Energy is one of the main concerns of humanity because energy resources are limited and costly. In order to reduce the costs and to use the energy for space heating effectively, new building materials, techniques and insulations facilities are being developed. Therefore, it is important to know which factors affect the space heating costs. This study aims to introduce the novel Rank Correlation Bayesian Network model and its application in analyzing the effects of dwelling characteristics on the space heating costs. The results show that the constructed Rank Correlation Bayesian Network model performed better than the Bayesian networks models estimated by Bayesian search, PC and Greedy Thick Thinning algorithms, which are kinds of structure learning algorithms having different kinds of estimation mechanisms to build Bayesian networks. The constructed Rank Correlation Bayesian Network model shows that the space heating costs of the dwellings are mostly affected by the heating systems used. Coal stoves, air conditioners and electric stoves appear to be the costliest heating systems. The second most important factor appears to be the existence of external wall insulation. The lack of external wall insulation almost doubles the space heating costs. The third most important factor is the building age. Dwellings on the ground floors and the first floors appear to pay the highest space heating costs. Therefore, dwellings on these floors need to be more effectively insulated. As the size of the dwelling increases the heating cost increases too. Another result is that facing directions and floor levels of the dwellings have the least effects on their space heating.