No abstract

Many studies have been conducted for modeling the underlying non-linear relationship between pricing attributes and price of property to forecast the housing sales prices. In recent years, more advanced non-linear modeling techniques such as Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have emerged as effective techniques to predict the house prices. In this paper, we propose a fuzzy least-squares regression-based (FLSR) model to predict the prices of real estates. A comprehensive comparison studies in terms of prediction accuracy and computational complexity of ANN, Adaptive Neuro Fuzzy Inference System (ANFIS) and FLSR has been carried out. ANN has been widely used to forecast the price of real estates for many years while ANFIS has been introduced recently. On the other hand, FLSR is comparatively new. To the best of our knowledge, no property prices prediction using FLSR was developed until recently. Besides, a detailed comparative evaluation on the performance of FLSR with other modeling approaches on property price prediction could not be found in the existing literature. Simulation results show that FLSR provides a superior prediction function as compared to ANN and FIS in capturing the functional relationship between dependent and independent real estate variables and has the lowest computational complexity.

Purpose In Malaysia, a vertical residential building (VRB) is still facing challenges associated with property management. The lack of experience in property management resulted in the management, acting unprofessionally that cause a lot of problems to the homeowners and their properties. The findings demonstrated that maintenance of the facilities and common areas in a VRB is essential to ensure its optimal performance over its life cycle. This has to be carried out efficiently and professionally by the qualified property manager. Unfortunately, not every property manager can perform all the tasks efficiently and professionally. Thus, this leads to an increasing number of complaints by unsatisfied homeowners’ particularly on the maintenance and management of the buildings and facilities. To satisfy the homeowners, the issue of transparency is one of the area concerns that need to be emphasised in the property management system practiced in Malaysia. This case study area of Klang Valley poses as one of the urban areas that has the highest number of vertical buildings, especially the medium cost of VRB. The purpose of this study is to explore the satisfaction level of homeowners towards the management system, maintenance services and facilities provided by the management of the medium cost VRB in the Klang Valley. Design/methodology/approach The study adopted a quantitative approach. The survey method was used as an appropriate method for inquiry of the data. Face to face survey was conducted with respondents at a medium-cost residential building in Klang Valley, Malaysia. Findings The findings demonstrated that homeowners living in medium-cost VRB in Klang Valley are satisfied and faced issues with the management system, maintenance service and services provided by the management of the medium-cost VRB. The only issue confronted by the homeowners with the medium-cost VRB is the commitment of the management towards their involvement in organising the activities, problem-solving and taking action on residents’ reports or complaints. Practical implications The findings suggest that appointing qualified property managers who understand property management has increased the performance of the management team. Most importantly, qualified property managers are equipped with knowledge in managing people, especially to create awareness on a sense of responsibility and belonging. Originality/value This study has bridged the research gap on property management of the medium-cost of VRB in Malaysia. This will add value to the management of the medium cost of VRB.

Various methods have been used previously to estimate housing sales prices to model the underlying non-linearity relation between housing attributes and the price of property. More advanced non-linear modelling techniques such as Artificial Neural Networks (ANN), Fuzzy Inference System (FIS) and Fuzzy Logic (FL) emerged recently to model the nonlinear relation between the independent variables and the price function. A new structured model for house prices prediction based on Fuzzy Logic is proposed. A fuzzy logic based regression model has proved to be effective to address many prediction problems used in business forecasting, marketing and insurance. This paper highlights the development of a theoretical formulation for sales price prediction through the utilisation of a fuzzy regression model by applying fuzzy logic and fuzzy inference system techniques. The results show favourable outputs which indicate superior prediction function when compared with ANN and FIS as well as indicate the fuzzy functional relationship between dependent and independent variables.