| In recent year's Chinese real estate industry sustained and rapid development, it has already become one of China's important pillar industries. The real estate market increasingly fierce competition, the real estate market mechanism is also in constant perfection and deepening, the investment decision makers aware of investment decision-making method analysis and the importance of the study. Investment decision makers in real estate development projects for investment decisions, they pay more and more attention to evaluation method, it is also related to investment decision and effectiveness of the scientific nature.Real options have broad prospects in the investment decision-making, financial management, and evaluation applications, Real option is gradually application in real estate investment decisions. Now, In the real estate project investment decisions, most experts using Black -Scholes model to evaluate, this method do not consider the real option different financial option features. The real estate development projects for investment decision, if simple duplicating financial option pricing model, the wrong conclusion chance is larger, it affects the investor right decisions.In this paper, the characteristics of the real estate investment projects are studied, and summarize the traditional net present value approach and its defects. Investment optimization is a key problem in the option pricing. It is a complicated non-linear continuous optimization problem to select the optimal strike price and expiration time in order to maximize the option price. This paper introduces real option pricing model base on artificial neural network and the optimal investment option pricing model base on particle swarm optimization, which can provide decision support for investors. Based on the Black-Scholes model, the algorithm is designed and realized. Finally, a typical simulation study is carried out to illustrate the validity of the algorithm. |
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