Enhancing Property Valuation in Post-War Recovery: Integrating War-Related Attributes into Real Estate Valuation Practices

Abstract

In post-war environments, property valuation encounters obstacles stemming from widespread destruction, population displacement, and complex legal frameworks. This study addresses post-war property valuation by integrating war-related considerations into the ISO 19152 Land Administration Domain Model, resulting in a valuation information model for Syria’s post-war landscape, serving as a reference for property valuation in conflict-affected areas. Additionally, property valuation is enhanced through visualization modeling, aiding the comprehension of war-related attributes amidst and following conflict. We utilize data from a field survey of 243 Condominium Units in the Harasta district, Rural Damascus Governorate. These data were collected through quantitative interviews with real estate companies and residents to uncover facts about property prices and war-related conditions. Our quantitative data are analyzed using inferential statistics of mean housing prices to assess the impact of war-related variables on property values during both wartime and post-war periods. The analysis reveals significant fluctuations in prices during wartime, with severely damaged properties experiencing notable declines (about −75%), followed by moderately damaged properties (about −60%). In the post-war phase, rehabilitated properties demonstrate price improvements (1.8% to 22.5%), while others continue to depreciate (−55% to −65%). These insights inform post-war property valuation standards, facilitating sustainable investment during the post-war recovery phase.

1. Introduction

Property valuation stands as a crucial linchpin in the national economic landscape [1,2], rooted in its ability to provide services meeting human needs, contributing to overall well-being [3]. In instances where such valuation practices necessitate alignment with established traditions governing property rights, a meticulous consideration of real estate conditions across diverse geographical locations becomes imperative [4]. The documentation of property rights unfolds within a jurisdiction-specific intricate framework that encompasses policies, laws, and strategic measures [5].

The Syrian conflict, initiated in 2011, has resulted in the widespread destruction of residences, the loss of real estate documentation, and compelled property sales [6]. Significantly, more than half of the population experienced displacement from their homes, particularly notable in southern cities [7]. A substantial portion of internally displaced individuals reported the absence of property documentation [8]. This issue is further compounded by the proliferation of legislation systematically undermining the property rights and legal standing of individuals lacking proximity, exemplified by Law 10 of 2018 [9]. This legal provision empowers the government to designate areas for redevelopment, imposing a requirement for property owners to register their claims within a specified timeframe. This poses formidable challenges for individuals who have been displaced or encounter obstacles in accessing proper documentation, resulting in a considerable number of people finding themselves without houses they possessed before the commencement of the conflict [10]. Beyond legal impediments, the vulnerability arising from the varied structures of neighborhoods in Damascus and its surrounding areas exacerbates the crisis. Despite potential indications of an end to armed hostilities, the ongoing lack of confidence in “community security” persists in these districts, posing a continued threat [11].

The recovery of the real estate sector in the aftermath of conflict poses challenges on a global scale [7]. Nations across the world grapple with difficulties in addressing the consequences of violence, including forced displacement, compromised security, and enduring property losses, persisting for decades following the cessation of hostilities [12]. In the preceding decades, an international legal and public policy framework has evolved pertaining to the recovery of housing and property in the aftermath of war [13]. Nevertheless, the mechanisms intended to mitigate the repercussions of property damage, partially address the issues of damage valuation [14], lacking a clear vision for achieving spatial empowerment within the valuation process.

This study formulates a systematic procedure for post-war real estate valuation, aiming to uphold property rights and promote equitable development. This procedure, particularly relevant in conflict-affected neighborhoods, differs from conventional approaches, such as the direct market comparison, which relies on comparing a property with a similar one recently sold in the same neighborhood, becomes impractical in post-war contexts due to unique war-related attributes that affect each valuation unit differently. Similarly, the income approach, which bases valuation on the property’s rental or lease income, proves unreliable when destruction renders income generation uncertain or impossible [15]. Additionally, while the cost approach appears logical, as it estimates property value by accounting for supposed improvements and deducting the physical, functional, and economic damage, calculating destruction costs in a descriptive manner often raises doubts about the method’s reliability [16].

This study stands out by prioritizing an understanding of the nuanced impact of war-induced damage on real estate. The core focus lies in tailoring the investment process to intricately facilitate a balanced and sustainable development in the post-war real estate landscape. Its effectiveness is evident when aligned with national land and property administration systems. Insights from the post-war Syrian experience inform this methodology, serving as a relevant case study.

Several international organizations are devoted to enhancing the quality and credibility of valuation practices globally. The International Valuation Standards Council (IVSC) plays a pivotal role in establishing rigorous standards that ensure consistency and transparency in the property valuation process. The IVSC also offers professional guidance on integrating sustainability features into valuations, thus fostering greater professionalism within the global valuation community [17,18,19]. Similarly, the Royal Institution of Chartered Surveyors (RICS) provides extensive valuation and appraisal standards through its Red Book. The Red Book emphasizes the processes and foundations of property valuation while granting valuers the flexibility to select the most appropriate methods. It serves as an invaluable resource for appraisers and other stakeholders. The latest updates to the RICS Global Red Book reflect contemporary advancements in sustainability and technology, thereby further refining valuation practices [20,21].

The International Organization for Standardization (ISO), one of the oldest non-governmental international bodies focused on property valuation, is designed to encompass all stages of the valuation process. It facilitates the integration of various data sources, including spatial, temporal, and attribute data [22]. In response to the contextual challenges delineated, an augmentation of the ISO 19152 Land Administration Domain Model (LADM), denoted as the valuation information model (LADM_VM), has been systematically devised. This augmentation integrates attributes germane to wartime exigencies, with the specific objective of formulating the LADM_VM post-war country profile for Syria as an indispensable component intrinsic to the overarching post-war recovery strategy. This country profile operates as a foundational construct, providing a systematic framework for the valuation information pertinent to individual Condominium Units (Condo-Unit)—identified as the central element in our property valuation model���as well as the broader entities within its purview, encompassing buildings, land parcels, neighborhoods, and districts.

