A household satisfaction index (HSI) model based on the American customer satisfaction index (ACSI) model was fit to the housing industry in China and used with the software LISREL to measure the HSI in Beijing. Specifically, the empirical study analyzes the HSI of low-priced housing in Beijing. The results show that the HSI model is valid and the customer satisfaction theory can effectively analyze the housing industry. The results can help illustrate the factors which most affect customer satisfaction, and can be used to not only enhance the quality of homes and promote the housing market, but also to improve the standard of living for lower income people and provide suggestions to policy makers.

A rough set method is presented in this paper to assess the credit of contractors. Unlike traditional methods, the rough set method deduces credit-classifying rules from actual data to predict new cases. The method uses a contractors' database with a genetic algorithm and an exhaustive reduction implemented using ROSETTA software that integrates rough set method. The classification accuracy of the rough set model is not as good as that of a decision tree, logistic regression, and neural network models, but the rough set model more accurately predicts contractors with bad credit. The results show that the rough set model is especially useful for detecting corporations with bad credit in the currently disordered Chinese construction market.