[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120091-en":3,"doc-seo-120091-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"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},120091,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","IDENTIFICATION OF DISTRESSED REAL ESTATE PROPERTIES USING NATURAL LANGUAGE PROCESSING - MACHINE LEARNING - A Project Presented for the Master of Science Degree","The Real Estate industry is an asset class involving constructing, buying, and selling property. Despite online marketplaces improving property transactions, distressed properties remain difficult to identify as an investment opportunity. This project applied machine learning to classify listings as distressed or nondistressed using textual features and natural language processing. Research questions evaluated how LDA topic modeling can identify distressed-related keywords and how supervised learning models can predict listing distress with strong predictive performance. Results showed 4-topic LDA achieved the highest coherence (94%). The Multi-Layer Perceptron model delivered the best classification with F1 of 94% and accuracy of 93%, enabling distressed probability prediction.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 12-2024\u003Cbr>IDENTIFICATION OF DISTRESSED REAL ESTATE PROPERTIES USING NATURAL LANGUAGE PROCESSING/MACHINE LEARNING Pavithra Sirigiri\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Business Analytics Commons, and the Real Estate Commons |  |\n\nRecommended Citation  \nSirigiri, Pavithra, \"IDENTIFICATION OF DISTRESSED REAL ESTATE PROPERTIES USING NATURAL LANGUAGE PROCESSING/MACHINE LEARNING\" (2024) . Electronic Theses, Projects, and Dissertations. 2096.  \n[https://scholarworks.lib.csusb.edu/etd/2096](https://scholarworks.lib.csusb.edu/etd/2096)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nIDENTIFICATION OF DISTRESSED REAL ESTATE PROPERTIES USING  \nNATURAL LANGUAGE PROCESSING/MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in  \nInformation Systems and Technology  \nby Pavithra Sirigiri December 2024  \nIDENTIFICATION OF DISTRESSED REAL ESTATE PROPERTIES USING  \nNATURAL LANGUAGE PROCESSING/MACHINE LEARNING  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nPavithra Sirigiri  \nDecember 2024  \nApproved by:  \nDr. Ahmed Elnoshokaty, Committee Chair  \nDr. Nima Molavi, Committee Member  \nDr. Conrad Shayo, Committee Member & Department. Chair, Information and  \nDecision Sciences  \n© 2024 Pavithra Sirigiri  \nABSTRACT  \nThe Real Estate industry is an asset class that involves constructing, buying, and selling property. Although technology has made progress in buying and selling real estate properties online , via Zillow and Redfin , finding distressed properties as an investment opportunity is lacking. This culminating project explored how to use machine learning to classify a property as distressed or nondistressed. The research questions are: Q1 . How can Natural Language Processing methods like Latent Dirichlet Allocation (LDA) be leveraged to identify distressed and non-distressed real estate properties? (Tijare & Rani , 2020) and Q2 . How can machine learning models help in categorizing real estate listings into distressed vs non-distressed properties based on textual features? (Narozhnyi & Kharchenko , 2024) . The findings in Q1 are that the LDA model with 4 Topic modellings generated the highest coherence score of 94% and identified the keywords associated with distressed properties. The findings for Q2 are choosing the Multi-Layer Perceptron (MLP) machine learning model , which had the highest F1 scores of 94% and accuracy of 93% and predicted the distressed properties probability. In Q1, we conclude that 4 Topic modelling generated the highest coherence values and hence the keywords predicted distressed properties. In Q2, we conclude that the MLP machine learning model generated the highest F1 score of 94% and testing accuracy of 93% and performed the best to predict distressed properties. Future study can be expanded by studying different asset classes like multifamily, commercial and mixed-use properties.  \nACKNOWLEDGEMENTS  \nAs a proud graduate student at California State University, San Bernardino, I would like to express gratitude towards my mentor catalysts: Dr. Conrad Shayo, Dr. Ahmed Elnoshokaty, Dr. Nima Molavi throughout my academic journey. I extend my gratitude to the Writing Center (Samual, Kenia, Jodi, Noor, and Brance) and Graduate studies for formatting support (Shelby Reeder) . Truly, they are a powerhouse ","cbCaif7WRIW7n6be","https://ap.wps.com/l/cbCaif7WRIW7n6be","pdf",1549410,1,90,"English","en",105,"# ABSTRACT\n# ACKNOWLEDGEMENTS\n# DEDICATION\n# TABLE OF CONTENTS\n# CHAPTER ONE: INTRODUCTION\n## Problem Statement\n## Investors’Point of View\n## Data Analysts’ Point of View\n## Research Questions\n## Explanation for the Research Questions\n## Objective\n## Organization of this Proj","[{\"question\":\"What problem does the project address in real estate investing?\",\"answer\":\"Distressed properties are hard to locate as investment opportunities even though online platforms have advanced property buying and selling. The project targets automated identification of distressed versus nondistressed listings.\"},{\"question\":\"How is Natural Language Processing used to identify distressed listings?\",\"answer\":\"Latent Dirichlet Allocation (LDA) topic modeling is leveraged to discover keywords and topic structures associated with distressed properties, evaluated using coherence performance.\"},{\"question\":\"Which machine learning model performed best for classifying listings?\",\"answer\":\"The Multi-Layer Perceptron (MLP) achieved the highest F1 score of 94% and accuracy of 93%, and it was used to predict the probability that a property is distressed.\"}]","IDENTIFICATION OF DISTRESSED REAL ESTATE PROPERTIES USING NATURAL LANGUAGE PROCESSING - MACHINE LEARNING - A Project Presented for the Master of Science Degree | PDF",1785728125,227,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identification-of-distressed-real-estate-properties-using-natural-language-processing-machine-learning-a-project-presented-for-the-master-of-science-degree","",{"@graph":36,"@context":85},[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/identification-of-distressed-real-estate-properties-using-natural-language-processing-machine-learning-a-project-presented-for-the-master-of-science-degree/120091/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the project address in real estate investing?","Question",{"text":75,"@type":76},"Distressed properties are hard to locate as investment opportunities even though online platforms have advanced property buying and selling. The project targets automated identification of distressed versus nondistressed listings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is Natural Language Processing used to identify distressed listings?",{"text":80,"@type":76},"Latent Dirichlet Allocation (LDA) topic modeling is leveraged to discover keywords and topic structures associated with distressed properties, evaluated using coherence performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best for classifying listings?",{"text":84,"@type":76},"The Multi-Layer Perceptron (MLP) achieved the highest F1 score of 94% and accuracy of 93%, and it was used to predict the probability that a property is distressed.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":21,"slug":95},"Story & Novel","story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]