Following a comprehensive analysis of the data models underpinning both the LADM_VM, war-related characteristics, and national geospatial standards, the proposed LADM_VM post-war model emerges as a conceptual framework. Our scholarly insights contribute to establishing this model as a reference data model, facilitating the development of property valuation practices tailored for post-disaster scenarios and recovery strategies.

The post-war recovery procedures require reinforcement through 3D modeling aligned with information-centric registration practices and legal regulations governing real estate [23]. Nowadays, the intricate physical and legal complexities inherent in urban environments underscore the imperative for property registration to transition from 2D to 3D paradigm [24]. In times of conflict, these complexities are exacerbated, primarily due to extensive property destruction. Consequently, the incorporation of 3D modeling becomes imperative to delineate the real estate status of buildings and neighborhoods. This ensures that property valuation is transparent, equitable, and, thereby, beneficial for all stakeholders with rights to the properties in question [25].

In this context, there’s an urgent need for a 3D visualization model to depict property distribution before and after war. This will enhance understanding of the post-war real estate landscape and aid targeted recovery efforts. Our study introduces a multilevel 3D real estate visualization model integrating physical entities and war-related attributes for post-war property valuation in Syria.

In this paper, we have systematically gathered data pertaining to 243 Condo-Units distributed across 41 buildings, providing a comprehensive overview of the real estate scenario in a neighborhood within the Harasta district, Rural Damascus Governorate, during the pre-war (2010), during-war (2019), and post-war (2023) periods. The analysis is substantiated by Mean Housing Prices per square meter (MHP/sqm), enabling a nuanced comparison of the war-related variables’ impact on the value of buildings and property portfolios. These comparisons are facilitated through the representation of two- and three-dimensional plans, serving as a supportive framework for the property valuation process post-war. We utilize mean prices as the measure of central tendency for several reasons. Firstly, the sample sizes are relatively small, and the mean provides a stable estimate and facilitates robust analysis. Additionally, our data exhibit characteristics of a relatively normal distribution without discernible extreme outliers that could unduly influence the mean. The inclusion of every data point in the calculation of the mean housing price ensures that each Condominium Unit or building contributes proportionally to the overall average, thereby providing a comprehensive representation of the dataset.

This study delves into fundamental inquiries concerning the establishment of post-war property valuation practices. How might one formulate these practices based on a transparent standardization model? Moreover, in what manner can 2D diagrams and 3D models function as instrumental tools in post-war visualization?

The research objectives present a significant scholarly contribution by addressing a notable gap in the existing literature, which predominantly concentrates on enhancing property valuation frameworks in isolation, neglecting the contextual factors arising from routine urban dynamics or exceptional circumstances such as disasters, which can profoundly influence real estate values. Initially, this study encompasses an exhaustive field survey that evaluates real estate conditions and values within a significantly affected neighborhood in Rural Damascus, where considerations for reconstruction initiatives remain overlooked. Secondly, this research introduces the LADM_VM post-war profile, thereby augmenting the framework to support the property valuation process within post-war recovery strategies. Thirdly, the study presents advanced 2D and 3D visualization models illustrating property conditions during war and post-war periods, effectively portraying war-related characteristics of real estate units and spaces.

Damascus, a city that has endured the ravages of war, seeks to build smart societies capable of effective, fair, and sustainable recovery. This study aims to enhance the smart transformation of real estate valuation practices during the post-war recovery phase by integrating the LADM_VM post-war model with property visualization models. This integration allows for the smart management of the real estate sector and post-war urban investment initiatives by enhancing the compatibility, transparency, and integration of property rights, responsibilities, and restrictions. Furthermore, the study aims to facilitate data-driven decision-making in post-war recovery investments, enabling cities to significantly improve smart economic recovery and attract sustainable investment.

2. Study Area

The study area encompasses a neighborhood situated in the district of Harasta within the Rural Damascus Governorate in Syria (Figure 1). Positioned directly behind the municipal building, this neighborhood underwent substantial qualitative destruction from 2015 to 2019. The impact included the complete collapse of some buildings and substantial damage to a considerable number of others, rendering them uninhabitable [11]. Simultaneously, the Harasta district experienced a notable influx of internally displaced persons amid intense bombing activities, leading to heightened tensions within the housing market both during and in the post-war period [26].

The neighborhood comprises 11 property portfolios, encompassing a total of 41 buildings, which include both administrative and commercial units, although the predominant nature of the neighborhood is residential. Situated at the entrance of the neighborhood is the municipal building, with the adjacent Al-Hal Market—a wholesale market. The remaining nine property portfolios accommodate a total of 243 Condo-Units and 16 commercial units. The data required for the study were collected for all Condo-Units, buildings, and real estate portfolios to cover the real estate situation of the entire neighborhood.

Throughout the wartime period, the neighborhood, akin to the broader region, endured significant devastation, warranting the classification of the Harasta district as a Severely Damaged District (SDD) [27]. This neighborhood was selected as a representative sample reflecting real estate conditions during and after the Syrian war. Its varying degrees of destruction offer valuable insights into post-war real estate dynamics. The extent of the neighborhood’s destruction is visually depicted in Figure 2.

Upon the cessation of bombing and destruction in 2019, five buildings were categorized as completely damaged (CD), with an additional five classified as severely damaged (SD). Concurrently, 36.6% of the buildings were moderately damaged (MD), while 24.4% were classified as lightly damaged (LD). Only six buildings remained undamaged (U). Table 1 offers an outline of the distinctive features linked to each gradation, serving as the foundation for property classification. The category “Repaired (R)” has been included to denote properties that underwent repair works following the war up to 2023.

