Analysis of the Role of Factors Affecting Land Price Using Geographical Weight Regression Model (Case study: Babolsar city)

Equitable access to land and its optimal use are one of the main components of sustainable development. The speed and the growth rate of land prices are related to the regional situation or land. Understanding the factors affecting land prices in Babolsar is the main goal of this research in order to plan the balance of prices. GIS software is used to extract and classify the data of this research. Our statistical population in this research is residential land plots in the city's neighborhoods. In order to determine the price of land, 330 land plots were taken as an example. The geographic weight regression model was also used to analyze the role of seven effective factors on land prices. The results showed that the average price of land in Babolsar is 1337000 Toman. Thirteen neighborhoods of the city were lower than the average and in contrast, nine neighborhoods of the city had a higher price level than the city average. Based on the results obtained from the weight regression model, the local determination coefficient (Local R2) for the dependent variable of land prices in Babolsar is between 0.57 and 0.79 Which shows good fit and high accuracy. As we move from the coastline to the southern parts of the city, the value of the land will be reduced. Neighborhoods close to the beach such as Ali Abadimir, Nokhostvaziri, Parking, Ketiben and ValiAsr and ... will have a higher price tag than other neighborhoods. In return, the southern neighborhoods of the city such as Miandasht, Ghaemie, Bibi Sarrozeh and parts of Hemmatabad and Yur mahalebala based on the estimated local land values, will have a lower price range than other areas of the city. The proximity to the coastline and the Proximity to the city center factor are among the most influential factors in the final value of land prices.