دانلود رایگان مقاله ارزیابی توده ای از زمین های کشاورزی با استفاده از اقتصاد سنجی کلاسیک و مدل سازی فضایی

ABSTRACT

Mass appraisals of properties traditionally use classical linear regression models (CLRMs); however, there has been the need to model the data spatially. Such modeling of the geographic effects has been used mainly in appraisals of urban areas, but the values of the properties in rural areas are also affected by the geographic location. This paper aims to use spatial regression econometric models in a sample of rural properties to elaborate the plan of values for an area of the North Fluminense Region – RJ, Brazil. The proposed methodology is to investigate and model the effects caused by the spatial autocorrelation on the CLRMs, evaluate their performance comparing them with the spatial models and produce the plan of values through ordinary kriging. The utilized sample consisted of 113 observations and 25 samples of verification. The performance of the obtained surfaces of values was evaluated through the Root Mean Squared Error (RMSE). The results showed that the spatial autocorrelation can have its effects controlled by Spatial Regression Models, because the Spatial Error Model (CAR) allowed to model the spatial dependence present in the residuals. Using the metrics of Akaike information criterion (AIC), R2 and likelihood function (LIK), the CAR model showed better fit in comparison to the CLRM. The results showed that the surface generated by the CAR model showed the best performance with the lowest RMSE. The combination of the methodologies of classical and spatial regressions and the use of geostatistical techniques were adequate to elaborate and obtain the plan of values for rural areas, to be used for various purposes, such as taxation, financing, expropriations, indemnities (in case of creation of conservation units or even in environmental disasters), among others.

6. Conclusions

The proposed methodology may be of great utility for the calculation and update of the Plans of Values for the rural areas and will also allow to analyze the sites where real estate appreciation occurs in the municipalities. Therefore, it may be used by municipal organs and state or federal public administration organs to obtain Plans of Values of rural areas for various purposes and applications.

The modeling through CLRM without the correct specification of variables related to the location proved to be inefficient, because the existence of spatial autocorrelation in the residuals of the least squares regression model was confirmed. The treatment of the spatial autocorrelation through the use of the spatial error model showed greater explanation capacity in relation to the least squares regression model for the utilized variables.

Regarding the use of Geostatistics to interpolate the values and generate the plan of values, the ordinary Kriging proved to be appropriate, allowing to generate values between the neighbors, considering that it is frequently difficult to obtain data in the field collection for the entire studied area, especially in rural areas.

Lastly, the combination of classical and spatial regression methodologies, and the combined use of geostatistical techniques proved to be adequate to elaborate and obtain the plan of values for rural areas.