In collaboration with AirDNA, this project applied statistical and machine learning methods to Airbnb guest review text to model supply–demand dynamics and macro-level impacts in the sharing-economy short-term rental market, characterizing behavioral patterns across user segments.
This project applied NLP and a pretrained BERT model on the text side to quantify guest preferences and trust toward hosts, and on the structural modeling side combined SEM, PCA, exploratory and confirmatory factor analysis, generalized linear regression, and LDA to support host decision-making under post-pandemic market uncertainty. The findings informed AirDNA’s industry-facing data products and analytics for the short-term rental market.