Elizabeth Thurman ECN 405 2L Model 1 Paper Cross Sectional Introduction: The cost of housing is an important factor when choosing where to live and also in considering the economic well-being of a state. High housing costs can be an indication of higher paying jobs and higher prices in general in a particular area. When housing prices are increased, it indicates an increased demand for housing in that area. Often, when prices are high, one must achieve a certain level of education in order to obtain a high paying job in a high-priced area. The question I would like to analyze is how personal income affects housing prices in a state, and also how level of education ties in with level of income to affect said housing prices. Therefore, in this paper, I will be using personal income and level of education as my testing variables in a regression analysis to see if the amount of money a consumer 1
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Elizabeth Thurman ECN 405 2L
Model 1 Paper
Cross Sectional
Introduction:
The cost of housing is an important factor when choosing where to live and also
in considering the economic well-being of a state. High housing costs can be an
indication of higher paying jobs and higher prices in general in a particular area. When
housing prices are increased, it indicates an increased demand for housing in that area.
Often, when prices are high, one must achieve a certain level of education in order to
obtain a high paying job in a high-priced area.
The question I would like to analyze is how personal income affects housing
prices in a state, and also how level of education ties in with level of income to affect
said housing prices. Therefore, in this paper, I will be using personal income and level
of education as my testing variables in a regression analysis to see if the amount of
money a consumer makes, and their level of education will have an effect on the
dependent variable, housing prices.
Also, I will be exploring the impact of income and education on home mortgages
throughout this paper by using five related studies in the form of journal articles. I will
be exploring how each article relates and shapes my model and question. I will then
pose my economic model that I will use to answer my question along with its estimation
and corresponding graphs.
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Further, I will test to see if I have evidence of heteroscedasticity which could
imply that I have a violation of constant variance in my error terms. I will then test for
endogeneity which will allow me to assess whether there is a correlation between an
independent variable and an error term. If the test results show that I do have an
endogenous model, my regression coefficient would cause the model to be biased, and
therefore violating the OLS rules for being the best estimator.
After testing for homoscedasticity and endogeneity, I will do a Ramsey reset test
for zero mean to see if my model is specified correctly, test for normality, and preform a
Wald test. Then, I will perform a final weighted estimation on my variables and then test
a binary model for states west versus east of the Mississippi River.
Review of the literature:
The article by Stephen Malpezzi, "Housing prices, externalities, and regulation in
US metropolitan areas" analyzes the determinants of housing prices which vary widely
across the United States. The study focuses mainly on city and metropolitan areas. It
uses a simple supply and demand framework to assess how regulatory actions effect
housing prices and uses factors such as income and population changes.
This article has helped me to shape my model because of the use of population
and income in relation to housing prices. I have included both as variables in my model
to study their effect. In Malpezzi’s article, I like that he related the housing prices to
metropolitan areas and chose to do my binary testing based on states east and west of
the Mississippi. This is because although both coasts tend to have a relatively large
number of metropolitan areas compared to the “inner states”, the east coast states
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seem to have more populated areas throughout. Highly populated areas tend to have
high demand for housing, thus increasing the prices of homes (or other housing
amenities). This is why I especially wanted to include population; however, I used
population for an entire state which will not give me the more specific relation to housing
prices that I would have liked.
The article "Education and income" by Hendrik S. Houthakker, analyzes income
levels in relation to a person’s level of education. It is performed as a cross sectional
analysis of different age groups across a single year. The article concludes that “capital
values increase uniformly as education increases”. However, it finds that those with
college levels 1-3 do not fare as well as those with only a high school diploma.
This concept led me to use the testing variable ‘level of education’ because those
who afford higher priced homes may be correlated with higher paying jobs which would
most likely require more than a high school diploma. Because the study found a
positive correlation between level of education and level of income, I decided it would
be a reasonable variable to use along with the amount of personal income.
In the article "Valuation of education and crime neighborhood characteristics
through hedonic housing prices" by Robin A. Dubin and Allen C. Goodman, housing
prices are studied as a bundle of neighborhood characteristics. Among the
characteristics in the bundle are those of crime and neighborhood schools. The study
found that housing values were influenced in the Baltimore metropolitan area
significantly when the two variables were studied together.
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This article inspired me to use state crime rates in 2010 to see how it affects the
cost of housing. Because one would intuitively guess that housing prices are cheaper
where there is more crime, I would like to test for a correlation. Many homeowners want
to live in an area where there is low crime not only for the safety of their family and safe
schools, but also for the safety of their belongings. Areas with high breaking and
entering rates may also drive the nearby cost of housing down. However, sometimes it
costs more to live in a city where the crime rates tend to be higher. This is another
reason I would also like to add crime rates as a variable. However, I will be using crime
as the amount of burglaries and will use it as a state data and not specifying it to
suburban, city, or rural housing, although this would be an interesting study.
