The ability to determine the optimal investment timing provides a strategic advantage for companies working in a competitive environment. While on the one hand, exogenous uncertainties may inhibit investment, under pressure from competitors, these firms can gain a competitive advantage if they are first movers. This thesis summarizes the concepts applied to Real Options and Game Theory methodologies to propose an evaluation methodology that contributes to the financial analysis of investments in the real estate market. The first model of the thesis is an econometric model that considers the interrelationships in the formation of prices that compose the real estate market dynamics of Rio de Janeiro, through a vector autoregression (VAR) model identified by directed acyclic graphs. The objective of this work is to determine the optimal strategy to exercise the investment option in the Nash equilibrium considering the uncertainty in the real estate demand in a region of Rio de Janeiro. To this end, the second model of the thesis adopted a modified version of the Grenadier (2002) methodology with a more adequate and robust specification of the uncertainties for the stochastic demand function, in which a stochastic factor elasticity was inserted. The parameters of the model were estimated using econometric tools based on real data from the real estate market in Rio de Janeiro. Monte Carlo simulations of demand were performed to compare the oligopolies in terms of levels of investment and quantity produced. The quantitative results obtained are intuitive in the sense that the larger the number of competitors, the lower the level of demand (threshold) required for investment in new units, whereas the greater the volatility of demand, the greater the demand threshold for the investment to be optimal.