# Provide a credible and realiable and cost-effective estimate of land value as of given point in time

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1 LOGO Automatic Land Parcel Valuation to Support the Land and Buildings Tax Information System by Developing the Open Source Software Bambang Edhi Leksono, Yuliana Susilowati, Andriyan Bayu Suksmono Graduate Program for Land Administration, Bandung Institute of Technology. Labtek IX-C 3 rd floor, Jl. Ganesha 1, Bandung, 4132, Indonesia. Tel Fax AUTOMATIC LAND PARCEL VALUATION The purpose of Land Valuation: Provide a credible and realiable and cost-effective estimate of land value as of given point in time

2 BACKGROUND Uncertainty Multicolinearity Land Value Characteristic Variable Location ANN Method Non Linearity ANN Method MRA Method Problem Identification & Conceptual Framework LAND VALUE METHOD Multiple Regression Analysis (MRA) MRA is one of the most widely used method for land valuation models. MRA is a statistically based analysis that evaluates linear relationship between a dependent (response) variable and several independent (predictor variable), and extracts parameter estimates for independent variables used collectively to estimate value in a mathematical model. Artificial Neural Network (ANN) ANN is Computation method applies approach of pattern recognition to solve problem. ANN can calibrate models that consist of both linear and nonlinear term simultaneously.

3 RESEARCH QUESTION 1 What is the most significant Variabel of the Land Value system? 2 What is the most proper model of the Land Value system? 3 How is the result of MRA method compare with the ANN Method? OBJECTIVES Objective The aim of this study is to develop the automatic land valuation method using spatial analysis and artificial neural network.

4 METHOD LAND VALUE METHOD Multiple Regression Analysis (MRA) Case Study analysis in using MRA Method Artificial Neural Network (ANN) Case Study analysis in using ANN Method Comparison between of the result of MRA Method and ANN Method Artificial Neural Network Method Computational process Mathematical Model Y = f(x) = v1 X-1 + v2 X-2 + v3 X v23 X-23 + bj

5 Architecture of Artificial Neural Network JL. KH A DAHLAN 1,, 5 1, 1,5 2, 2,5 3, Mathematical Model: Hj = f(x) = v1 X-1 + v2 X-2 + v3 X v23 X-23 + bj Y = g(h) =g( f(x)) = w1 H-1 + w2 H-2 + w3 H w46 H-46 + bk LAND VALUE VARIABLES Central Business District Higher Education Facility Health Care Facility Transportation Facility Public School Facility Land Value

6 SPATIAL DATA OF LAND VALUE YEAR 27 VARIABLES MEASUREMENT Direct Measurement of Centroid Measurement of Buffer

7 Characteristic of Transportation Facility to the Land Value Scatter Diagram Nilai Tanah dan Jarak ke JL. KH A DAHLAN 1,, 5 1, 1,5 2, 2,5 3, Characteristic of Central Business Distric to the Land Value Nnilai Tanah (Rp/m2) Nnilai Tanah (Rp/m2) ALUN-ALUN 1,, 1, 2, 3, 4, Jarak (m) BANDUNG SUPERMALL 1,, 5 1, 1,5 2, 2,5 3, 3,5 Jarak (m) Nnilai Tanah (Rp/m2) PASAR KOSAMBI 1,, 1, 2, 3, 4, Jarak (m) JL. KH A DAHLAN 1,, 5 1, 1,5 2, 2,5 3,

8 COMPARISON OF MRA AND ANN METHOD Nilai Tanah (Rp/m 2) JL. KH A DAHLAN 1,, 5 1, 1,5 2, 2,5 3, Jarak (meter) Nilai Tanah (Rp/m 2) JL. KH A DAHLAN 1,, 5 1, 1,5 2, 2,5 3, Jarak (meter) Multiple Regression Analysis (MRA) Artificial Neural Network (ANN) LAND VALUE DATA YEAR 26

9 LAND VALUE MODEL Multiple Regression Analysis (MRA) Method Contrast Land Value Variation LAND VALUE MODEL Artificial Neural Network (ANN) Method Smooth Land Value Variation

10 COMAPRISON OF THE RESULT SPATIAL INTERPOLATION OF LAND VALUE DATA YEAR 26

11 SPATIAL INTERPOLATION OF LAND VALUE MODEL Multiple Regression Analysis (MRA) Method SPATIAL INTERPOLATION OFLAND VALUE MODEL Artificial Neural Network (ANN) Method

12 COMAPRISON OF THE RESULT CONCLUSSION Multiple regression analysis (MRA) is the most widely used method for calibrating model. The used of MRA has been the long standing choice for calibration of land value model. MRA is a statistically based analysis that evaluates linear relationship between a dependent (response) variable and several independent (predictor variable), and extracts parameter estimates for independent variables used collectively to estimate value in a mathematical model. Artificial neural network can calibrate models that consist of both linear and nonlinear term simultaneously.

13 CONCLUSSION LINEARITY ASSUMPTION CANNOT BE SUPPORTED BY THE LAND VALUE VARIABLES THE USE OF NONLINEAR METHOD IS RECOMMENDED FOR THE LAND VALUE MODELING The land value modeling using spatial analysis and artificial neural network is a promising method for the automatic land valuation activities. LOGO

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