Summary
Single-family houses are typically the most important component in their owners’ portfolios. Buying a home is a major financial transaction for most households. Yet, unlike assets traded in financial markets, getting a quote for the current market value of a house is not an easy task because houses are very heterogenous assets. They differ, among other things, in size, location, age and maintenance. In this paper, we describe a web-based, almost realtime prediction of prices for single family homes in Berlin, Germany. Based on an extended hedonic regression model and estimated from a rich data set covering all house transactions in Germany’s capital, this online service delivers predictions for homes with user-specified characteristics. We describe the statistical model and how its predictions are implemented on the computer to allow seamless interaction between its users and the data base containing the model estimates.
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Notes
1 We would like to thank participants of the Computational Finance Workshop of Compstat 2002 for useful comments, and the Gutachterausschuss für Grundstückswerte in Berlin for providing the data and for fruitful discussions. The usual disclaimer applies. Financial support from the Deutsche Forschungsgemeinschaft, SFB 373 Quantifikation und Simulation Ökonomischer Prozesse, is gratefully acknowledged.
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Schulz, R., Sofyan, H., Werwatz, A. et al. Online Prediction of Berlin Single-Family House Prices. Computational Statistics 18, 449–462 (2003). https://doi.org/10.1007/BF03354609
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DOI: https://doi.org/10.1007/BF03354609