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Estimating the value of real estate has applications in fields as diverse as taxation, buying and renting properties, expropriation and urban regeneration. Determining the most objective, accurate and acceptable value for real estate by considering spatial criteria is therefore important. One stochastic method used to determine real estate values i...
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... Beyoğlu, the highest land values are seen in the eastern strip of İstiklal Street, one of the city's most famous streets, and the Marmara Sea in the south. Places with high nominal value are distributed along the shoreline of the Golden Horn and the Sea of Marmara ( Figure 5). Regions with higher nominal values in Gaziosmanpaşa centre on the Merkez Neighbourhood, and on the borders with Bayrampaşa and Eyüpsultan districts to the south. ...
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... The effects of accessibility and mobility on property value have also been studied [25,26]. In countries with no public records of average house prices, many researchers have created property valuation maps [27][28][29][30]. Many studies have used the power of GIS to examine the relationships between urban morphology and economic growth. ...
In the face of pressing global challenges such as climate change, socioeconomic inequalities, and rapid urbanization, ensuring sustainable development in the regions has become essential. The COVID-19 pandemic has highlighted how vulnerable cities are to unforeseen crises and underscored the urgent need for proactive urban planning strategies capable of navigating dynamic and unpredictable futures. In this context, the use of geographic information systems (GIS) offers researchers and decision makers a distinct advantage in the study of spatial data and enables the comprehensive study of spatial and temporal patterns in various disciplines, including real estate valuation. Central to the integration of modern technology into real estate valuation is the need to mitigate the inherent subjectivity of traditional valuation methods while increasing efficiency through the use of mass appraisal techniques. This study draws on extensive academic literature comprising 103 research articles published between 1993 and January 2024 to shed light on the multifaceted application of GISs in real estate valuation. In particular, three main areas are addressed: (1) hedonic models, (2) artificial intelligence (AI), and mathematical appraisal models. This synthesis emphasizes the interdependence of numerous societal challenges and highlights the need for interdisciplinary collaboration to address them effectively. In addition, this study provides a repertoire of methodologies that underscores the potential of advanced technologies, including artificial intelligence, GISs, and satellite imagery, to improve the subjectivity of traditional valuation approaches and thereby promote greater accuracy and productivity in real estate valuation. By integrating GISs into real estate valuation methodologies, stakeholders can navigate the complexity of urban landscapes with greater precision and promote equitable valuation practices that are conducive to sustainable urban development.
... On the other side, raster maps of geographic data were produced with the Euclidean Distance in GIS software. The nominal land valuation model was created by multiplying the weights by the pixel values (Mete and Yomralioglu, 2019). In Afyonkarahisar, value estimation was made using the nominal valuation method in 120 neighborhoods. ...
... Based on the scoring used in the nominal valuation method, it is similar to the GeoValueIndex. The studies, which are land readjustment (Yomralioglu, 1993), value prediction (Nişancı and Yomralıoğlu, 2002), and the basis for the valuation (Droj et al., 2010;Mete and Yomralioglu, 2019;Unel and Yalpır, 2014) were implemented with the nominal valuation method. In the study of Yomralioglu (1993), a nominal asset value-based land readjustment model was developed. ...
The aim of this study is to develop a value-based GeoValueIndex with AHP weights and GIS for the criteria of the Mersin University (MEU) Çiftlikköy Campus real properties, and it is referred to as the “GeoValueIndex” in this study. GeoValueIndex is a symbolic value that combines geographic and non-geographic features of real properties. The data of the real properties on the campus were collected and arranged for mass appraisal. One of the Multi Criteria Decision Analysis (MCDA) methodologies, Analytic Hierarchy Process (AHP), was used to weight the criteria. GeoValueIndex was calculated by multiplying each parcel’s geographic and non-geographic data by their weights and adding them. GeoValueIndex Map is obtained by associating GeoValueIndex and parcel in GIS software. GeoValueIndex of real properties save time, effort, and cost in mass appraisal processes. There are many techniques for doing GeoValueIndex operations, and the ones presented in this study are only proposals.
... Some 3.9 Information system model Running one or more models in any GIS project is a common part of the process. The mentioned model is frequently seen especially in studies using GIS data that need to be updated [107], [108]. Modelbuilder is used to automate and associate a set of geoprocessing tools in ArcToolbox. ...
