Real estate assessment of agricultural lands outside the zoning plan with artificial neural networks and multiple regression analysis methods: The case of Aksaray, Bahçesaray and Kırımlı rural districts

Year 2023, Volume: 4 Issue: 1, 1 - 12, 24.10.2023

Kamil Karataş , Hakan Karaduman

https://doi.org/10.48053/turkgeo.1213142

Cited By: 1

Abstract

This study aims to determine the market value of the agricultural lands in the rural Bahçesaray and Kirimli districts, which are outside the zoning plan, where agricultural production continues in Aksaray Province, Central District, in Turkey, by mass valuation methods. It is also aims to provide value estimation and value map production with the help of geographic information systems (GIS). Using the sales data from 125 parcels in the study area, the market value of the real estates for which the value is unknown in the region, was estimated. The most frequently used criteria in the assessment of agricultural lands were determined, and the valuation was carried out with Multiple Regression Analysis (MRA) and Artificial Neural Networks (ANN). By means of the assessment and the valuation study, the performance of the valuation methods was compared, and it was determined that the best result according to the test data was the valuation with ANN. In the performance analysis conducted with ANN, the Coefficient of Determination (R²)=0.87, Mean Absolute Percentage Error (MAPE)=0.192, Mean Absolute Error (MAE)=0.047 and Root Mean Square Error (RMSE)=0.059 was found. Moreover, according to the proportional standards guide determined by the International Association of Assessing Officers (IAAO), the performance measurement, the values derived for the Coefficient of Dispersion as (COD)=19.58 and Price-Related Differential as (PRD)=1.02 were also found to be within acceptable limits. Since the valuation of agricultural lands is a less studied subject, there are few articles in the literature. For this reason, it will be useful to increase such as article and evaluate the results applying it region by region. In this study, estimates were found with MRA and ANN methods and value maps were created.

Keywords

Agricultural land, Land valuation, ANN, MRA, GIS

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