# RECORD 8674ce1937729ab9e1f1084511ae7a29ecdbc635
# TITLE: The Role of Subjective Perceptions and Objective Measurements of the Urban Environment in Explaining House Prices in Greater London: A Multi-Scale Urban Morphology Analysis
# YEAR: 2023.0  VENUE: ISPRS Int. J. Geo Inf.  DOI: 10.3390/ijgi12060249
# AUTHORS: Sijie Yang; K. Krenz; Waishan Qiu; Wen-Jing Li
# PAGES: 21  CANDIDATE TABLE PAGES (1-based): [9, 13, 14, 15, 16]
# ABSTRACT: House prices have long been closely related to the built environment of cities, yet whether the subjective perception (SP) of these environments has a differing effect on prices at multiple urban scales is unclear. This study sheds light on the impact of people’s SP of the urban environment on house prices in a multi-scale urban morphology analysis. We trained a machine learning (ML) model to predict people’s SP of the urban environment around properties across Greater London with survey response data from an online survey evaluating people’s SP of street view image (SVI) and linked this to house price data. This information was used to construct a hedonic price model (HPM) and to evaluate the association between SP and house price data in a series of linear regression models controlling location information and urban morphological characteristics such as street network centralities at multiple urban scales, quantified using space syntax (SS) methods. The findings show that SP influences house prices, but this influence differs depending on the urban scale of analysis. Particularly, a sense of ‘enclosure’ and ‘comfort’ are important factors influencing house price variation. This study contributes by introducing SP of the urban environment as a new dimension into the traditional HPM and by exploring the economic impact of SP on the house price market at multiple urban scales.


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6.2
3.099
The Role of Subjective Perceptions
and Objective Measurements of the
Urban Environment in Explaining
House Prices in Greater London: A
Multi-Scale Urban Morphology
Analysis
Sijie Yang, Kimon Krenz, Waishan Qiu and Wenjing Li
Article
https://doi.org/10.3390/ijgi12060249


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models. Backpropagation and its application to neural networks were first proposed and
further developed by Rumelhart et al. [86]. This multilayer perceptual model can cope with
arbitrarily complex patterns for classification and provides sufficient multi-dimensional
function mapping. As shown in Figure 5, in this study, the view indexes of 30 physical
elements in a single SVI, e.g., Vsky, Vwall, Vtree, constitute the input layer. After the function
calculation in the hidden layer, the output layer yields the scores of this image on eight
subjective perceptual attributes, e.g., Simageability, Senclosure, Shuman scale. The BP neural net-
work model was used as a classifier to rate each SP variable of each SVI as an integer score
ranging from 1–5 based on the information from the SVI segmentation. The data for the
pre-trained model are derived from the results of the online perception survey based on
300 pre-determined SVIs and view indexes extracted from these images. Eighty percent of
the data are used as a training set to build the BP neural network model, while 20% are
used as a testing set to assess the model’s accuracy.
 
Figure 5. Algorithms diagram of Backprop Neural Network.
Accuracy, Precision, Recall, and F1 Score evaluate the prediction performance of
a model. The best-performing model was then used to predict the SP scores for all
70,059 SVIs. The streetscape perception scores within 1 km of a property were averaged to
represent the quality of the street environment in the property’s neighborhood. However,
it is acknowledged that what constitutes a neighborhood can vary in size and scale based
on individual preferences. Descriptive statistics for the SP scores of the street environment
around each property are presented in Table 2. In Figure 6, one can see that the SVIs are seg-
mented according to physical features to obtain visual indices of different physical features.
Subsequently, the subjective streetscape perception scores are predicted by the ML model.
Table 2. Descriptive statistics of all variables.
Variable
Description
Count
Mean
Std.Dev.
Min
Max
Data Source
PRICE
£/m2, dependent variable
49,603
6793.59
3427.28
117.77
91,866.95
LR-PPD data
House Structure attribute
H1_FLARA
Total floor area (m2)
49,603
91.25
57.15
6.26
4373.00
EPCs data
H2_INSUP
House insulation
performance
49,603
2.58
1.63
1.00
5.00
H3_LIGTP
House lighting performance
49,603
3.69
1.52
1.00
5.00
H4_HOTWP
House hot water performance
49,603
3.80
0.89
1.00
5.00
H5_CO2EM
House CO2 Emission
49,603
2.00
1.62
−1.40
66.00


