# RECORD 25220943ab063e0fe03168460d2005c02cf25de0
# TITLE: Mass Appraisal of Property Tax Base based on Multivariate Linear Regression—Empirical Study on Four Districts of Jinan
# YEAR: 2013.0  VENUE: nan  DOI: 10.2991/icetis-13.2013.254
# AUTHORS: Shan Tian
# PAGES: 5  CANDIDATE TABLE PAGES (1-based): [5]
# ABSTRACT: In this paper, a multivariate linear regression analysis is made to the effective transaction data of 226 housing stocks in four administrative regions (Tianqiao District, Shizhong District, Lixia District, and Huaiyin District) of Jinan, a mass appraisal model of real estate values is established, and also the transaction prices of 226 housing stocks are compared with the appraised prices. In this study, it is thought that an efficient, low-cost, objective, and accurate mass appraisal can be made to property tax base through the establishment of a unified real estate information database. In the mass appraisal system, geography information technology should be integrated so as to improve the objectivity and accuracy of the appraisal results.


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Mass Appraisal of Property Tax Base based on Multivariate Linear 
Regression—Empirical Study on Four Districts of Jinan 
Shan TIAN 
School of Economic Management, Shandong Women's University, Jinan, Shandong, China 
Tianshanhaoxinqing@163.com 
Keywords: Property tax base; mass appraisal; multivariate linear regression 
Abstract. In this paper, a multivariate linear regression analysis is made to the effective transaction 
data of 226 housing stocks in four administrative regions (Tianqiao District, Shizhong District, 
Lixia District, and Huaiyin District) of Jinan, a mass appraisal model of real estate values is 
established, and also the transaction prices of 226 housing stocks are compared with the appraised 
prices. In this study, it is thought that an efficient, low-cost, objective, and accurate mass appraisal 
can be made to property tax base through the establishment of a unified real estate information 
database. In the mass appraisal system, geography information technology should be integrated so 
as to improve the objectivity and accuracy of the appraisal results.  
Introduction 
Since the uniformly standardized collection of the property tax was proposed in the Third 
Plenum of the 16th CPC Central Committee on October 14, 2003 and relevant charges were 
canceled, a lot of fundamental work has been made by China’s government to the collection of 
property tax. However, there are many problems necessary to solve, and property tax base and tax 
rate should be primarily considered because they directly play a decisive role in the amount of 
payable tax and also has a tie with the fairness of the taxation. Tax base is the basic amount of the 
payable tax for calculation, so it is the basis of tax assessment. The main basis for determining the 
base of the property tax collected in the transferring process of real estate lies in the appraisal value 
of property. Therefore, accurately estimating the appraisal value of real estate tax is the foundation 
of determining property tax base and deciding payable property tax amount, and also the key to the 
success of collecting property tax. The appraisal scope of real estate value for the purpose of 
taxation generally includes all real estates in the country, so the amount in appraisal is huge. 
Appraisal time-point generally refers to the unified appraisal base date provided in the tax law, and 
the time points are required to be consistent. To ensure the fairness of the taxation, appraised objects 
should be periodically assessed. Therefore, time and cost should be considered seriously in the 
appraisal, and mass appraisal techniques are necessarily introduced to the appraisal of property tax 
base. 
Mass appraisal is a new computer technology based appraisal method system, in which a 
corresponding asset appraisal model is established with three principles of asset appraisal and also 
the appraisal model is repeatedly corrected and evaluated with mathematical statistics technology 
and other related technologies. Mass appraisal rose in foreign countries in the 1970s, but has 
developed into a new appraisal mode of great influencing power currently. At present, property tax 
base is commonly assessed with mass appraisal in the countries such as the United States, Britain, 
Germany, and Finland using market value as tax assessment basis. 
As early as 1919, scholars in the western world had applied the multivariate regression analysis 
in statistics to price appraisal. Due to the absence of computer technology at that time, early 
technical analyses on mass appraisal were manually completed by economists using multivariate 
regression analysis, so a lot of time and energy were required to cost. Later, with the popularization 
of information technology and computer technology, computer-assisted mass appraisal (CAMA) has 
been widely applied to property tax base appraisal, public administration value appraisal, and the 
mass real estate value appraisal in business activities. Now, this technique has been used in the 
International Conference on Education Technology and Information System (ICETIS 2013)
© 2013. The authors - Published by Atlantis Press
1111