3. Data and Methods

The study workflow commenced with an extensive field survey assessing real estate conditions and values within a significantly impacted neighborhood in Rural Damascus. Subsequently, the research introduces the LADM_VM post-war profile, enhancing the existing LADM and LADM-VM frameworks by incorporating war-related attributes. Following this, we present advanced 2D and 3D visualization models illustrating property conditions during war and post-war periods, effectively portraying war-related characteristics of real estate units and spaces. The illustrative Figure 3 serves as a visual representation of the workflow outlined in this paper.

Our survey aims to assess the impact of war and its associated variables on property values within the neighborhood, visualized in 3D to facilitate post-war investment processes. This necessitates a meticulous field study to uncover implicit facts, with data sourced from multiple sources.

Accessing reliable property market information is crucial for professional valuation practices, yet it often presents challenges due to the incomplete and fragmented nature of market data. Recognizing the reliability of valuers’ own databases, it is essential to develop a comprehensive databank in collaboration with key stakeholders in the property market, such as estate agents and property owners [28]. Research in real estate valuation has utilized various data collection methods to ensure the comprehensiveness and comparability of data, thereby enhancing its reliability and robustness. These methods include field surveys, questionnaires, and interviews with stakeholders, providing a nuanced understanding of market structures, the size and types of valuation practices, and the intrinsic and extrinsic characteristics of complex commodities like residential properties [29,30,31].

The 2D AutoCAD map, vital for this study, was acquired from municipal authorities. This map, reflecting the 2011 masterplan based on the latest governmental updates in the district, delineates the outer boundaries of the neighborhood, property portfolios, land parcels, and buildings. The severity of the area’s devastation was ascertained through the Syrian Cities Damage Atlas, a comprehensive thematic evaluation synthesized from satellite-based assessments of damages incurred in 2019 [27].

Detailed data pertaining to Condo-Units was gathered through a quantitative interview survey, aiming to monitor property characteristics and changing prices across pre-war, during-war, and post-war phases. The interviews, conducted between May and August 2023, lasted approximately two hours each, with a total of 35 interviews. Participants included both local real estate companies and neighborhood residents who resettled in the area after 2019, ensuring diverse representation across demographic variables such as age, occupation, and socioeconomic status. This comprehensive approach was adopted to capture a wide spectrum of perspectives and experiences pertinent to post-war property valuation dynamics within the neighborhood.

The interviews involved a purposive sampling approach, wherein domestic real estate companies were selected as primary sources for insights into real estate prices during different periods. These companies were chosen based on their extensive experience and knowledge of the local real estate market. Additionally, interviews were conducted with local residents (both owners and tenants) to provide firsthand information on property prices, architectural features, and constructional conditions. A summary of the interview respondents and some key insights is presented in Table 2.

The interview items were formulated through a comprehensive review of existing literature and consultation with subject matter experts. During the interview survey, a semi-structured approach was utilized to strike a balance between flexibility and direction. This method facilitated the exploration of predetermined core inquiries concerning property valuation dynamics in post-war settings, while also affording participants the opportunity to articulate their experiences and perspectives freely. Interrogations and data collection encompassed the following points:

• Area of the property;
• Floor area of the building;
• Age of the property and the building;
• Type of property;
• Legal state of ownership;
• Pre-war condition;
• Price of the property in Syrian Pound (SYP) in 2010;
• During-war condition;
• Price of the property in SYP in 2019;
• Post-war condition;
• Price of the property in SYP in 2023.

Regarding prices, conversions to United States dollars (USD) were conducted using the average exchange rate applicable for each of the specified three years. This conversion aimed to mitigate the significant inflation in the Syrian pound, thereby isolating its impact on the real changes in property prices. Additionally, adjustments were made for the relatively straightforward inflation associated with the USD. These adjustments were based on the latest information from the Bureau of Labor Statistics regarding inflation, as provided in the Consumer Price Index.

The selection of a suitable 3D visualization platform, aligned with the objectives of this study, involves critical considerations, including (1) comprehensive modeling capabilities, (2) dedicated focus on 3D information modeling, (3) interoperability, (4) rendering and visualization tools, and (5) alignment with the project’s scope and prerequisites. For our specific objectives related to visualizing war-related attributes affecting property values, the decision was made to leverage both 3ds Max and Revit Architecture. The 3ds Max program was selected for its extensive and adaptable modeling capabilities, ideal for intricate visualizations [32]. Meanwhile, Revit’s specialized Building Information Modeling (BIM) functionality, enabling the creation of intelligent 3D models embedded with data, proved essential for managing building information during both war and post-war phases [33,34,35,36].

The cadastral database adheres to the Syrian legal and cadastral framework, modeled on the global standard LADM [22]. This database incorporates various classes interconnected through different types of associations, facilitating a continuous flow of information. While preserving the main LADM classes, additional classes were introduced to align with the Syrian cadastral framework and post-war conditions, serving the real estate post-war recovery strategy. The draw.io UML modeling tool was employed for the development of the database conceptual schema. This tool enabled us to create UML class diagrams, capturing entities and relationships in our models, as well as workflows and processes related to the property valuation database. Leveraging UML via draw.io, we developed comprehensive visual representations of our LADM_VM post-war database schema, improving our understanding of the underlying data structures and workflows.

4. Results

4.1. LADM_VM Post-War Profile

The LADM_VM post-war profile underscores the importance of real estate valuations as a crucial component in facilitating the necessary development outlined within post-war recovery strategies. This initiative aims to safeguard and enhance property rights across both formal and informal real estate sectors. Figure 4 illustrates the general structure of our LADM_VM post-war profile. White-colored classes (prefixed with LA_) represent the LADM, while orange-colored classes (prefixed with VM_) represent the LADM_VM. Classes beginning with “War Profile” and colored red denote the post-war profile.

