instance, Jumia.com, which is an online platform combining an e-commerce marketplace, classified websites, and applications, is widely used across the continent. In Nigeria, Property Pro (formerly Jumia) is the number one property transaction website, covering about 65 percent of the Nigerian online real estate market (Nairametrics 2018).

Additionally, EMDEs’ websites, particularly those that do not have a regional spread like Jumia.com in Africa, have limited formal standards regarding data entry. Many websites in emerging economies do not have consistent data on an array of property characteristics and provide somewhat limited information on the listed properties. Often, they only include some pictures of the property, the listed price, and a phone number through which the seller can be reached. Formal developers, who have started to also list newly built properties on online platforms (in addition to their own online or offline platforms), provide slightly more structured information on the transacted property. Yet, they are bound by the format of the online platform, which often only requires submission of property price and size. Few developers provide exhaustive property descriptions in free text format, which, if extracted, needs to be processed for data analysis through text mining. Hence, property data obtained through web scraping in emerging economies, in our experience, will not be as exhaustive in terms of obtaining different property features as found in publications on developed markets (cf. Section 2.2).

Furthermore, across websites in EMDEs, there is no standardized way to record the size of the property. Sometimes, the website does not provide the option to insert size information at all. In addition, userprovided information on size might not necessarily comply with the unit required by the platform (e.g., users insert square feet even though the platform requires square meters). The matter is even more complicated in economies where local measurement units for properties are used alongside more “standardized” measurements. South Asian websites (Nepal; Pakistan) allow for the insertion of different size units including Biga, Kattha, Dhur, Ropani, Aana, Paise, and Daam, alongside square feet and square meters. However, not all users consistently specify the measurement unit making data cleaning cumbersome. In addition, particularly for houses, size data can be somewhat muddled as it is unclear whether the plot size or the usable property size is indicated. To account for this difference, we distinguish apartments and houses (cf. Section 4.1) and, where available, use plot size for houses to also account for the value of the land, which – in some EMDEs – can be a significant portion of the property price (cf. Section 2.2).

Finally, in some EMDEs, listing websites do not specify whether a property is for rent or for sale. Usually, rental properties can easily be distinguished by relatively low prices. However, in some EMDEs, it is common to pay one year’s rent upfront. In these instances, it is challenging to discern low sales prices from annual rents in cases where listings do not distinctly indicate sale versus rent. In addition, price data do not always include a currency marker, which is mainly problematic in countries where both euros and US dollars are used to transact properties in addition to local currencies.

When the aim is to estimate representative property prices of the housing stock in a country or region, scraping at least 0.5-1 percent of the number of HHs in that area is considered to be a large enough sample (cf. Bricongne et al. 2019). The same principle applies when the statistical population being analyzed is the universe of transacted properties in the market over a given period: in developed economies where residential property markets are formalized, it is possible to obtain the total number of transactions (the 13