The Housing Recommendation System Uses Multi-Criteria Decision-Making Methods

Economic and population growth, increasing urbanization, changing habits, new welfare requirements, lower interest rates have led to increased demand for housing in cities. However, conditions many cities are slightly alarming, while is a primary need the community. Selecting low-income people (LIP) that meets criteria required by LIP not an easy task. Because most of decisions made did utilize detailed information. Therefore, recommendation system required. This study aims develop selection best suits their wishes. integrated two multi-criteria decision-making (MCDM) methods: Best Worst (BW) method, which has fewer pairwise comparisons compared other MCDM methods selecting Technique Order Preference Similarity Ideal Solution (TOPSIS) method determining recommendations according Based on analysis results, ten dominate sequentially: Location, Land Size, Down Payment, Public Facilities, Price, Booking Fee, Home Design, House Specifications, Quality, Ownership Credit. Furthermore, sensitivity results showed robustness score this approach was high. The model could recommend