Estimating House Prices in Emerging Markets and Developing Economies : A Big Data Approach

Despite the relevance of house prices for a variety of stakeholders as well as for macroeconomicand monetary policy making, reliable, publicly available house price data are largely absent in emerging markets anddeveloping economies. Filling this void, this paper presents a systematic approach to collecting, analyzing, andassessing private property prices in emerging markets and developing economies. The paper uses data scraped from fivecountries’ largest real estate websites where private properties are listed for sale, to obtain price data andproperty attributes to establish a comprehensive data set that allows for both intra- and inter-country comparison ofresidential property prices. It then outlines the usability of these data by employing random forest estimation topredict the price of a standard housing unit—the basic houseprice—that is comparable across countries. While this approach is also applicable to filling wide data gaps in theprovision of private property prices in developed economies, the paper focuses on how this approach can be applied toemerging markets and developing economies, where private property price data are particularly scarce.