The document discusses an econometric model created by Joseph Gallego to analyze the relationship between housing prices and various factors using sample data of 88 houses provided by Jenna Stearns. The model found property value, number of bedrooms, square footage, and lot size explain 83% of housing price variability. It showed positive correlations between price and property value, bedrooms, and lot size, but a negative correlation with square footage. However, a t-test revealed only property value and bedrooms significantly impacted prices at the 90% confidence level, while square footage and lot size did not. Therefore, the final model considers only property value and bedrooms as key determinants of housing prices from this sample data.

1. Date: April 18, 2014
To: Jenna Stearns
From: Joseph Gallego, Real Estate Analyst
Subject: Econometrics Model for Housing prices
Dear Ms. Stearns:
Thank you for approaching me for advice on real­estate pricing. From the sample data of the 88
houses you provided, I was able to create an economic model that examines the relationship between
the value of the house (in dollars) and the multiple factors that may affect housing prices. These factors
are the value of the property, the number of bedrooms, the amount of square feet, and the size of the lot.
In the following analysis, I will conduct a regression of the four factors explained above.
After conducting a regression analysis of housing prices with respect to the asset’s value,
bedrooms, square feet, and lot size, my economic model suggests that there is a high R­squared value
of 83% (Appendix 1). This percentage explains how much of the variance in the housing prices is
explained by our variables. Ideally, we would want to get a high R­squared value because a higher
value shows a stronger correlation between the variable factors and housing prices. The high R­squared
value we obtained conveys that the property value, number of bedrooms, amount of square feet, and lot
size explain 83% of housing prices.
Furthermore, the coefficients within the economic model are obtained by deriving the
estimation equation by each of the variable factors (Appendix 1). The coefficients gathered illustrate
that there is a positive correlation between the value of the asset, the number of bedrooms, and the lot
size on the house’s price, but a negative correlation with the amount of square feet. According to the
model, housing prices increase by 90 cents for every dollar increase in the assets value. Additionally,
housing prices also increase by $11,602 for every additional bedroom; and, 59 cents for every increase
in square feet of the lot, which in my opinion makes sense. However, the model also explains that the
price decreases by 52 cents for every increase in square feet of the house. Logically, price should
naturally increase for every square foot added to the property. Therefore I believe that the last portion
of the model does not make sense and will be further examined in a statistics test.
Lastly, a t­Statistics is used to test the significance of the asset’s value, bedroom number,
housing size, and lot size. If a variable is significantly different from zero, we can conclude that it does
impact the housing prices. If it is not significantly different from zero, then it doesn’t affect housing
prices compared to the significant factors. In order to do so, I used the t­statistic of each variable and
compared them to a two­sided statistical test. Hopefully, this test will explain the coefficient housing
square feet enigma found in the previous paragraph. According to the t­values, we can determine that
the asset’s value and the number of bedrooms statistically affect housing prices to a 90% confidence
level; whereas, the housing square feet and the lot size does not pass our 90% confidence level test and
can be equated to zero (or insignificant). From this test, we can determine that housing square feet and
lot sizes are insignificant variables compared to the asset’s value and the number of bedrooms. In
conclusion from the t­statistic, we can produce a new economic model and eliminate the house’s square
feet and lot size as significant factors that affect housing prices from the sample data. In this case, the
significant factors that determine house prices from the sample data are in fact the asset’s value and the
number of bedrooms.
Attached is an appendix with the necessary visual aids that supplement my analysis. I hope you
find this information to be relevant to our company’s goals. If you have any further questions, feel free
to email me at josephgallego@umail.ucsb.edu. Thank you, and I look forward to working with you for
the micro paper 2 assignment.

2. Date: April 18, 2014
To: Jenna Stearns
From: Joseph Gallego, Real Estate Analyst
Subject: Econometrics Model for Housing prices
Appendix:
Model: Comparison of housing price with value of the asset, number of bedrooms, space in square feet
and lot size.
Regression on E­Views: ls hprice c hassess bdrms sqrft lotsize
Estimation Equation:
=========================
HPRICE = C(1) + C(2)*HASSESS +
C(3)*BDRMS + C(4)*SQRFT +
C(5)*LOTSIZE
Substituted Coefficients:
=========================
HPRICE = ­38887.0154265 +
0.908299068323*HASSESS +
11602.4896345*BDRMS ­
0.517459901499*SQRFT +
0.586729214593*LOTSIZE
1.645 ­ 90% two tailed
1.960 ­ 95% two tailed
2.576 ­ 99% two tailed