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Table 2. Cont.
Variable
Description
Count
Mean
Std.Dev.
Min
Max
Data Source
Description
Values
Count
Percent
Avg.Price
Avg.Area
Data Source
H6_FLLEV
Floor level
1: Low
15,959
32.17%
6908.76
65.03
EPCs data
2: Mid
32,093
64.70%
6698.90
105.08
3: High
1551
3.13%
7571.03
74.85
H7_PROTY
Property
type
1: Detached
3464
6.98%
6123.85
174.60
2: Flat
24,841
50.08%
7539.41
67.13
3: Semi
7961
16.05%
5683.22
113.49
4: Terrace
13,337
26.89%
6244.62
101.24
H8_MENER
Main energy
source
1: Electricity
4626
9.33%
6602.96
63.06
2: Gas/LPG
38,167
76.94%
6409.88
97.83
3: Oil/Coal
101
0.20%
6684.66
100.98
4: Others
6709
13.53%
9111.52
73.11
Subjective urban perception variable
Count
Mean
Std.Dev.
Min
Max
Data Source
S1_IMBLY
Perceived imageability
49,603
3.05
0.36
1.00
4.16
SVIs, Investi-
gation data,
ML results
S2_ENCLS
Perceived enclosure
49,603
2.86
0.46
1.50
4.65
S3_HMSCL
Perceived human scale
49,603
3.06
0.26
1.00
4.29
S4_TRANS
Perceived transparency
49,603
2.82
0.20
2.00
4.00
S5_CMPLY
Perceived complexity
49,603
3.24
0.27
1.33
4.00
S6_SAFTY
Perceived sense of safety
49,603
3.24
0.22
1.00
4.50
S7_COFRT
Perceived sense of comfort
49,603
3.03
0.21
2.00
4.00
S8_INTST
Perceived level of interest
49,603
3.09
0.17
2.00
4.33
Objective urban perception variable
Count
Mean
Std.Dev.
Min
Max
Data Source
Location Attribute (Urban morphology)
L1_D2CBD
Cost network distance to CBD
49,603
0.30
0.15
0.02
0.81
OS data
L2_POSDT
Postcode District
49,603
/
/
/
/
M1_INT400
Space syntax-Integration[HH]
(R400)
49,603
23.18
6.63
3.56
68.69
M2_CH400
Space syntax-Choice (R400)
49,603
108.54
59.18
0.00
902.55
M3_INT800
Space syntax-Integration[HH]
(R800)
49,603
49.09
20.54
3.56
176.85
M4_CH800
Space syntax-Choice (R800)
49,603
788.61
485.01
0.00
6399.87
M5_INT2000
Space syntax-Integration[HH]
(R2000)
49,603
155.50
88.85
3.56
575.72
M6_CH2000
Space syntax-Choice (R2000)
49,603
11,141.62
7914.72
0.00
47,980.45
M7_INT6000
Space syntax-Integration[HH]
(R6000)
49,603
650.86
459.06
3.56
2148.27
M8_CH6000
Space syntax-Choice (R6000)
49,603
272,714.0
249,841.4
0.00
1,552,460
Neighbourhood and Environment Attribute (Urban Functional Property)
F1_DENLS
Density of urban services
(within 1 km)
49,603
341.28
384.77
0.00
5515.00
OS data
F2_DENWK
Density of workplace (within
1 km)
49,603
337.43
463.93
1.00
5880.00
F3_DENAT
Density of attraction (within
1 km)
49,603
27.36
40.80
0.00
572.00
F4_D2UDG
Distance to TfL station (km)
49,603
0.75
0.58
0.00
7.26
TfL data
F5_A2UDG
Accessibility to TfL station
(within 3 km)
49,603
15.94
13.04
0.00
70.00
F6_D2SCH
Distance to quality school
(km)
49,603
0.56
0.40
0.00
6.94
Ofsted data
F7_A2SCH
Accessibility to quality school
(within 3 km)
49,603
24.93
14.76
0.00
83.00


