A novel approach to commercial property valuation: Successive term indexing and its competitive implications

The development of innovative valuation models in the commercial real estate sector is crucial for enhancing competitiveness, as they provide stakeholders with the accuracy and adaptability needed to navigate and capitalize in an increasingly complex market landscape. This paper aims to improve initial valuations provided by real estate agents by updating their timeliness while also enhancing their accuracy. Leveraging statistical techniques based on hedonic regression, it introduces a novel mechanism, successive term indexing, enabling the reassessment of widely varying commercial properties with initial assessments that are at least one term old, where a term typically encompasses one year. The model's novelty lies in its approach to indexing individual property characteristics across two successive terms, using a k-means algorithm to categorize all numerical variables and stepwise selection. The former enables differing index values for varying sizes, while the latter allows dynamic evaluation of the significance of regressors in time. Applied to data from a banking institution, the model showcases strong predictive accuracy with unique reassessment ratios ranging from -3.8% to 5.2% for 2022. With its nuanced analysis of market dynamics and indexing capabilities, the mechanism intersects the elements of PPIs and AVMs, presenting a significant methodological advancement and a practical, simple-to-use tool for valuation.