effectsofpollution.pdf

The Effect of Pollution on the Value of Houses

Econometric AnalysisWalter Sosa-EscuderoSpring 2009

A really classic paper:

Harrison, D. and Rubinfeld, D., 1978. Hedonic prices and the demand for clean air, Journalof Environmental Economics andManagement, 5, 81-102

Motivation

How can we measure the willingness to pay for clean air?

Standard problem in public finance: free riding. No incentives to reveal willingness to pay

If pollution affects prices of houses, this can be used to measure willingness to pay. Families in fact pay more (less) to live in less (more) polluted places.

One strategy: compare values ofhouses with different levels ofpollution.

There is a problem with this strategy.

Methodology

A hedonic model to explain what determines the value of the houses.

The model is used to decompose how different characteristics of a house contribute to the total price.

In this hedonic model the level of pollution is included as one of the characteristics who may be explaining the value of the houses.

The role of the regression model is to isolatethe contribution of pollution from other competing factors.

Data and Variables

Data: classic paper by Harrison and Rubinfeld (1978).

Explained variable: VALUE: average value of occupied houses in

Boston (thousands of $).

Explanatory variables NITOX: concentration of nitrogen oxides (parts per

million, annual average concentration). CRIME: crime rate in the locality (crimes per capita,

in %).

Variables

ROOMS: Average rooms per dwelling. AGE: proportion of housing built before 1940. DIST: average distance to five major

employment centers in the Boston area (km). ACCESS: index of accessibility to highways of

the radial Boston area. TAX: tax rate ($ / $ 10,000). PTRATIO: ratio of students per teacher.

The Hedonic Model

Ex-ante conjecturesSince,

What signs do we expect for j? Positive Coefficients : 3 , 6 Negative Coefficients: 1, 2 , 4, 7, 8 Coefficients without conjecture: , 5

+ ptratio + tax + access + dist +

age +rooms + nitox + crime + = value

ii8i7i6i5

i4i3i2i1i

506 ..., 1, i =

ji

ij

x

valueE

=

][

OLS estimationregress value crime nitox rooms age dist access tax ptratio

Source | SS df MS Number of obs = 506-------------+------------------------------ F( 8, 497) = 118.99

Model | 28064.0746 8 3508.00932 Prob > F = 0.0000Residual | 14652.221 497 29.48133 R-squared = 0.6570

-------------+------------------------------ Adj R-squared = 0.6515Total | 42716.2956 505 84.586724 Root MSE = 5.4297

------------------------------------------------------------------------------

value | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+----------------------------------------------------------------

crime | -.1834488 .0364887 -5.03 0.000 -.25514 -.1117576nitox | -22.81088 4.160742 -5.48 0.000 -30.98569 -14.63607rooms | 6.371512 .3923866 16.24 0.000 5.600571 7.142453

age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031

access | .272282 .072276 3.77 0.000 .1302777 .4142863tax | -.0125921 .0037702 -3.34 0.001 -.0199995 -.0051847

ptratio | -1.176787 .1394154 -8.44 0.000 -1.450703 -.9028705_cons | 28.40667 5.365948 5.29 0.000 17.86393 38.9494

------------------------------------------------------------------------------

Distance from downtown (dist):------------------------------------------------------------------------------



age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031

access | .272282 .072276 3.77 0.000 .1302777 .4142863tax | -.0125921 .0037702 -3.34 0.001 -.0199995 -.0051847

ptratio | -1.176787 .1394154 -8.44 0.000 -1.450703 -.9028705_cons | 28.40667 5.365948 5.29 0.000 17.86393 38.9494

------------------------------------------------------------------------------

Expected value decreases in $ 1335 per kilometer from downtown Boston.

Crime rate:------------------------------------------------------------------------------



age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031

access | .272282 .072276 3.77 0.000 .1302777 .4142863tax | -.0125921 .0037702 -3.34 0.001 -.0199995 -.0051847

ptratio | -1.176787 .1394154 -8.44 0.000 -1.450703 -.9028705_cons | 28.40667 5.365948 5.29 0.000 17.86393 38.9494

------------------------------------------------------------------------------

An increase in 1 % in the crime rate, decreases the average value of houses in $183

The effects of pollution

Pollution (nitox):------------------------------------------------------------------------------



age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031

access | .272282 .072276 3.77 0.000 .1302777 .4142863tax | -.0125921 .0037702 -3.34 0.001 -.0199995 -.0051847

ptratio | -1.176787 .1394154 -8.44 0.000 -1.450703 -.9028705_cons | 28.40667 5.365948 5.29 0.000 17.86393 38.9494

------------------------------------------------------------------------------

An increase in one unit in the index of concentration of nitric oxide will decrease the average value of houses in $ 22,810.

Suppose the government can implement a policy that reduces pollution in 5% in a certain neighborhood. What would be theexpected increase in the value of houses in that neighborhood

Given a level of contamination x, a 5% reduction implies a decreas in contamination in 0.05 x.

According to our estimates, this reductionproduces an increas in the expected valueof houses in $1140x (22810 * 0.05* x).

For a neighborhood with average contamination (0.55ppm) this implies anincrease in the value of houses of $627.

What is the social benefit of implementingthis policy? Suppose each of the housesincreases its value in $627 and that thereare N hosues. Is the cost of reducingcontamination in 5% greater than $627 N (this is more or less the maximum familiesshould be willing to pay to have pollutiondecreased).

effectsofpollution.pdf

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