Do Listings Lie? How Airbnb Language Predicts Price
Text analytics across 61,521 listings in 16 cities
Premium words like “luxury” and “stunning” predict nightly price three times more strongly than generic positive sentiment.
Imagine two flats on Airbnb that are genuinely identical: same neighbourhood, same number of bedrooms, same square footage, same amenities, same guest rating. One is described as a "stunning luxury apartment." The other is described as a "nice place to stay." Does the first one charge more? And if so, is that because the property is actually better, or purely because of how it was written up?
To answer that, we took 261,894 Airbnb listings, cleaned them down to 61,521 across 16 English-speaking cities, and tested how much of the nightly price could be explained by the description text alone, separately from the physical facts of the property.
Two things stood out. First, being generically positive barely mattered: a broad "positivity score" of the description had almost no relationship with price. But specific premium words like "luxury," "stunning," and "breathtaking" had a relationship three times stronger. In other words, sounding enthusiastic is worthless; sounding expensive is not.
Second, and more importantly, that premium-language effect survived when we controlled for the property itself, comparing listings with the same size, type, location, and rating. So the effect isn't just "nicer flats get described more nicely." The language carries its own price premium on top of what the property justifies, which is exactly the two-identical-flats scenario above.
Why it matters
The words a host chooses are worth money. Writing "luxury apartment with stunning views" instead of "nice flat, great location" is associated with a real price difference, even when the two properties are otherwise the same. For hosts, that makes the listing description a lever worth pulling. For Airbnb, it's a way to spot listings that oversell themselves.
R² on a 20% held-out test set: how much price variance each model explains.
Correlation with nightly price. Premium vocabulary tracks price three times more strongly than generic positivity, and holds at 0.17 once property features are controlled for.
Dataset, tools and how it was done+
Dataset: Public Airbnb listings dataset, 16 English-speaking cities
Tools: R · glmnet (LASSO) · tidytext · topicmodels (LDA) · sentimentr
- Cleaned 261,894 listings to 61,521 across 16 cities; log-transformed price for near-normality
- Metadata-only LASSO benchmark (8 property variables): R² = 0.569
- Text-only TF-IDF LASSO (1,415 selected words): R² = 0.446
- Combined model (metadata + TF-IDF + LDA topics): R² = 0.660, a 9-point lift over metadata alone
- Trained on London (11,394 listings), tested cross-city generalisation on 15 held-out cities