This study analyzes the Ames Housing Dataset using CatBoost and LightGBM models to explore feature importance and causal relationships in housing price prediction. We examine the correlation between SHAP values and EconML predictions, achieving high accuracy in price forecasting. Our analysis reveals a moderate Spearman rank correlation of 0.48 between SHAP-based feature importance and causally significant features, highlighting the complexity of aligning predictive modeling with causal understanding in housing market analysis. Through extensive causal analysis, including heterogeneity exploration and policy tree interpretation, we provide insights into how specific features like porches impact housing prices across various scenarios. This work underscores the need for integrated approaches that combine predictive power with causal insights in real estate valuation, offering valuable guidance for stakeholders in the industry.

Home owners could soon save money on loans and insurance by making their homes more energy efficient, while banks and insurers gain new insights into the sustainability of their residential portfolios, thanks to Adelaide-based fintech startup ValAi. ValAi's core product, known as Greenhouse, is an app that allows residential homeowners to see their home's energy use and climate resilience in real time. It includes advanced machine learning and artificial intelligence to provide practical tips that allow homeowners to save money and improve the value of their property by making it more sustainable and energy efficient. Meanwhile, for banks and insurers, the platform will fill a vital need by providing data on the sustainability of the properties in their residential portfolios at an individual asset level. The platform could potentially allow banks to make energy efficiency improvements a condition on the loans they issue.

As technology and data capabilities advance, there is an increasing focus on leveraging data that will enable innovation and deliver new value for customers. Geospatial data is an emerging area of opportunity in the property sector, and it is fast being utilised by agile "proptechs", developers and data scientists. Buildings, like everything, occupy space. For residential and commercial enterprises, the contextual data attached to properties is fertile ground for innovation. Whether helping property developers better understand the spatial contexts of sites or providing homebuyers with easier access to the detail they need, geospatial data is being used more than ever to create solutions that deliver value across the sector.