arXiv 1 Jul 2026 · Machine Learning
arXiv:2607.00280 · PDF · DOI · OpenAlex · Extracted main text
Airbnb is a community based on connection and belonging -- many hosts on Airbnb are everyday people who share their worlds to provide guests with the feeling of connection and being at home; Airbnb strives to connect people and places. Among our efforts to connect guests and hosts, we provide tools to enable hosts to set competitive prices, which helps improve affordability for guests while helping hosts get more bookings. We also personalize the guest experience to show them the listings that match their needs. To help inform these efforts, we combine economic modeling and causal inference techniques to understand how guests book stays based on the prices hosts set, among other factors, and how that preference varies across different guests and listings. Such understanding helps us identify opportunities for Airbnb to support the marketplace and better connect guests and hosts. For example, understanding how much guests respond to different prices helps optimize the tools that we provide to hosts, in order to enable hosts to choose and set competitive prices that further balance demand and supply. As another example, understanding heterogeneity in guest preferences helps us personalize the guest experience and better match them with the listings that meet their needs, based on how much they respond to different prices and other factors.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Stephen Berry, James Levinsohn, and Ariel Pakes (1995) Automobile prices in market equilibrium | 0.644 | 2 | 2 | 100% |
| 2 | Mihajlo Grbovic (2017) Search ranking and personalization at airbnb | 0.405 | 1 | 1 | 100% |
| 3 | Mihajlo Grbovic and Haibin Cheng (2018) Real-time personalization using embeddings for search ranking at airbnb | 0.405 | 1 | 1 | 100% |
| 4 | Jean-Pierre Dubé, Ali Hortacsu, and Joonhwi Joo (2021) Random-coefficients logit demand estimation with zero-valued market shares | 0.405 | 1 | 1 | 100% |
| 5 | Amit Gandhi and Aviv Nevo (2021) Empirical models of demand and supply in differentiated products industries | 0.405 | 1 | 1 | 100% |
| 6 | Amit Gandhi, Zhentong Lu, and Xiaoxia Shi (2023) Estimating demand for differentiated products with zeroes in market share data | 0.405 | 1 | 1 | 100% |
| 7 | David Holtz, Ruben Lobel, Inessa Liskovich, and Sinan Aral (2020) Reducing interference bias in online marketplace pricing experiments | 0.405 | 1 | 1 | 100% |
| 8 | Joy Jing and Jing Xia (2023) Prioritizing home attributes based on guest interest | 0.405 | 1 | 1 | 100% |
| 9 | Ramesh Johari, Hannah Li, Inessa Liskovich, and Gabriel Y Weintraub (2022) Experimental design in two-sided platforms: An analysis of bias | 0.405 | 1 | 1 | 100% |
| 10 | Thu Le and Alex Deng (2023) The price is right: Removing a/b test bias in a marketplace of expirable goods | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 12 scored citations.