Expert opinion vs. artificial intelligence: new evidence on building age estimation

Matthias Soot, Daniel Kretzschmar and Alexandra Weitkamp

ERES from European Real Estate Society (ERES)

Abstract: The year of construction is one of the most important building features, indicating factors such as building morphology, building material types and construction technologies. Knowing the specific year of construction of a building helps to estimate energy consumption and demolition waste, answers questions about urban renewal processes in the context of urban planning, improves the accuracy of material stock models and material cadasters and advances MFA and LCA. Additionally, the building age is relevant for real estate value and rent estimations. In Germany, detailed information for individual buildings is not publicly available due to data protection regulations. Traditionally, this information is collected by experts who roughly classify buildings into an age category based on specific visual building characteristics. In recent years, the possibilities of extracting construction age information from the visual appearance of building façades via ground image data have been increasingly explored via deep convolutional neural networks (DCNN). An easy way to derive Information about images are pretrained Visual Language Models (VLM) and Large Language Models (LLM). The question arises as to whether the laborious estimation of the age of a building by humans still guarantees better results in times of freely accessible LLMs, or whether AI can beat the experts. In our study, we conducted a comprehensive survey of more than 350 real estate experts, each of whom was asked to evaluate five randomized images. Analogously, we asked the AI to do the same by using image recognition to ask ChatGPT to estimate the age of construction. The results show that the AI achieves significantly better results than individual experts across all building age groups. Only by sorting the experts according to their experience, local expertise and their own expertise assessment, and by combining the experts' estimations, certain groups of experts manage to beat the AI.

Keywords: building age; construction year; large language models; real estate experts (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2025-01-01
New Economics Papers: this item is included in nep-big, nep-eur and nep-ure
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