# RECORD 71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7
# TITLE: The Effect of War on Spatial Differentiation of Real Estate Values and Urban Disorder in the Damascus Metropolitan Area
# YEAR: 2024.0  VENUE: Urban Science  DOI: 10.3390/urbansci8040183
# AUTHORS: Mounir Azzam; Valerie Graw; A. Rienow
# PAGES: 27  CANDIDATE TABLE PAGES (1-based): [16, 17]
# ABSTRACT: The Syrian war, which commenced in 2011, transformed the Damascus real estate market due to heightened insecurity and tenure disputes. Using the hedonic price models, including 2411 housing transactions over the period 2010–2022, this study aims to understand the spatial dynamics of the real estate market in wartime. Our findings indicate that war variables have had a significant impact on the differentiation of property prices. Notably, property attributes have a more substantial impact on real estate values than district location, with severely damaged buildings in Damascus City resulting in an 89% decline in prices, while prices in Rural Damascus districts have decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google Earth Engine. Our findings show that correlations were highly differentiated, particularly among Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall between 2015 and 2019, they improved slightly after 2019. This study guides decision makers in mitigating severe property value variations across war-affected urban areas by fostering spatial justice in property rights and promoting balanced investment and sustainable real estate development during the post-war recovery phase.


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Citation: Azzam, M.; Graw, V.;
Rienow, A. The Effect of War on
Spatial Differentiation of Real Estate
Values and Urban Disorder in the
Damascus Metropolitan Area. Urban
Sci. 2024, 8, 183. https://doi.org/
10.3390/urbansci8040183
Academic Editors: Shivanand Balram,
Raja Sengupta and Jorge Rocha
Received: 12 September 2024
Revised: 18 October 2024
Accepted: 18 October 2024
Published: 22 October 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
The Effect of War on Spatial Differentiation of Real Estate Values
and Urban Disorder in the Damascus Metropolitan Area
Mounir Azzam *
, Valerie Graw and Andreas Rienow
Institute of Geography, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany;
valerie.graw@ruhr-uni-bochum.de (V.G.); andreas.rienow@ruhr-uni-bochum.de (A.R.)
* Correspondence: mounir.azzam@ruhr-uni-bochum.de
Abstract: The Syrian war, which commenced in 2011, transformed the Damascus real estate market
due to heightened insecurity and tenure disputes. Using the hedonic price models, including
2411 housing transactions over the period 2010–2022, this study aims to understand the spatial
dynamics of the real estate market in wartime. Our findings indicate that war variables have had a
significant impact on the differentiation of property prices. Notably, property attributes have a more
substantial impact on real estate values than district location, with severely damaged buildings in
Damascus City resulting in an 89% decline in prices, while prices in Rural Damascus districts have
decreased by 50%. Additionally, this study examines the urban texture of Damascus using correlation
and homogeneity statistics derived from the gray-level co-occurrence matrix obtained from Google
Earth Engine. Our findings show that correlations were highly differentiated, particularly among
Rural Damascus districts, with a total decline of 87.2%. While homogeneity values decreased overall
between 2015 and 2019, they improved slightly after 2019. This study guides decision makers
in mitigating severe property value variations across war-affected urban areas by fostering spatial
justice in property rights and promoting balanced investment and sustainable real estate development
during the post-war recovery phase.
Keywords: real estate differentiation; spatial justice; hedonic price; urban texture analysis; reconstruction
strategy
1. Introduction
The political economy of urban processes for Syrian cities has been dominated for
decades by the socialist system. Although the central ideology was egalitarian, there was
significant rural–urban variation [1,2]. The urban fabric of Damascus was distinguished by
clusters of cellular communities in which rural–urban interdependence triggers the orga-
nizing collective consumption, where real estate markets are among a set of items subjected
to this consumption in order to alleviate the bottleneck of urban inflation [3,4]. Within
each district, there was a high level of social mixture in the high-quality neighborhoods,
and there were significant contrasts with the adjacent informal housing quarters. Despite
many clear observations of urban disparity in the Damascus metropolitan area, especially
in the aftermath of the war [5–7], the understanding of spatial inequality of property values
remains unsystematic.
Living in proximity to areas affected by conflict poses significant risks, which should
be taken into account when valuing real estate [8,9]. Syria has endured more than a decade
of war, resulting in extensive property damage and reducing many cities to rubble [10].
