| House prices are of interest to the general public and government agencies for many reasons. The complexity and practicality of house price modeling have attracted many researchers. In this dissertation, attempts are made to explore the dependence structure in time and space among houses using over 130 thousand house price observations in San Jose from 1991 to 2012. Innovative spline methods are utilized to build a forecasting model incorporating both hedonic, spatial and temporal information. The use of splines greatly reduces the number of variables needed in the model without sacrificing for precision. Moreover, the recession period (2008--2010) was given special care because it behaved differently from the rest of the 22 year time period. The model proposed in this dissertation uses both repeat sales and single sale transactions, and is able to produce an overall price index for the whole region, as well as predictions for individual houses. The final model, which includes an autoregressive spatio-temporal error term, is shown to have better predictive abilities than other competing methods in the literature. |
