The U.S. housing price indexes are subject to measurement problems that severely impair their ability to capture the true risk. In this paper, we seek alternative methodology of utilizing latent-variable statistical methods and provide new insight to understand housing market dynamics. Housing prices are assumed to respond to external forces as proxies by way of a set of macroeconomic variables and Önancial indexes. Latent variable models allow us to extract interpretable common information about unobserved real estate returns. This methodology of this paper is based on the framework in Bai & Ngís papers (2002 &2006). We applies a pure statistical approach to extract the latent factors and, more importantly, examines whether the observed macro factors are exact factors according to the information criteria proposed by Bai &Ng (2006). For robustness, we test both OFHEO repeated sales index and MRAC median home price index. We found geographical pattern of factor loadings for housing price appreciation at MSA level. The results indicate that income, consumption and GDP is a comparatively accurate factor.