This study employs the statistical method of Multiple Linear Regression analysis (MLR) to develop an Automated Valuation Model (AVM) for estimating land values by utilizing transaction-based data in Limassol, Cyprus. The authors focus on the confidence level and accuracy of the value estimated by an AVM. Thus, the developed AVM was tested in two contrasting areas of Limassol in terms of location characteristics and market conditions. Most AVMs contain a statistical method to generate the estimated value of a real estate property. However, the outcome of a statistical method is verified by statistical measures. Therefore, if the validation of the predicted value for its accuracy derives from the statistical metrics of the model, then the explanatory variables cannot remain constant. It is implied that the AVM in order to grant the highest statistical metrics for a given property valuation requires different combination of independent variables in different locations, which means that the parameters of the model should change or adjust for every case to obtain the best fit model. The authors demonstrate that the best fit model is obtained when several models are executed with alternative combinations of variables. Hence, the best fit to the regression is given by the model with the better statistical measures when compared to the other models. Consequently, the predicted value is supported by statistical significance and can be adopted at a high confidence level.
A recent study in property valuation literature, indicated that the vast majority of researchers and academics are focusing on Mass Appraisals rather than on further developing the existing methods. Researchers are using a variety of mathematical models from the field of Machine Learning and Artificial Neural Networks, which are applied to real estate valuations, with high accuracy. On the other hand, it appears that the professional valuers do no use those sophisticated models on their daily practice, using essentially the traditional 5 methods. At that point, authors deal with the ethical question that arises and that is whether those models can replace the judgment of the individual valuer. As in many other aspects of scientific research, and in particular in artificial intelligence applications, human intelligence is still dangerous to be replaced by machine intelligence (like i.e. the self-driving cars). Despite the fact that those models are proved to be extremely accurate in academic test cases, in real-world applications, they cannot be used without the audit of an experienced valuer. The aim of this work is to investigate the capabilities of such models and how they can be used in order to improve valuer’s work.
Industrial development forms an important segment of many economies. The importance and significance of these industries has led to numerous studies being carried out globally, with the aim of identifying the factors affecting industrial land values whilst also enhancing stakeholder knowledge and allowing firms or investors to identify optimum locations. However, it is evident that there is a knowledge gap in the market for information regarding industrial land in Cyprus. This study looked to identify the main factors which affect land prices in the primary industrial areas within Cyprus’s largest district; Nicosia. The study’s aim is to firstly enhance all property stakeholders’ existing knowledge of industrial land prices in Nicosia, but then also goes a step further by identifying the main factors affecting industrial land values. Extensive literature review determined that the variables can be categorized into three main areas; physical and legal characteristics, locational characteristics and economic indicators. The importance of each variable differs greatly between regions and countries, but nonetheless there are evident themes which apply to all industrial locations around the world. Following the identification of these factors, primary and secondary data was collected surrounding all industrial land sales for the period 2008Q1-2018Q2. All raw data was analysed, filtered and processed accordingly, before finally being used within a multiple regression analysis with the use of IBM SPSS. The analysis carried out allowed the primary variables to be identified and quantified with the use of the regression models, resulting in a forecasting equation with an accuracy of 68.7%.
When the European Commission, International Monetary Fund and European Central Bank arrived in Cyprus to assist for a sustainable solution on the crisis on the banking sector, one of the first things they ordered was a New General Valuation (a mass appraisal that would revalue all properties in Cyprus as on 1st of January 2013), that it would be used for taxation purposes. The above indicates the importance of property mass appraising tools. This task was successfully conducted by the Department of Lands and Surveys. Authors aim to move a step further and implement the use of GIS and GWR techniques to improve the results of the New General Valuation. On a sample of comparative evidences for flats in Nicosia District, GIS was used to measure the impact of spatial attributes on real estate prices and to construct a prediction model in terms of spatially estimating apartment values. In addition to the structural property characteristics, some spatial attributes (landmarks) were also analysed to assess their contribution on the prices of the apartments, including the Central Business District (CBD), schools and universities, as well as the major city roads and the restricted zone that divides the country into two parts; the occupied by Turkish area and the Greek area. The values of the spatial attributes, or locational characteristics, were determined by employing GIS, considering an established model of multicriteria analysis. The price prediction model was analysed using the OLS method and calibrated based on the GWR method. The results of the statistic process indicate an accuracy of 81.34%, showing better performance than the mass valuation system applied by the Department of Land and Surveys in Cyprus with accuracy of 66.76%. This approach suggests that GIS systems are fundamentally important in mass valuation procedures in order to identify the spatial pattern of the attributes, provided that the database is comprised by a sufficient number of comparable information and it is continuously updated.