4.1.1. The LADM

Property valuation systems rely on accurately identifying property units and associated immovable rights. This emphasis on precise identification aligns with robust land administration systems, including cadaster and land registry [37]. The ISO 19152:2012 LADM serves as a descriptive model, offering a fundamental reference for efficient land administration. This initiative commenced in 2008 with the initial proposal emerging from International Federation of Surveyors activities dating back to 2002. After a protracted duration, the LADM achieved international standard status with its first edition in December 2012. ISO standards typically undergo periodic revisions within a 6 to 10 year cycle [38].

The revision of the LADM, initiated in May 2018, aimed to enhance tools for bolstering tenure security and advancing land and property rights universally. Given the intricacies of land administration, this revision involves diverse stakeholders such as the ISO, UN-Habitat, and World Bank, among others. This collaborative effort underscores the complex nature of land administration, necessitating collective engagement for its improvement [38]. In this regard, the LADM furnishes a comprehensive ontology, delineating standardized terminologies essential for consistent land administration practices within a flexible conceptual schema [39]. The LADM’s role extends to facilitating the development of software applications tailored for land administration purposes and streamlining data exchange processes among disparate land administration systems [40,41].

Certain rights may exist beyond the confines of legal norms and might even be deemed illegitimate, yet their documentation remains pertinent during the recognition phase. The functionality embedded within the LADM, particularly its specialized application in the social tenure domain, facilitates an approach that advocates for the recognition, compilation, maintenance, and publication of all property rights, regardless of their nature or legal standing [39].

Figure 5 depicts the class structure of the LADM, illustrating the constituent classes, their respective attributes, and the interrelationships between classes. The foundational structure of the LADM revolves around four primary classes: LA_Party, LA_RRR, LA_BAUnit, and LA_SpatialUnit. LA_Party delineates the entities associated with property rights, encompassing individuals or authorities. LA_RRR is instrumental in modeling rights, restrictions, and responsibilities, wherein a right may confer entitlement to a property owner. LA_BAUnit signifies basic administrative units that can be subdivided into multiple spatial units [22].

Concerning LA_SpatialUnit, a pivotal constituent within the LADM, its primary function resides in establishing the linkage between legal spatial entities and physical components [42]. This classification embodies two crucial sub-classes essential to this investigation: LA_LegalSpaceBuildingUnit, which intricately details legal spaces associated with buildings, and LA_LegalSpaceUtilityNetwork, specifically designed to represent infrastructure data that notably impacts the valuation units [43]. This encompassing portrayal includes vital elements such as electrical, road, heating, ventilation, air conditioning, and water supply networks, each significantly contributing to the determination of the valuation unit’s worth. Furthermore, Figure 5 illustrates the association between Valuation::VM_Valuation, which serves as the fundamental core of our LADM_VM, and the LADM.

4.1.2. The LADM_VM

The LADM, an advancement in the cadastral system, typically manages legal data associated with immovable properties, encompassing parcels, attached buildings, or Condo-Units. A valuation database, however, is structured to store information concerning individual parcels, buildings, combined parcels and buildings, and Condo-Units, each of which may undergo separate valuation procedures [44]. The aim of the LADM_VM is to define the semantics of valuation registries managed by governmental bodies and articulate their interconnections with other databases within land administration systems [45].

Figure 6 provides an outline of our LADM_VM. The VM_Valuation class serves as the central component within our model, designed explicitly to define crucial valuation information and related activities. Its primary focus revolves around the output data generated during property valuation processes, aiming to ensure the sustainable preservation of property rights in post-war scenarios. This class defines several valuation attributes, including the valuation ID, valuation date, and valuation type, while also encapsulating the ultimate assessed value and valuation approach attributes.

Valuation of real estate stands as a pivotal aspect for all enterprises, encompassing a broad spectrum of purposes necessitating valuations. These purposes span various needs, such as assessments for purchase and sale transactions, property transfers, tax evaluations, expropriation considerations, and investment and financing decisions [46,47]. The determination of a unit’s value often involves preliminary estimations through diverse approaches, including the hedonic pricing method, market comparison approach, income capitalization approach, and cost approach. Consequently, the VM_ValuationMethod class is tailored to specifically define and organize information pertinent to these mentioned valuation approaches.

Sales statistics in real estate rely on transaction price indexes to monitor market dynamics and generate periodic sale statistics reflecting transaction types, average prices, and fluctuations in property prices [48]. These indexes are constructed and regularly updated utilizing information sourced from declarations submitted by the involved parties in property transactions. In our LADM_VM, support is derived from the VM_TransactionPrices class, which encompasses attributes delineating the information contained within contracts. These attributes include the contract ID, contract date, transaction type, and transaction price in both USD and SYP.

The subsequent class, VM_SalesStatistics, is formulated to encapsulate sales statistics generated via the examination of transaction prices. Beyond basic identifiers such as the ID, date, and location of statistics, this class comprises attributes that delineate the number of samples integrated into the statistical analysis, alongside calculated metrics including the mean, minimum, and maximum price per square meter.

Our extended model introduces a parent class, VM_ValuationUnit, along with sub-classes—VM_LandParcel, VM_Building, and VM_CondominiumUnit—to delineate parcels, buildings, Condo-Units, and their respective attributes essential for Syrian valuation authorities. Given that this paper concentrates on exploring the utilization of spatial characteristics of property units to deduce war-related impacts on property values and their incorporation into valuation registries, this section exclusively details the spatial components within the LADM_VM. Consequently, the introduction of the class VM_ValuationUnit fulfills the objective of encapsulating the essential recording units within valuation registries. This class encompasses a valuation unit type attribute, which categorizes various types of valuation units, including land parcels, buildings, floors, or Condo-Units. Moreover, VM_ValuationUnit integrates key elements such as date, approach, and assessed value, all of which are crucial to the valuation process.

