Cascaded Fuzzy Analytics Based Model for Determining Rental Values of Residential Properties

Abstract

The world’s property marketplace continues to experience enormous growth in infrastructure geared towards enhancing the quality of neighborhoods, such as physical landscaping and aesthetics, which have pushed rental values above reasonable bounds. The practice of ascertaining the market value of properties makes use of underlying key characteristics, especially in cities across the globe. Again, the rental values of property vary differently from place to place on the basis of characteristics (or factors). Studies are ongoing in determining the best factors needed to accurately arrive at appropriate market and rental values for properties. This study proposes a cutting-edge approach based on cascaded fuzzy logic controls to pair up distinct property characteristics identified by various professionals and literally works. The housing dataset was collected and used to construct the membership functions, the inference engine, and validate the proposed property rental value model. The outcomes revealed that the cascaded fuzzy analytics model was the inverse of the regression model, as the minimal MSE (0.05628) supported a good prediction of residential property values when compared to the regression model (R = 0.7320), whose value must be close to 1 to be a good estimate. Again, the proposed cascaded fuzzy analytics model (0.05628) was an improvement over the regression model (0.09619) in terms of MSE and standard error of estimation. These revealed the capability of the proposed model in determining residential property prices at a lower error rate than statistical inference approaches like regression estimation models.