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id caadria2025_817
authors Oki, Takuya and Shimomura, Toshiki
year 2025
title Predicting Housing Preference of Households Raising Children Using Rental Property Big Data and Questionnaire
source Dagmar Reinhardt, Nicolas Rogeau, Christiane M. Herr, Anastasia Globa, Jielin Chen, Taro Narahara (eds.), ARCHITECTURAL INFORMATICS - Proceedings of the 30th CAADRIA Conference, Tokyo, 22-29 March 2025, Volume 1, pp. 325–334
summary This study addresses the challenge of aligning housing supply with the diverse demands of families with children, which vary based on household characteristics and children's growth stages. While various housing options exist, comprehensive data-based analyses of how well these options meet families' needs are limited. To tackle this, the study developed a method using a large-scale rental housing dataset, machine learning, and a web questionnaire. The research involved 7,855 respondents with or planning to have children. Using LIFULL HOME’S dataset, 3LDK properties with detailed interior and floor plan images were analysed. Participants evaluated pairs of properties based on their preferences for interiors, layouts, or both. These evaluations trained machine learning models to predict preferences with over 70% accuracy, as indicated by F-values. The study revealed no strong link between interior preferences and spatial component proportions, highlighting the need for future analyses considering spatial quality. However, floor plan preferences allowed properties to be grouped into high, medium, and low categories, with unique adjacency patterns identified for each group. This method enables quantitative evaluation of housing proposals, facilitating revisions to better meet residents' needs. Ultimately, it aims to bridge the gap between housing demand and supply for families with children.
keywords Households Raising Children, Rental Housing, Preference, Deep Learning, Questionnaire
series CAADRIA
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