An improved spatial error model for the mass appraisal of commercial real estate based on spatial analysis: Shenzhen as a case study

• This paper formulates a spatial error model to implement mass appraisal of commercial properties.
• Fuzzy mathematics, econometrics and GIS are introduced into the proposed method.
• Different results are compared between the spatial error model and traditional method (linear regression).
• The proposed method has a better performance than traditional mass appraisal method.
• The advantage allows us to increase the usages of our technique for property tax reform.

Due to the rapid urbanization in China, the government must explore a new sustainable source of fiscal revenue. Therefore, property tax reform has been the topic of active discussion. As an important technical support for property tax reform, mass appraisal is an effective technique for implementing the tax base assessment. To achieve high precision and reduce the cost of valuation, an innovative framework for commercial real estate appraisal is proposed through the introduction of a spatial error model (SEM), fuzzy mathematics and econometrics. Several modifications are made to the conventional SEM so that it can be extended to the solution of commercial property mass appraisals. The fuzzy mathematics approach is adopted to specify the spatial weight matrix (SWM). Additionally, based on econometrics, the impacts of the entity and locatio0n factors on commercial real estate prices are discussed. The proposed method is then implemented for commercial real estate, based on the empirical analysis of Shenzhen. Experiments demonstrate the accuracy of the assessment results and that the consistency among different appraisal objects is improved. Further, through the introduction of fuzzy mathematics, we extend the SWM construction from a single variable to multi-variables. This advantage allows us to increase the usage of our technique and to provide technical support for the undergoing property tax reform in the Chinese real estate market.