  Home sale prices are formed given the transaction actors economic interests,
which include government, real estate dealers, and the general public who buy
or sell properties. Generating an accurate property price prediction model is a
major challenge for the real estate market. This work develops a property price
classification model using a ten year actual dataset, from January 2010 to
November 2019. The real estate dataset is publicly available and was retrieved
from Volusia County Property Appraiser of Florida website. In addition,
socio-economic factors such as Gross Domestic Product, Consumer Price Index,
Producer Price Index, House Price Index, and Effective Federal Funds Rate are
collected and used in the prediction model. To solve this case study problem,
several powerful machine learning algorithms, namely, Logistic Regression,
Random Forest, Voting Classifier, and XGBoost, are employed. They are
integrated with target encoding to develop an accurate property sale price
prediction model with the aim to predict whether the closing sale price is
greater than or less than the listing sale price. To assess the performance of
the models, the accuracy, precision, recall, classification F1 score, and error
rate of the models are determined. Among the four studied machine learning
algorithms, XGBoost delivers superior results and robustness of the model
compared to other models. The developed model can facilitate real estate
investors, mortgage lenders and financial institutions to make better informed
decisions.
