Motivation : Suppose we had data on house prices and their determinants across the whole of Greater Sacramento area and we wanted to model the house...

Motivation: Suppose we had data on house prices and their determinants across the whole of Greater Sacramento area and we wanted to model the house prices as a function of these determinants. Typically, we will run a regression of house prices on a set of structural attributes of each house, such as the age and floor area of the house; a set of neighborhood attributes, such as crime rate or unemployment rate; and a set of location attributes, such as distance to a major road and a school. Some other important variables to consider in the current market conditions are variables such as: interest rate, mortgage loans, subprime lending etc. The output from this regression would be a set of parameter estimates, each estimate reflecting the relationship between the house price and an attribute of the house. It would be quite usual to publish the results of such an analysis in the form of a table describing the parameter estimates for each attribute and commenting on their sign and magnitude, possibly in relation to a priori set of hypotheses. Similarly let us say we wanted to investigate the determinants of sales of leading retail outlets in the region. We would select a dependent (sales) and several independent variables (advertisement spending, lagged sales, population density, income levels, employment/unemployment levels, taxes, credit availability, and inflation levels) to model the relationships.

Group Formation, Expectations and Presentation: The class will form a team of TWO members in each group or individually. It is the responsibility of the group to get the data, export data in a spreadsheet, run a regression analysis and follow the instructions below to analyze and write a project report in not more then five pages (excluding tables, graphs, and maps). You will use double space word processing using 11-12 font size letters. The reports should be professionally prepared. This is a team effort and each member of the team will contribute to produce the final product i.e. report. You should submit a word processed and grammatically error free document. Please proof read the document before submitting it. Failure to do so will mean points being taken off.

Format (Each report will contain the following sections)

I. Cover Page

·        Title of Report

·        Think of a nice name for your team: Names of team members

II. Introduction [5 points]

• What motivates you to do this study?
• Include a few lines about the importance of this research.
• Example-- real estate--Regional and Neighborhood characteristics (housing, crime rates, rental rates, vacancy rates, population growth, economic growth profile, employment characteristics)
• Example—marketing research-determinants of sale of retail sector in California—past sales trends, advertisement, income levels, population trends,
• Use graphs, tables to report statistics of larger economy

III. Literature Review [10 points]

• Review two references
• What is the theory that you are testing?
• What do other authors say? How does your study differ from others?
• What is the relationship between house price and housing attributes? What are the findings of other studies?
• Cite some Californian studies or national studies. Search your own references using JSTOR, INGENTA, library, google scholar etc.,

IV. Problem Statement [10 points]

·        What is your question?

·        Propose a hypothesis. What is it that you are testing?

·        What are your null and alternate hypotheses statements?

V. Methodology [20 points]

·        How will you implement the project? What steps will you use to get the results?

·        What method will you use?

·        What are your data sources?

·        What variables have you selected?

·        What are the rationales of for the variables you have selected?

·        Each group will use FIVE explanatory variables and fifty time periods or observations

·        You will develop a multiple regression model--

§ Must include dummy variables or lagged variables (either one of both or both from same category)

§ Four explanatory variables

·        Each group will develop and run two models of any of these two kinds—SIMPLE AND MODIFIED MODELS

§ Examples:

·        Different time periods

·        Levels of complexity (multiple regression with dummies vs. model that includes lags)

·        Cross-section versus time series data

·        What years and regional units have you chosen?

§ Years, county and or MSAs, time series data

VI. Analysis [50 points]

·        Report Results from Statistical Analysis

·        R square, adjusted R square, descriptive statistics (mean, standard deviation)

·        Tests of Significance (5%) and coefficient interpretation of each variable and P value interpretation

·        What is the relationship between the dependent variables and independent variables?

·        Has the analysis contributed to a better understanding of the determinants of the phenomenon?

VII. Conclusions [5 points]

·        Report your findings succinctly in a few lines.

References

·        Provide a list of references used in writing this report.

Appendix

·        Include a copy of statistics on a spreadsheet.

Useful Data Websites

California Statistics/Data Sources

California Rand Statistics

California Finance data

American Fact Finder

US Census

National Parks    

http://www.nps.gov/

Economic http://www.economagic.com/

http://www.dof.ca.gov/HTML/FS_DATA/LatestEconData/Data_home.htm

Bureau of economic analysis

US CENSUS

American Fact Finder

http://portal.hud.gov/hudportal/HUD?src=/states

yahoo.com for finance data

http://www.edmunds.com/industry-center/analysis/drive-by-numbers-tesla-in-all-50-states.html

http://trade.gov/mas/manufacturing/oaai/tg_oaai_003649.asp

Research Articles

JSTOR

Ingenta.com

Library, CSUS