Advanced forecasting model on land market value based on USA real estate market
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This research presents a time series estimation and prediction methods with the use of classic and advanced forecasting tools. Our discussion about di erent time series models is supported by giving the experimental forecast results, performed on several macroeconomic variables. Also, the main section deal with the experience of using such data in econometric analysis. Besides, the implementation of SAS and R software improve the parameter estimation and forecasting accuracy. The objective in providing crucial statistical techniques is to enable government and investors to make informed decisions regarding real estate. Most importantly, we obtain how to add value to business and apply skills set real estate in a real world environment. Eventually, the summary of various existing forecasting models can provide information to develop an appropriate forecasting model which describes the inherent feature of the series.
Thesis (Ph.D.)-- Wichita State University, College of Liberal Arts and Sciences, Dept. of Mathematics, Statistics and Physics