Real-Estate Price Prediction Using Time Series Forecasting Methods


  • Arnav Wadehra Aspiring Scientists' Summer Internship Program, 2019
  • Dr. Setareh Rafatirad Department of Information Sciences and Technology, Volgenau School of Engineering, George Mason University



Accurate price prediction in real-estate investment can be an indispensable tool in generating capital and ensuring financial success. This work investigates the use of time series forecasting models in predicting residential property prices in Virginia. The time series model used in this study was the Autoregressive Integrated Moving Average (ARIMA) model, due to its flexibility to fit well into many varieties of time series and the coherence of the associated Box-Jenkins methodology. The results show that ARIMA models can indicate short-term market direction and provide an understanding of whether the movement will be small or large. An investor or consumer will find it useful to incorporate forecasting methods into their investment strategy to understand the upcoming direction of the market and maximize monetary gains.





Abstracts from the 2019 Aspiring Scientists' Summer Internship Program