EVALUASI NJOP DAN HARGA PASAR DENGAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) (STUDI KASUS: KECAMATAN MULYOREJO, KOTA SURABAYA, JAWA TIMUR)

Abstract

The city of Surabaya is one of the cities that has very rapid development which has a relatively high land value. Where Mulyorejo Subdistrict is one of the districts that has developed very rapidly, which become one of the new trading areas. In this study, a Geographically Weighted Regression modeling for land values in Mulyorejo Subdistrict will be conducted. Using NJOP data and market price data. Where in GWR modeling, different mathematical models and parameters will be obtained in each zone. The result is 126 data models, which consist of 63 NJOP data models and 63 market price data models. Then do a T-test and calculate the value of R-Square. In T-test results is each zone have different influence (x). Whereas in the calculation of R-Square value the result is NJOP data has a R-Square value of 72.4% to explain all variables (x) to the variable (y) and the market price data has a R-Square value of 70.6%. From the results of R-Square value it can be seen that variable (x) used in GWR modeling has a large influence to explain the variable (y), so that GWR modeling where used in NJOP data and market price data is significant.Keywords: GWR, market Price, NJOP, R-Sqaure, T-test