House Price Prediction using Machine Learning Algorithm - The Case of Karachi City, Pakistan

Abstract

House prices are a significant impression of the economy, and its value ranges are of great concerns for the clients and property dealers. Housing price escalate every year that eventually reinforced the need of strategy or technique that could predict house prices in future. There are certain factors that influence house prices including physical conditions, locations, number of bedrooms and others. Traditionally predictions are made on the basis of these factors. However such prediction methods require an appropriate knowledge and experience regarding this domain. Machine Learning techniques have been a significant source of advanced opportunities to analyze, predict and visualize housing prices. In this paper, Gradient Boosting Model XGBoost is utilized to predict housing prices. Publicly available dataset containing 38,961 records of Karachi city is attained from an Open Real Estate Portal of Pakistan. Lot of work has been done in predicting house prices across many countries, however very limited amount of work has been done for predicting house prices in Pakistan. Our proposed house price prediction model is able to predict 98% accuracy.