Abstract:
The real estate market of Nairobi has been booming quickly with the price of apartments depending on location, amenities, and the market forces. Conventional approaches to valuation that use the past and market judgement can hardly be accurate or efficient. This paper presents and verifies a machine learning model to estimate the price of apartments in Nairobi. Online sources and Kenya National Bureau of Statistics (KNBS) were used to gather data and three regression algorithms of Linear Regression, Random Forest (RF), and Gradient Boosting Machines (GBM) were compared. The models were trained, tested and validated to find out the predictive accuracy. These findings indicated RF and GBM were more successful than Linear Regression and Support Vector Machine (SVM) with an accuracy of 86.30 and 84.40, respectively. The importance of features analysis allowed determining the apartment size as the key factor that determines the price after which came the number of bedrooms and bathrooms. The research paper suggests that RF and GBM should be used to create a web-based prediction tool, which will provide real estate experts and investors in Nairobi an accurate, transparent, and reliable pricing model. In general, the results prove that machine learning models are effective to predict the non-linear behaviour of apartment prices, and they are better than traditional valuation methods.
