new repeat-sales house price index that describes house values at the contract date when the price is determined rather than the closing date when the property is transferred. Other big data price collection efforts include, for instance, scraping of online listings in Great Britain (e.g., Rae 2015), the US (e.g., Boeing et al. 2021), the Netherlands (ten Bosch & Windmeijer 2014), <mark>Türkiye</mark> (Keskin & Watkins 2017), Japan (Sadayuki 2018), or China (Hu et al. 2019; He et al. 2019; Wang et al. 2020).

In their data collection efforts, most authors focus exclusively on a localized housing market (i.e., a particular region, city, or neighborhood) in developed countries for which well-structured property listing websites with a plethora of private properties listed for sale are available. Additionally, while very comprehensive in scope, most efforts of web scraping of private property prices are centered on developed markets. Similar approaches in EMDEs, particularly in low-income economies, are scarce. Notable exceptions are, for instance, Gnagey and Tans (2018), who collate a data set of over 64,000 properties in 2016 from listing websites to estimate house prices in Indonesia. The authors find that desirable housing attributes, structural quality, advantageous location on major thoroughfares, and secure land tenure increase property asking prices.

Almost all studies that collect price data from online listing websites focus on only one or few markets within a particular region. One notable exception is a recent _HouseLev_ database project that assembled house prices for 40 countries, mainly European and advanced economies, including some emerging economies such as Türkiye or the Russian Federation (Bricongne et al. 2019). The authors do not solely rely on web scraping for all 40 countries. They instead relate to national accounts data and implement web scraping as a “fallback methodology” in case of missing data. As the authors use both methods, national accounts as well as web scraping, for a sub-sample of European countries, they can compute the median level of estimated upward bias arising from the use of listed rather than transaction prices, which is then applied as a correction factor to improve comparability of price level data obtained with the two methods (Bricongne et al. 2019: 6). _HouseLev,_ to the best of our knowledge, is the most comprehensive web scraping project of private property prices, primarily focused on developed economies.

Advanced price estimation techniques have also evolved with the increased usage of big data approaches to collecting property price data from listing websites. Traditionally, the hedonic price model, which draws on Lancaster’s consumer theory, has long been the predominant model to estimate property prices (Lancaster 1966; Rosen 1974). Property prices are modeled in multiple regression analysis, assessing the association between property price and several hedonic attributes through parametric estimation (Oladunni & Sharma 2016). Attributes frequently applied in hedonic price models include, for instance, number of bathrooms, number of bedrooms, area size, neighborhood, or accessibility of the property (e.g., Borba & Dentinho 2016; Can 1992; Krol 2013). While very simple in their interpretation, hedonic price models require the fulfillment of strong model assumptions, including functional form of the conventional hedonic pricing model, homoscedasticity, independence, and the absence of multicollinearity (e.g., Anderson 2000; Pérez-Rave et al. 2019).<sup>10</sup> In recent years, the applicability of alternate methods to the hedonic price estimation has expanded and machine learning (ML) has emerged as an alternative to predicting house prices (Borde et al. 2017; Čeh et

> 10 For a critical overview of the different prediction algorithms commonly used for house price predictions, see Montero & Fernández-Aviles (2018).

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