Bayesian Modelling of a Standard House Configuration Model to Analyze Housing Feature Impacts in Newly Developed Suburbs without Historical Sales

Abstract

There is a recent trend of entire new suburbs being designed and built to solve the housing crisis all around the world. The aim of this study is to anticipate the value of housing features in newly developed suburbs using a Bayesian approach. We present the Standard House Configuration Model, where housing feature impacts are analyzed relative to the configuration of a standard house for easy interpretation. The benefit of using a Bayesian approach is that we describe housing feature impacts with highest density intervals, which more closely resemble the intuitive understanding of probability intervals than statistical confidence intervals. Our case study on newly developed suburbs in Auckland, New Zealand, demonstrates that the posterior distributions from our model effectively capture the complex relationship between housing features and sale price (R2 value of 93%). The proposed model is cross-validated on four recently developed suburbs in Auckland. For comparable suburbs, our model is able to make reasonably accurate price predictions without using any historical sale records from the target suburb. This indicates that the insights into housing feature impacts are applicable to other new suburbs still in the planning stage and, therefore, have the potential to support future suburb developments.