Difference-in-Difference Estimation: Garbage Incinerators and Home Prices

The price of a 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 in the form of neighborhood characteristics such as quality of schools, crime, and even the proximity to garbage incinerators. A garbage incinerator was built in North Andover, Massachusetts and the goal is to figure out what kind of impact the garbage incinerator had on home prices over the course of two years. Kiel and McClain estimated the effect of the garbage incinerator on home prices and found that there was only a negligible effect which was statistically insignificant.

What is the Difference-in-Difference Estimation?

  • A linear regression that is used in policy analysis when there exist a treatment and a control group and two time periods before and after.
  • A more accurate way of verifying that the average differences between treatment and control groups across time are really meaningful.
  • It is a way of eliminating unobserved heterogeneity, in other words it is a way of eliminating fixed factors that might have an impact between treatment and control groups.

Figure 1:  Linear Regression Model with Difference-in-Difference Estimator

The regression for the average difference between Massachusetts home prices before and after the incinerator demonstrate that the incinerator didn’t impact the prices of homes in any significant way. The reason that home prices are lower is probably not because the incinerator was build, but because home prices were lower that maybe the incinerator was built.

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 and have been converted with the natural number e

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.
y81nrincDifference in price from being near the incinerator in 1981 compared to 1978
_cons-Value of house in 1978 that is far from the incinerator, in natural logarithm, to convert to regular price =exp(11.28)= $79,221

  • 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 cannot reject the hypothesis that living near the newly build incinerator did not cause a decrease in home prices.
  • There appears 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 near by.

Figure 3:  After controlling for other factors that are important in determining home prices y8nrinc is still statistically insignificant.

Interpretation of Regression Coefficient Changes and Control Variables:

  • 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%.

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