A Two-Phase Spatio-Temporal Interpolation Framework for Housing Values

High-resolution historical housing value data are essential for urban analysis, socioeconomic research, and policy evaluation. However, housing values reported by the U.S. Census are only available at decennial intervals and are subject to changes in administrative boundaries, resulting in substantial spatial and temporal gaps. This study proposes a two-phase spatio-temporal interpolation framework to reconstruct annual block-group-level housing values in California by explicitly decoupling spatial and temporal dependence. In the first phase, ordinary kriging is applied independently to each census year (1990, 2000, 2010, and 2020) to reconstruct spatially continuous housing values on a unified 2020 block-group geometry, leveraging strong spatial autocorrelation in housing markets. In the second phase, temporal interpolation and extrapolation are performed independently at each block group using a locally constrained inverse time-distance weighting (ITDW) approach that exploits short-range temporal autocorrelation while avoiding unrealistic global temporal trends. Validation against American Community Survey data demonstrates robust predictive performance, with R² values ranging from 0.64 to 0.95 for interpolated years (2013–2019) and from 0.68 to 0.84 for the extrapolated years (2021–2023). The results indicate that housing values are more strongly influenced by nearest temporal neighbors than by long-range temporal trends, and that explicitly separating spatial and temporal processes enables stable and accurate reconstruction of fine-scale housing values from sparse historical observations.