Predicting house price via gene expression programming

Purpose – The housing sector is one of the main sources of economic growth in both developing and developed countries. Although many methods for modeling house prices have been proposed, each has its own limitations. The present paper aims to propose gene expression programming (GEP) as a new approach for prediction of housing price. Design/methodology/approach – This study introduces gene expression programming (GEP) as a new approach for predicting housing price. This is the first time that this metaheuristic method is used in the housing literature. Findings – The housing price model based on the gene expression programming is compared with a least square regression model that is derived from a stepwise process. The results indicate that the GEP‐based model provides superior performance to the traditional regression. Originality/value – Data used in this study is derived from the Household Income and Expenditure Survey (HIES) in Iran that is conducted by the Statistical Center of Iran (SCI). Housing price model is estimated by administering the questionnaires of this survey in Hamedan Province. To show the applicability of the derived model by GEP technique, it is verified applying parts of the data, namely test data sets that were not included in the modeling process.