Affiliations: [a]
School of Construction Management and Real Estate, Chongqing University, Chongqing, China
| [b]
School of Built Environment, University of Reading, Reading, Berkshire, UK
Correspondence:
[*]
Corresponding author. Deheng Zeng, School of Construction Management and Real Estate, Chongqing University, Chongqing, China. E-mail: thomasmahd@sina.com.
Abstract: The precision of price appraisal is directly linked to the sound development of a market-oriented economy and public benefit. Market comparison approach is a widely used price-appraisal algorithm. However, when the sample size is small, the computing precision of the market comparison approach is severely impaired. To realize precise, intelligent computing under the conditions of a small sample size and a low degree of proximity between samples and the object to be evaluated, this study adopted grey correlation and fuzzy mathematics to optimize the market comparison approach for price appraisal. The proposed algorithm used grey correlation analysis to quantify the weight of various attributes influencing price appraisal. The price correlation between samples and the object to be evaluated was confirmed. The fuzzy mathematical method was then used to obtain the relative weight of samples. The proposed intelligent price-appraisal algorithm was constructed to improve the market comparison approach. Finally, several residences in Hangzhou, Zhejiang province, China were chosen for an empirical analysis and for the verification of the feasibility of the improved price-appraisal algorithm. Results suggest that (1) The mean error of the algorithm is only 0.99%, indicating high computing precision; (2) When the number of objects to be evaluated is large, a computer system can be used to aid the intelligent computing and to efficiently perform intelligent computing. In conclusion, the proposed algorithm can maintain high computing precision under the conditions of a small sample size and a low degree of proximity, and the algorithm can be used as a theoretical basis to realize intelligent mass appraisal. Overall, the proposed algorithm is worthy of further development because it is highly operational.
