The revolutionary development of the digital world has become our new normal. Almost everything in the face of the world can be digitally visualized. The current global pandemic has forced us to change how we see the world. In real estate planning, most of the planners have already started using third party and open-source Geographic Information System (GIS) softwares in developing spatial plans of their city. However, the basis for coming-up with an assessed/market value is still manually computed by the City Assessor's Office which is time-consuming and are already usually outdated. The study aimed to create a land valuation model using the different spatial factors that
affect residential land value through spatial analysis in a GIS software. Experts in planning and appraisal were asked to answer a survey in the form of the combination of Analytic Hierarchy Process (AHP), Bipolar AHP and Weights by Ranking to obtain how each factor and their proximity to a residential area affects the value of land. Dagupan City, noted as one of the major developing cities in the country where proper planning is mostly needed, was chosen as
the study area. The valuation model was created by adding the corresponding weight of each factor and analyzing them at a 1-meter by 1-meter resolution. The top three features that affect the value of a residential land are the distance of the different type of road network, presence of hazards, and proximity to landfill, while the land features with the least effect are land’s proximity to institutional, leisure, and livelihood areas. In addition, most of the factors give an increase in the value of land if they are near the residential area, except for landfill with a negative effect and cemeteries with a neutral effect. It can be observed that’s there’s a large gap between the market and zonal value of the area. Using Multiple Regression Analysis, the output, Nominal Land Value Map, and the Zonal Value of the City were used as independent variables, and they were related to the Market Value of the City per Ordinance No. 2086-2017. Two Hundred Fifty (250) sample points were used for the calibration, and another 250 points were used for validation that is evaluated for the whole city at 50 sample points per cluster. They were tested into three (3) cases considering equal values (Case 1), weighted values (Case 2), and only top five (5) weighted values (Case 3). The model was further evaluated using F-test at 0.05 level of significance that resulted to P-value 1.06E-08, a statistically significant result in Case 2 using a city-wide calibration, and a statistically significant result for Cluster 1 and 4 unanimously in the cluster calibration. An agreed result of not statistically significant can be found in all three (3) cases for Clusters 3 and 5, and a not statistically significant result for Cluster 2 for Cases 1 and 2 only. It can be observed that the top affecting feature can be found in Cluster 3 and 5. These characteristics might have affected the value of land and might have contributed a change in the approach to value these areas which is not yet integrated in the current market value of the area. Considering that Market/Assessed values are updated every three (3) years, Local Government Units (LGUs) may apply the developed standards in order to relate zonal and market values to the spatial characteristics of the land being valued. The standards may be used to come-up with a quantitative value of each parcel. It would help LGUs to have a sound basis for increasing their taxes for a particular zone and help them in prioritizing infrastructure and other development activities. The study show significant p-value but there is still a low RMSE and low r-squared that can be improved by incorporating additional variables, such as adding other factors that may affect land value, trying other regression techniques, and considering expansion to other land uses.