Factors affecting housing prices in Yangtze River Delta Cities during 2000 to 2023

This thesis aims to develop a framework for identifying and evaluating the important factors affecting housing prices in the Yangtze River Delta region. These factors include urbanization rate, population, debt ratio, GDP, and resident income over the past 20 years for four cities: Shanghai, Hangzhou, Suzhou, and Nanjing. The Pearson correlation analysis and linear regression analysis are methods used to find and analyze the factors with the strongest correlation. These two methods enable a direct identification of the factors that most significantly affect housing prices, providing insights into their impact and predicting trends for these factors in future years. To investigate key drivers of housing price surges in China's Yangtze River Delta (2000–2023), I analyzed historical data (avg. price ↑490% to 10,400¥/m²; Shanghai ↑12x to 50,000¥/m²) from authoritative sources (Zhang, 2024; Anjuke, 2024), designed a quantitative framework using Pearson correlation and linear regression to test five factors (population, urbanization rate, GDP, disposable income, debt ratio) across Shanghai, Hangzhou, Suzhou, and Nanjing, and established predictive models for future trends. This empirical approach addresses critical gaps in regional-specific housing economics literature.

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Publication Details

Journal
Interdisciplinary Humanities and Communication Studies
Published
2025-08-26
DOI
https://doi.org/10.61173/ebwh1d62
Primary Topic
Regional Development and Environment
Type
article
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Factors affecting housing prices in Yangtze River Delta Cities during 2000 to 2023

Anjie Sha
Interdisciplinary Humanities and Communication Studies
Regional Development and Environment
article

Factors affecting housing prices in Yangtze River Delta Cities during 2000 to 2023

Anjie Sha
article en

Abstract

This thesis aims to develop a framework for identifying and evaluating the important factors affecting housing prices in the Yangtze River Delta region. These factors include urbanization rate, population, debt ratio, GDP, and resident income over the past 20 years for four cities: Shanghai, Hangzhou, Suzhou, and Nanjing. The Pearson correlation analysis and linear regression analysis are methods used to find and analyze the factors with the strongest correlation. These two methods enable a direct identification of the factors that most significantly affect housing prices, providing insights into their impact and predicting trends for these factors in future years. To investigate key drivers of housing price surges in China's Yangtze River Delta (2000–2023), I analyzed historical data (avg. price ↑490% to 10,400¥/m²; Shanghai ↑12x to 50,000¥/m²) from authoritative sources (Zhang, 2024; Anjuke, 2024), designed a quantitative framework using Pearson correlation and linear regression to test five factors (population, urbanization rate, GDP, disposable income, debt ratio) across Shanghai, Hangzhou, Suzhou, and Nanjing, and established predictive models for future trends. This empirical approach addresses critical gaps in regional-specific housing economics literature.

Interdisciplinary Humanities and Communication StudiesVol. 1(4)
Openalex Percentile: Top 70%
Regional Development and Environment
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