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Table 2: Coefficients 
Model 
Non-standardized Coefficients 
Standardized 
Coefficients 
t 
Sig. 
Co-linearity Statistics 
B 
Std. Error 
Tolerance 
VIF 
(Constant) 
-59506.228 
50917.800 
 
-1.169 
0.244 
 
 
Geographic 
Position 
58080.342 
8630.594 
0.239 
6.730 
0.000 
0.979 
1.021 
The Number of 
Verandas 
-35112.641 
14006.309 
-0.092 
-2.507 
0.013 
0.914 
1.094 
Building area 
6972.190 
363.181 
0.713 
19.198 
0.000 
0.893 
1.120 
Housing  type 
29134.010 
11631.539 
0.099 
2.505 
0.013 
0.782 
1.279 
Decoration 
25098.127 
8085.780 
0.110 
3.104 
0.002 
0.973 
1.028 
Facilities 
105639.321 
26027.895 
0.158 
4.059 
0.000 
0.816 
1.226 
 
Table 3: The ratio between market value and appraisal value 
No. 
Market 
value 
Si 
(RMB) 
Appraisal 
value 
Ai 
(RMB) 
ARi=Ai/Si 
|ARi-M|
No.
Market 
value Si 
(RMB) 
Appraisal 
value 
Ai 
(RMB) 
ARi=Ai/Si 
|ARi-M| 
1 
800,021 
739,821.96
0.92 
0.5011 
12 
850,000
883,447.32 
1.04 
0.0043 
2 
850,000 
794,543.01
0.93 
0.1002 
13 
850,000
889,152.54 
1.05 
0.0111 
3 
876,000 
841,314.04
0.96 
0.0746 
14 
830,000
874,848.08 
1.05 
0.0190 
4 
860,000 
831,851.35
0.97 
0.0677 
15 
850,000
901,573.25 
1.06 
0.0257 
5 
850,000 
826,027.18
0.97 
0.0632 
16 
850,000
907,278.47 
1.07 
0.0324 
6 
849,966 
830,460.43
0.98 
0.0579 
17 
960,000
1,028,254.20 
1.07 
0.0361 
7 
850,000 
833,375.02
0.98 
0.0546 
18 
870,000
934,555.47 
1.07 
0.0392 
8 
860,000 
852,767.92
0.99 
0.0434 
19 
820,000
904,048.47 
1.10 
0.0675 
9 
850,000 
863,206.52
1.02 
0.0195 
20 
850,000
939,015.44 
1.10 
0.0697 
10 
830,000 
858,033.77
1.03 
0.0012 
21 
900,000
999,559.56 
1.11 
0.0756 
11 
850,000 
882,180.35
1.04 
0.0029 
22 
850,000
983,060.66 
1.16 
0.1215 
Median 
850000 
878514.21 
 
 
Median ratio 
 
1.03 
 
 
Mean 
854,818
881,744.32 
 
 
Mean ratio 
 
1.03 
 
 
Discrete coefficient 
 
 
 
6.54 
Price-related difference 
 
 
 
1.00 
 
Building Area 
LA 
Numerical 
Variable 
The building area of each property ( Unit: m2) 
The Number of Toilets 
W 
Numerical 
Variable 
The number of the toilets in the property of each 
transaction case (Unit: room) 
Housing Type 
RS 
Classified 
Variable 
multi-storey housing (≦7)=0; 
Small high-rise housing (8~12, including 12)=1; 
High-rise housing (>13)=2 
Years of Housing 
AGE 
Numerical 
Variable 
Base period is the year of 2012, and the year of the 
housing in 2012 is 0 (Unit: year) 
Decoration 
DS 
Classified 
Variable 
Blank or clear-water=1; simple decoration=2; 
medium  decoration=3;  refined decoration=4; 
luxury decoration=5 
Facilities 
I 
Dummy 
Variable 
Non double-gas source facilities=0; double-gas 
source facilities=1 
The Number of Floors 
L 
Numerical 
Variable 
The number of floors in the property of each 
transaction case (Unit: floor) 
1115