Multiple regression model Essay Example | Topics and Well Written Essays - 1750 words

Multiple regression model - Essay ExampleDespite the fact that at that place are numerous factors affecting the set up market, this paper testament focus mainly on these four factors since they are the greatest determinants of the housing market. The comparison among real augury prices and unemployment rates is rather an interesting one. The 1970s and 1980s national housing bubbles showed the true relationship between unemployment and house prices. The data from the housing bubbles indicated that real house prices declined until the rate of unemployment was at peak. Following the late 1980s housing bubbles, the Caser-Shiller index was of the suggestion that prices reduced for a few years afterward the unemployment rate peaked. Several studies also support this arguments hence the conclusion that house prices and unemployment rate exhibit a rather negative relationship. There is a coefficient of correlation between house prices and pomposity. In fact several researchers show that the relationship these two variables are 0.18-which is not strong but positive. The fact is the global inflation has been relatively low for quite a lot of time and the interest rates have fallen dramatically during this low inflation rate period. An make up in money supply in the economy causes inflation and house prices to increase. As mentioned earlier, there are a lot more factors that affect house prices and the relationship they exhibit is not as strong compared to the relationship that exist between inflation and house prices. One of the other factors is the rate of interest in the economy. Low interest rates means that home buyers can easily afford to buy a home. This leave behind increase the pick up hence at last increasing the demand of the homes. In large cities like London-where availability to land is limited-you will realize a more distinct effect of inflation. Countries with high nation are always characterized with high house prices. This is because high p opulation will always increase the demand for the houses hence pushing up the housing prices. The piece of tail line is if the construction industry is not able to satiate the demand for homes, the supply-demand imbalance will explain the unprecedented increase in real house prices. The economical state of the country is also important in determining prices of the houses. Countries with high GDP are experienced with high per capita income hence high demand for housing units which results to higher housing prices. This explains the reason as to why buying a house in a developed country is expensive as compared to underdeveloped or developing countries. This paper will try to analyze the relationship that exists between house prices GDP, interest rates, population and unemployment rates. Through these variables, the paper will try to determine how house prices are affected by interest rates, GDP, population and unemployment rate in a country. A regression model will be developed that will eventually be used to project the level of house prices in the future. Objective of the withdraw The main goal of this study is to determine how house prices are affected by factors such as interest rates, GDP, population and unemployment rates. Assumptions of the study Assumptions are vital concept of empirical studies. besides like any other empirical study, this study applies some statistical assumptions in order to achieve the much needed results. These assumptions include The mean departure is zero The data is normally distributed The variance of the two