in EMDEs, with rapidly expanding formal housing markets, quantity and type of housing are important elements to capture. They provide context to changes in prices as the sample over which prices are indexed changes, and as price and quantity and type of housing supplied are highly interrelated. Also, they have important policy implications in the context of markets’ ability to supply homes for different market segments, and formal developers’ ability to go reach lower income groups. The measurement of these dimensions of the housing markets is absent across EMDEs.

Third, primary data collection for actual, non-indexed house prices is still somewhat limited and, if available, almost exclusively covers advanced economies. With a few notable exceptions, there is a severe lack of contributions in the literature on property price estimations in EMDEs. Property price data for EMDEs are virtually non-existent – both for within-country assessments, and even more so for crosscountry comparison. Most studies on house prices obtain data from readily available sources such as land registries, real estate agencies, or commercial banks, or tap into established indices. Given the significant effort to collect original data on house prices, there are very limited efforts. Since the scope and focus of these studies differ or as they purely rely on price indices, comparing property prices across studies is not feasible.

Fourth, efforts to investigate property prices in EMDEs mainly converge to analyze the determinants of _mean_ house prices. Distributional efforts in property price collection for different income segments within emerging economies are largely absent. CAHF is unique in its effort to approach the house price estimation from the perspective of low-cost developers. Yet, CAHF takes it to the other extreme. It only collects the _cheapest_ price of a house built by a formal developer in African countries and does not factor in otherwise transacted housing units in the formal housing market. While insightful, this approach does not allow pricing the entire housing market, offering an understanding of the quantity of “affordable” houses available to different income segments.

Lastly, studies assessing the historical developments of private property prices are concerned with measuring financial (in)stability, which they attribute to distorted household mortgage debt to asset values ratios. A myriad of studies estimating house prices in developed economies were published after the collapse of the housing bubble and the resulting financial crisis in 2008/2009 mainly concerned with estimating how to identify housing bubbles in the first place. An examination of property prices from the perspective of affordability and demand-supply mismatches for different income segments is absent. In addition, studies assessing house price data usually draw on different methodologies to estimate property prices and rely on varying data sources. Hence property price estimations are not comparable across studies and scholars have only recently started to use big data approaches to collect actual property price data for a larger number of economies. Yet, their focus mostly remains on developed countries.

To fill these gaps, our paper extends the novel approaches in collecting house prices through a web scraping approach to emerging economies, thus addressing the substantial data gap in EMDEs. Further, this paper offers a methodology contributing to a more distributional understanding of private property prices in emerging economies. It also provides a comprehensive methodology to estimate a standard house price that 10