Thomschke (2018) who show that housing prices are largely determined by the physical location of the property. In addition, the number of bedrooms and the size of a private property are consistently found to be positively related to the property price (e.g., Fletcher et al. 2000; Garrod and Willis, 1992; Rodriguez and Sirmans, 1994). Other attributes studied include crime rates (e.g., Ceccato & Wilhelmsson 2020), or proximity to transportation (e.g., Zhang et al. 2021; Zong & Li 2016). Other authors estimate that house prices have particularly increased since the financial crisis in 2008 due to the rise of economic activity paired with unusually low mortgage interest rates in most advanced economies (Claessens & Schanz 2019). Also, price changes in major cities are hypothesized to be driven by institutional investors trying to find high yields or safe assets in a low-interest rate environment (IMF 2018; Gauder et al. 2014).

While there is a plethora of literature on property price determinants in developed economies, studies on EMDEs are scarce. Some single-country studies focusing on EMDEs are analyzing existing house price data that are published by commercial banks such as e.g., Absa, Standard Bank, and First National Bank for South Africa (e.g., Balcilar et al. 2011; Luüs 2005). Other authors collect their own data by either surveying real estate agencies to estimate the relative importance of housing attributes to house prices (Owusu-Manu et al. (2019) for Ghana), by surveying developers (Libertun de Duren (2018) for peri-urban areas in Brazil and Mexico), or by conducting a household survey to collect data on housing costs (Uwayezu & de Vries (2020) for Kigali city in Rwanda). High property prices in EMDEs are often attributed to prohibitively high building costs due to the need to import materials, the shortage of local skills, and the absence of financial mechanisms that allow for materials to be bought in bulk (e.g., Gardner & Pienaar 2019). While unique in their efforts to shed some light on the housing market in emerging economies, these studies provide only a snapshot of the housing market of one country (or a handful of countries) – often with a regional focus or a focus on the biggest cities.

# 2.3 Big Data Approaches and Machine Learning for Private Property Price Estimation

In addition to traditional approaches of data collection of private property prices discussed in the previous section, in recent years and with the gaining momentum of big data and machine learning in economics, more studies started to gather property price data from online listing websites. While less than a decade ago, most private properties were listed for sale in local newspapers or with private realtors, today, much of the listing activity has moved to websites concentrating on housing advertisements.

Analyses that draw on property price data collected from listing websites allow for fine-grained spatial and temporal assessments of the entire housing market. Further, big data approaches to private property prices will enable one to investigate a particular housing market in more detail or add distributional aspects to the mostly averaged house prices made available by indices discussed in Section 2.1.

A predecessor of web scraping approaches to collect property price data includes Kim’s (2007) study on Vietnam. The author manually collated over 5,000 observations on property prices and property attributes drawing on classified advertisements in Vietnam’s most prominent newspaper. Applying a hedonic price model, Kim assesses the price differences between Hanoi and Ho Chi Minh City to investigate the impact of social norms on property prices. Over time and with the increased penetration of property listing websites, private property price collection efforts have transitioned to online listings where data collection can be automated. Anenberg & Laufer (2017), for instance, use listing information to construct a new house price index to monitor house price developments in the US. Using property listings, the authors construct a 7