Human Expertise vs. Artificial Intelligence: New evidence on building age estimation - Experts survey and ChatGPT answers

The construction, operation, and demolition of buildings account for a significant portion of global energy demand and CO2 emissions. Preserving existing buildings and encouraging renovation is crucial for sustainability. However, detailed information about the construction age of buildings is sparse. This study explores the potential of large pre-trained visual language models (VLMs) like ChatGPT to estimate building age, comparing their accuracy to that of real estate experts. The research involves a comprehensive survey of experts and the application of ChatGPT promts to a dataset of building images. The results show that ChatGPT makes more accurate statements about the age of buildings than individual experts. Only the collective intelligence of many experts provides better results than the VLM. This suggests that VLMs should be used more extensively than before to determine the age of buildings. Using this knowledge, urban planning can be tailored more specifically to necessary renovations in order to extend the life cycles of buildings and close loops in line with the principle of circularity.