Research On The Investment Risk Of Real Estate Project

In our country, the rapid development of real estates leads to superfluous investments, a series of problem, such as, the investment scale being too large, the investment structure being unreasonable, high house price, too much house of commodity, and many ground laying idle etc. It can be shown that the trade of real estates is still being the elementary phase in our country, and eyeless upsurge of investment would bring tremendous risk loss to investors. In view of above suggestion, it is necessary to analyze scientifically all kinds of risk factors in the course of investment, which may effective, avoid or reduce investment risks.First, in this paper, two basic methods, i.e., investment investigation and investment simulation are given. It is introduced systematically that event tree (ET) and fault tree analysis (FTA) to simulate investment risks (IRs). The reason why ET and FTA are used to analyze important factor, is too much factors of IRs should be considered. Form the view of the logic, the FTA is a microscopical and static analysis approach, which belongs to deduction analysis. But the ET is a macroscopical and dynamic analysis approach, which belongs to induce analysis and is unsuitable to study the variational course. In general, two approaches may be combined to make decisions in fact. Some typical measure approaches for IRs, such as Break-Even Analysis, Sensitivity Analysis, Probability Analysis and Monte Carlo method are all given in this paper. It should be highlighted that the approaches have different angle views, emphasize particularly on problems, and different application scope.Second, after recognizing all kinds of real estates IRs, a critical core for risks management of projects is how to evaluate reasonably IRs. In this paper, two traditional evaluation approaches are analyzed and their inherence drawbacks are pointed out as follows. For the AHP, five drawbacks are given. (1) The AHP is unsuitable to analyze the inner dependent relations among elements clusters. AHP thinks that element clusters are inner independent, but complex dependent relations rises among risk factors, so the AHP may not be used to evaluate complex dependent issues in this paper. (2) During the course of decision using the AHP, When the quality of complex factors is more than nine, more scaling work need to be done by experts or decisions, thus they usually feel tedious and cause decision confusion, so the result of comparisons is undependable. (3) The adjustment of comparison matrices is forcible, namely, the condition of CR<0.1 is necessary, so some preference information of the expert can't be reflected in comparison matrices. (4) The phenomena of rank reversals among existing alternatives may occur when introducing of new alternatives or deleting an existing alternative. However, at present, the explanation on"rank reversal"is still on debate. (5) the restriction of pairwise comparisons to a 1 to 9 scale, which necessarily imposes inconsistency of responses (for example, if A is strongly preferred to B, measured as a 5 on the AHP scale, and B is strongly preferred to C, then to be consistent in comparing A and C, the strength of preference should be 5×5, i.e. 25, which is not possible).The main drawback of fuzzy composite evaluation approach is the subjectivity of decision-maker is too strong. To overcome the above disadvantages, in this paper, artificial neural network (ANN) which is developed in recent years is applied to evaluate synthetically the investment risks of real estates projects, and the steps of evaluation approach based on the back propagation (BP) are dwelled on. The ANN is successfully applied in risk evaluation, which is the spark point of this paper.Finally, two treatment approaches of investment risks for projects, namely, the controlling approach and the financing approach are introduced respectively. An illustrated example of the evaluation of investment risks for a real estate project shows that the given evaluation approach is reasonable and feasible, which can be used in many complex decisions.