Regression Analysis To Forecast the Demand of New Single Family Houses in USA

Publication Year:
2015
Usage 1682
Downloads 1612
Abstract Views 70
Repository URL:
http://repository.stcloudstate.edu/mme_etds/15
Author(s):
Nayal, Lama
Tags:
Regression; Analysis; Forecast; Demand; Houses; USA
paper description
Forecasting the market demand is a very critical step in planning all kinds of business including construction business. This study was conducted to develop a robust regression model that enables construction companies predicting the demand of new single family houses in the USA. The study identified each of inflation rate, mortgage rate, GDP, Personal consumption, unemployment rate, and population as independent variables that may affect the market demand of new single family houses. The data were collected over 21 years, evaluated, and sorted according the nature of the relationship between each independent variable factor and the market demand of new single family houses. The data reflected double conversion in relationship between GDP, Personal consumption, and population and the market demand due to the financial crises and the beginning of the recovery after it. The Dummy variables technique was used to identify the periods of before the financial crisis, during the financial crises, and after it. The dummy variables have been added to the model to handle the fluctuation in these data sets. The study concluded that the unemployment rate variable and the personal consumption variable are the most important factors that affect the market demand of new single family houses in the USA. A regression model was developed to be used to predict the market.