Assessing the Explanatory Power of Dwelling Condition in Automated Valuation Models

Abstract

This study investigates the role of dwelling-condition attributes in automated valuation models (AVMs) by utilizing detailed dwelling-condition assessment reports in Norway. A hedonic linear regression model, a gradient boosted decision tree, and a support vector machine were trained and evaluated to predict the sale price of dwellings in three urban regions. The study aims to evaluate the explanatory power of condition attributes in AVMs through a comparison of predictive performance with and without these attributes. The results indicate that the inclusion of condition attributes significantly improves the accuracy of all models across all regions. Furthermore, the models show consistent results regarding which condition attributes are important and their relationship to the price. The study finds that the condition of the bathroom has a high impact on the price, while the condition of doors, roof, and exterior extensions has a low impact. The study concludes that dwelling condition holds explanatory power in both linear and nonlinear AVMs, which can benefit researchers, practitioners, and homeowners looking to renovate. The findings highlight the importance of including detailed dwelling-condition attributes in AVMs and provide insights into the valuation of different aspects of a dwelling.