Detecting Illicit Investment in Real Estate: A Machine‑Learning Approach to Rare‑Event AML Risk

Detecting Illicit Investment in Real Estate: A Machine‑Learning Approach to Rare‑Event AML Risk | 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 Detecting Illicit Investment in Real Estate: A Machine‑Learning Approach to Rare‑Event AML Risk Mark Lokanan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8941478/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Illicit investment in residential real estate poses persistent challenges for anti–money‑laundering (AML) enforcement due to ownership opacity, fragmented data, and the scarcity of confirmed criminal cases. This study evaluates supervised machine‑learning and neural‑network models to determine their ability to detect properties linked to criminal activity and to identify the most influential predictors of illicit investment. Using a rare‑event dataset and cross‑validated modelling framework, the analysis shows that meaningful patterns can be detected despite extreme class imbalance. Across logistic regression, Random Forest, XGBoost, CART oversampling experiments, and an artificial neural network, consistent predictors—market_value, owner_legal_person, owner_owns_multiple, land_acres, and out_of_state_owner—emerge as central risk indicators. The findings support Rational Choice Theory by illustrating how offenders exploit structural vulnerabilities to maximize utility. Policy implications include property‑level risk scoring, early‑warning systems, and enhanced support for gatekeepers. Machine‑learning approaches show strong potential to strengthen real‑estate AML frameworks. Money laundering real estate machine learning rare‑event classification Rational Choice Theory beneficial ownership opacity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8941478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599614846,"identity":"28f82ef5-451c-484e-af35-b5f917a98172","order_by":0,"name":"Mark Lokanan","email":"data:image/png;base64,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","orcid":"","institution":"Royal Roads University","correspondingAuthor":true,"prefix":"","firstName":"Mark","middleName":"","lastName":"Lokanan","suffix":""}],"badges":[],"createdAt":"2026-02-22 21:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8941478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8941478/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104000163,"identity":"551894ee-12f2-4f19-9845-4169dfcdcb49","added_by":"auto","created_at":"2026-03-05 13:42:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":569050,"visible":true,"origin":"","legend":"","description":"","filename":"CriminalProperties.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8941478/v1_covered_32d661d9-d3f7-4375-8aab-5697118701ab.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDetecting Illicit Investment in Real Estate: A Machine‑Learning Approach to Rare‑Event AML Risk\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Money laundering, real estate, machine learning, rare‑event classification, Rational Choice Theory, beneficial ownership opacity","lastPublishedDoi":"10.21203/rs.3.rs-8941478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8941478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIllicit investment in residential real estate poses persistent challenges for anti\u0026ndash;money‑laundering (AML) enforcement due to ownership opacity, fragmented data, and the scarcity of confirmed criminal cases. 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