We examine the time-series relationship between house prices in eight Southern California metropolitan statistical areas (MSAs). First, we perform cointegration tests of the house price indexes for the MSAs, finding seven cointegrating vectors. Thus, the evidence suggests that one common trend links the house prices in these eight MSAs, a purchasing power parity finding for the house prices in Southern California. Second, we perform temporal Granger causality tests. The Santa Anna MSA temporally causes house prices in six of the other seven MSAs, excluding only the San Luis Obispo MSA. The Oxnard MSA experiences the largest number of temporal effects from six of the seven MSAs, excluding only Los Angeles. The Santa Barbara MSA proves the most isolated. It temporally causes house prices in only two other MSAs (Los Angeles and Oxnard) and house prices in the Santa Anna MSA temporally cause prices in Santa Barbara. Third, we calculate out-of-sample forecasts in each MSA, using various vector autoregressive and vector error-correction 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 how theses time-series models can predict out-of-sample peaks and declines in house prices after in 2005 and 2006. Recursive forecasts, where we update the sample each quarter, provide reasonably good forecasts of the peaks and declines of the house price indexes.
The Journal of Real Estate Finance and Economics – Springer Journals
Published: Mar 12, 2010
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
All the latest content is available, no embargo periods.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud