Change Characteristics and Multilevel Influencing Factors of Real Estate Inventory—Case Studies from 35 Key Cities in China

High inventory is a common issue in urban real estate markets in many countries, posing a threat to the sustainable development of macroeconomics and society. This study built an analytical framework for the evolution of real estate inventory and its driving mechanisms and conducted an empirical stu...

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Bibliographic Details
Published inLand (Basel) Vol. 10; no. 9; p. 928
Main Authors Zhao, Sidong, Li, Weiwei, Zhao, Kaixu, Zhang, Ping
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2021
Subjects
China
Cities
Consumption
Correlation analysis
drive mechanism
Economic analysis
Empirical analysis
Evolution
Heterogeneity
Housing
housing market
Inventory
Macroeconomics
Real estate
Research methodology
spatial analysis
Spatial heterogeneity
Sustainable development
Trends
Vacancies
vacancy
United States > US
China
Germany
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Summary:High inventory is a common issue in urban real estate markets in many countries, posing a threat to the sustainable development of macroeconomics and society. This study built an analytical framework for the evolution of real estate inventory and its driving mechanisms and conducted an empirical study on 35 key cities in China. The findings show that, first, China has a huge real estate inventory with significant spatial heterogeneity. Second, the real estate inventory in China first rises and then falls, presenting an inverted U-shaped change trend; however, the spatial heterogeneity first falls and then rises, characterized by a U-shaped evolutionary change. Third, the present characteristics and evolutionary paths vary among different types of real estate inventory, mainly showing growth, stability, and inverted U-shaped changes. Fourth, the influencing factors of real estate inventory are increasingly diversified, and different factor pairs show bifactor-enhanced and nonlinearly-enhanced interaction effects, with a more intricate and complex driving mechanism. Fifth, four types of policy areas were divided according to the Boston Consulting Group Matrix, and it is recommended that the design of de-stocking policies should be dominated by “key factors” for cities in the stars and cows policy areas, while “important factors” and “auxiliary factors” should be equally emphasized for cities in the question policy area; the cities in the dogs policy area should keep the status quo as much as possible with avoidance of undesirable or excessive interventions.
ISSN:2073-445X
2073-445X
DOI:10.3390/land10090928