Predictive Modeling of Land Value Using Machine Learning: a Case Study of Nairobi Metropolitan Area.

Abstract

Predicting land value is a complex task with significant implications for real estate, urban planning, land rates and land investment decisions. Traditional methods of land valuation often rely on manual assessment and historical data analysis, leading to potential inaccuracies and inefficiencies. This challenge has necessitated the need of a model that will assist the land assessors and investors to detect and predict land value. Land assessors and investors around the world have also turned to technology to improve their precision in land valuation. The rise of artificial intelligence has shown tremendous impact over the years in several sectors. The emergence of machine learning (ML) techniques has provided new opportunities to develop predictive models capable of estimating with higher accuracy and efficiency the land value. Machine learning is a subset of artificial intelligence that enables software applications to enhance their accuracy in data prediction, assess current performance, and make improvements for future data. This work is focused on the development of a predictive model for estimating land value using machine learning algorithms. The objective is to identify optimal approaches for predicting land value, providing valuable insights for stakeholders such as land investors, land assessors, urban planners, and tax authorities. The study aims to determine the most effective characteristics, including location, land size, topography, soil type, and existing developments that affects the land valuation. A comprehensive data collection effort will be undertaken, gathering information from diverse sources such as land sale transactions and land parcels attributes. The research will explore a range of machine learning algorithms, including linear regression, random forests, gradient boosting, and support vector machines (SVM), for model development. In addition, ensemble learning techniques like bagging, boosting, and stacking will be examined to enhance model performance. The predictive model developed will be rigorously evaluated using appropriate metrics, including mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R^2). Furthermore, techniques for model interpretability will be employed to uncover the key factors influencing land value. The study will also utilize cross-validation techniques to test and validate the model, ensuring its robustness. This research aims to contribute to the advancement of machine learning-based methodologies in land valuation, addressing the dynamic needs of the real estate industry and urban planning.