# RECORD 23a821c97160e852a6c2dd5c6097aa79bfac7ed3
# TITLE: Prediction and analysis of residential house price using a flexible spatiotemporal model
# YEAR: 2022.0  VENUE: Journal of Applied Economics  DOI: 10.1080/15140326.2022.2045466
# AUTHORS: Lu Wang; Guang-Xing Wang; Huan Yu; Fei Wang
# PAGES: 22  CANDIDATE TABLE PAGES (1-based): [15]
# ABSTRACT: ABSTRACT House price prediction has traditionally been approached using linear or spatial linear hedonic models and focused on big cities. In this study, we developed a flexible spatiotemporal model (FSTM) to explore the spatiotemporal characteristics of the residential house price and the impact factors in middle-small cities. The FSTM integrated both spatial and temporal components of the residential house price, accounted for its spatiotemporal characteristics, and reproduced its spatial variability and temporal trends. The results showed that the governmental policy had a significant influence on the house price and led to the characteristics being different from those in big cities. The significant factors also included the density of roads, the density of banks, density of supermarkets, the area used by public and user shared area within a building. This study implied that FSTM provided the potential for spatiotemporal prediction of the residential house price in the middle-small cities.


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Wang, Lu; Wang, Guangxing; Yu, Huan; Wang, Fei
Article
Prediction and analysis of residential house price using a
flexible spatiotemporal model
Journal of Applied Economics
Provided in Cooperation with:
University of CEMA, Buenos Aires
Suggested Citation: Wang, Lu; Wang, Guangxing; Yu, Huan; Wang, Fei (2022) : Prediction and analysis
of residential house price using a flexible spatiotemporal model, Journal of Applied Economics, ISSN
1667-6726, Taylor & Francis, Abingdon, Vol. 25, Iss. 1, pp. 503-522,
https://doi.org/10.1080/15140326.2022.2045466
This Version is available at:
https://hdl.handle.net/10419/314172
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to the year 2004, although their p-values were larger than the significant level of 0.05, 
indicating a statistical insignificance. This implied that the decrease of the residential 
house price was slight and not significant. Starting in 2005, the coefficients became 
positive, increased over years, and showed statistically significant effect on the residential 
house price at the significant level of 0.05 except the year 2006 in which the p-value was 
0.054, slightly smaller than 0.05. This indicated that overall, the increase of the residential 
house price over the years was statistically significant.
For the factors that drove the spatial variability of the residential house price, there 
were only five factors that had the p-values smaller than 0.05, including DensityRoad, 
DensitySupmarket, DensityBank, AreaUsedP and AreaShareAreaB. DensityRoad with 
a negative coefficient significantly affected the residential house price, meaning that the 
higher the density of roads, the lower the residential house price. Within a city, the higher 
road density often implies an old downtown where there is much noise and the houses 
were old and had lower quality, which would reduce one’s desirability of buying houses. 
DensitySupmarket also had a significant negative effect on the residential house price. In 
addition to noise, super markets often cause safe problems. Moreover, the residential 
houses are usually far away from super markets. Banks (DensityBank) provided conve­
nience for residents and thus had significantly positive an effect on the residential house 
price. The areas used by public (AreaUsedP) provided facilities such as recreation and 
sport areas for the residents and thus enhanced the extraction for the residents to buy the 
houses. The shared areas within buildings had a positive coefficient, seemed not reason­
able because the residents paid more for the residents shared areas. In fact, the residents 
liked to live in the buildings that had good facilities such as elevators and sport rooms.
Table 1. The coefficients of flexible spatiotemporal model based on the 
sample data of residential house price.
Coefficient
Standard error
p-value
(Intercept)
2974.66
222.83
0.000
as.factor(Time)2002
−120.30
277.37
0.665
as.factor(Time)2003
−164.37
260.14
0.528
as.factor(Time)2004
−456.48
290.14
0.116
as.factor(Time)2005
417.38
296.57
0.160
as.factor(Time)2006
575.53
298.60
0.054
as.factor(Time)2007
792.12
316.11
0.012
as.factor(Time)2008
1723.92
336.21
0.000
as.factor(Time)2009
2172.30
321.50
0.000
as.factor(Time)2010
3341.63
406.18
0.000
DisCenter
−130.66
135.56
0.335
DensityRoad
−411.78
135.61
0.002
DensityMarket
88.65
140.72
0.529
DensityBusS
−108.46
111.14
0.329
DensityTeleCom
−107.58
153.38
0.483
DensitySupmarket
−219.79
111.02
0.048
DensityPost
−57.82
99.85
0.563
DensityBank
423.41
197.04
0.032
AreaUsedP
234.20
78.76
0.003
UserShareAreaB
267.51
79.71
0.001
Density_2003Pop
79.39
112.92
0.482
JOURNAL OF APPLIED ECONOMICS
515


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Although other spatially driving factors did not significantly affect the residential 
house price, their coefficients were meaningful. DisCenter had a negative coefficient, 
suggesting that the larger the distances to the city center, the smaller the residential house 
price values. DensityBusS, DensityTeleCom and DensityPost all had negative coefficients 
because of noise and safety reasons. Moreover, the population densities of all the years 
were involved in the FSTM but removed except the population density of the year 2003 
with a positive coefficient.
3.4. The spatiotemporal predictions and accuracy assessment using FSTM
The obtained FSTM was used to make the spatiotemporal predictions of the residential 
house price that consisted of the spatial distribution maps of the residential house price 
for the years from 2001 to 2010. The 10-fold cross validation led to a RMSE value of 2087 
Chinese Yuan/m2, a relative RMSE of 58.8% (Table 2), a RMSPE value of 0.568 and 
a MAPE value of 0.41 (Table 3). The RMSPE and MAPE were statistically slightly 
significantly larger than zero at the significant level of 0.05 (Table 3).
In Table 2, the large RMSE and relative RMSE values were caused mainly by the large 
overall coefficient of variation, 86.4%. The annual relative RMSE ranged from 31.9% to 
114.2%. The extremely large value of the annual relative RMSE, 114.2%, occurred in 
the year of 2005 mainly because in 2005 there was one extremely large house price, 56,616 
Chinese Yuan/m2, collected from a business center, while the annual sample mean of 
the year 2005 was 3377 Chinese Yuan/m2. When developing the FSTM, we did not 
Table 2. The accuracy assessment of predicted residential house price values from 
FSTM based on root mean square error (RMSE) and relative RMSE (=RMSE×100/ 
sample mean).
Year
Sample mean
Average estimate
RMSE
RRMSE
2001
2825
2825
1726
61.1
2002
2763
2763
1784
64.6
2003
2695
2695
1472
54.6
2004
2468
2468
1288
52.2
2005
3377
3377
3857
114.2
2006
3710
3710
1546
41.7
2007
4170
4170
1488
35.7
2008
5055
5055
2950
58.3
2009
5442
5442
2010
36.9
2010
6727
6598
2147
31.9
Overall
3551
3551
2087
58.8
Table 3. Comparison of performance results for models as a spatial predictor (RMSPE and MAPE).
RMSPE MAPE R Square
Cross 
Validation 
Method
Independent Variable Set
FSM
0.568
0.410
0.275
10 fold cross 
validation
PricePer ~ List((Time)+ (DisCenter+DensityRoad+DensityMarket 
+DensityCar+DensityCom+DensitySupmarket+DensityPost 
+DensityBank+LandComArea+LandShareArea 
+Density_2003Pop)
516
L. WANG ET AL.