Improving housing valuation for taxation: the Norwegian case

Abstract Several forms of taxation incorporate the market value of housing into their tax base, making accurate and up-to-date property valuations important. However, due to limitations in available information, assessing residential property values poses a significant challenge for tax authorities. This paper presents and examines the simple, inexpensive and transparent methodology currently used in Norway to assign market values to individual dwellings for wealth taxation purposes. The method uses predictions generated through hedonic regression models based on housing transaction data. We present two versions of the method: the current model, introduced in 2010, and a refined version that substantially improves prediction accuracy, through the use of data from smaller geographical units. This latter enhancement incorporates machine learning techniques for geographical clustering.