Under the conditions of market economy, the healthy development of the real estate market is of great significance to the normal operation of the real estate economy and the sustainable development of the real estate economy. Therefore, it is necessary to be able to accurately evaluate the development of the real estate industry and adopt appropriate macro-control measures in time. Taking Xiamen as an example, this paper adopts the way to combine objective information entropy weight method with subjective order relationship analysis method to determine the weight of group decision makers on program attributes and balance the importance of each index. According to the comprehensive attribute value of each scheme, the weight of decision makers is calculated by using entropy weight method for giving the evaluation result of the scheme. The results show that the overall situation of the development of real estate industry in Xiamen is more optimistic, and the weight of each index after the normalization of land transfer price is the highest. Based on the research conclusions, this paper proposes corresponding assessment commendations as a reference for relevant parties.

In this paper, we will start from constructing a healthy and sustainable higher education system by using the Analytic Hierarchy Process (AHP) to evaluate four guideline layers and fifteen measure layers, calculate the corresponding weights of each indicator and pass the consistency test. Then our model will be applied to three countries, China, Germany and Canada. And then each indicator will be scored high, medium and low by questionnaire and expert evaluation, and the combination of AHP and fuzzy comprehensive evaluation method (FCE) will be used to quantify the comprehensive scores of higher education systems in these three countries.

The three indicators of the number of schools, educational personnel and full-time teachers have a correlation with college size, which can be quantified. Therefore, by collecting data from the last five years in China, a grey forecasting model (GM) was used to predict the quantitative changes of these three indicators in the coming years and to propose targeted policies for implementation. The predicted values are analysed by residual tests for accuracy. The effectiveness of the policy is evaluated by passing the accuracy test.