Housing prices in China follow Zipf’s law

This study verifies that housing prices in China follow Zipf's law by analyzing the average housing prices in 200 Chinese cities between 2015 and 2023. The Kolmogorov-Smirnov (KS) test further supports this conclusion. Furthermore, the study explores housing price trends in different city tiers and uses rank clocks to reveal price fluctuations, which is not captured by the Zipf's law. Using nine-year housing price time series data, the ARIMA and ConvLSTM models generate short-term forecasts. The forecasting results are evaluated using various indicators, and KS tests on the predicted prices show a better conformity to Zipf's law. This paper also builds a dynamic housing price model, offering a new perspective to understand and predict housing price trends. The results of this paper can shed light on the real estate market.

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Publication Details

Journal
PLoS ONE
Published
2025-05-30
DOI
https://doi.org/10.1371/journal.pone.0324239
Citations
1
Primary Topic
Housing Market and Economics
Type
article
Field-Weighted Citation Impact
3.13
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article

Housing prices in China follow Zipf’s law

Baozhong Zheng, Yide He, Yue Kai
1 citations
PLoS ONE
Housing Market and Economics
3.13
article

Housing prices in China follow Zipf’s law

Baozhong Zheng, Yide He, Yue Kai
article en
1 citations

Abstract

This study verifies that housing prices in China follow Zipf's law by analyzing the average housing prices in 200 Chinese cities between 2015 and 2023. The Kolmogorov-Smirnov (KS) test further supports this conclusion. Furthermore, the study explores housing price trends in different city tiers and uses rank clocks to reveal price fluctuations, which is not captured by the Zipf's law. Using nine-year housing price time series data, the ARIMA and ConvLSTM models generate short-term forecasts. The forecasting results are evaluated using various indicators, and KS tests on the predicted prices show a better conformity to Zipf's law. This paper also builds a dynamic housing price model, offering a new perspective to understand and predict housing price trends. The results of this paper can shed light on the real estate market.

PLoS ONEVol. 20(5)
Tongji University (CN), Shanghai University of Engineering Science (CN)
Sustainable cities and communities
Openalex Percentile: Top 8%
Housing Market and Economics
3.13
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