Electronic Science and Technology ›› 2024, Vol. 37 ›› Issue (1): 1-8.doi: 10.16180/j.cnki.issn1007-7820.2024.01.001

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Research on Real Estate Price Index Based on Sparrow Search Optimization SVR Model

LAN Ruijie,MENG Weigao,GENG Jinqiang   

  1. School of Urban Geology and Engineering,Hebei GEO University,Shijiazhuang 050031,China
  • Received:2022-07-31 Online:2024-01-15 Published:2024-01-11
  • Supported by:
    Social Science Foundation of Hebei(HB18GL021)

Abstract:

In order to solve the data acquisition lag problem of traditional economic indicators as an influencing factor of housing prices, and the uncertainty of parameter selection in the machine learning model when predicting housing prices, the network search data is used as the explanatory variable of the house price index, and Sparrow Search Algorithm(SSA) is used to establish the SSA-SVR(Support Vector Regression)model to optimize the penalty factor C of SVR and the parameter g of the RBF(Radical Basic Function) kernel function in this study. Comparison among the established SSA-SVR model with PSO(Particle Swarm Optimization)-SVR, GA(Genetic Algorithm)-SVR,WOA(Whale Optimization Algorithm)-SVR, GS(Grid Search)-SVR and benchmark SVR show that the correlation coefficient of SSA-SVR(0.99), root mean square error(6.71), mean absolute error(5.24), mean square error(45.13) and mean absolute percentage error(0.26%) are better than those of the other five models. The results show that the SVR model optimized by the sparrow search algorithm has better global optimization ability in housing price prediction, which can improve the prediction accuracy and prediction ability of the model.

Key words: sparrow search algorithm, optimization algorithm, SVR model, data lag, parameter uncertainty, network search data, real estate price index, house price forecast

CLC Number: 

  • TP39