In a deeper exploration of the spatial characteristics inherent to valuation units, the model introduces distinct classes such as VM_District and VM_NeighbourhoodUnit. These classes delineate spatial and administrative delineations within the valuation framework, aiming to capture the influence of spatial dimensions on the valuation process, thereby elucidating the spatial variability in real estate values within urban landscapes. Additionally, the model incorporates VM_LandParcel, VM_Building, and VM_CondominiumUnit classes, specifically focusing on the physical attributes pertinent to valuation. Each of these classes is interlinked with VM_ValuationUnit, enabling comprehensive analyses based on spatial or functional clusters.

The valuation process often falls short in capturing the comprehensive market value of a property, resulting in revenue inadequacies and inequities within the valuation framework [49]. Consequently, there is a necessity to broaden the valuation process beyond the mere physical boundaries of the property. This expansion allows for a more comprehensive control over the spatial influence exerted by the surrounding area on the real estate’s value [50]. The VM_District class functions as a representative entity of the district to which the valuation unit pertains. This particular class comprises attributes corresponding to district identification, area, and location coordinates. Moreover, it includes supplementary attributes detailing the count of buildings, dwellings, parks, and residents, as well as details regarding pre-war conditions and proximity to the city center.

Similar considerations apply to the VM_NeighborhoodUnit class, portraying a self-contained residential zone that typically encompasses a group of residences and accompanying services [51]. Such zones are often characterized by unique features such as streets, squares, or gardens. Essentially, they represent socially homogeneous units wherein inhabitants coexist and utilize local amenities and services situated in close proximity [52]. This class encapsulates fundamental attributes associated with neighborhood identification, area, type, counts of buildings and residents, housing density, the quality of infrastructure, prevailing and anticipated land use, and the distance from the district center.

The VM_LandParcel class serves as a representation of cadastral parcels primarily utilized for taxation purposes, based on official land use designations. Within this class, attributes include parcel identifiers extracted from the Syrian cadastral information system, area, type, and pre-war condition. For parcels designated as developed areas, supplementary attributes are included, encompassing the number of buildings, dwellings, and parking spaces within the parcel. Significantly, in adherence to the technical guidelines of the INSPIRE data specification on land use, attributes detailing both current and planned land uses have been integrated [53].

The VM_Building class delineates the essential physical attributes integral to the valuation of buildings. It encapsulates a comprehensive array of fundamental characteristics pertaining specifically to the building itself, encompassing facets such as its use type (e.g., residential or commercial), area, age, construction materials, cladding quality, heating systems, and pertinent details regarding internal elements like the number of floors, apartments, availability of elevators, and parking provisions. Various forms delineate the correlation between buildings and the underlying land parcel on which they stand, thereby allowing the VM_Building class to serve either as an autonomous unit for valuation or as an integrative component within the context of the associated parcels.

The VM_CondominiumUnit class is derived from the LandInfra standard [54] and serves as a repository to document primary physical attributes of Condo-Units, constituting the smallest valuation unit within our model. As such, it captures significant characteristics specific to a Condo-Unit, incorporating key elements like area, finishing material, energy performance, number of rooms, pre-war condition, and quality of view. Moreover, this classification includes essential data concerning shared joint facilities (e.g., staircases and roofs) and accessory parts (e.g., cellars and storage rooms). These jointly owned facilities are allocated based on the proportional ownership shares of the individual units. In contrast, accessory parts within the Condo-Units are privately owned by the respective unit holders [55].

4.1.3. The Extension of LADM_VM Incorporating War Attributes

The LADM_VM post-war profile emphasizes the necessity of integrating war-related attributes into the real estate valuation process during the post-war recovery phase. This profile comprehensively addresses spatial and physical dimensions relevant to valuation units, as well as the parties involved in valuation practices, rights, restrictions, and associated responsibilities. Additionally, it includes crucial code lists containing specific values that delineate war-related attributes pertinent to property valuation in Syria (Figure 7).

The documented effects of war on properties, ranging from direct outcomes such as destruction, looting, and theft to the indirect consequences stemming from proximity to previous conflict zones, underscore the importance of thorough record-keeping within valuation registries. This meticulous recording process plays a crucial role in real estate surveys, enabling a comprehensive understanding of its influence on spatial disparities within property valuations. To address this need, we developed specific classes prefixed with War_Profile, which are linked to all sub-classes of VM_ValuationUnit. Commencing from the smallest valuation unit, War_Profile::VM_CondominiumUnit, these classes extend to War_Profile::VM_District, which denotes the largest valuation unit within our model.

The “damageType” attribute signifies the type of damage sustained by the building, while “damageDescription” provides details on how the damage occurred and the circumstances surrounding it, thereby improving the precision and accuracy of valuation processes. The “damageLevel” attribute categorizes the extent of damage in both the building and Condo-Units. To facilitate this categorization, a VM_DamageLevel code list was devised, comprising six distinct categories, CD, SD, MD, LD, U, and R, as mentioned in Section 2. Furthermore, quality-related attributes were delineated for the valuation unit, encompassing essential facets for assessment such as qualityOfConstruction, qualityOfFinishing, and qualityOfInfrastructure. Additionally, a date attribute was introduced to indicate the specific date of damage occurrence, providing temporal context to the assessment process.

