Housing Prices in Fujian Province: A Linear Regression Analysis of Fuzhou

Hanyu Zhuang

doi:10.54097/5f1c9014

Authors

Hanyu Zhuang School of No.5 High School, Quanzhou, Fujian, China

DOI:

https://doi.org/10.54097/5f1c9014

Keywords:

House price prediction; linear regression; ordinary least squares

Abstract

This study used multiple linear regression to investigate the key points affecting housing prices in Fuzhou, Fujian Province. According to data from 20 residential communities, 5 variables are examined: building area, building age, distance to the nearest metro station, distance to the central business district (CBD), and proximity to key schools. The results show that metro distance has the strongest effect: one kilometer closer to a metro station increases price by 4,856 RMB/m². School proximity adds 8,923 RMB/m², while each additional year of building age reduces price by 413 RMB/m². The model explains 71.2% of price variation (R² = 0.712), and all variables are statistically significant. These findings support land value capture for metro funding and education equity policies to reduce housing pressure. Limitations include a small sample size (20 communities) and cross-sectional data. Future research should expand the number of the samples and include more detailed values especially variables.

References

[1] Fujian Provincial Bureau of Statistics. Fujian real estate market report, January–August 2025. Fuzhou: Fujian Provincial Bureau of Statistics; 2025.

[2] Zhang P, Zhuge AD, Tian Y. Wealth inequality in urban China: Sources and mechanisms. Fin Trade Econ. 2025;(7).

[3] Xu XY, Li CH, Qu N, Wang JW, Wu SD. MGWR analysis of second-hand housing prices in Fuqing City. J China West Norm Univ Nat Sci Ed. 2023;44(1):29.

[4] Huang CC, Wang XW, Li LN. Urban rail transit and housing prices: A case study of Fuzhou Metro Line 1. Geogr Res. 2021;40(10):2808–2822.

[5] Lin Z, Wang WL, Gong J, Lin T. Multi-scale effects of housing price determinants in Fuzhou central area. Trop Geogr. 2023;43(8):1536–1546.

[6] Craven BD, Islam SM. Ordinary least-squares regression. The SAGE dictionary of quantitative management research. 2011; 1:224-8.

[7] Dempster AP, Schatzoff M, Wermuth N. A simulation study of alternatives to ordinary least squares. Journal of the American Statistical Association. 1977 Mar 1;72(357):77-91.

[8] Phillips PC, Park JY. Asymptotic equivalence of ordinary least squares and generalized least squares in regressions with integrated regressors. Journal of the American Statistical Association. 1988 Mar 1;83(401):111-5.

[9] Pohlmann JT, Leitner DW. A comparison of ordinary least squares and logistic regression (1). The Ohio Journal of Science. 2003 Dec 1;103(5):118-26.

[10] Zdaniuk B. Ordinary least-squares (OLS) model. InEncyclopedia of quality of life and well-being research 2024 Feb 11 (pp. 4867-4869). Cham: Springer International Publishing.

[11] Rzhetsky A, Nei M. Statistical properties of the ordinary least-squares, generalized least-squares, and minimum-evolution methods of phylogenetic inference. Journal of molecular evolution. 1992 Oct;35(4):367-75.