Analysis of Factors Influencing Shanghai Real Estate Prices: A Multiple Linear Regression Approach Using Official Data (2021-2024)

Tianwei Yu

doi:10.47297/wspdecWSP2515-797315.20261002

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Analysis of Factors Influencing Shanghai Real Estate Prices: A Multiple Linear Regression Approach Using Official Data (2021-2024)

Volume 10, Issue 2

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Analysis of Factors Influencing Shanghai Real Estate Prices: A Multiple Linear Regression Approach Using Official Data (2021-2024)
Analysis of Factors Influencing Shanghai Real Estate Prices: A Multiple Linear Regression Approach Using Official Data (2021-2024)
University of California, Davis, CA 95616, USA
Vol. 10, Issue 2, Pages: 77-82(2026)
Published：28 February 2026
DOI：10.47297/wspdecWSP2515-797315.20261002
Accepted：

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Yu Tianwei.Analysis of Factors Influencing Shanghai Real Estate Prices: A Multiple Linear Regression Approach Using Official Data (2021-2024)[J].Development Economics of China,2026,10(02):77-82.
DOI：

Yu Tianwei.Analysis of Factors Influencing Shanghai Real Estate Prices: A Multiple Linear Regression Approach Using Official Data (2021-2024)[J].Development Economics of China,2026,10(02):77-82. DOI： 10.47297/wspdecWSP2515-797315.20261002.

摘要

Abstract

This paper will analyse the macroeconomic factors that determine the residential property prices in Shanghai by using multiple linear regression analysis on the official government data between January 2021 and December 2024. The dependent variable is the National Bureau of Statistics 70 Cities Property Price Index

whereas the independent variables are the GDP growth rate

interest rate

and CPI inflation. The information was obtained in reputable databases

such as the NBS China

the World Bank

and the People Bank of China. The regression was estimated with the help of R statistical software

and the Newey-West heteroskedasticity and autocorrelation-consistent (HAC) standard errors were used to address the time-series properties. The findings show that the GDP growth (betta= - 0.117

0.001) and CPI inflation ( betta = -0.209

0.001) have a significant negative impact on the housing prices

but interest rates have a positive effect (betta = 0.723

p = 0.033). The model accounts 80.9% of the price variance (R

= 0.809

F = 14.77

p = 0.001). These counter-intuitive results highlight the overwhelming role of government policy interventions in the Chinese housing market

and they add to a better comprehension of the interaction between macroeconomic variables and regulatory processes in the determination of real-estate prices in emerging economies.

关键词

Keywords

references

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