The proximity of a conflict zone significantly influences property values during wartime and impacts the potential for development during the post-war recovery phase. In light of this, we formulated the War_Profile::VM_DistanceCharacteristic class aimed at pinpointing essential criteria related to proximity. Similar to other classes, the descriptive characteristic serves the purpose of documenting datasets utilized in the analysis of property values concerning their proximity to conflict zones (e.g., measuring unit, measuring methods, and data quality). The “distanceTo” attribute denotes the proximity of a property unit to a conflict zone, while the “distanceType” attribute specifies the nature of this proximity. Furthermore, the “rateOfAccessibility” assesses the ease of accessing a valuation unit, considering the level of difficulty influenced by war and its resulting damages on accessibility. This rating system ranges from one to five, with a rating of one indicating limited accessibility.

The management of property valuation registries demands ongoing updates of derived characteristics within the LADM_VM post-war profile. Throughout periods of conflict, both internal and external changes in real estate correspond to destruction patterns. The destruction of infrastructure in the Damascus metropolitan area surged after 2015 due to aerial bombardment [56,57]. Subsequent to this, some districts experienced internal disturbances like looting and vandalism due to security lapses, prompting population displacement [58].

In post-conflict periods, it is crucial to comprehend the spatial and physical transformations occurring in real estate, particularly in cities like Damascus, which have experienced a gradual and spontaneous recovery since 2019, albeit with significant complexities [59]. Hence, conducting regular assessments covering internal and external factors is essential for accurate record-keeping. Adjusting analysis frequency based on property and neighborhood traits is vital for thorough valuation. The responsibility of determining update intervals can be assigned to the local municipality, considering the unique characteristics of properties and neighborhoods. This authority is tasked with periodically reassessing conflict impacts on the real estate sector within specified timelines. These re-evaluation processes ensure that the devised recovery strategy remains aligned with the present situation, promoting sustainability and safeguarding property rights.

4.2. Property Visualization of Post-War Valuation

A substantial body of literature has technically advanced property valuation practices, making them smoother, more transparent, and more precise. For instance, spatial analysis techniques using Geographic Information System (GIS) technology have been employed to develop national land information services, examining the impact of accessibility on real estate values [60]. Moreover, value maps that illustrate the locational values of comparable properties employ a GIS intelligent interface for straightforward comparative analysis. This method addresses value differences due to physical characteristics and attributes any remaining discrepancies to locational factors by shading properties according to their locational value [61]. Additionally, detailed geographical representations of individual properties have been created using GIS-based valuation models, further improving property valuation processes [62].

The landscape of property valuation in the post-war period is a challenging terrain, necessitating the development of intricate and specialized valuation models [63,64]. Our models are crafted to adeptly maneuver through the intricate web of challenges and nuances intrinsic to the process of recovery. The scars left by war—be it infrastructural damage, socioeconomic shifts, or spatial reconfigurations—mandate novel practices to understand the evolving worth and dynamics of properties. This section delves into two essential components: the “neighborhood 3D valuation model” and the “property 3D valuation model”. These frameworks provide tailored methodologies for comprehending post-war property dynamics, assessing collective spatial changes, and meticulously scrutinizing individual units.

4.2.1. Neighborhood 3D Valuation Model

In the context outlined earlier, the application of 3ds Max was pivotal in crafting a 3D depiction of our neighborhood unit, accomplished through the conversion of field survey data into 2D AutoCAD plans. This 3D model served as a foundational structure for integrating war-related attributes specific to the buildings and Condo-Units situated within the neighborhood. Particularly notable was the deliberate application of color schemes, strategically employed to delineate varying degrees of real estate conditions arising from wartime devastation. This categorization encompassed a wide spectrum, categorizing observed damages into six distinct gradations, CD, SD, MD, LD, U, and R units, as mentioned in Section 2. This method of classification was instrumental in systematically recording the varied post-war conditions. By adopting this visualization manner, a comprehensible representation of the post-war urban environment and the current state of property rights within the locality was facilitated.

The development of a 3D neighborhood model, designed to encompass war-related attributes based on preceding property classifications, commenced with the acquisition of a 2D AutoCAD map illustrating the neighborhood’s layout, land parcels, and building units (Figure 8). This map, obtained from municipal authorities, aligns with the 2011 masterplan and has been supplemented with data reflecting building conditions due to the war’s impact. These additional data were sourced from the Syrian Cities Damage Atlas, representing a thematic assessment derived from satellite-identified damage [27].

Alongside these resources, our on-site field study significantly contributed vital data, enabling a comprehensive assessment of building conditions and property attributes within the neighborhood. This phase encompassed rigorous field surveys, capturing essential information spanning pre-war conditions in 2010, categorized across five distinctive classifications (Excellent, Good, Fair, Poor, and Critical).

Furthermore, the valuation process involved documenting war-induced damages observed in 2019, marking the conclusive year of conflict in the Damascus metropolitan area, alongside post-war assessments recorded in 2023 (Table 3). These assessments were supported by MHP/sqm recorded in their respective years. The amalgamation of spatial data from the municipal map with empirical field data underwent iterative refinement, culminating in the creation of a 3D model detailing all Condo-Units across the entire neighborhood. Each unit was color-coded to represent its war-related condition, thereby enriching the visual representation (Figure 9).

4.2.2. Property 3D Valuation Model

The transition from neighborhood valuation to meticulous examination, encompassing individual buildings and residential units, demands a comprehensive pre- and post-war field study. Our procedure commences with assessing the condition of Condo-Units and extends to the valuation of their influence on buildings, property portfolios, and ultimately, the entire neighborhood. Figure 10 presents the post-war condition of building No. 6 within property portfolio B.