It is estimated that the world population will reach 8.5 billion in 2025 and 9.7 billion in 2050. It is foreseen that the land will be used more intensively in the future due to the mentioned population increase. For this reason, the management of limited and non-renewable land is a necessity. While planning the land management, the management of the real estates (e.g. building, parcel) and the legislations (e.g. law, regulation) are other issues that need to be planned.
While the real estate is a source of income for the citizens due to its economic value, it has been the subject of tax for the states. Therefore, the need to legally evaluate, define and registry real estates has appeared. This need is also present in the Cadastre 2014 vision. In the current study, the main motivation is to present the legal legislations that real estates are subject to due to different criteria (e.g. owner, location, type, intended purpose, acquisition form, encumbrance). This presentation can be made both by using the province, county, neighborhood/village, block, parcel information of the real estates and by clicking on the real estates. In this context, a GIS-based real estate legislation information system was designed in the Foça/İzmir, which was selected as the study area. For this purpose, a comprehensive national legislation inventory was prepared by determining active laws (general and private) regarding real estate. In the continuation of the study, the real estates were associated with the legislation, taking into account their location and attributes. As a result of the mentioned association, the legislation information is located in the attribute table together with the guiding link. In this way, it is foreseen that the stakeholders will be prevented from experiencing aggrievements after the purchase and sale processes in the free market. Pecuniary loss and intangible damages (e.g. penalties, usage constraints, loss of immovable) that may arise in lawsuits, transactions and implementations will be minimized. In addition to all these, a model has been established to automate the updating of the data used in the information system. At the end of the study, the diversity of services for each real estate has been increased. As a result, legislative information, which will enable more accurate decisions to be made, is presented to the user in a inquirable form with a simple interface.
... Nominal valuation provides calculated parametric scores of weighted criteria which affect real estate values [18]. This approach provides a distribution of land values as parametric quantities using scientific approaches without requiring the market value [19]. Nominal valuation is stochastic method based on statistical models that require analysis by computers since these method is applied for a large number of real estate [10]. ...
In addition to the usual concerns that governments face, managing agriculture and agricultural areas is a significant issue. As a result, a country's agricultural production is highly dependent on its land potential. The transformation of agricultural areas into construction sites will be more attractive for the proprietors if they are not valued at their actual worth. This study applies and compares two different valuation methods, income and nominal land value approaches, to evaluate land in rural area, Cankiri Province, Catalelma Village, Central Turkey. In Turkey, income valuation method is typically applied for dealing with financial matters related to agricultural areas. On the other hand, one of the statistical methods, nominal valuation, was also used in the study area by applying the spatial analysis functions of geographical information system (GIS). In this study, four sample parcels representing three different land use capability classes (two irrigable, one medium, and one barren) were selected for comparison of two valuation methods. The results obtained during this research demonstrate that the nominal valuation approach produced more accurate results than the income method, and it may be used in the country's local administrative units due to available data.
... Yet, it is difficult to carry out detailed (street or parcel level) data analysis/analytics in GIS since the precise location information is missing. Locational factors like proximity to important places, sea or forest views, flat topography have a significant effect on the property value (Kiel and Zabel 2008, Mete and Yomralioglu 2019, Wyatt 1997. So although the linked PPD-EPC data contains many attributes for properties in England and Wales, it still lacks the locational factors needed to build more accurate regression models (Clark and Lomax 2018). ...
... There are a wide range of mass valuation methods like Multiple Regression Analysis (Benjamin et al. 2020, Yilmazer and Kocaman 2020, Zurada et al. 2011, Hedonic Pricing (Lisi 2019, Peterson and Flanagan 2009, Yamani et al. 2019, Nominal Valuation (Mete and Yomralioglu 2019, Yomralioglu 1993, Yomralioglu and Nisanci 2004, Geo-4 This is an Accepted Manuscript of an article published by Wiley Online Library in Geographical Analysis on 14 October 202214 October , available online: https://doi.org/10.1111 graphically Weighted Regression (GWR) (Dimopoulos and Moulas 2016, Huang et al. 2010, Wang et al. 2020), Ensemble Methods (Alfaro-Navarro et al. 2020, Aydinoglu et al. 2021, Gnat 2021, and ANN (Demetriou 2017, Lee 2021, Yalpır 2018. ...
