Modeling the Determinants of Urban Short-Term Rental Prices in Gambia: A Case of Greater Banjul Area

Modeling the Determinants of Urban Short-Term Rental Prices in Gambia: A Case of Greater Banjul Area | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling the Determinants of Urban Short-Term Rental Prices in Gambia: A Case of Greater Banjul Area Rodrigue Samb, Dr. Uttam Kumar Roy, Dr. Manavvi Suneja This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8074344/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Short-term rentals (STR) are forming a growing part of the accommodation offer to international and domestic tourists. Yet, the drivers of their prices and spatial structures are understood poorly in sub-Saharan African settings. The present study explores the determinants of STR pricing in the GBA, focusing on spatial proximity to POIs, attributes of accommodation, and hosts. Based on an enriched set of 5657 Airbnb listings (September 2024–July 2025), we performed spatio-temporal feature engineering including booking seasonality, length of stay, and distances to beaches, tourist, and entertainment areas. The price distribution showed that the market was mainly budget to midrange (USD 20–60 per night), with a few high-end accommodation options. Correlation and model interpretation analyses showed that coastal proximity, property size, and entire-home listings significantly increase prices, while distance from amenities and non-super-host status reduce them. Based on the six machine learning prediction models, Random Forest and XGBoost had the highest prediction accuracy (R² = 0.92–0.93). The results from SHAP analysis confirm that the determinant of price in GBA mainly depends on location features. The results inform spatially targeted policy strategies of tourism development, affordability, and sustainable urban development, and provide new empirical evidence on STR urban patterns in Africa. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Nov, 2025 Editor assigned by journal 15 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 10 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8074344","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":545507137,"identity":"f7ae94ea-e74d-4956-9afc-cd294b0a9ec6","order_by":0,"name":"Rodrigue Samb","email":"","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"prefix":"","firstName":"Rodrigue","middleName":"","lastName":"Samb","suffix":""},{"id":545507138,"identity":"dd95df44-3a94-4708-9f8c-e8b2febef01b","order_by":1,"name":"Dr. Uttam Kumar Roy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACHhBhw8DAD6ITDKCiCQS1pDEwSDaAtRiQoMXgAJhrgE8tBBicOfvwMU+CXZ7x8bMPGB4U/MlnYD/8gOHhDjxazrYbG/MkJBebnUk3ADnMsoEnzYAh8QweLefZ2KR5fzAnbjuQBvYL0GU5DAyJbQS08CTUJ27ufwbVwv+GgJazbSAthxM3SMBskSBgi+SZY8yGcxKOF0vceMZwIMHA2IBN4pnBAXxa+M6kMT54k1Cdx9+fxvjwxx85A37+5IcPf+LRAgJMPNC4OwAi2GAMfIDxB4HoHgWjYBSMghEOABMHSTnkg/oPAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":true,"prefix":"Dr.","firstName":"Uttam","middleName":"Kumar","lastName":"Roy","suffix":""},{"id":545507139,"identity":"d154d63d-bd9e-49b3-afb4-60bde18bc74c","order_by":2,"name":"Dr. Manavvi Suneja","email":"","orcid":"","institution":"Indian Institute of Technology Roorkee","correspondingAuthor":false,"prefix":"Dr.","firstName":"Manavvi","middleName":"","lastName":"Suneja","suffix":""}],"badges":[],"createdAt":"2025-11-10 08:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8074344/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8074344/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98628563,"identity":"c16c8113-53c1-4a61-9c92-af66f3a95bc9","added_by":"auto","created_at":"2025-12-19 17:11:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1601855,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedPriceManuscriptLSRS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8074344/v1_covered_3f61ae60-a1b2-4b4e-884b-ab24a138d37f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eModeling the Determinants of Urban Short-Term Rental Prices in Gambia: A Case of Greater Banjul Area\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"letters-in-spatial-and-resource-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lsrs","sideBox":"Learn more about [Letters in Spatial and Resource Sciences](http://link.springer.com/journal/12076)","snPcode":"12076","submissionUrl":"https://submission.springernature.com/new-submission/12076/3","title":"Letters in Spatial and Resource Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8074344/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8074344/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eShort-term rentals (STR) are forming a growing part of the accommodation offer to international and domestic tourists. Yet, the drivers of their prices and spatial structures are understood poorly in sub-Saharan African settings. The present study explores the determinants of STR pricing in the GBA, focusing on spatial proximity to POIs, attributes of accommodation, and hosts. Based on an enriched set of 5657 Airbnb listings (September 2024\u0026ndash;July 2025), we performed spatio-temporal feature engineering including booking seasonality, length of stay, and distances to beaches, tourist, and entertainment areas. The price distribution showed that the market was mainly budget to midrange (USD 20\u0026ndash;60 per night), with a few high-end accommodation options. Correlation and model interpretation analyses showed that coastal proximity, property size, and entire-home listings significantly increase prices, while distance from amenities and non-super-host status reduce them. Based on the six machine learning prediction models, Random Forest and XGBoost had the highest prediction accuracy (R\u0026sup2; = 0.92\u0026ndash;0.93). The results from SHAP analysis confirm that the determinant of price in GBA mainly depends on location features. 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