Given this, the impact of war on property values is particularly pronounced in affected
districts, where it can be expected that prices for damaged or conflict-adjacent properties
will be lower than for those unaffected by conflict. In this study, the pertinent question is to
what extent can war negatively impact the spatial differentiation of real estate values, and
how can they be monitored?
Urban Sci. 2024, 8, 183. https://doi.org/10.3390/urbansci8040183
https://www.mdpi.com/journal/urbansci


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poral dependencies within the hedonic model. Nonetheless, these temporal classifications
have been instrumental in our analysis of mean housing prices before, during, and after
the war, as detailed in Sections 3.2 and 3.3.
Models 1 and 2 introduce property attributes for 12 districts in the Damascus Gov-
ernorate and 37 surrounding districts in the Rural Damascus Governorate, respectively,
to determine the relationship between the property values and the effects of war. Models
3 and 4 introduce district attributes and monitor the spatial differentiation of property
values by comparing transactions in different areas. The hedonic models report coefficients,
standard errors, and p-values to indicate the mathematical change, accuracy, and statistical
significance of estimates. The coefficients of each variable have been interpreted as percent-
age changes in property prices relative to a baseline reference. Given that the dependent
variable is transformed to the natural logarithm of real estate transaction prices, we apply
the exponential function to these coefficient estimates to facilitate their interpretation as per-
centage changes associated with a one-unit change in each respective independent variable.
Specifically, we compute the percentage change using the formula (exp (β) −1) × 100.
3.4.1. Property Characteristics
Looking at the coefficients and standard errors in models 1 and 2 show that all war
variables are statistically significant at the one percent level and represent large implied
economic impacts (Table 3). There is a strong statistical association between the decline
in property values and the extent of building destruction. This suggests that, as buildings
become more destroyed, prices will drop more. Despite the high negative signs, the
magnitudes indicate a potentially considerable willingness to pay for homes with poor
structural conditions. However, this trend should be viewed with suspicion, as it may
be motivated by investment rather than living, as suggested by the saying “buy on the
cannons, sell on the trumpets” [80,81].
Table 3. Hedonic regression results with property attributes.
Variable
Model 1
Damascus Districts
Model 2
Rural Damascus Districts
Intercept
10.8546 (0.3768) ***
9.0971 (0.2714) ***
Area
0.0032 (0.0008) ***
0.0104 (0.0011) ***
Age
−0.0018 (0.0025)
−0.0040 (0.0020) *
State_p
0.3427 (0.0368) ***
0.2380 (0.0246) ***
View
0.1568 (0.0379) ***
0.1489 (0.0247) ***
F_right
−0.6878 (0.3477) *
−0.3506 (0.2494)
P_right
−0.6146 (0.3457)
−0.3471 (0.2478)
N_bed
0.1361 (0.0337) ***
−0.0201 (0.0268)
N_bath
0.0600 (0.0530)
−0.0436 (0.0370)
N_balcony
0.0491 (0.0514)
0.0229 (0.0345)
Parking
−0.0572 (0.0692)
0.2003 (0.0734) **
Elevator
−0.0411 (0.0799)
0.0432 (0.0420)
SDB
−2.2511 (0.4437) ***
−0.6999 (0.0974) ***
MDB
−1.3450 (0.1674) ***
−0.6056 (0.0596) ***
LDB
−1.3786 (0.1184) ***
−0.5678 (0.0552) ***
UB
−1.2019 (0.0826) ***
−0.4687 (0.0539) ***
R2
0.603
0.474
Adjusted R2
0.596
0.469
Number of observations
900
1511
Legend: * p < 0.05; ** p < 0.01; *** p < 0.001.
Model 1 indicates that SDB in Damascus City reduces prices by about 89%, while MDB
and LDB decrease prices by about 74%. Although UB properties should have significantly
higher values, the coefficients show similar large decreases of around 70%. However, in
model 2, which depicts the situation in Rural Damascus districts, the figures indicate that


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Urban Sci. 2024, 8, 183
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property values decrease proportionally less compared to Damascus City. Prices drop by
50% for SDB and gradually decrease based on the level of damage, to reach 37% for UB.
The results of the regression analysis indicate that the war variables had a dominant
effect on real estate values compared to other independent variables. The coefficients of age,
ownership, full and partial right ownership, and number of rooms were not statistically
significant. In model 1, a one-unit increase in the share of the area led to a 0.3% increase
in the sales price, while a one-year increase in the age of the property led to only a 0.18%
decrease. Similarly, in model 2, an increase of one unit in the share of the area and
age of the property was associated with only a 1% increase and 0.4% decrease in sales
price, respectively.