... In order to build more accurate regression models, there is a need for the inclusion of locational factors, which are highly correlated with land price (Kiel and Zabel 2008, Mete and Yomralioglu 2019, Wyatt 1997. GIS provides numerous spatial analysis tools that can be utilized for revealing locational criteria effects on the property price. ...
Geographic Information Systems (GIS) and Machine Learning methods are now widely used in mass property valuation using the physical attributes of properties. However, locational criteria, such as as proximity to important places, sea or forest views, flat topography are just some of the spatial factors that affect property values and, to date, these have been insufficiently used as part of the valuation process. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for mass valuation of residential properties. GIS-based Nominal Valuation Method was applied to carry out proximity, terrain, and visibility analyses using Ordnance Survey and OpenStreetMap data, than land value map of Great Britain was produced. Spatial criteria scores obtained from the GIS analyses were included in the price prediction process in which global and spatially clustered local regression models are built for England and Wales using Price Paid Data and Energy Performance Certificates data. Results showed that adding locational factors to the property price data and applying a novel nominally-weighted spatial clustering algorithm for creating a local regression increased the prediction accuracy by about 45%. It also demonstrated that Random Forest was the most accurate ensemble model.
... The element that we can see this effect best is the value of the real estate. GIS is also required in the production of value maps (Locurcio et al., 2020;Mete and Yomralioglu, 2019). For an important application such as the property tax system, GIS should be utilized in the most effective use. ...
... Random Forest), respectively. In addition to these techniques, MCDA-based methods such as Fuzzy Logic, Nominal method, Analytic Hierarchy Process (AHP) and FAHP can also be used effectively in the mass appraisal of real estates (Yalpir, 2018;Aydinoglu et al., 2020;Yilmazer and Kocaman, 2020;Chen et al., 2017;Mete and Yomralioglu, 2019;Yalpir et al., 2013;Selim, 2009). ...
In this study, for mass real estate appraisal forecasting, the hybrid mathematical model has been developed by combining Cobb-Douglas one of the nonlinear regression models, and linear modeling. The real estate attributes that create the model were grouped under four main-title: local, spatial, physical and legal features. While Cobb-Douglas was used for the value forecast based on the real estate attributes in each part of the model, an integrated model was created with a linear approach. As a different approach, local and spatial features, which are among the real estate attributes, were used as indexes for reasons such as preventing data confusion in the model and using according to the spatial analysis results of distances. Local and spatial index were prepared with the Fuzzy Analytic Hierarchy Process (FAHP) method to use within the model. For indexes, in the central districts of Konya, 10 local-specific attributes were used, while 12 spatial-specific attributes. The data set has been prepared using legal and physical attributes with market values collected from 457 parcels in the study area. Local and spatial attributes were added as indexes to the data set used in the hybrid model. In addition, modeling was done with the data set used in the Cobb-Douglas Hybrid Model (C-DHM) according to the Linear Multiple Regression Analysis (Linear MRA) method. The developed C-DHM’s results was integrated with Geographical Information Systems (GIS). The performance values between the hybrid model and market values were examined. Results showed that R2 value for C-DHM and Linear MRA used as indexes was found to be 0,85 and 0,80. When the values obtained from C-DHM and market value are compared, it is seen that model gives successful results.
... PPD-EPC data contains many physical attributes about properties which are beneficial for price prediction in Machine Learning based appraisal process. However, there is a need for inclusion of locational factors, which are highly correlated with land price, in order to build more accurate regression models (Wyatt, 1997;Kiel and Zabel, 2008;Mete and Yomralioglu, 2019). GIS provides numerous spatial analysis tools that can be utilized for revealing locational criteria effects on the property price. ...
Property value is needed in many transactions such as purchase and sale, taxation, expropriation, capital market activities. Geographic Information Systems (GIS) and Machine Learning methods are widely used in automated mass appraisal practices. Analysing the Machine Learning based mass appraisal applications, it is seen that locational criteria are insufficiently used during the price prediction process. Whereas, locational criteria like proximity to important places, sea or green areas views, smooth topography are some of the spatial factors that extremely affect the property value. In this study, a hybrid approach is developed by integrating GIS and Machine Learning for mass appraisal of residential properties in England and Wales.