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3. Results
3.1. Subjective Urban Perception Prediction
3.1.1. Accuracy of the Machine Learning Prediction Model
The prediction performance and model evaluation of the BP neural network model
on the SP scores of the urban street environments are shown in Table 3. The model
performed on average moderate or above for all subjective perceptual variables, with
values greater than 0.5 based on a generally accepted rule of thumb evaluation criteria. The
best-performing perceptual prediction models are models of ‘imageability’ (S1), ‘human
scale’ (S3), ‘transparency’ (S4), ‘sense of comfort’ (S7), and ‘level of interest’ (S8), all with
accuracy greater than 0.7, of which ‘human scale’ has the highest accuracy of 0.795. The
prediction models for ‘enclosure’ and ‘sense of safety’ are less accurate at 0.643 and 0.652,
respectively, while the model for ‘complexity’ has the lowest accuracy at 0.579. The accuracy
of the SP model predictions in this study is relatively high, ranging from 0.58 to 0.80, which
is a significant improvement compared to previous studies, with R2 of Verma, et al.’s
prediction model [98] ranging from 0.20 to 0.66, and R2 of Qiu, Zhang, Liu, Li, Li, Xu and
Huang’s perception prediction model [30] ranging from 0.47 to 0.61.
Table 3. Performance of BP Neural Network predictions.
Perception
Accuracy
Precision
Recall
F1-Score
Criteria
S1. Imageability
0.710
0.731
0.725
0.721
Good
S2. Enclosure
0.643
0.630
0.666
0.629
Moderate
S3. Human Scale
0.795
0.785
0.780
0.779
Good
S4. Transparency
0.722
0.723
0.732
0.724
Good
S5. Complexity
0.579
0.628
0.613
0.613
Moderate
S6. Sense of Safety
0.652
0.657
0.664
0.659
Moderate
S7. Sense of Comfort
0.720
0.726
0.732
0.716
Good
S8. Level of Interest
0.711
0.737
0.724
0.729
Good
Evaluation criteria: >90% very good, 70–90% good, 60–70% moderate, <60% low.
A higher accuracy indicates fewer variations in survey scores, which in turn may imply
that these SP dimensions can be better understood by respondents [5,30]. Imageability (S1),
human scale (S3), transparency (S4), sense of comfort (S7), and level of interest (S8), all
show higher levels of accuracy whereas complexity (S5) showed lower levels of accuracy,
hinting towards a more ambiguous perception of the latter.
3.1.2. Spatial Heterogeneity of Urban Subjective Perception
Figure 7 shows the spatial distribution of SP prediction results, with clear spatial
patterns for some SP variables. For the three SP variables of ‘imageability’ (S1), ‘enclosure’
(S2), and ‘complexity’ (S5), there is a strong tendency for the ratings to cluster geographically
towards the city center, with enclosure being the most pronounced. This phenomenon
can be explained by the high density of buildings in the city center, where the presence of
buildings makes the street space more impressionistic and more enclosed and adds to the
diversity and complexity of the urban space. For the three variables ’human scale’ (S3),
‘transparency’ (S4), and ‘sense of safety’ (S6), the geographical distributions are reversed,
with scores highly concentrated in the periphery of the city. The urban periphery, with
fewer buildings and more greenery, is considered by respondents to have a ‘human scale’
(S3), ‘transparency’ (S4), and a ‘sense of safety’ (S6). The spatial distributions of ‘sense of
comfort’ (S7) and ‘level of interest’ (S8) ratings are scattered across the research region, with
interest scores being the most dispersed, suggesting that people’s perceptions in these two
SP dimensions are more complex.


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Figure 7. Spatial distribution of properties with SP scores.
3.2. Spatial Hedonic Price Model Result
3.2.1. Correlation of Each Attribute Group with House Prices
Table 4 shows the strength of the four attribute dimensions that make up the HPM in
explaining house prices (p < 0.01). Location attributes, i.e., urban morphology attributes,
are the strongest in explaining house prices (R2 = 0.427), followed by SP scores (R2 = 0.342)
and neighborhood attributes (R2 = 0.339). The weakest influencing attribute group is the
set of house structure attributes (R2 = 0.085).
Table 4. Model performance of each attribute group.
OLS Diagnosis
House Structure Attributes
Location Attributes
(Urban Morphology)
Neighborhood Attributes
(Urban Function)
Subjective Perception Scores
Adjusted R2
0.085 ***
0.427 ***
0.342 ***
0.342 ***
Pr. (F-statistic)
0.000 ***
0.000 ***
0.000 ***
0.000 ***
*** p < 0.01 level.
3.2.2. Results of Regression Models with Multi-Scale Urban Morphology
The OLS model results are shown in Table 5, and the impact ranking of each of the
five urban scale models is shown in Figure 8 respectively. The baseline model (Model 0)


