This study is based on survey data on investor expectations for 40 metropolitan statistical areas (MSAs) in the US over the period from 2003:Q2 to 2014:Q2. The paper has two main objectives. These are firstly to identify whether expected rates of return across different commercial real estate markets are positively spatially correlated and secondly to analyze the role of core markets like New York, Washington, DC, Los Angeles, San Francisco, and Chicago in the information-spreading process. All the tests conducted are conditional on the maintained hypothesis that expected returns across different markets are spatially uncorrelated. To carry out these tests, we regress a measure of excess return in each of the 40 markets on (contemporaneous) measures of expected returns in other markets, controlling for the interaction between (neighboring) markets. We find very large spatial interaction coefficients across different markets. Our basic estimates yield a spatial correlation coefficient of near one and statistically significant at standard levels. Chow tests allow rejection of the hypothesis that core markets like New York, Washington, DC, Los Angeles, San Francisco, and Chicago are no different than markets like Cleveland, Columbus, Orlando, Pittsburgh, Salt Lake City, St. Louis, and Tampa in explaining the covariances of the rates of return among different commercial real estate markets. Tests of structural change also imply that core markets currently play a more prominent role that in the past in the transfer of information from one market to another.
The Journal of Real Estate Finance and Economics – Springer Journals
Published: Sep 28, 2016
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