[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120384-en":3,"doc-seo-120384-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120384,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Achieving Fairness in Zoning Laws with Machine Learning - College Honors Program Thesis","Zoning is a powerful regulatory tool that classifies land use to improve compatibility among neighboring parcels, yet local decisions are often made through opaque board processes, raising concerns about bias and fairness. In the United States, zoning has historically favored single-family housing, contributing to economic barriers and limiting access for certain communities. This study frames zoning classification as a supervised learning problem and uses extensive publicly available geographic, demographic, and infrastructural data for Worcester County, Massachusetts. Results indicate that accurate predictions require relatively complex models, and counterfactual analysis using socioeconomic features helps assess how factors influence zoning decisions. The work supports further research on computational methods for fair zoning designation.","College of the Holy Cross  \nCrossWorks  \n\n| College Honors Program | Honors Projects |\n| --- | --- |\n| 5-2025\u003Cbr>Achieving Fairness in Zoning Laws with Machine Learning\u003Cbr>William Schimitsch\u003Cbr>College of the Holy Cross, [wfs716@gmail.com](wfs716@gmail.com)\u003Cbr>Follow this and additional works at: [https://crossworks.holycross.edu/honors](https://crossworks.holycross.edu/honors)\u003Cbr> Part of the Computer Sciences Commons |  |\n\nRecommended Citation  \nSchimitsch, William, \"Achieving Fairness in Zoning Laws with Machine Learning\" (2025) . College Honors Program. 729.  \n[https://crossworks.holycross.edu/honors/729](https://crossworks.holycross.edu/honors/729)  \nThis Thesis is brought to you for free and open access by the Honors Projects at CrossWorks. It has been accepted for inclusion in College Honors Program by an authorized administrator of CrossWorks.  \nAchieving Fairness in Zoning Laws with Machine Learning  \nWilliam Schimitsch  \nCollege of the Holy Cross  \nWorcester, MA  \nMay 2025  \nAbstract  \nZoning is a powerful regulatory tool that determines how municipalities use and develop land. The goal of zoning is to classify land use (e.g., residential, commercial, industrial) to maximize compatibility among neighboring parcels. Local zoning decisions, however, are made by small-sized boards, often through an opaque process, which raises concerns about bias and fairness. In the United States, zoning has historically prioritized single-family housing and thus created economic barriers that limit access to certain communities. Given the task of classifying land use and the wealth of geographical, demographic, and infrastructural data describing each parcel, the problem of bias in zoning could benefit from an algorithmic decision-making process aimed at equity. This paper explores zoning classification as a supervised learning task based on existing data in a locality (Worcester County, Massachusetts) . We extensively collect publicly available data from multiple sources to predict zoning labels with machine learning models. Our results show that accurate predictions here require relatively complex models. Furthermore, we perform counterfactual analysis using socioeconomic features to explore their influence on current zoning decisions. Taken together, this exploratory study aims to assess zoning through machine learning and highlights opportunities for future work to apply computational techniques for fair zoning designation.  \nContents  \n1 Introduction 3  \n1.1 Zoning ............................................ 3  \n1.2 Machine learning ...................................... 4  \n1.3 Fairness ........................................... 5  \n1.4 Counterfactual analysis .................................. 5  \n2 Methods 6  \n2.1 Features ........................................... 6  \n2.2 Data collection ....................................... 7  \n2.3 Zoning classification task ................................. 9  \n2.4 Models ............................................ 11  \n3 Results 12  \n3.1 Spatial completeness .................................... 12  \n3.2 Model performance ..................................... 12  \n3.3 Counterfactual testing ................................... 14  \n4 Discussion 16  \n5 Future Works 17  \n5.1 Fair classification ...................................... 17  \n5.2 Clustering .......................................... 17  \nReferences 18  \n1 Introduction  \nLand use and building regulations have been formally enacted in the United States of America since the early 20th century, when legislators addressed the challenges of increased immigration and industrialization (Chandler and Dale 2001) . The sociopolitical climate of the country today is evidently vastly different than it was one hundred years ago. Land and community composition as well as incentives for zoning have completely transformed, and the country faces new issues, such as a housing and cost of living crisis (Kahlenberg 2023) . Taken together, zoning ","cbCaii9TjAO5Jbyx","https://ap.wps.com/l/cbCaii9TjAO5Jbyx","pdf",13533381,1,22,"English","en",105,"# 1 Introduction\n## 1.1 Zoning\n## 1.2 Machine learning\n## 1.3 Fairness\n## 1.4 Counterfactual analysis\n# 2 Methods\n## 2.1 Features\n## 2.2 Data collection\n## 2.3 Zoning classification task\n## 2.4 Models\n# 3 Results\n## 3.1 Spatial completeness\n## 3.2 Model performance\n## 3.3 Counterfactual testing\n# 4 Discussion\n# 5 Future Works\n## 5.1 Fair classification\n## 5.2 Clustering","[{\"question\":\"What problem does the paper address in zoning decisions?\",\"answer\":\"The paper addresses bias and fairness concerns arising from opaque local zoning decision processes and the way zoning has historically prioritized single-family housing.\"},{\"question\":\"How is zoning classification modeled in this study?\",\"answer\":\"Zoning classification is treated as a supervised learning task that predicts zoning labels using collected geographic, demographic, and infrastructural data from Worcester County, Massachusetts.\"},{\"question\":\"What do the results show about model accuracy and complexity?\",\"answer\":\"Accurate zoning label predictions require relatively complex machine learning models, rather than simpler approaches.\"},{\"question\":\"What is the purpose of the counterfactual analysis in the paper?\",\"answer\":\"Counterfactual analysis uses socioeconomic features to examine how these factors may influence current zoning decisions, supporting an exploratory assessment of fairness.\"}]","Achieving Fairness in Zoning Laws with Machine Learning - College Honors Program Thesis | PDF",1785729766,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"achieving-fairness-in-zoning-laws-with-machine-learning-college-honors-program-thesis","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/achieving-fairness-in-zoning-laws-with-machine-learning-college-honors-program-thesis/120384/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address in zoning decisions?","Question",{"text":75,"@type":76},"The paper addresses bias and fairness concerns arising from opaque local zoning decision processes and the way zoning has historically prioritized single-family housing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is zoning classification modeled in this study?",{"text":80,"@type":76},"Zoning classification is treated as a supervised learning task that predicts zoning labels using collected geographic, demographic, and infrastructural data from Worcester County, Massachusetts.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results show about model accuracy and complexity?",{"text":84,"@type":76},"Accurate zoning label predictions require relatively complex machine learning models, rather than simpler approaches.",{"name":86,"@type":73,"acceptedAnswer":87},"What is the purpose of the counterfactual analysis in the paper?",{"text":88,"@type":76},"Counterfactual analysis uses socioeconomic features to examine how these factors may influence current zoning decisions, supporting an exploratory assessment of fairness.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]