Predicting House Sale Prices in Newly Developed Suburbs Without Historical Sales

There is a recent trend of housing developers designing masterplan suburbs based on new urbanist principles to solve the housing crisis all around the world. The aim of this study is to anticipate the value of housing features in recently developed suburbs using a Bayesian approach, and investigate the price premiums of masterplan suburbs in the Auckland housing market. 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 using highest density intervals, which more closely resemble the intuitive understanding of probability intervals than statistical confidence intervals. Our case study on the newly developed suburbs of Fairview Heights, Oteha, Stonefields, Long Bay, Flat Bush, Silverdale, and Hobsonville 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 91.5%). From analyzing the seven newly developed suburbs in Auckland, New Zealand, we find evidence that masterplan suburbs have higher price premiums than traditional suburbs. Our model estimates that masterplan suburbs offer a 17.47% to 20.16% price premium compared to non-masterplan suburbs. 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.

Name thesis:degree_name

PhD

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Engineering

Grantor dc:publisher

ResearchSpace@Auckland

Year dc:date.issued

2025

Author dc:creator

• Lin, Christina Yin-Chieh

Advisors dc:contributor.advisor

• Kempa-Liehr, Andreas
• Mason, Andrew

• Machine Learning
• Property Development

Statement dc:rights

• Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Licence dc:rights.uri

• https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm

Handle dc:identifier.uri

https://hdl.handle.net/2292/72936

OAI identifier oai:identifier

oai:researchspace.auckland.ac.nz:2292/72936