Geospatial land valuation modeling using the integrated multivariate analysis and AHP | |
| Author | Bencure, Jannet Colubio |
| Call Number | AIT Diss. no.RS-20-02 |
| Subject(s) | Land cover--Remote sensing Geospatial data Multivariate analysis Decision-making--Mathematical models |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Remote Sensing and Geographic Information Systems |
| Publisher | Asian Institute of Technology |
| Abstract | Updated and realistic land value is essential not only for private individuals but also for government agencies in all aspects of land administration and on their day-to-day land dealings. Despite its significance, land valuation undertaking remains disorganized. The process itself is unregulated that lack concrete scientific, legal, and practical foundations. The previous valuation approaches that confined to regression analysis inherit some significant limitations especially when applied to massive number of parcels for it cannot comprehend the real-world factors. On the other hand, multicriteria decision analysis (MCDA) approach enables to grasp important factors, however, it only provides relative land value. This study combined these two methods so that each limitation is compensated. The study aimed at developing a geospatial land valuation model by involving land experts in important phases of development. The analytic hierarchy process (AHP), a MCDA tool, enables these factors to be included in the model, hence providing a realistic result. The land valuation model (L VM), developed in this study, is an inclusive approach wherein experts are involved in the selection and weighing of factors through the AHP. The model was validated using root mean squared error (RMSE) and compared with multiple regression analysis (MRA) through a case study in Baybay City, Philippines. The final model considered 15 factors and its performance (RMSE = 0.526) is comparable to MRA (RMSE = 0.460), while MRA resulted to only six (6) significant factors in final model. It was also found out that the value derived from L VM is five to hundred times higher than the assessor-based market value and zonal values. As such, total land value and potential worth of real property tax of entire Baybay City is estimated to be 132billion and 450million Philippine Pesos (l USD ~ PhP50.00), respectively. |
| Year | 2020 |
| Type | Dissertation |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Tripathi, Nitin Kumar;Miyazaki, Hiroyuki (Co-Chairperson) |
| Examination Committee(s) | Sarawut Ninsawat;Kim, Sohee Minsun |
| Scholarship Donor(s) | Commission on Higher Education-Philippines Visayas State University, Philippines;Asian Institute of Technology Fellowship |
| Degree | Thesis (Ph.D.) - Asian Institute of Technology, 2020 |