The article "Housing and income" by Alan R. Winger finds that income and
housing do have a relationship and should involve other factors such as the permanent
income of an individual. In the past, studies have concluded that there is a relationship;
however, the specific factors involved where not clear. The article notes that you must
take into account that the decision to purchase a home was made in the past relative to
when each study is performed. Therefore it is harder to conclude what factors are
considered when a consumer decides to buy their home. The article mentions a paper
by Margaret Reid who studied the effects and also found that there is a strong
correlation between income and the housing market.
Because a strong correlation was found, and the two variables of housing related
to income are widely studied, I was inspired to also test for a correlation between the
two. If I were to analyze a second model, I would include a test for income over a
certain time period to see if it fluctuates with housing prices because income can also
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fluctuate. Thus, what a consumer may be able to afford in one year, may not be true in
the next.
Similarly, in the article, "Housing and permanent income: Tests based on a
three-year re-interview survey” by Lee, Tong Hun, the author points out that as
consumption is tested against income, income is usually tested at a point in time.
Instead, he notes, particularly with the consumption and purchase of mortgage loans
(housing), it is better to test the relationship with a fixed income over time. This is
because purchasing power may fluctuate and although a consumer may be making a lot
in a particular year, they may not make as much in following years and therefore not
affect the mortgage market as greatly.
This article gave some great insight into the notion that it must be kept in mind
we are testing income and housing at one point in time. Although results were found,
indication that income over time is a more accurate measure when related to housing
consumption, I decided to keep my variable of income because I am interested to see if
there is any correlation with housing prices combined with other factors such as the
population’s level of education, level of crime, GDP per capita, etc.
When testing the dependent variable (housing prices) against all other variables,
initially I find that burglary has a negative correlation with housing prices. This was as I
expected. All variables seem to have a correlation with my dependent variable;
however, GDP per capita seems to have the least correlation when compared with other
variables. Also, this variable has a negative beta, which I expected the correlation to be
positive because the income of a state increases as taxes increase which would mean
the housing costs more.
The following are correlation graphs. This gives a visual display the relationship
between each variable against the dependent variable.
Personal income appears to have a generally positive correlation, as expected.
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It appears as though total state taxes remain around one area with varying house
prices. This makes sense since state income may be in the same general area, the
prices of its houses vary much more greatly.
Level of education appears to have a positive correlation with housing prices, as
expected.
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State burglary appears to vary. As burglary levels remain around a certain number,
house prices seem to vary. However, it appears as burglary rates increase, housing
prices don’t stay at high levels.
GDP per capita appears to have a roughly positive correlation, however not very
strongly. I expected a stronger correlation.
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Unemployment rate does not appear to have a very strong correlation; however unemployment tends to stay around a certain level while house prices vary.
The population variable does not appear to have a high correlation with the housing
price variable.
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Conclusion/Test Results:
In conclusion, I have found that housing prices are more correlated with my
testing variable income, than with the testing variable level of education. In my final
estimation I found that my variables are significant, and that burglary is negatively
correlated as I expected.
Due to the high F-stat in my Breusch-Pagan-Godfrey test for heteroscedasticity
(Appendix B), I find that I reject the null concluding that my model has implication of
heteroscedasticity. Therefore, my error terms may be correlated to some degree and
also have varying distributions and variances.
In my test for endogeniety found in (Appendix B) I found that because my t-stat of
-0.586935 was less than my t-crit of 2.02, I fail to reject the null, concluding that I do not
have endogeneity. Therefore, my variables are not correlated with the error term.
In the Ramsey reset test for zero mean I find that my model is specified correctly.
The normality test shows that my model is not a normal distribution. The Wald test
shows that my testing variables are not jointly significant at a value of 0. In the final
weighted estimation I found that most all of my variables tend to be significantly
correlated with the exception of GDP per capita, as it was in my initial estimation.
Therefore, I would conclude that while I would want to include more data
variables, housing prices are related to the level of one’s income. There is also a higher
correlation between housing prices and burglary rates. It has a negative correlation,
meaning as burglary rates are higher, housing prices tend to be lower. If I created a
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new model, or added onto this model, I would do more testing for crime rates and use it
as a testing variable rather than a control variable.
References:
Article 1
Dubin, Robin A., and Allen C. Goodman. "Valuation of education and crime neighborhood characteristics through hedonic housing prices." Population and environment 5.3 (1982): 166-181.
Article 2
Houthakker, Hendrik S. "Education and income." The Review of Economics and Statistics (1959): 24-28.
Article 3
Lee, Tong Hun. "Housing and permanent income: Tests based on a three-year reinterview survey." The Review of Economics and Statistics (1968): 480-490.
Article 4
Malpezzi, Stephen. "Housing prices, externalities, and regulation in US metropolitan areas." Journal of Housing Research 7 (1996): 209-242.
Article 5
Winger, Alan R. "Housing and income." Economic Inquiry 6.3 (1968): 226-252.