Utilizing Revit 2024 software, we facilitated the capture and transfer of property data impacted by war into a 3D model of architectural structures. This methodology enables a thorough valuation of damage severity across individual spaces, categorized into severity levels (SD, MD, LD, U, and R elements). Utilizing BIM functionalities, property data were systematically input, encompassing base, wall, ceiling, and floor finishes, as depicted in Figure 11. This figure showcases a 3D model specifically detailing building No. 6 within property portfolio B. Consequently, this process meticulously portrays post-war conditions within digital models, facilitating an assessment of damage across different spatial zones. Such representations serve as tools for stakeholders engaged in post-conflict scenarios, providing a visual narrative of destruction and offering quantitative data metrics that contribute to a deeper understanding of affected areas. Furthermore, these models aid urban planners, policymakers, and humanitarian organizations in their strategic planning endeavors for post-war rehabilitation initiatives.

To accurately assess temporal changes in property prices, it is imperative to adjust prices for USD inflation over two key periods: from 2010 to 2019 (wartime period) and from 2019 to 2023 (post-war period). An analysis of mean prices per square meter reveals a significant decline in prices during the wartime phase, particularly affecting properties categorized as MD (buildings No. 3 and 5, experiencing approximately a 48% decrease in price) and SD (buildings No. 1 and 6, witnessing a 47% and 61% decrease in price, respectively). Following the conflict, a limited number of buildings underwent repair and cladding restoration efforts, resulting in a continued decline in property prices, albeit at a slower rate compared to the wartime period. For instance, building No. 5 experienced a modest 11% decrease in price after being reclassified from MD to LD, while building No. 6 saw a decrease of 28% following its transition from SD to MD (Table 4).

However, upon conducting an in-depth analysis of building 6 within property portfolio B, notable shifts were observed predominantly among properties categorized as SD, exemplified by Condo-Units No. 5, 6, 7, and 8, experiencing significant declines in price at 78.2%, 76.7%, 72.9%, and 73.7%, respectively. This trend is followed by MD Condo-Units No. 1, 9, 10, and 11, facing depreciations of 52.5%, 67.7%, 58.7%, and 61.6%, respectively. Notably, properties that suffered lighter damage exhibited a less pronounced impact on prices, as evidenced by Condo-Units No. 2, 3, and 4, which experienced relatively modest declines in prices of 45.3%, 40.6%, and 38.1%, respectively (Table 5).

Following the conflict, certain properties, particularly Condo-Units No. 1, 2, 9, 10, and 11, which underwent rehabilitation for housing purposes, subsequently demonstrating an improvement in value upon reclassification as R. Condo-Units 1, 10, and 11 experienced price increments of 22.5%, 8%, and 1.8%, respectively. However, Condo-Unit No. 2 witnessed a decrease of 6.1%, while Condo-Unit No. 9 maintained its price. This disparity in prices can be attributed to varying levels of damage sustained during the war and the quality of post-war repairs. Conversely, SD properties continued to depreciate due to the onerous and costly restoration processes. Condo-Units No. 5, 6, 7, and 8 exhibited further declines in price from 2019 to 2023 of 55.3%, 54.5%, 60.4%, and 65.3%, respectively.

Property prices must be documented not solely to bolster real estate registries but also to transparently manage and disseminate them. The assessment of changes in price should be clear and easily accessible to the public and relevant authorities. Therefore, in this study, we utilized Cesium ion as a robust and secure platform for 3D geospatial data visualization and streaming to any device. Cesium ion is widely adopted by a diverse group of users in the domains of land administration and property management. Figure 12 illustrates a simple example of the building No. 6 model on the Cesium ion platform “https://ion.cesium.com/stories/viewer/?id=21300965-9d34-43d4-b530-e9456d201ac7 (accessed on 2 July 2024)”, accompanied by a table detailing real estate prices before, during, and after the war. In future research endeavors, there is a concerted effort to develop a smart approach for modeling buildings and neighborhoods damaged by disasters, supporting them with temporal and spatial analyses to enhance the sustainability of spatial development processes in the post-war recovery phase.

5. Discussion

The post-war valuation practices, incorporating tailored conceptual and geometric models visualized in 3D, underwent rigorous evaluation through a case study centered on a distinct neighborhood in the Harasta district of the Rural Damascus Governorate. It is imperative to note that the discourse presented herein is primarily informed by authors with expertise in architecture and geography. Nevertheless, the broader discussion warrants consideration of post-disaster property valuation within a multifaceted framework, encompassing social, cultural, and economic dimensions, thus necessitating interdisciplinary insights for development strategies.

Property valuation methods should work on introducing real estate laws and establishing legal frameworks to safeguard property rights while fostering investment prospects. However, amid societal transformations catalyzed by disasters, enhanced property valuation survey procedures are imperative. Such procedures should underscore collaborative efforts among stakeholders, each contributing pertinent data to ensure optimal transparency in the valuation process. The interview survey emerges as a primary method for comprehensively gauging post-war property conditions, particularly in the absence of governmental or non-governmental studies addressing the volatile nature of the real estate market.

We opted for an interview survey of the real estate market within a specific neighborhood due to its merits in offering detailed and adaptable insights into participants’ perspectives and experiences concerning various aspects of real estate transactions and market trends. Additionally, this form of survey facilitates rapport building between the interviewer and participant, potentially eliciting more candid responses and richer data. Despite their advantages, interview surveys in real estate research also present limitations. Firstly, findings from such surveys may not readily generalize to a broader population, given their reliance on a small sample within a specific neighborhood. Moreover, participants may be inclined to withhold sensitive information during non-governmental surveys, particularly concerning financial and security matters.

