Development of an Income-Based Decision Support System for Residential Selection in Bang Khen District, Bangkok

Abstract

This research aimed to 1) examine the economic, social, and locational factors affecting housing selection among residents in Bang Khen District; 2) design and develop a web-based decision support system for housing selection by applying the 30% income rule as the basis for calculating an appropriate rental budget; and 3) evaluate the system performance and user satisfaction. The study employed a Research and Development (R&D) approach. The population was divided into two groups: apartment and condominium housing data in Bang Khen District, Bangkok, and potential users seeking housing accommodation. The sample, selected through purposive sampling, consisted of 50 housing units and 30 system users. The research instruments included the web application, a system performance evaluation form, and a user satisfaction questionnaire. Data were analyzed using mean and standard deviation.

            The results revealed that the key factors used in the system design included average monthly income, preferred location, and type of accommodation. The developed system was capable of performing all major functions, including user data input, rental budget calculation based on the 30% income rule, housing filtering, automatic email notification of results, and dashboard-based summary reporting for administrators. The performance evaluation showed that the system design and user interface aspect had a mean score of 4.80 (S.D. = 0.34), system performance had a mean score of 4.67 (S.D. = 0.46), and security and database management had a mean score of 4.74 (S.D. = 0.46), all at the highest level. Users also reported that the system helped reduce the time required to search for housing, improved the accuracy of rental budget calculation, and supported more appropriate housing decisions based on their financial capacity. Therefore, the developed system has strong potential for practical application in supporting urban housing decisions effectively.