Table 7 The optimal regression model for the ANLI time series prediction of each provincial capital.

From: Study on Average Housing Prices in the Inland Capital Cities of China by Night-time Light Remote Sensing and Official Statistics Data

City

Regression model

R2

Changchun

ANLI = −0.01829Y2 + 74.13Y − 75090

0.7997

Changsha

ANLI = 0.06177Y2 − 247.2Y + 247400

0.8891

Chengdu

ANLI = 0.06861Y2 − 274.4Y + 274300

0.8672

Chongqing

ANLI = 0.01298Y2 − 51.95Y + 51970

0.9354

Guiyang

ANLI = 0.05905Y2 − 236.6Y + 236900

0.8707

Harbin

ANLI = −0.0007979Y2 + 3.633Y − 4071

0.7085

Hefei

ANLI = 0.01278Y2 − 50.19Y + 49270

0.7457

Hohhot

ANLI = 0.0321Y2 − 128.3Y + 128300

0.8707

Kunming

ANLI = 0.04935Y2 − 197.5Y + 197700

0.8766

Lanzhou

ANLI = 0.0353Y2 − 141.3Y + 141400

0.8032

Nanchang

ANLI = 0.0296Y2 − 118.3Y + 118200

0.7849

Taiyuan

ANLI = 0.04523Y2 − 181.1Y + 181200

0.6653

Urumqi

ANLI = 0.06364Y2 − 254.9Y + 255300

0.8960

Wuhan

ANLI = 0.1048Y2 − 419.3Y + 419600

0.8644

Xi’an

ANLI = 0.051Y2 − 203.7Y + 203500

0.8800

Xining

ANLI = 0.01513Y2 − 60.31Y + 60100

0.8229

Yinchuan

ANLI = 24.81Y2 − 99330Y + 99440000

0.9650

Zhengzhou

ANLI = 0.03563Y2 − 141.3Y + 140100

0.9074

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