Abstract- House prices in Indonesia tend to increase every time. This is due to the increasing demand for residential sector every year, especially in urban areas. Prediction of house prices is important, especially, for property investors and potential buyers, so they can make careful planning related to home sales and assessing asset prices as a references in loan approval. There are several factors affect the house price, including physical attributes, concepts, location, as well as several economic factors prevailing predictions. However, the difficulty of applying the fuzzy inference system is establishing the membership function and choosing the rules to be used. This research goal is to extract fuzzy rules (Membership functions and inference rules), which can be used to pr3edict house prices based on nearby objects location. K-Means clustering method is used to extract initial values to form fuzzy membership function and inference rules of several groups of residential. This research produces a good-interpretability fuzzy system shows a satisfactory results of predictions.