Regression Algorithm-Based Machine Learning Model for Apartments' Price Prediction in Nairobi City

The real estate industry in Nairobi has shown a phenomenal growth due to the economic dynamics in the city and prices of apartments differ depending on the area, facilities and the market forces. Traditional techniques of valuation which are based on experience are generally unrealistic and ineffective. This paper developed a machine learning predictive model, specific to the Nairobi real estate market, based on internet listing and KNBS data. Three regression algorithms; linear regression, random forest (RF) and gradient boosting machines (GBM) were trained, tested and validated under a comparative framework. Its findings indicated that RF (86.30%) and GBM (84.40%) performed better than linear regression and support vector machines (SVM) when it comes to the prediction of apartment prices. According to a key feature analysis, apartment size was the most important factor, then came the number of bedrooms and bathrooms. The last web-based model is a RF and GBM based model that offers a more precise and transparent pricing tool to buyers, sellers and real estate professionals. Such results indicate the effectiveness of the machine learning models grounded in the algorithms, in capturing the non-linear nature of the apartment pricing, in comparison with the more conventional methods of valuation.