摘要：Economic crises are not new in the world economy, wherein one of the first occurred in the seventeenth century, the Dutch tulipmania. The latest, a speculative bubble in the real estate market in the United States in 2008, sparked the interest of some researchers in analyzing the behavior of the real estate market in many countries. To mitigate the effects of the crisis that spread across the world, the Brazilian government took several of countercyclical measures, and one of which was to reduce the rules for granting credit in order to increase the supply of financing in various sectors. In the housing market, this credit expansion resulted in a significant increase of prices in real estate, and after that the concern arose of the possibility of a speculative bubble in this sector emerged. Thus, this study will examine whether macroeconomic variables can explain the behavior of the amount of credit to the real estate industry. For this, the methodology used was the method of Ordinary Least Squares - OLS, using the variables listed in the literature to analyze the volume of granting real estate loans. The variables were analyzed in the first differences of logarithms. The estimation results of the study showed that the most significant variables were the differences of the variables benchmark interest rate SELIC, the average income of the main job RENDA and the amount of money available in market M1, in their respective lags. However, estimates made in the explanatory power of the model was not more than about 20%, which may indicate that the variables that explain the behavior of the volume of granting real estate loand were not identified by existing studies in the Brazilian market. It can also be a sign that the behavior of the real estate market is not being explained by economic fundamentals and that is an evidence of a speculative bubble behavior in the sector. One problem of the study is limited data found in property prices to make a more complete analysis. To further study new methods for the analysis of the time series may be used.
