Application of various modeling techniques to analyze a housing condition survey dataset

Housing in our society is always associated with a wide range of social and economic issues. Examination of the differences between different community sectors based on different social and economic factors in regard to housing can help to address questions raised in public and political debates not just about housing - but about the underlying parity of economic provision, for which housing is often considered to be a prominent proxy. In this paper, the data mining techniques are used to analyze a regional housing condition survey dataset. Three data mining modeling techniques are applied, namely linear and nonlinear regression modeling and neural nets. Advanced algorithm is used to build the models more efficiently. Interesting patterns extracted using these data mining modeling techniques are presented and advantages and disadvantages of the three data mining techniques are discussed.