Modeling home prices using realtor data

Iain Pardoe


It can be challenging when teaching regression concepts to find interesting real-life datasets that allow for an analysis that puts all the concepts together in one large example. For example, concepts like interaction and predictor transformations are often illustrated though small-scale, unrealistic examples with just one or two predictor variables that make it difficult for students to appreciate how these concepts might be applied in more realistic multi-variable problems. This article addresses this challenge by describing a complete multiple linear regression analysis of home price data that covers many of the usual regression topics, including interaction and predictor transformations. The analysis also contains useful practical advice on model building - another topic that can be hard to illustrate realistically - and some novel statistical graphics for interpreting regression model results.

Keywords: graphics, indicator variables, interaction, linear regression, model building, quadratic, transformations

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Last updated: November 16, 2006

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© 2006, Iain Pardoe, Lundquist College of Business, University of Oregon

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