3.4.2. District Characteristics
Introducing a set of war variables as control variables in district attribute models 3
and 4, Table 4 shows a significant impact on the pattern of hedonic results in terms of
coefficient magnitude and statistical significance. This leads to a better monitoring of
spatial differentiation of housing prices and more credible estimates of war’s impact on
property values based on district damage classification. Model 3 shows that State_N, QI,
and accessibility are statistically significant categorical variables, attracting a positive price
premium of 17.5%, 47.3%, and 20% in the sales price for each category, respectively.
Table 4. Hedonic regression results with district attributes.
Variable
Model 3
Damascus Districts
Model 4
Rural Damascus Districts
Intercept
9.6587 (0.6956) ***
8.8499 (0.6902) ***
State_n
0.1616 (0.0516) **
0.3971 (0.0294) ***
HD
−0.0184 (0.0505)
0.1048 (0.0217) ***
QI
0.3872 (0.0496) ***
0.1436 (0.0262) ***
Regular
−0.3343 (0.5304)
−0.1010 (0.6822)
Slums
−0.2968 (0.5293) ***
−0.0509 (0.6800)
Accessibility
0.18214 (0.06331) **
−0.0496 (0.0214) *
D_city_center
0.00006 (0.00004) ***
0.000040 (0.000009) **
SDD
−0.4491 (0.1621) **
−0.6483 (0.0526) ***
MDD
−1.2068 (0.2198) ***
−0.4209 (0.0709) ***
UD
−0.7933 (0.0931) ***
−0.5484 (0.0733) ***
D_conflict_point
0.00005 (0.00002) ***
0.000002 (0.000016)
R2
0.427
0.432
Adjusted R2
0.420
0.427
Number of observations
900
1511
Legend: * p < 0.05; ** p < 0.01; *** p < 0.001.
Model 4 reveals that State_N has the greatest impact on property values in Rural
Damascus districts, with a 48.7% increase in selling price for each score, followed by QI,
which drives a 15% increase. HD also has a significant association, resulting in an 11%
increase in selling price. However, HD in model 3 is associated with a negative sign
(−1.8% decrease in selling price), indicating that high-density housing during wartime is
relatively safe and can increase the property value, while conflict-prone areas that have
been converted to low-density residences because of the destruction. In contrast, low-
density neighborhoods in Damascus City are commonly perceived as exclusive housing
areas with high market values. Conversely, informal settlements are frequently associated
with overcrowding, substandard housing conditions, and decreasing property prices.
The war dummy variables in district attributes resulted in negative coefficients similar
to those observed in property attributes. However, model 3 shows that the impact of war is
more pronounced in the Damascus Governate. For instance, at Tadamon (MDD), property
prices plummeted by 70%, while Al Kadam (SDD) decreased by 36% and UD by 54.7%. In
contrast, model 4 indicates that MDD areas had the smallest decline of approximately 34%,


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Urban Sci. 2024, 8, 183
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although there were no significant spatial differences. On the other hand, SDD and UD
resulted in a decrease of 48% and 42%, respectively.
As might be expected, real estate values become more important when looking at
property features, irrespective of the district’s condition. In the post-war period, the results
indicate a higher willingness to pay for well-maintained housing in SDD, which offers
affordable property prices and attractive future prospects.
The analysis reveals that proximity to conflict points is statistically significant only
in model 3 and has a positive sign in both models, indicating that the distance to conflict
points has a significant impact on property values. In other words, residents are paying less
to reside close to conflict points. Specifically, a one-kilometer decrease in distance to conflict
points lowers property values by 5% in Damascus districts and by only 0.2% in Rural
Damascus districts. The distance to the city center, measured from the Great Umayyad
Mosque, is also statistically significant in both models and has a positive sign. However,
this does not necessarily lead to improved property values. This finding suggests that the
real estate market in Damascus is less centralized.
3.5. Texture Analysis
This section systematically explores the impact of spatial changes on real estate differ-
entiation by analyzing the changes in GLCM homogeneity and correlation values in relation
to property prices. The study compares results between two phases: the most heavily
damaged phase of the war in Damascus (2015–2019) and the post-war phase (2019–2022)
following local reconciliation agreements between conflicting parties. The GLCM homo-
geneity values ranged from 0.0057 to 0.0296, while the correlation values ranged from 0.38
to 0.9, categorized into low and high classes based on the midpoint (Figure 7).
Figure 7. Reference image, homogeneity plan, and correlation plan for 2015, 2019, and 2022 in the
study area.