We examine the time-series relationship between housing prices in eight Southern California
metropolitan statistical areas (MSAs). First, we perform cointegration tests of the housing price
indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one
common trend links the housing prices in these eight MSAs, a purchasing power parity finding
for the housing prices in Southern California. Second, we perform temporal Granger causality
tests revealing intertwined temporal relationships. The Santa Anna MSA leads the pack in
temporally causing housing prices in six of the other seven MSAs, excluding only the San Luis
Obispo MSA. The Oxnard MSA experienced the largest number of temporal effects from other
MSAs, six of the seven, excluding only Los Angeles. The Santa Barbara MSA proved the most
isolated in that it temporally caused housing prices in only two other MSAs (Los Angels and
Oxnard) and housing prices in the Santa Anna MSA temporally caused prices in Santa Barbara.
Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive
(VAR) and vector error-correction (VEC) models, as well as Bayesian, spatial, and causality
versions of these models with various priors. Different specifications provide superior forecasts
in the different MSAs. Finally, we consider the ability of theses time-series models to provide
accurate out-of-sample predictions of turning points in housing prices that occurred in 2006:Q4.
Recursive forecasts, where the sample is updated each quarter, provide reasonably good forecasts
of turning points.