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uses traditional location attributes, i.e., the property’s network cost distance to the CBD
(L1_D2CBD) and its postcode district (L2_POSDT). The other models (Model 1–4) apply
different scales of integration and choice as location attributes of the HPM. Table 5 shows
the regression model’s performance and diagnostic outcomes. The adjusted R2 indicates
a comparable association with house prices among the five models. When comparing
Model 0 and Model 4, the overall performance of the two models is comparable after
replacing the traditional location attributes with the urban morphology variables of space
syntax (R2 = 0.496, R2 = 0.494). However, the integration variable in Model 4 (M7_INT6000,
M8_CH6000) increases the models standardized coefficients, as shown in Figure 8. Such
a result suggests that integration captures characteristics of the urban environment that
influence house prices that traditional location attributes have been unable to represent.
Table 5. Regression with multi-scale urban morphology: results and diagnosis.
Model 0
Model 1
Model 2
Model 3
Model 4
Location
Attribute
Baseline
(L1, L2)
M1, M2
(R400)
M3, M4
(R800)
M5, M6
(R2000)
M7, M8
(R6000)
Adjusted R2
0.496
***
0.482
***
0.484
***
0.486
***
0.494
***
Pr. (F-statistic)
0.000
***
0.000
***
0.000
***
0.000
***
0.000
***
Variable
Coef.
P > t
Coef.
P > t
Coef.
P > t
Coef.
P > t
Coef.
P > t
CONSTANT
***
***
***
***
***
House Structure attribute
H1_FLARA
−0.007
−0.009
**
−0.012
**
−0.014
**
−0.016
***
H2_INSUP
−0.009
**
−0.006
−0.001
−0.001
0.004
H3_LIGTP
−0.039
***
−0.037
***
−0.036
***
−0.036
***
−0.035
***
H4_HOTWP
0.002
0.002
0.001
0.002
0.003
H5_CO2EM
0.054
***
0.059
***
0.059
***
0.06
***
0.059
***
H6_FLLEV
0.068
***
0.062
***
0.063
***
0.062
***
0.066
***
H7_PROTY
−0.026
***
−0.02
***
−0.021
***
−0.021
***
−0.02
***
H8_MENER
0.122
***
0.135
***
0.136
***
0.134
***
0.137
***
Subjective urban perception variable
S1_IMBLY
0.024
***
0.032
***
0.031
***
0.035
***
0.029
***
S2_ENCLS
0.135
***
0.157
***
0.16
***
0.164
***
0.125
***
S3_HMSCL
0.043
***
0.037
***
0.035
***
0.033
***
0.027
***
S4_TRANS
−0.022
***
−0.009
***
−0.009
***
−0.01
***
−0.017
***
S5_CMPLY
0.028
***
0.034
***
0.028
***
0.028
***
0.026
***
S6_SAFTY
−0.039
***
−0.051
***
−0.051
***
−0.052
***
−0.048
***
S7_COFRT
0.112
***
0.124
***
0.126
***
0.123
***
0.112
***
S8_INTST
−0.043
***
−0.044
***
−0.044
***
−0.041
***
−0.037
***
Objective urban perception variable
Location Attribute (Urban Morphology)
L1_D2CBD
−0.098
***
/
/
/
/
L2_POSDT
0.136
***
/
/
/
/
M1_INT400
/
0.115
***
/
/
/
M2_CH400
/
−0.052
***
/
/
/
M3_INT800
/
/
0.217
***
/
/
M4_CH800
/
/
−0.14
***
/
/
M5_INT2000
/
/
/
0.286
***
/
M6_CH2000
/
/
/
−0.185
***
/
M7_INT6000
/
/
/
/
0.324
***
M8_CH6000
/
/
/
/
−0.087
***


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Table 5. Cont.
Model 0
Model 1
Model 2
Model 3
Model 4
Neighborhood Attribute (Urban Functional Property)
F1_DENLS
0.135
***
0.111
***
0.088
***
0.087
***
0.102
***
F2_DENWK
−0.137
***
−0.177
***
−0.165
***
−0.164
***
−0.181
***
F3_DENAT
0.419
***
0.486
***
0.479
***
0.456
***
0.437
***
F4_D2UDG
−0.028
***
−0.019
***
−0.018
***
−0.021
***
−0.02
***
F5_A2UDG
0.048
***
0.044
***
0.046
***
0.041
***
0.009
***
F6_D2SCH
0.041
***
0.031
***
0.032
***
0.03
***
0.034
***
F7_A2SCH
−0.071
***
−0.039
***
−0.045
***
−0.067
***
−0.126
***
** p < 0.05 level, *** p < 0.01 level.
 
