The application of 3D city models in real estate assessment. An introduction towards possible applic

STUDENT CONTEST 2018 The application of 3D city models in real estate assessment. An introduction towards possible applications.

Abstract The usage of geospatial data is becoming more widespread due to recent trends in the geographic information systems (GIS), surveying and real estate assessment industries.

CityGML provides the industry standard in 3D city models and is therefore a good framework for the discussion of how geospatial data can be used in real estate assessment. The combination of both 3D city models and real estate assessment might form a fertile relationship in advancing both industries. The application of geostatistics enhances the accuracy of traditional models of multiple regression. Moreover, the further implementation of geospatial data might take the accuracy to a next level in the future. Using GIS both industries of surveying and real estate assessment find a platform to be combined. The goal of this paper is to get interest from both industries and to push towards a discussion between consumers and suppliers of the datasets. With this discussion, a fertile future can be secured for both industries.

Key words: CityGML, 3D city models, real estate assessment,

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STUDENT CONTEST 2018 §1 Introduction In recent years, geographic information systems (GIS) became available for many people.

Working stations are no longer necessary because most laptops fulfil the system requirements nowadays (Poletti 2012). Alongside this development, more open data is made operable (Bidanset and Lombard 2014). With this development GIS is used in a far wider field and geographic thinking is making its way into the workplace in many fields.

The combination of geographic information systems (GIS) and real estate assessment, and more specifically computer assisted mass appraisal (CAMA), has been an important research topic over the last decades. However, the combination is not unambiguously, because there are multiple ways of implementing geospatial data into automated valuation models (AVM) (Bidanset and Lombard 2014). AVMs reduce cost and time of real estate appraisal therefore the international association of assessing officers (IAAO) is pushing appraisal by AVMs (Bidanset and Lombard 2014). Combining AVMs with geographical information can enhance the overall performance of the models. Due to this and earlier mentioned recent trends such as the availability of GIS to the masses and the accessibility of evermore open data it is necessary that people who work with the combination are in complete understanding of the subject (Bidanset and Lombard 2014)(Poletti 2012). The aim of this paper will be to introduce open geospatial consortium (OGC) CityGML and its level of detail and to outline some of the most important possibilities of the integration of GIS and real estate assessment. Outlining al of the possibilities would be a time consuming and far to elaborate work therefore this paper will focus on the applications and its main focus is to draw attention towards the combination of 3D building models and real estate assessment.

The paper will consist of a literature review and every example will be handled only briefly.

The rest of this paper consists of a further explanation of CityGML levels of detail. Thereafter the combination of GIS and real estate assessment will come forward. Before last, the combination will be taken into discussion and last the paper will draw a conclusion upon the combination of GIS and real estate assessment.

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STUDENT CONTEST 2018 §2 CityGML The CityGML 3D city model became the international standard of the open geospatial consortium (OGC) back in 2008. The extended version dates back to March 2012, a wide range of tools and applications support the 3D city model (Gröger and Plümer 2012).

CityGML covers almost all spatial objects such as terrains, street furniture and most importantly buildings. Within the CityGML model, a concept of levels of detail (LOD) is being handled. These levels of detail for the building model will be explained further in the next part of this paper. Further applications towards real estate assessment will be outlined later. However the main focus of this paper is not to stress the importance of CityGML, it is to draw attention towards the possibilities of geospatial data in assessing real estate to perceive a fairer estimation of the values as these values are used as a tax base in many countries among them The Netherlands.

The levels of detail are currently under revision due to the discussion and forthcoming of CityGML version 3.0, therefore version 2.0 is being used as stepping stone for this paper (Biljecki, Ledoux and Stoter 2016). §2.1 LOD0 The first level of detail is the most straightforward one and is represented by a (most of the times) flat polygon which can be elevated by usage of an attribute value such as numbers of stories, this usage takes the LOD0 to a 2.5D representation (Biljecki, Ledoux and Stoter 2016). The level 0 of detail further contains the function of the building and the year the building was built for the building as a whole (Gröger and Plümer 2012). Figure 1 shows the CityGML feature structure as UML instance diagram.

Fig. 1 LOD0 UML instance diagram (Source: Gröger and Plümer 2012, p.17)

§2.2 LOD1 The main improvement of LOD1 is the possible separation of the building into separate building parts, which all can have their own attribute data such as building height and year of completion. This greatly improves the usability of the 3D model for example to do shadowing simulations and energy demands (Biljecki, Ledoux and Stoter 2016).

Fig. 1 LOD1 UML instance diagram (Source: Gröger and Plümer 2012, p.17).

