San Diego and Riverside, indicates a price appreciation since 1999, all else equal, of 100
percent, 123 percent, and 131 percent, respectively.
Submarket dummy variables are not reported in the regression outputs, but they
serve for achieving best possible control of relative location in the metropolitan market
and their inclusion improves significantly the fitness of all models.
Next, I present a second model for the retail properties, OLS Model II, which is
identical in its specification to the second model used with the office properties sample.
The model adds to the model specification, given by equation (18) a third order
expansion of the latitude and longitude coordinates, which has the objective of effectively
controlling for the absolute location of the property. The third order expansion, rather
than a simple linear form, is entered in the equation to draw a price surface based on
location. The model specification is given by equation (23).
Table 21 presents regression statistics from OLS Model II. Controlling for absolute
location improves the R-squares of market regressions by 0.5 to 1 percent. No significant
changes in the estimated coefficients are observed. With respect to the key variables of
interest results are re-confirmed. The coefficient on the variable representing a
replacement exchange, EXREPL, is positive and significant in 13 of the 15 regressions.
The magnitude of the estimated coefficients remains largely unchanged. This reconfirms
the evidence that buyers of replacement properties are paying significant price premiums
in the majority of the markets. Results with respect to relinquished exchanges, EXRELQ,
and a combination of relinquished and replacement exchanges, RELQ REPL, remain the
same.
The coefficient on the variable representing a purchase by an out-of-state buyer,