In developing our post-war valuation practices, several challenges emerged, particularly in acquiring data for the case study. Restricted inhabitation in the district, compounded by security blockades, posed significant obstacles to movement. Additionally, the absence of official building plans complicated the valuation process, requiring alignment with the 2011 masterplan established after a prior field survey. Damaged archives at real estate offices further hindered database acquisition. Thus, a field engineering survey under governmental or international supervision may offer the most viable alternative to ensure accurate matching of ownership data and real estate status with engineering boundaries, facilitating 3D visualization of war attributes on properties. Scholars advocate for future collaboration between municipalities in affected regions and real estate valuation initiatives, guided by a well-defined yet adaptable valuation model. Such collaboration can enhance efficiency, particularly in data collection, saving time and expediting the overall valuation process.

The results obtained from our study offer valuable insights into different aspects of the real estate landscape. By collecting both descriptive and numerical data from our case study, we gained a better understanding of what factors should be considered when valuing properties after a war. This understanding was reflected in our LADM_VM post-war model and 3D visualization models. The importance of our research lies in its development of realistic valuation models that accurately reflect the post-war conditions of Syrian society. These models are crucial for documenting and safeguarding property rights at a time when there is an urgent need for thorough documentation, revaluation, and restoration of post-war properties. Utilizing advanced valuation and representation models ensures that real estate investment in the context of post-war recovery is conducted transparently and systematically. This method is expected to significantly enhance the economy and revitalize Syrian cities that have been devastated by years of conflict and displacement.

While these models were tailored to the Syrian case study, they were designed to be useful for any community facing natural or human-made risks. To date, an ISO 19152 LADM that incorporates wartime conditions has not been developed. These unique circumstances significantly affect urban life and the real estate sector. Our research is globally significant, as the post-war LADM_VM can be applied to urban areas facing real estate crises and destruction due to disasters. This model aims to normalize the property valuation process post-disaster, aiding economic recovery and growth. It is important to note that these models should be adapted to fit the specific data of each community. Therefore, any post-war property valuation procedure needs to align with both national guidelines and local standards.

Upon analyzing the outcomes, several unforeseen discoveries surfaced, prompting a deeper inquiry. For example, some properties showed an irrational fluctuation of prices despite having similar architectural and construction conditions. Furthermore, a dichotomy emerged wherein some properties underwent restoration efforts, while others remained derelict, their ownership status and future prospects shrouded in uncertainty. Such circumstances cast doubt upon the fate of these properties within the framework of sustainable real estate investment. This underscores the imperative of tailoring valuation models to the idiosyncrasies of each community, alongside accounting for the aftermath of any hazards, which may yield unpredictable outcomes.

Post-war real estate valuation standards differ significantly from those under normal circumstances. Neglecting our effort to develop post-war specific valuation models could lead to several negative consequences, such as misleading market information, inaccurate property value assessments, poor investment decisions, and financial losses. Standardized valuations can also cause value discrepancies and legal disputes, potentially reigniting conflicts. Thus, it is essential to present our post-war property valuation model that accounts for the unique conditions of properties during and after the war.

While the proposed models offer significant contributions to post-war recovery, several limitations should be noted. Firstly, their direct applicability to other disaster-affected case studies without substantial modification is limited, as the models are meticulously tailored to local contexts and specific community needs. Secondly, the innovation of this study in incorporating war characteristics into the LADM-VM is both an advantage and a limitation. The absence of comparable benchmarks in the existing literature makes comprehensive validation challenging. Lastly, although our model includes critical war-related attributes, it does not fully account for certain long-term and indirect effects of war, such as environmental pollution. Nonetheless, these limitations highlight areas for future research aimed at post-war recovery and the development of property valuation standards in the aftermath of natural and human-made disasters.

6. Conclusions

This paper presents post-war practices that incorporate war-related factors into the property valuation process. Through an analysis of a neighborhood comprising 41 buildings with 243 Condo-Units, this study seeks to develop a valuation information model serving as a reference data model for post-war recovery strategies. In conflict zones such as Rural Damascus, the real estate sector and property rights face significant challenges, including the loss of ownership documents due to displacement and extensive property destruction. Legislative actions exacerbate these issues by progressively undermining property rights.

Property values, in conjunction with war-related attributes, must be transparently documented to ensure equitable real estate valuation and property rights management. Leveraging the post-war LADM_VM addresses the urgent need for precise post-war valuation, extending beyond individual properties to incorporate the broader impact of conflict at the neighborhood and district levels. The model aspires to comprehensively address all factors influencing property value within an integrated framework, serving as a reference data model for conflict-affected areas. By expanding the LADM_VM to include country-specific characteristics, a country profile can represent them in the transparent valuation process, facilitating sustainable real estate investment during the post-war recovery phase.

This paper advocates for a transparent post-war valuation model using 3D visualization (3D Max and Revit). It serves as a supportive tool for real estate data during and after conflict. Findings from the targeted neighborhood’s field study show the significant impact of destruction size on property prices, with SD Condo-Units experiencing a decrease around 75%. The inquiry extends its analysis to examine the impact of post-conflict improvements on property valuation. Notably, the observed increment varies contingent upon the quality of enhancements, showcasing a substantial 22.5% rise for the highest-quality improvements. However, some properties continued to decrease in price or remained unchanged despite undergoing simple improvements.

This study introduces innovative post-war property valuation practices. Subsequent research endeavors could refine these practices to accommodate diverse hazard types, spanning both anthropogenic and natural disasters. Emphasis should be placed on elucidating the post-hazard repercussions and the dynamic variables influencing real estate valuations. Moreover, a pivotal agenda for future research involves advancing towards the comprehensive automation of the property valuation model, employing a sophisticated algorithmic analysis and machine learning techniques. This agenda should strive to optimize the intricate process of determining and visualizing property databases, specifically within the LADM. Its application extends across various use cases, encompassing property valuation, fraud detection, and value forecasting. This necessitates the meticulous aggregation of pertinent data for identified use cases, including transaction data, property attributes, disaster characteristics, and other relevant datasets.