Post-wildfire Housing Recovery Simulation via an Agent-based Model

Post-wildfire Housing Recovery Simulation via an Agent-based Model | 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 Article Post-wildfire Housing Recovery Simulation via an Agent-based Model Rodrigo Costa, Ali Nejat, Sara Hamideh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7330389/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract As climate change increases the frequency and severity of disasters, proactive planning for post-disaster housing recovery is essential to mitigate long-term social and economic disruption. Computational models can support this planning by simulating potential recovery trajectories, yet many existing approaches are limited by overwhelming data requirements or narrow applicability to past events. Here, we introduce RAAbIT (Recovery Assessment using Agent-based Tools), a novel agent-based model designed to simulate housing recovery using data available within weeks of a disaster. RAAbIT models individual households, insurers, and contractors as agents governed by empirical behavior rules, and incorporates modifiable system-level constraints, such as contractor availability, to reflect context-specific recovery dynamics. We demonstrate the model’s utility by hindcasting two California wildfires—the 2017 Tubbs Fire in Santa Rosa and the 2018 Camp Fire in Paradise—and capturing their divergent recovery trajectories. Despite similar hazards, the two communities experienced significantly different reconstruction outcomes, with Santa Rosa rebuilding 57% of destroyed homes and Paradise only 9% within five years. RAAbIT can reproduce temporal and spatial patterns of recovery observed in building permit and construction data. By balancing generalizability with data realism, RAAbIT provides a flexible and transferable tool for post-disaster recovery planning, supporting more effective decision-making under uncertainty and enhancing community resilience in the face of escalating climate risks. Earth and environmental sciences/Climate sciences Scientific community and society/Developing world Social science/Environmental studies Earth and environmental sciences/Natural hazards Full Text Additional Declarations No competing interests reported. Supplementary Files Supplemental.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Dec, 2025 Reviews received at journal 20 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 09 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 24 Aug, 2025 Editor assigned by journal 24 Aug, 2025 Editor invited by journal 22 Aug, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 19 Aug, 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-7330389","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":507847101,"identity":"60c45c03-9d6e-487e-9ba3-20556760467b","order_by":0,"name":"Rodrigo Costa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYHACA4YEOLsCSj8gUgtjA8MZIMUGxAm4lUO0MMC0MLYRocW8vXnbhwe/bBj429ufP/g573Bi//zmYx8SKkAiB7BqkTlzrHhGYl8ag8SZM4aNvdsOJ844xpY8I+EMSAS7XRISOcYMiT2HGQwkchgbeLcdzm04xgMUaTuMEiw4tKQ/bPw753Du/GP8nyFa+B/g1pLwA6QlwbCZt+Fw7oZjPMwQLRI4bOE5VsyQ2JDGA/LLbJlj6fUbj6UBDTkDFLmBwxb25s2MP/7YyAFD7MHHNzXWxnKHDz9m+FABFOnHbgsYAKODB0MQUwQF/MEvPQpGwSgYBSMcAAD9xF/IGlAZZAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Waterloo","correspondingAuthor":true,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Costa","suffix":""},{"id":507847102,"identity":"46c8a689-6f5e-417b-9d15-6cef45bd0797","order_by":1,"name":"Ali Nejat","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Nejat","suffix":""},{"id":507847103,"identity":"a73b216f-b001-43bc-b75d-dc3e0bdffc3f","order_by":2,"name":"Sara Hamideh","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Hamideh","suffix":""}],"badges":[],"createdAt":"2025-08-08 22:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7330389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7330389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90351840,"identity":"e07797a7-755e-477a-b0df-4f52075d022a","added_by":"auto","created_at":"2025-09-01 18:06:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3570268,"visible":true,"origin":"","legend":"","description":"","filename":"CostaTeX.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7330389/v1_covered_7a66e045-a39e-48ea-9725-a0592c223eb5.pdf"},{"id":90351637,"identity":"92a72da3-4742-4d74-894a-02de888ec676","added_by":"auto","created_at":"2025-09-01 17:58:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":422960,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7330389/v1/c3c01235419a86ec4ed162b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Post-wildfire Housing Recovery Simulation via an Agent-based Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7330389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7330389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As climate change increases the frequency and severity of disasters, proactive planning for post-disaster housing recovery is essential to mitigate long-term social and economic disruption. 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