Formação dos preços de imóveis residenciais em Curitiba/PR: Um estudo de caso a partir das equações de preços hedônicos

Abstract

This research aims to analyze the formation of property prices in the city of Curitiba, Paraná state in Brazil. In this case study is used as data, Sinapi indices, the CUB and the updated property values. For both the organization of the city is presented by Curitiba division in specific areas and sectors according to their characteristics. The average price of CUB, seeks to recognize and identify which factors are included in the training of their value and their ratings as property type and pattern. It also seeks to identify the significance of the value of hedonic prices in the final price of city property and what the attributes that most influence the valuation for these uses as basis the equation of hedonic prices. Linear regression is used to study and analyze the behavior of the variables that compose it, and in addition, is used fieldwork to check the characteristics of the chosen neighborhoods and their influence on price formation. As a result we obtained the symmetry of some variables with respect to the price of real estate and other variables that are asymmetrical, proving mathematically by linear regression that real estate prices in the region of Curitiba are influenced by the equations of hedonic prices of Rosen (1974). The work also shows the amenities found in the city of Curitiba, as well as a trend, shown graphically, a reduction in property prices in that the neighborhoods away from the Batel region which is close to the city center. Another result is verified that the Batel neighborhood has the largest number of accessibility to health and the third quarter with the largest number of the third lazer accessibility and also in accessibility and consumer education. The Center neighborhood, Batel, Green Water and Rebouças are neighborhoods that holds together the greatest health accessibility numbers, education and lazer from the fifteen neighborhoods analyzed are 82, 74 and 1,088 respectively showing that there is easier access to these three items for the residents of these regions. By linear regression method was possible to verify which variables most influence positively or negatively in property prices. The private area m² has a positive influence on the formation of 76.1% of that price, this index proves that the more private area of the larger property its price. The pattern of neighborhood income of the household head is influenced by 46%, consumption and accessibility to health 33%, the distance from the business center in km influences in 26.4%, the number of floors at 25.4%, the property located in prime area 25.1%, these variables help to value the property. On the other hand the amount of crime homicides (security) influences negatively by 32.7% and the number of apartments per floor also influences negatively 27.8% demonstrating that crime and the greatest number of apartments per floor are negative factors, make the fall prices.