Date of Award

11-2015

Culminating Project Type

Starred Paper

Degree Name

Engineering: M.E.M

Department

Mechanical and Manufacturing Engineering

College

College of Science and Engineering

First Advisor

Ben Baliga

Second Advisor

Hiral Shah

Third Advisor

Balasubramanian Kasi

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Keywords and Subject Headings

Regression, Analysis, Forecast, Demand, Houses, USA

Abstract

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.

Comments/Acknowledgements

Firstly, I would like to express my sincere gratitude to my advisor Dr. Ben Baliga for the continuous support of my Master study and related research, for his patience, motivation, and immense knowledge. His guidance helped me in all the time of research and writing of this thesis.

Besides my advisor, I would like to thank the rest of my thesis committee: Dr. Hiral Shah, and Dr. Balasubramaian, for their insightful comments and encouragement.

In addition, I would like to thank Randal D. Kolb, The statistic specialist in St. Cloud statistic station for his guidance and support through this study.

Last but not the least, I would like to thank my family: my parents, my brother, my sister, my husband and my kids for supporting me spiritually throughout writing this thesis and my life in general.

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