  Developing an accurate prediction model for housing prices is always needed
for socio-economic development and well-being of citizens. In this paper, a
diverse set of machine learning algorithms such as XGBoost, CatBoost, Random
Forest, Lasso, Voting Regressor, and others, are being employed to predict the
housing prices using public available datasets. The housing datasets of 62,723
records from January 2015 to November 2019 are obtained from Florida Volusia
County Property Appraiser website. The records are publicly available and
include the real estate or economic database, maps, and other associated
information. The database is usually updated weekly according to the State of
Florida regulations. Then, the housing price prediction models using machine
learning techniques are developed and their regression model performances are
compared. Finally, an improved housing price prediction model for assisting the
housing market is proposed. Particularly, a house seller or buyer, or a real
estate broker can get insight in making better-informed decisions considering
the housing price prediction. The empirical results illustrate that based on
prediction model performance, Coefficient of Determination (R2), Mean Square
Error (MSE), Mean Absolute Error (MAE), and computational time, the XGBoost
algorithm performs superior to the other models to predict the housing price.
