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Polyvios Protopapas

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Proceedings Article | 27 June 2019 Paper

Factors affecting the price of industrial land in the district of Nicosia, Cyprus

Polyvios Protopapas, Thomas Dimopoulos

Proceedings Volume 11174, 1117419 (2019) https://doi.org/10.1117/12.2536530

KEYWORDS: Data modeling, Buildings, Tolerancing, Legal, Analytical research, Roads, Databases, Error analysis, Statistical analysis, Nanolithography

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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%.

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