Also in LOD1, different types of roofs can be defined which refer to the actual building in reality however not to the representation in LOD1 (Gröger and Plümer 2012). Further, the terrain intersection curve (TIC) represents the line of contact between the building and the terrain, the TIC is available in every level of detail (Gröger and Plümer 2012). The UML instance diagram is an extension of the UML instance diagram of LOD0. The UML instance diagram of LOD1 is being shown in figure 2.

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STUDENT CONTEST 2018 §2.3 LOD2 To add even further detail to the 3D city model LOD2 enriches the model with a couple highly usable concepts. The most important extras are the possibility to add generalized roof structures and the addition of boundary surfaces for walls, roofs and even the ground surface on which the building stands on (Gröger and Plümer 2012). The applications of the improved model are wide. For example, every possible application of earlier LODs can be done with this level of detail with a more improved accuracy. In addition, the addition of generalized roof structures adds the possibility to carry out solar potential estimations (Biljecki, Ledoux and Stoter 2016). Furthermore, even buildings that are not enclosed completely can be handled by LOD2 with the concept of ClosureSurfaces. The concept allows buildings to still be represented as a closed object when in reality the building is not fully enclosed by walls.

This allows computations of volume and they can be neglected when they are not necessary for the intended application (Gröger and Plümer 2012). The UML instance diagram is an extension of the UML instance diagram of LOD1. The UML instance diagram of LOD2 is being shown in figure 3.

There is a drawback to LOD2, which comes from the handling of roofs. Overhangs are allowed in LOD2 yet they are not required. Therefore, LOD2 levels exist with both buildings with overhangs and buildings which have overhangs but without representation of them in the model. Due to the fact that overhangs require a more time consuming and more costly way to be acquired most LOD2 models consist of buildings without registered overhangs (Biljecki, Ledoux and Stoter 2016).

Fig. 3 LOD2 UML instance diagram (Source: Gröger and Plümer 2012, p.17).

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STUDENT CONTEST 2018 §2.4 LOD3 In LOD3, openings and detailed roof structures are being added into the 3D city model.

Openings such as windows can be defined by their own geometry and attributes. The more detailed roof structures allow the model to contain dormers and chimneys again with their own attributes and geometry (Gröger and Plümer 2012). LOD3 gives earlier mentioned application a greater accuracy and makes way for new possible applications such as heat loss estimations and luminance mapping (Biljecki, Ledoux and Stoter (2016). The UML instance diagram is an extension of the UML instance diagram of LOD2. The UML instance diagram of LOD3 is being shown in figure 4.

Due to the high level of 3D content obtaining LOD3 is a costly process that is mostly done by terrestrial and airborne laser scanning combined. It can however also be obtained using CAD or BIM. Even the usage of architectural plans, procedural modelling and ground imagery are being used to achieve LOD3 (Biljecki, Ledoux and Stoter 2016). Because of these high costs of obtaining LOD3 data, most datasets are restricted to smaller areas. Current research is being carried out to automate the process specifically for the detection of openings (Biljecki, Ledoux and Stoter 2016).

Fig. 4 LOD3 UML instance diagram (Source: Gröger and Plümer 2012, p.18).

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STUDENT CONTEST 2018 §2.5 LOD4 LOD4 adds inside geometries and openings such as doors between rooms. It also adds moveable objects such as tables (Gröger and Plümer 2012). LOD4 allows inside navigation among other applications. Furthermore, it adds a greater accuracy to the earlier mentioned applications. The UML instance diagram is an extension of the UML instance diagram of LOD3. The UML instance diagram of LOD4 is being shown in figure 5.

Fig. 5 LOD3 UML instance diagram (Source: Gröger and Plümer 2012, p.18).

§3 Applications of geospatial data considering real estate assessment The possible applications of geospatial data are wide concerning real estate assessment. The applications have been used since the early 80’s (Poletti 2012). In the next paragraph, an outline will be made of the most important applications. Furthermore, for every application the lowest level of detail will be stressed. As said above the applications will improve in accuracy when a higher level of detail is being used.

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STUDENT CONTEST 2018 Poletti states that the resources needed for application of GIS for real estate assessment can be divided into three different categories: technological, human and financial resources (2012).

