Assessment of Effective Factors on Urban House Prices Using Artificial Neural Network؛ Case Study: District 2 of Tabriz

Housing is a fundamental component of the household consumption bundle. In fact, for most households, the purchase of a home is their single most important financial transaction. The housing market can be influenced by macro-economic variables, spatial differences, characteristics of community structure, and environmental amenities. Heterogeneity of house and how consumers rank different characteristics of a house led to price changes and fluctuations. So that, one house with similar physical attributes in different urban regions will show different prices. This research, looking for recognition effective factors on house Prices and estimating prices of housing units in the District two of Tabriz. This research based on applied and Correlational researches. The data were collected through survey and inquiry from real-estate agents. Statistical population is the houses in district two of Tabriz which is 56107. Cochran formula estimated 384 sample size and for desirable estimation 400 house were randomly selected as a sample of research. Artificial neural network (ANN) is employed in this paper to analyze housing values. In determining the house prices "physical variables" have 53.8 percent, "distance variable" has 39.2 percent and "environmental variables" has 7 percent. The findings of research indicate which "floor area" variable with 28/4 percent, "distance from treatment centers" variable with 4/4 percent, "distance from health centers" variable with 5.1 percent and "building facades" variable with 4.6 percent has the highest share of house prices. In this research, were used MATLAB 2013 and ArcMap 10.4.