Abstract
It is of the prospective home buyers and sellers’great interests to reference a true estimate of the residential housing value. Often times, sale prices at the same neighborhood are used to determine the market value of the real property. It is generally recognized that spatial data are more informative since they contain more variation and less collinearity among the variables. The use of spatial data results in a greater availability of degrees of freedom, and hence increases the efficiency in the estimation. This project uses R to develop a spatial regression-based home value estimation model to forecast residential housing market value in Sacramento and the surrounding area. With the goal of providing a robust estimation of home values based on real sale data, the project has identified critical variables for determining housing values and developed a spatial regression-based home value estimating model.