The Land Market Value, defined as the total value of land and quantity data are derived from data on housing values, is an important factor in the estimation of structure costs using price indexes for housing and construction costs. In this paper, we gathered and analyzed 34 years' national data on past and present real estate transaction. According to the characteristics of raw data, we tried to develop the potential Decomposition, Smoothing, ARIMA and other advanced forecasting models with appropriate transformations. Specifically, we employed an innovation space state underlying certain forecasting model. For regression analysis, we involved GDP, CPI, Construction Cost Index, population, unemployment rate, inflation rate and Purchasing Manage Index in multivariate statistical model. Most importantly, we obtained how to add value to business and apply skills set to real estate in a real world environment. The goal in providing crucial statistical method is to enable government and investors to make informed decisions regarding real estate.