Charles Tiebout (1956), in his work “A Pure Theory of Local Expenditures”, provides a vision of the workings of the local public sector, acknowledging many similarities to the features of a competitive market, however omitting any references to local taxation. Contrary to other researchers’ claim that the Tiebout model and the theory of fiscal decentralization are by no means synonymous, this paper aims to expand Tiebout’s theory, by adding the local property tax in the context, introducing a fair, ad valorem property taxation system based on the automated assessment of the value of real estate properties within the boundaries of local authorities. Computer Assisted Mass Appraisal methodology integrated with Remote Sensing technology and GIS analysis is applied to local authorities’ property registries and cadastral data, building a spatial relational database and providing data to be statistically processed through Multiple Regression Analysis modeling. The proposed scheme accomplishes economy of scale using CAMA procedures on one hand, but also succeeds in making local authorities self-sufficient through a decentralized, fair, locally calibrated property taxation model, providing rational income administration.
N. Abu Jaber, Y. Abunnasr, A. Abu Yahya, N. Boulad, O. Christou, G. Dimitropoulos, T. Dimopoulos, K. Gkoltsiou, N. Khreis, P. Manolaki, K. Michael, T. Odeh, A. Papatheodoulou, A. Sorotou, S. Sinno, O. Suliman, N. Symons, T. Terkenli, Vassilis Trigkas, M.G. Trovato, M. Victora, M. Zomeni, I. Vogiatzakis
Following its application in Northern Europe, Landscape Character Assessment has also been implemented in Euro-Mediterranean countries as a tool for classifying, describing and assessing landscapes. Many landscape classifications employed in the Euro-Mediterranean area are similar in philosophy and application to the ones developed in Northern Europe. However, many aspects of landform, climate, land-use and ecology, as well as socio-economic context are distinctive of Mediterranean landscapes. The paper discusses the conceptual and methodological issues faced during landscape mapping and characterisation in four East-Mediterranean countries (within the MEDSCAPES project): Cyprus, Greece, Jordan and Lebanon. The major hurdles to overcome during the first phase of methodology development include variation in availability, quality, scale and coverage of spatial datasets between countries and also terminology semantics around landscapes. For example, the concept of landscape - a well-defined term in Greek and English - did not exist in Arabic. Another issue is the use of relative terms like 'high mountains,' ‘uplands’ ‘lowlands’ or ' hills'. Such terms, which are regularly used in landscape description, were perceived slightly differently in the four participating countries. In addition differences exist in nomenclature and classification systems used by each country for the dominant landscape-forming factors i.e. geology, soils and land use- but also in the cultural processes shaping the landscapes - compared both to each other and to the Northern-European norms. This paper argues for the development of consistent, regionally adapted, relevant and standardised methodologies if the results and application of LCA in the eastern Mediterranean region are to be transferable and comparable between countries.
KEYWORDS: Geographic information systems, Remote sensing, Data modeling, Buildings, Databases, Systems modeling, Orthophoto maps, 3D modeling, Amplifiers, System integration
This paper aims to examine how CAMA, GIS and Remote Sensing are integrated to assist property taxation. Real property tax apart from its fiscal dimension is directly linked to geographic location. The value of the land and other immovable features such as buildings and structures is determined from specific parameters. All these immovable assets are visible and have specific geographic location & coordinates, materials, occupied area, land-use & utility, ownership & occupancy status and finally a specific value (ad valorem property taxation system) according to which the property tax is levied to taxpayers. Of high importance in the tax imposing procedure is that the use of CAMA, GIS and Remote Sensing tools is capable of providing effective and efficient collection of this property value determining data. Furthermore, these tools can track changes during a property’s lifecycle such parcel subdivision into plots, demolition of a building and development of a new one or track a change in the planning zone. The integration of these systems also supports a full range of business processes on revenue mobilization ranging from billing to taxpayers objections management.
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