Considering the scope of this paper the main focus is on the technological and the financial category however, human resources will be taken into account as they are as important. §3.1 The integration of centrality into real estate assessment The first, and probably the most straightforward, application of geospatial data in real estate assessment are derived from old theories of land value. Understanding these theories and applying them in real estate assessment can greatly improve the understanding on how geospatial data can actually increase the fairness of assessment. For Fig 6. Bid rent curve (Source: PennState College of Earth and Mineral Sciences via: https://www.eexample: Garcia and colleagues developed education.psu.edu/geog597i_02/node/842) a model of assessment in which a variable is being taken into account on how far a building is from Plaza de Gabriel Lodares in the Spanish city of Albacete (2008). The distance in this example is Euclidian towards the plaza, within the model a value is accepted that influences the value of the building (Garcia et all. 2008). This idea of implementing geospatial data of centrality into an computer assisted mass appraisal (CAMA) model derives from one of the oldest theories in land use namely the Von Thünen model, later worked out

by William Alonso into what is now called the Bid rent curve (Alonso 1960). The basic idea of the Bid rent curve is that land prices are higher near the city centre. Therefore, housing and land use near the centre is of a higher value as opposed to housing and land use near the outskirts of the city (Alonso 1960). The bid rent curve is shown in figure 6, which shows that prices differ among land uses yet near the centre the prices are always higher (Alonso 1960).

Possible discussion towards this theory can be the fact that cities showed patterns of inner city decay during the second part of the 20th century and coherent decay in land prices within these city centres. In addition, the emergence of multinuclear city regions is in conflict with the traditional bid rent curve. This discussion can be made more interesting using another old theory in geography namely the central place theory of Christaller.

The central place theory adds hierarchy towards centres therefore applying the bid rent curve towards more places with different heights of the perceived land prices of those centres is a possibility (King 1985). Applying this theory places can be ranked as far as necessary to get to a fairer assessment of real estate. The central place theory as defined by Christaller states a boundary condition of flat surface with an evenly distributed population.

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STUDENT CONTEST 2018 Using GIS centres can be defined on accepted points that improves the distance computations towards the actual centres and their given place within the hierarchy.

To stress the importance of distance towards services even more Thériault and Des Rosiers add the concepts of mobility and accessibility into the models of real estate assessment (1999). They state that the implementation of mobility and accessibility gives the model a great advantage of models using mere Euclidian distances and census data (Thériault and Des Rosiers 1999). To implement accessibility and mobility into CAMA models a transportation-orientated GIS is needed, which is costly.

Summarizing the application of distances Fig 7. Central place theory (Source: King 1985, p.34) towards centres can be done using LOD0 datasets and can be enhanced by using the central place theory and implementation of accessibility and mobility. As is stated above more good data, or a higher LOD, enhances the accuracy of the model but it does also raise the cost of acquiring the data and running such a model especially for a wider region. §3.2 Spatial statistics Another implementation of geospatial data into computer assisted mass appraisal is the concept of spatial statistics as opposed to traditional linear hedonic specification models. The workings of geostatistics are far beyond scope of this paper but the main concept can and needs to be covered if talking about implementation of GIS in real estate assessment.

Concepts such as geographically weighted regression (GWR), spatial lag models (SLM) and artificial neutral networks (ANN) are more flexible compared to traditional statistics such as multiple regression models (Bidanset and Lombard 2014) (McCluskey et all 2013).

The implementation of these concepts gives models the accuracy needed to reach the international standards as defined by the international association of assessment officers (IAAO) (Bidanset and Lombard 2014). Because of this the IAAO pushes research into these concepts as research still needs to be carried out to fully fine tune the concepts for application in the working place (Bidanset and Lombard 2014) (McCluskey et all 2013). Human resources play an important role within the usability of spatial statistics due to the complexity of its nature (Poletti 2012).

Geostatistics can be carried out with LOD0 datasets however; they can be taken to a higher level of accuracy using a higher LOD. It is however important to stress that 3D geostatistics might be future noise in this moment. Furthermore, the implementation of mobility and accessibility might enhance the models even more (Thériault and Des Rosiers 1999). §3.3 3D geospatial data and real estate assessment 8 Luc Hermans Word count:3501 Address Offices in Brussels : Rue du Nord 76, BE – 1000 Bruxelles. Tel +32/2/217.39.72 Fax +32/2/219.31.47 E-mail: maurice.barbieri@clge.eu - www.clge.eu

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STUDENT CONTEST 2018 3D geospatial data clears the way for many applications. In the paper of Gröger and Plümer, an outline is made of the most important applications, yet real estate assessment is not one of them. They stretch that 3D geospatial data can be used to carry out noise simulations, energy applications, disaster management among other things (Gröger and Plümer 2012). Zhang and colleagues further explain the implementation of 3D geospatial data for the execution of sunlight exposure computations (2014). In paragraph 2 more possible applications have been brought forward which can all have some impact on the value of real estate.

