Determinants of house prices in China: a panel-corrected regression approach

Mei Liu () and Qing-Ping Ma ()
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Mei Liu: NingboTech University
Qing-Ping Ma: The University of Nottingham Ningbo China

The Annals of Regional Science, 2021, vol. 67, issue 1, No 3, 47-72

Abstract: Abstract This paper uses annual data from 31 provinces, municipalities and autonomous regions in China from 2000 to 2018 to examine the determinants of Chinese house prices with a panel data regression method. Our finding suggests that land price, loans of real estate developers, per capita saving and the proportion of people with college or above educational degrees significantly drive up house prices, while the number of unemployed population has a significant negative impact. These five variables account for 72 percent variations in house prices across the country. Other economic fundamentals such as inflation, interest rate, per capita gross domestic products (GDP), and rent cost do not have significant influences on house prices. The econometric model is then used to assess the existence of house price bubbles in those provincial-level divisions in China by comparing their actual price levels with those predicted by the model. If the trend of house price growth during 2002–2014 found in the present study is the true long-run trend of house prices in China, with a few exceptions, the house prices in most provincial-level divisions are not overvalued by the end of 2014.

JEL-codes: L74 R21 R31 R38 R52 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s00168-020-01040-z

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