Difference-in-Difference Estimation: Garbage Incinerators and Home Prices | Master of Economics	
Posted on August 8, 2010 by JJ Espinoza	
The price of a home can be affected by current interest rates, unemployment rates, and a host of other macroeconomic factors. Home prices are also subject to microeconomic externalities. These externalities can take the form of neighborhood characteristics such as quality of schools, crime, and even the proximity to garbage dumps. To quantify the impact of a building a garbage incinerator in North Andover, Massachusetts, had on home prices, Kiel and McClain estimated it impacts using a Difference-in-Difference Model. The effect of the garbage incinerator on home prices and found that there was only a negligible effect, which was statistically insignificant.
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What is Difference-in-Difference?
Linear regression is used in policy analysis when there exist a treatment and a control group and two time periods before and after treatment.
A more accurate way of verifying that the average differences between treatment and control groups across time are significant.
It is a way of eliminating unobserved heterogeneity; in other words, it is a way of eliminating fixed factors that might impact treatment and control groups.
Figure 1: Linear Regression Model with Difference-in-Difference Estimator
The regression below demonstrates that the incinerator didn’t impact the prices of homes in any significant way. Building the incinerator did not lower home prices; the incinerator was build where home values were already suffering.
Figure 2: This figure shows the difference -in- difference estimation for the treatment group post policy. In other words, it shows the average treatment effect of home prices near the incinerators post-policy.
The interpretation of these coefficients are a little tricky, but one thing to keep in mind is that the numbers are in natural logarithmic form since it is better to get figures in percentages.
Coefficient Explanation-All coefficients are in natural logs:
y81-The change in the average price of homes between 1978 and 1981 that are away from the incinerator
nearinc- The effect of being near the incinerator in 1978.
y81nrinc-Difference in price from being near the incinerator in 1981 compared to 1978
cons-Value of a house in 1978 that is far from the incinerator, in natural logarithm, to convert to regular price =exp(11.28)= $79,221
Controlling for Additional Variables
The coefficient that we are interested in is the one y81nearinc coefficient of – 6.26% with a p-value of 45.3 percent under the hypothesis that y81nearinc is statistically insignificant from zero. One can not reject the hypothesis that living near the newly build incinerator did not cause a decrease in home prices. There appear to be other factors that are much more important in determining home prices than the presence of an incinerator. The following regression shows how other factors are much more significant in determining the change in home prices than whether or not there is a new garbage incinerator nearby.
Figure 3: After controlling for other factors that are important in determining home prices y8nrinc is still statistically insignificant, but just barely.
y81 – the time trend in home prices for the control group is much less pronounced 14% increase as opposed to a 19% increase when you don’t control for relevant variables.
y81nrinc – is slightly larger and still insignificant at the 10% level, indicating that the new incinerator probably didn’t have ANY affect on home prices in a 3 mile radius.
nearinc – goes from being highly statistically significant to becoming statistically insignificant and much smaller in this new regression.
Significant Control Variables- (bath) an additional bathroom adds 12% to a homes value, (area) an extra 100 feet in area adds about 1% to a persons home price, and (age) every 10 years of aging reduces a homes value by about 8.2%.
JJ Espinoza is Senior Full Stack Data Scientist, Macroeconomist, and Real Estate Investor. He has over ten years of experience working in the world’s most admired technology and entertainment companies. JJ is highly skilled in data science, computer programming, marketing, and leading teams of data scientists. He double-majored in math and economics at UCLA before going on to earn his master’s in economics, focusing on macro econometrics and international finance.
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