All of these implementations are important on their own yet they might all have an influence on the value of real estate. A garden facing south might increase the value of a property and noise pollution might decrease the value. The applications of using 3D city models thus find common ground in real estate assessment. They are a long way to being included in computer assisted mass appraisal but it can become a possibility if the datasets are available and unambiguous.

Most of these 3D implementations need a LOD3 or higher to be carried out due to the addition of openings in this LOD and are therefore more expensive. However, the application in real estate assessment can become front-runner in more specific and challenging applications of the 3D city model because of the social interest of real estate assessment. §4 Discussion Real estate appraisal has the potential to become one of the most important costumers of 3D city models. With CityGML being the international and industry standard, the combination of both the datasets and the usage of them is of great importance. However, real estate assessment is not being considered one of the most important applications by some (Gröger and Plümer 2012).

On both sides, improvements need to be made. CityGML needs to get a finer definition of LODs because a building can have LOD3 yet without having all openings and roof overhangs being registered (Biljecki, Ledoux and Stoter 2016). So on the surveying part of the spectrum steps need to be taken and CityGML 3.0 might be an important step towards that direction (Gröger and Plümer 2012). Real estate assessors have a long way to go in understanding and implementing geospatial data and geostatistics in their assessment practices (Bidanset and Lombard 2014)(McCluskey et all 2013). Also, the human factor of understanding data and its applications plays in important role in the near future of the combination of 3D city models and real estate assessment (Poletti 2012).

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STUDENT CONTEST 2018 §5 Conclusion The aim of this paper is to outline the combination of 3D city models and real estate assessment. Although not nearly every possibility is being covered, the aim is to get discussion going between consumers and providers of data. With CityGML becoming the industry and international standard back in 2008 it seems logical to focus the discussion on how the geospatial data is being registered within that framework from a viewpoint of real estate assessment. From the viewpoint of CityGML, it is important to get enough consumers and attention to raise enough funds and interest to further develop and fine-tune the model into the untouchable 3D city model. Because real estate value is a tax base in many countries, the combination might be extremely prolific for the future due to interest of national and regional governments and their available funds to carry out the assessment and to help further development of models.

Both industries are too big to watch each other develop from a distance. Discussion on how to best develop the relation is needed to keep the applications as widespread as they now seem to be becoming. This paper is a small step towards getting that discussion going all around both industries. Further research into the implementations of 3D city models is needed on the side of real estate assessment. On the other side, uniform and bigger datasets are needed to get from the academic world into the field of practice. Interest is by no discussion the starting point of a fertile relationship between both industries. Discussion is the means by which the goal of actual widespread usage can be perceived.

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STUDENT CONTEST 2018 Literature Alonso, W. (1960). A theory of the urban land market. Papers in Regional Science, 6(1), 149157.

Bidanset, P. E., & Lombard, J. R. (2014). Evaluating spatial model accuracy in mass real estate appraisal: A comparison of geographically weighted regression and the spatial lag model. Cityscape, 16(3), 169-182.

Biljecki, F., Ledoux, H., Stoter, J. (2016). An improved LOD specification for 3D building models. Computers, Environment, and Urban Systems, vol. 59, pp. 25-37.

García, N., Gámez, M., & Alfaro, E. (2008). ANN+ GIS: An automated system for property valuation. Neurocomputing, 71(4-6), 733-742.

Gröger, G., & Plümer, L. (2012). CityGML–Interoperable semantic 3D city models. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 12-33.

King, L. J. (1985). Central place theory. Regional Research Institute, West Virginia University Book Chapters, 1-52.

McCluskey, W. J., McCord, M., Davis, P. T., Haran, M., & McIlhatton, D. (2013). Prediction accuracy in mass appraisal: a comparison of modern approaches. Journal of Property Research, 30(4), 239-265.

PennState College of Earth and Mineral Sciences via: https://www.eeducation.psu.edu/geog597i_02/node/842) (Last consulted on 9-8-2018) Poletti, A. (2012). Leveraging GIS to Enhance Real Estate and Urban Areas performance.

Aestimum, 317-332.

Thériault, M., Des Rosiers, F., & Vandersmissen, M. H. (1999). GIS-based simulation of accessibility to enhance hedonic modeling and property value appraisal: an application to the Quebec city metropolitan area. Faculté des sciences de l'administration de l'Université Laval, Direction de la recherche.

Zhang, H., Li, Y., Liu, B., & Liu, C. (2014). The application of GIS 3D modeling and analysis technology in real estate mass appraisal-Taking landscape and sunlight factors as the example.

The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(4), 363.

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