No abstract

This research study explored the impact of an urban spatial plan on land value; it drew on the value model based on space syntax as a systematic technical piece of software that explains and analyzes the relationship between land use and land value. The study proposes a framework based on the urban spatial network. We selected the Kirkuk city master plan for evaluation purposes and hypothesized that there would be a relationship between the space syntax of the essence of urban spatial integration and the price of land. Therefore, the case selected was evaluated in three aspects of analysis: the urban spatial function, which involves the integration rates of the city’s street network and connectivity; the urban land price assessments in the context of the city’s spatial grids; and the expansion of land use distribution, including residential and commercial grounds, to explain the changing economic value of the spatial relationship in the land market. OSM, AutoCAD, depth map X8, QGIS 3.16, and SPSS were used for data cluster analysis, spatial network preparation and analysis, and correlation analysis. The results showed that the urban spatial plans and comprehensive urban socioeconomic and environmental factors had a significant impact on land price. This result can enhance the future spatial design and the economy of metropolitan areas.

Recent literature has highlighted the critical issue of urban land value and cost; properly assessing land use costs, particularly for residential and commercial purposes, is crucial in influencing urban development and investments. Therefore, the objective of this research is to create a model for land pricing that considers the urban street networks and hierarchy; by analyzing the spatial plan of the city using space syntax and evaluating the economic impact on land value, the study aims to identify the factors that influence land prices. Furthermore, the study intends to investigate the correlation between urban spatial networks, street hierarchy, and land price to create a predictive model for urban spatial land pricing. Ultimately, the study has successfully built a model for predicting the price of urban land. The case selected is evaluated and compared in three aspects of the analysis, including the urban axial assessments and urban street width, to find out their impacts on the real estate’s land price in the context of the land use distributions, which are predominantly residential and commercial types of uses. Depth map X8, SPSS, and QGIS 3.16 were used for the study evaluations and assessments. The study found that land prices are influenced by factors such as integration, connectivity, and street width. Commercial zones with good integration and wider roads tend to command higher prices, while narrow local roads generally have lower prices. This result can enhance future urban design regarding urban economy improvements and land costs.

Overcoming the issue of land value and cost in urban areas will not provide a miraculous solution to the problems there. Appropriate land use cost, especially for residential and commercial land, is just one of the issues to be settled in the debate. Therefore, this study aims to build a new urban land price determination model by investigating the urban syntactical analysis, street width, and their economic effects on land value. The study attempts to determine the impact of syntactic analysis of streets and street width on land prices; it also seeks to identify the factor most affected by the land cost. Ultimately, the study built a model for urban land price prediction. The case selected is evaluated and compared in three aspects of the analysis, including; the urban axial assessments and urban street width, to find out their impacts on the real estate’s land price in the context of the land use distributions, which are predominantly residential and commercial types of uses. Depth map X8, SPSS, and QGIS 3.16 were used for the study evaluations and assessments. The result showed a significant impact of the urban street network on the price of land; this result can be used to enhance future urban design regarding urban economy improvements.