Enhancing Automated Valuation Models: Integrating Heating Energy Demand Analysis for Real Estate Property Valuation

In the realm of Automated Valuation Models (AVM) for real estate, incorporating nuanced features can significantly enhance the accuracy of property valuation. We are introducing a novel feature in our AVM framework aimed at capturing the impact of heating energy demand on the market value of real estate properties. Leveraging a combination of machine learning techniques and statistical modeling, our approach involves two key steps.First, utilizing a robust dataset of real estate transactions, we employ XGBoost models to predict heating energy demand for properties lacking such information. This imputation process enables us to generate comprehensive estimates of heating energy demand across a diverse range of properties.Secondly, we integrate tensor interaction effects within Generalized Additive Models (GAM) to analyze the relationship between heating energy demand and property value, considering crucial factors such as the construction year of the real estate objects. By incorporating tensor interaction effects, we are able to capture complex nonlinear relationships and interactions, allowing for a more nuanced understanding of how heating energy demand influences property valuation over time.Through the implementation of this advanced feature, our AVM framework offers real estate practitioners and stakeholders a more comprehensive tool for accurately assessing property values. This research contributes to the evolving landscape of real estate valuation methodologies, demonstrating the efficacy of combining machine learning with statistical modeling techniques to capture multifaceted influences on property value.