The rental price adjustment mechanism is a fundamental component of the model for forecasting future office space requirements. This is an important area of study given the increasing significance of office buildings in the urban environment. This has resulted from the large growth in service oriented employment. Very little academic work has been completed in this area because of the lack of sufficient data. To date, only the U.S. market has been examined. The objective of this thesis is model the rental price adjustment mechanism in the Canadian office market. The intent is to further test the theory in this area, provide a comparison with the results obtained in the U.S., and provide some insight into the workings of the Canadian office market. This thesis reviews the relevant literature on inventory theory, and empirical work performed on the housing market and on data from the U.S. office market. The review points to a series of propositions about the rental price adjustment mechanism in the office market, the most important being the strong relationship between rents and vacancies. The extensions to the model developed in this paper are the specification of the vacancy variable in non linear terms and an attempt to include some proxy for growth expectations. The model is tested using data from Montreal, Toronto, Edmonton, Calgary, and Vancouver. The data has been collected primarily from the Royal LePage Market Survey. Visual inspection of the data uncovers unique characteristics in each individual office market. The underlying reasons point to the importance of integrating growth expectations in the model. The regression results support some degree of asymmetric price behaviour, however the specification of the vacancy variable in non linear terms is not conclusive. Inflation expectations seem to be important as landlords attempt to pass inflationary rises on to the tenants. Operating costs and interest rates do not appear to be significant factors in the model. This leads to the conclusion that they are not important in the cost of holding inventory in the short run. Finally, the proxy used for growth expectations is not significant. The most likely reason for this result is that the variable is not properly specified. The low explanatory power of the model may be attributed to the misspecification of the growth proxy and limitations in the data set. Both of these factors should be considered in future work in this area. / Business, Sauder School of / Graduate