Publication: Estimating House Prices in Emerging Markets and Developing Economies: A Big Data Approach
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2023-02
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2023-02
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Despite the relevance of house prices for a variety of stakeholders as well as for macroeconomic and monetary policy making, reliable, publicly available house price data are largely absent in emerging markets and developing economies. Filling this void, this paper presents a systematic approach to collecting, analyzing, and assessing private property prices in emerging markets and developing economies. The paper uses data scraped from five countries’ largest real estate websites where private properties are listed for sale, to obtain price data and property attributes to establish a comprehensive data set that allows for both intra- and inter-country comparison of residential property prices. It then outlines the usability of these data by employing random forest estimation to predict the price of a standard housing unit—the basic house price—that is comparable across countries. While this approach is also applicable to filling wide data gaps in the provision of private property prices in developed economies, the paper focuses on how this approach can be applied to emerging markets and developing economies, where private property price data are particularly scarce.
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“Behr, Daniela M.; Chen, Lixue; Goel, Ankita; Haider, Khondoker Tanveer; Singh, Sandeep; Zaman, Asad. 2023. Estimating House Prices in Emerging Markets and Developing Economies: A Big Data Approach. Policy Research Working Papers;10301. © World Bank. http://hdl.handle.net/10986/39421 License: CC BY 3.0 IGO.”
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