Figure 8. Impact ranking in models based on absolute standardized coefficients.
For models 1–4, while different scales of urban morphology are used as the location
attribute, no significant change in R2 can be observed. Meanwhile, the density of attractions
(F3_DENAT) exhibits the highest standardized coefficient among all models, implying that
being close to functional opportunities has a greater influence on house prices than being
closer to generic opportunities. Other important functional variables include the density of
workplaces (F2_DENWK) and the density of urban services (F1_DENLS).
The models also show that the effect of people’s SP varies depending on varying
urban morphological scales. At a relatively large scale, i.e., a radius of 6 km (Model 4),
the integration variable (M7_INT6000) can better explain house price variation, than the
SP variables ‘enclosure’ and ‘sense of comfort’ (S2_ENCLS, S7_COFRT). The importance
diminishes as the scale decreases (Model 2, Model 3) and eventually falls below some SP
variables (Model 1). The choice variable has a similar trend in Models 1–3 but is not as
noticeable as the integration value.
In terms of SP attributes, ‘enclosure’ and ‘sense of comfort’ can best explain house price
variation, followed by ‘safety’ and ‘interest’, whereas ‘imageability’, ‘transparency’ and
‘complexity’ are the lowest. This result indicates that when holding other conditions equal,


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people’s SP plays a role in shaping the house price market. Particularly areas featuring a
sense of enclosure and which are also comfortable to live in tend to be more valued than
other places.
4. Conclusions
The findings show that SP of the built environment has a significant effect on house
prices. This effect is higher than neighborhood attributes (i.e., urban functions) or house
structure attributes. However, our analysis has shown that location attributes (i.e., urban
morphology) have a higher importance than SP attributes. This suggests that people’s SP
variables of the urban environment have significant impacts on house prices, above some
objective urban environment attributes.
The HPM based on multi-scale urban morphology shows that when comparing urban
morphological attributes with SP attributes, the smaller the scale of urban morphology
(i.e., the more local the measure), the more important is the SP. Moreover, the larger the
scale of urban morphology (i.e., the more global the measure), the more important are the
urban morphological attributes. For the different SP variables, ‘enclosure’ (S2_ENCLS)
and ‘sense of comfort’ (S7_COFRT) have more significant impacts on house prices than
other variables. This study adds the SP dimension to the house price assessment model,
helping to explore the impact of SP on house prices at different scales of urban morphology.
Future related research could refer to such a framework using SVI data and SP surveys
to incorporate people’s SP of the urban streetscape near the property into house price
assessment at multiple urban scales supported by SS techniques.
5. Limitation and Future Work
There are several limitations to this study. First, this study only used visual indices
from SVI segmentation in building an SP prediction model. The prediction accuracy
of this approach may be improved by increasing the quality and precision of SVI data
extraction and expanding the volume of perception survey data: (i) more features, such as
HSL histograms and Blob detection, may be added to complement the physical features
presented by the SVIs; (ii) more field experiences and surveys may be added to the study to
cross-validate the SP predictions and minimize the bias introduced by web image surveys.
Second, in the SP survey, some participants’ understanding of the concepts of SP may not
accurately capture their perception. A better questionnaire design and question setting as
well as a larger sample size could improve this limitation. Future research could involve a
larger more representative sample including urban design professionals to provide a more
accurate and efficient assessment of the street environment in images. In addition, VR
technology and panoramic SVIs can be used in the perception survey sessions to provide
participants with a more realistic environment and more accurate exploration of their SP.
Finally, many studies have shown that spatial dependence and non-stationarity violate the
basic assumptions of OLS regression. Due to spatial correlation effects, OLS regression
models may be biased in their coefficients and report incorrect significance. More tests for
OLS regression may be carried out, such as Moran’s I and robust Lagrange multiplier (LM)
tests, to see if lags and errors due to spatial correlation exist. Given there are spatial lags or
errors in OLS regression models, other regression models that solve for spatial dependence
may be introduced for more accurate multi-factor impact studies, including the Kelejian–
Prucha’s model (SAC) [99] and geographically weighted regression (GWR) [100]. These
models can effectively manage spatial dependence and non-stationarity-related issues and
achieve higher R2 values, thereby enhancing the models’ precision and providing stronger
data support for the paper’s conclusions [51,55].
Author Contributions: Conceptualization, Sijie Yang, Kimon Krenz, and Waishan Qiu; methodology,
Sijie Yang, Kimon Krenz, and Wenjing Li; software, Sijie Yang; validation, Sijie Yang; formal analysis,
Sijie Yang; investigation, Sijie Yang; resources, Wenjing Li; data curation, Sijie Yang; writing—original
draft preparation, Sijie Yang; writing—review and editing, Kimon Krenz and Waishan Qiu; visualiza-