[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-291210-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-291210-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","a-statistical-machine-learning-approach-to-identify-rental-properties-from-public-data-sources","A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources","","A statistical machine learning framework is developed to identify potential rental properties using existing public data when rental registries are incomplete or unavailable. The study addresses challenges in distinguishing residential rentals from owner-occupied units and other commercial properties, and it explains how predictive models are created, validated, and applied. The method is motivated by policy and research needs such as owner sampling, displacement and tenant mobility analysis, local enforcement support, and efficient disaster-related outreach.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/a-statistical-machine-learning-approach-to-identify-rental-properties-from-public-data-sources/291210/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/a-statistical-machine-learning-approach-to-identify-rental-properties-from-public-data-sources/291210.png","ImageObject",300,407,{"name":42,"@type":43},"Valentina","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is identifying individual rental properties difficult in many jurisdictions?","Question",{"text":63,"@type":64},"Rental registries often vary widely in availability and completeness. Where registries are missing, tax assessor data may be insufficient to clearly separate rentals from owner-occupied and other commercial units.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What problem does the project team’s method address?",{"text":68,"@type":64},"The method creates and tests statistical machine learning models to identify potential rental properties from existing data sources, improving researchers’ ability to solve data interpretation and manipulation issues before projects proceed.",{"name":70,"@type":61,"acceptedAnswer":71},"How can the approach be useful for researchers and practitioners?",{"text":72,"@type":64},"It supports tasks such as generating rental owner samples, tracking displacement and tenant mobility, studying tenant and property outcomes, and helping local officials target likely rental properties for licensing, inspections, or program outreach—especially during emergencies.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},291210,1791073984,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":112,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","Data Shop  \nData Shop, a department of Cityscape, presents short articles or notes on the uses of data in housing and urban research. Through this department, the Office of Policy Development and Research introduces readers to new and overlooked data sources and to improved techniques in using well-known data. The emphasis is on sources and methods that analysts can use in their own work. Researchers often run into knotty data problems involving data interpretation or manipulation that must be solved before a project can proceed, but they seldom get to focus in detail on the solutions to such problems. If you have an idea for an applied, data-centric note of no more than 3,000 words, please send a one-paragraph abstract to [chalita.d.brandly@hud.gov](chalita.d.brandly@hud.gov) for consideration.  \nA Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources  \nDaniel Kuhlmann  \nUniversity of Arizona  \nJane Rongerude  \nIowa State University  \nLily Wang  \nGeorge Mason University  \nGuanNan Wang  \nCollege of William and Mary  \nIntroduction  \nFor academic researchers and practitioners alike, identifying individual rental properties can be incredibly useful but is often difficult due to insufficient and incomplete data. Although some cities have ordinances that require residential rental property owners (RRPOs) to register their properties, the availability and completeness of these registries vary dramatically from place to place. In places without rental registries, tax assessor data can provide some information but often not enough to clearly distinguish residential rentals from owner-occupied units and other commercial properties. As part of a larger project surveying RRPOs, the project team developed and tested statistical  \nCityscape: A Journal of Policy Development and Research • Volume 25, Number 2 • 2023 Cityscape 367 U.S. Department of Housing and Urban Development • Office of Policy Development and Research  \n(machine) learning methods and predictive models to identify potential rental properties from existing data sources. This article describes the process for creating the models, suggests potential applications for the methods, and discusses how researchers and practitioners can use these models and methods in their work.  \nBackground  \nMany policy and research contexts exist in which the ability to identify individual rental properties and the owners of those properties are useful. For example, for this project, the team needed to generate a sample of rental property owners across multiple jurisdictions for a survey-based study investigating RRPO characteristics and behaviors. Similarly, identifying who owns rental properties can help researchers track investment behavior, understand market dynamics, and study other owner-related housing questions. Identifying locations and addresses of rental properties is useful for researchers when tracking displacement and tenant mobility or contacting tenants for surveysand interviews. Likewise, differentiating rentals from owner-occupied properties can be useful for those studying property and tenant outcomes, such as property neglect and evictions.  \nIn addition, being able to identify rental properties may be useful for practitioners and local government officials. For example, local housing officials might want to identify likely rental properties in their jurisdictions to track whether RRPOs comply with rental licensing and inspection laws. Similarly, in places without rental licensing requirements that need full disclosure of owner data, practitioners might use these methods to distribute program information and other resources to tenants or RRPOs. Local jurisdictions can benefit from knowledge about the individuals or entities investing in their rental housing markets, the location of those entities, and whether their holdings are expanding or consolidating in particular neighborhoods. The COVID-19 pandemic and the subsequent housing policy","cbCaidVF04dcSiUr","https://ap.wps.com/l/cbCaidVF04dcSiUr","pdf",137882,12,"English","# Introduction\n## Background\n## Methods for Identifying Rental Property (summary)\n## Model Creation and Applications","[{\"question\":\"Why is identifying individual rental properties difficult in many jurisdictions?\",\"answer\":\"Rental registries often vary widely in availability and completeness. Where registries are missing, tax assessor data may be insufficient to clearly separate rentals from owner-occupied and other commercial units.\"},{\"question\":\"What problem does the project team’s method address?\",\"answer\":\"The method creates and tests statistical machine learning models to identify potential rental properties from existing data sources, improving researchers’ ability to solve data interpretation and manipulation issues before projects proceed.\"},{\"question\":\"How can the approach be useful for researchers and practitioners?\",\"answer\":\"It supports tasks such as generating rental owner samples, tracking displacement and tenant mobility, studying tenant and property outcomes, and helping local officials target likely rental properties for licensing, inspections, or program outreach—especially during emergencies.\"}]","A Statistical Machine Learning Approach to Identify Rental Properties From Public Data Sources | PDF",1789643747]