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Regression Analysis

In this project, we are required to forecast number of houses sold in the United States by creating a regression analysis using the SAS program. We initially find out the dependent variable which known as HSN1F. 30-yr conventional Mortgage rate, real import of good and money stock, these three different kinds of data we considered as independent variables, which can be seen as the factors will impact the market of house sold in USA.

Intuitively, we thought 30-yr conventional mortgage rate is a significant factor that will influences our behavior in house sold market, which has a negative relation with number of house sold. When mortgage rate increases, which means people are paying relatively more to buy a house, which will leads to a decrease tendency in house sold market. By contrast, a lower interest rate would impulse the market.

We believe that real import good and service is another factor that will causes up and down in house sold market. When a large amount of goods and services imported by a country, that means we give out a lot of money to other country. In other words, people have less money, the sales of houses decreased. Otherwise, less import of goods and services indicates an increase tendency in house sold market. We can see it also has a negative relationship with the number of house sold.

Lastly, we have money stock as our third impact factor of house sold. We considered it has a positive relationship with the number of house sold. When the supply of money increases, it will leads to a increasing in house sold. People are more willing to spend when they have more money in their hands. Vice versa, people will spend less in case of their money run out quickly if money stock decreases.

We have monthly data for HSN1F, 30-yr conventional mortgage rate and M3 money stock, and quarterly data for real import of good...

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