... The BWM has been utilized to establish the weights and ranks of alternatives in numerous domains due to its simplicity, the clear understanding of the range of evaluations to be undertaken by the decision-maker before conducting a comparison, and the elimination of potential bias from the decision-maker when undertaking pairwise comparisons [20]. According to the best knowledge of the authors, few studies exist [16,29] in the literature that combined BWM and GIS in nominal land valuation; these studies focused on the effects of resolution differences on the nominal values. ...
... , W * n ) for the criteria. The optimal weights and consistency of the factors are calculated as Equation (2) [26,27,29]. ...
... In this study, a nominal land value map was produced based on many criteria that affect land value and the weight of the criteria determined with BWM differ from previous studies [6,11,12,18]. BWM was used [16,29] in weighting the criteria to produce nominal land value map, but many criteria were different from the current study, such as highway junctions, rapid-transit bus stations, quays, Bosphorus view, historical places, and hazardous areas, due to the location and characteristics of the study area. ...
Land valuation can be used in various areas, such as taxation, property acquisition, rental, expropriation, urban regeneration, and land readjustment. As a result, determining the value of land accurately by considering multiple criteria to eliminate the impact of subjectivity on the value of land is important. Nominal valuation is a statistical method for determining land values based on multiple criteria. In nominal valuation, criteria can be managed, analyzed, and integrated using Geographic Information Systems (GIS). This study produced a land value map using a nominal valuation method based on multiple criteria. The main criteria and sub-criteria have been determined according to the previous studies. Criteria weights were determined using the Best-Worst Method (BWM), which is one of the Multi-Criteria Decision-Making methods. A nominal land value map of the Atakum district of Samsun was produced by integrating criteria maps using GIS and compared with a value map based on current land sales.
... Using GIS and BIM in property valuation activities provides easy data manipulation, advanced automation, and robust spatial analysis capabilities. It also has great potential to increase assessment accuracy since it establishes a scientific and objective approach [52]. GIS analysis can be utilized to assess environmental factors like proximity, terrain, network, traffic density, sunlight exposure. ...
Real estate values are needed and used in many finance, engineering, and construction operations. It is significant to assess property values in a standard-based, objective manner. Utilizing Geographic Information Systems (GIS) and Building Information Modelling (BIM) technologies, real estate values can be assessed with three-dimensional (3D) geospatial and built environment analysis. In this study, criteria that affect real estate value are grouped as environmental, physical, legal, and socio-economic factors. Then, the Industry Foundation Classes (IFC)-based 3D property valuation model is designed. By proposing new property sets and properties, features and their attributes are mapped with entities and data types in the IFC schema to demonstrate the likely use of IFC data for property valuation. Conducting BIM&GIS analysis for both external and internal criteria, property values can be assessed by using the created holistic model. It is thus aimed to develop a valuation framework that can be used as a reference in several value-based applications such as property taxation, urban renewal, and land share calculation.
... Nominal valuation is a method that provides weighted parametric values of the criteria which affect land values (Yomralioglu 1993;Mete and Yomralioglu 2019). The distribution of values between lands can be seen easily by using Nominal Valuation Method. ...
Cloud computing enables performing computations and analysis tasks and sharing services in web-based computer centres instead of local desktop systems. One of the most used areas of cloud computing is geographic information systems (GIS) applications. Although Desktop GIS products are still used in the community frequently, Web GIS and Cloud GIS applications have drawn attention and have become more efficient for users. In this study, a serverless Cloud GIS framework is implemented for the land valuation platform. In order to store, analyse, and share geospatial data, the Aurora Serverless PostgreSQL database is created on Amazon Web Services (AWS). While adopting Aurora Serverless PostgreSQL as a database management system, a simple point in polygon analysis conducted to compare the performances with Amazon Relational Database Service (RDS) instance. Results showed that the serverless database responded to the query faster and scaled up during high workload to decrease latency. Hence, parcel vector data, which conveys ownership information and land values attributes, is shared directly from the PostGIS database as vector tiles. Besides S3 and AWS Lambda services are used for storing and disseminating raster-based land value map tiles. To visualize all shared data and maps through a web browser, open source web mapping library Mapbox GL JS is used.
