# 3.3 Limitations

While applying a web scraping approach to obtain price data in EMDEs is a very cost-effective and efficient way to collect data, particularly compared to conducting expensive surveys, there are several limitations that pertain particularly in the context of EMDEs.

To start with, the web scraping approach does not necessarily yield data that are representative of all properties in the market as we are only able to capture properties of sellers with access to internet and who are able and willing to post their property online. By the same token, accessing house prices on real estate listing websites in emerging economies requires buyers to have access to these online listings. This might not always be the case, especially in lower-income segments of a given market. Particularly in developing areas, information density is low and might lead to data blind zones (Li et al. 2019). Recent research shows that online platforms used for home sales, even in developed markets, may reproduce and even intensify existing forms of inequality within cities (Boeing et al. 2021; Angelo & Vormann 2018).

While internet access is less of a concern in the five countries we outline here (cf. Annex 1), expanding the methodology to other countries might become problematic. In Burundi, for instance, only 5 percent of the overall population use the internet either via computer, mobile phone or other digital devices (World Bank 2022). In comparison, in Brazil, close to 74 percent of the population use the internet (World Bank 2022). In countries with relatively low internet penetration rates, HHs might adhere to alternative pathways to buy properties: personal interactions with real estate agents, classified ads in newspapers or through informal, personal interaction. Hence, the web scraping approach might not be suitable to capture local property markets where online advertisements are not frequently used and might, therefore, weaken the generalization of the results to localized markets.

Second, the price data collected are concentrated in countries’ biggest business cities and urban centers. This is not surprising, since urban centers are the place where most new housing units are being built, responding to the accelerated urbanization rates currently observed in EMDEs. Further, urban dwellers are more likely to formally transact their property and to use online sources to sell or buy properties. Given the diversity of urbanization across EMDEs, the level of geographical disaggregation differs significantly. Disaggregated data for geographical areas beyond the major business city might not be sufficiently large to provide price estimations beyond the largest urban area. Due to data limitations beyond the biggest business cities and the absence of location markers on many housing listing websites in EMDEs, highly complex and spatially heterogenous housing markets cannot fully be delineated.

Third, the data listed on real estate websites include newly developed properties and the secondary housing market, which might bias house price estimations. In addition, some new housing developments are sold as investment projects, often tailored towards foreign investors. These properties are usually sold in bulk, i.e., entire apartment complexes containing several apartment units. Accounting for this potential bias, we conduct careful, multivariate outlier removal. In addition, we differentiate between property types (apartments and houses) and provide distinct price estimates for both property types.

Fourth, the final transaction price is likely to be different from the listed price, which often appears to be the price ceiling that precludes the possibility of sales at higher prices (Horowitz 1992). Furthermore, the listed prices advertised online represent the user-inserted price, which could include either the appraised 19