allows for consistent price comparison across countries. These data can then facilitate the extension of the scope of the analysis to affordability assessments of property price data and the segmentation of the housing market – particularly focusing on EMDEs.

# 3. Data Collection and Processing: House Price Data in Emerging Economies

In this section, we outline how we collect house price data through a web scraping approach for five markets: Albania, Costa Rica, Morocco, Pakistan, and South Africa. We demonstrate how a big data approach, hitherto employed mainly in developed economies with good data quality, can also be extended to EMDEs to collect price data efficiently. We collected 200,000 unique property transactions for these five countries in an otherwise data-scare environment. The web scraped data reflect the entire housing market and complement the available indexed data that (mostly) report average property prices only.

We selected these five economies to cover different regions and factors in varying country contexts to highlight specificities of web scraping and data processing in EMDEs. These include, for instance, types of properties listed, unique forms of data entry specific to EMDEs, or cultural aspects. We do not strive for the representativeness of these five countries for all EMDEs but seek to exemplify the unique challenges of applying a web scraping approach in EMDEs. Nevertheless, transferring this approach to other EMDEs, especially those with lower data quality, will come with additional unique challenges (as discussed in more detail in Section 3.3).

## 3.1 Web Scraping House Prices in EMDEs

The transaction price would be the ideal source to obtain comparable property price data. Typically, these data can be found in land registries or tax authorities, collated from real estate agencies, collected through online surveys, or obtained through appraisals or valuations as part of the mortgage process. However, none of these sources are feasible for automated data collection in EMDEs as the various institutions holding these price data do not yet have a standardized way of collecting, publishing, or even digitizing them. In markets characterized by lax regulation or enforcement, transacting parties may under-declare property prices to avoid negative ramifications with respect to paying additional registration costs or taxes.

We opted to collect property price data for EMDEs through a web scraping approach of real estate websites. While not yielding transaction prices, obtaining listing prices of formal properties is a viable alternative to gathering price data in EMDEs, where data are otherwise non-existent. At least in the context of developed markets, strong evidence exists that listing prices are correlated and a good leading indicator for transaction prices (e.g., Ardila et al. 2021; Anenberg & Laufer 2017; Lyons 2019). For each economy, we scrape property prices and additional data points for all available listings, capturing, to the extent possible, location aspects. Scraping unique property transactions has several advantages: first, they allow us to provide actual property price data of an entire housing market in EMDEs, facilitating analysis beyond aggregated or indexed data. Particularly in EMDEs, we expect significant differences in prices between the biggest business city and rural areas, and more considerable skewness in data even within cities. Second, by web scraping property prices of the entire formal housing market, we can analyze sub-markets in greater detail. Third, collecting house price data from the entire formal, online housing market allows us to also capture the quantity of housing available at different price segments and can, therefore, provide an overview on the 11