Abstract
In this paper, the annually average Defense Meteorological Satellite Program-Operational Linescan System (DMSP/OLS) night-time light data is first proposed as a surrogate indicator to mine and forecast the average housing prices in the inland capital cities of China. First, based on the time-series analysis of individual cities, five regression models with gross error elimination are established between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) adjusted by annual inflation rate or not from 2002 to 2013. Next, an optimal model is selected for predicting the ACRHPs in 2014 of these capital cities, and then verified by the interval estimation and corresponding official statistics. Finally, experimental results show that the quadratic polynomial regression is the optimal mining model for estimating the ACRHP without adjustments in most provincial capitals and the predicted ACRHP of these cities are almost in their interval estimations except for the overrated Chengdu and the underestimated Wuhan, while the adjusted ACRHP is all in prediction interval. Overall, this paper not only provides a novel insight into time-series ACRHP data mining based on time-series ANLI for capital city scale but also reveals the potentiality and mechanism of the comprehensive ANLI to characterize the complicated ACRHP. Besides, other factors influencing housing prices, such as the time-series lags of government policy, are tested and analysed in this paper.
Highlights
The real estate industry is an important manifestation in the process of urbanization, and the housing price is a vital economic indicator reflecting the sustainability of regional development
Elvidge et al.[20] used the Defence Meteorological Satellite Program Operational Linescan System (DMSP-OLS) data to study the relationship between gross GDP, electric power consumption and light area in 21 countries and found that the light area was highly correlated to GDP and electric power consumption
Hefei is taken as an example and five regression models between average night-time light intensity (ANLI) and average commercial residential housing price (ACRHP) are established so that the optimal regression model can be obtained by comparing the R2
Summary
The real estate industry is an important manifestation in the process of urbanization, and the housing price is a vital economic indicator reflecting the sustainability of regional development. From 2005 to 2015, the price-income ratio is the nominal house price divided by the nominal disposable income per head (https://data.oecd.org/price/housing-prices.htm) of China’s leading real estate cities, Beijing and Shenzhen, increased from 7.69 to 13.37 and 5.95 to 15.54 by respectively calculating them from data sources on the websites: Shenzhen Municipal Statistics Bureau: http://www.sztj.gov.cn/; Beijing Municipal Bureau of Statistics: http://www.bjstats.gov.cn/tjsj/; National Bureau of Statistics of the People’s Republic of China: http://data.stats.gov.cn/search.htm For housing markets, this rate is described as “affordable”, which is one of the key measures for a region’s socio-economy stability and should not exceed three times gross annual household income in general[7]. Previous studies have demonstrated that night-time light remote sensing data have been successfully used in social-economy factors, such as population migration and distribution, GDP, urbanization and so on. In order to guarantee that our study data are more reliable, we eliminated the abnormal errors of a few years and selecting an optimal mining model from several models to ensure the reliability of the results
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