Please write INTERPRTATION, LIMITATIONS and CONCLUSION by using the data in Master Regression Worksheet.
New house construction has a significant impact to the US Economy.
For every new house built, 2.97 jobs are created and $110,957 are paid in taxes (Emrath, 2014). This estimate for 2014 has not drastically changed since 2008 (Liu & Emrath, 2008).
The jobs created are not only required to build the house, but also to manufacture and deliver all the products and designs for the large range of materials used in the constructing.
New house development and population movement and growth causes new public and private infrastructure. New hospitals, roads, schools, shopping malls and national retail chains etc. all use estimates of population growth and housing development to manage capital expenditure.
According to Builder Magazine, the top 100 new house construction companies had a total revenue of $90bn in 2015 (Builder_Magazine, 2016).
Residential Fixed Investment (RFI) is a measure of new single and multi-family homes and house remodeling. RFI has averaged 4.5% of GDP over the last 35 years (Logan, 2016) and was 3.6% in 2016.
Accurately forecasting the quantity of new houses enables businesses to be more efficient. They will be able to better plan and manage their capital expenditure and human resources.
The quantity of localized new housing also effects sale prices of existing housing. This influences the decision making process of buyers and sellers. If it is known that new housing in a specific area will begin to be constructed, sellers may try and move their properties earlier, and buyers may wait till the new houses are ready for sale.
Unfortunately, the number of new houses sold fluctuates each year. Over the last 50 years, the average has been 651,000, with a maximum of 1,283,000 in 2005 and a minimum of 306,000 in 2011.
The aim of this study is to identify variables that will help support forecasting of house sales to better improve management of capital and resources.
The methodology used was:
1. Brainstorm independent variables
2. Develop a regression model
3. Analyze the results to identify variables that will help improve forecasting of new house sales.