[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124103-en":3,"doc-seo-124103-105":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},124103,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Leveraging Machine Learning in Housing Price Prediction in Nairobi County - Dissertation","Housing prices have shaped social and economic debates in Kenya, where conventional statistical approaches to pricing have become less effective amid big data and rapid technological change. This dissertation applies machine learning to housing price modelling and prediction in Nairobi County, identifying key housing features, comparing ML models, forecasting prices using unseen production-style data, and informing policy on price determination. Primary data was collected in Shauri Moyo and Kibra, complemented by key informant interviews from the State Department for Housing. The study trained and evaluated fourteen ML/deep learning models on secondary data, with LightGBM performing best, and provides data-driven recommendations for developers and policymakers.","LEVERAGING MACHINE LEARNING IN HOUSING PRICE PREDICTION IN  \nNAIROBI COUNTY.  \nJENNIFER WAMBUI NDUATI  \nMPPM  \nADMISSION NUMBER 135508  \nA DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF PUBLIC POLICY MANAGEMENT OF STRATHMORE UNIVERSITY BUSINESS SCHOOL.  \nSTRATHMORE BUSINESS SCHOOL  \nSTRATHMORE UNIVERSITY  \nNAIROBI, KENYA  \nDECLARATION  \nI declare that this work has not been previously submitted and approved for the award of a degree by this or any other University. To the best of my knowledge and belief, the dissertation contains no material previously published or written by another person except where due reference is made in the dissertation itself.  \n© No part of this dissertation may be reproduced without the permission of the author and Strathmore University  \nName of Candidate: Jennifer Wambui Nduati  \nApproval  \nThe dissertation of Jennifer Wambui Nduati was approved by the following:  \nName of Supervisor: Dr. John Olukuru  \nSchool/Institute/Faculty: Strathmore Business School  \nDr. Ceaser Mwangi  \nExecutive Dean  \nStrathmore University Business School.  \nDr. Bernard Shibwabo  \nDirector, Office of Graduate Studies  \nDEDICATION  \nI dedicate this dissertation to my husband Tony and our two sons Chris and Ian for their unwavering support, prayers, love and encouragement during this journey.  \nACKNOWLEDGEMENT  \nTo my supervisor Dr. Olukuru who always inspired me to trudge on and always availed himself. Your guidance, support and encouragement saw me through this journey. To Tim, Chakaya and Janice for aiding with data collection and analysis  \nTo my mums Mary and Eunice for their prayers and cheering me on. To Sandra and Reuben for their support and always having our little ones when I needed a break.  \nI thank the Almighty God for guidance, strength, protection and sustaining me.  \nABSTRACT  \nHousing prices have in the recent years dominated social and economic discussions in both developed and developing countries such as Kenya. Literature shows that modelling housing pricing in Kenya is predominantly based on conventional statistical theory and methodologies that are increasingly becoming sub-optimal in the wake of big data and technological advancements. To fill this gap, this research leveraged Machine Learning (ML) in housing price modelling and prediction in Nairobi County. The project further sought to achieve four objectives, namely: 1) to determine the features that significantly influence housing prices in Nairobi County; 2) to compare different ML models and techniques used to predict housing prices in Nairobi County; 3) to predict housing prices in Nairobi County using trained ML models based on unseen data in production; and, 4) to make policy recommendations on determination of housing prices. Primary data was collected from homeowners and potential homeowners from Shauri Moyo and Kibra. Further, Key Informant Interviews (KII) were undertaken with respondents from the State Department for Housing. The survey achieved a response rate of 85.3% and established that participants strongly agreed that; presence of tarmacked road within 5 Kilometres (Km), electricity connection/generator, a hospital within 5Km, a school within 5 Km, internet connectivity, age of the house et cetera significantly determined housing prices. Secondary data on the other hand was [retrieved from Property24.co.ke. Fourteen ML/ Deep Learning models were trained](retrieved from Property24.co.ke. Fourteen ML/ Deep Learning models were trained), optimized and tested based on the evaluation metrics; Root Mean Squared Error (RMSE) and R-Squared. Insights from the secondary data showed that; number of bedrooms, bathrooms, parking lots, location and type of the house accounted for at least 88% of variations in the predicted house sale price in half of the ML models. The best ML candidate was the Light-Gradient Boosted Machine (Light GBM) with a RMSE of 11.21635 and an R-Squared score of 88.65%. The ","cbCaioGJkGGtW8vq","https://ap.wps.com/l/cbCaioGJkGGtW8vq","pdf",10754635,1,106,"English","en",105,"# Declaration\n# Dedication\n# Acknowledgement\n# Abstract\n# Table of Contents\n# List of Figures\n# List of Tables\n# Abbreviations\n# Operational Definition of Terms","[{\"question\":\"Which housing features were found to significantly influence housing prices in Nairobi County?\",\"answer\":\"Participants strongly agreed that features such as presence of a tarmacked road within 5 km, electricity connection or generator, a hospital within 5 km, a school within 5 km, internet connectivity, and the age of the house significantly determined housing prices.\"},{\"question\":\"How were the machine learning models evaluated in the dissertation?\",\"answer\":\"Fourteen ML and deep learning models were trained, optimized, and tested using evaluation metrics including Root Mean Squared Error (RMSE) and R-Squared (R²).\"},{\"question\":\"What model performed best for housing price prediction, and what was the key performance outcome?\",\"answer\":\"The best-performing model was Light-Gradient Boosted Machine (LightGBM), achieving an RMSE of 11.21635 and an R-Squared score of 88.65%, while Elastic Net was the least performing.\"}]","Leveraging Machine Learning in Housing Price Prediction in Nairobi County - 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