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The Effect of Pollution on the Value of Houses

The Effect of Pollution on the Value of Houses. Econometric Analysis Walter Sosa-Escudero Spring 2009. A really classic paper: Harrison, D. and Rubinfeld, D., 1978. Hedonic prices and the demand for clean air, Journal of Environmental Economics and Management, 5, 81-102. Motivation.

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The Effect of Pollution on the Value of Houses

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  1. The Effect of Pollution on the Value of Houses Econometric Analysis Walter Sosa-Escudero Spring 2009

  2. A really classic paper: Harrison, D. and Rubinfeld, D., 1978. Hedonic prices and the demand for clean air, Journal of Environmental Economics and Management, 5, 81-102

  3. 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.

  4. One strategy: compare values of houses with different levels of pollution. • There is a problem with this strategy.

  5. 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 isolate the contribution of pollution from other competing factors.

  6. 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 %).

  7. 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.

  8. Summary statistics Variable | Obs Mean Std. Dev. Min Max -------------+----------------------------------------------------- value | 506 22.53281 9.197104 5 50 crime | 506 3.613525 8.601545 .0063 88.9762 nitox | 506 .5546951 .1158777 .385 .871 rooms | 506 6.284634 .7026172 3.561 8.78 age | 506 68.5749 28.14886 2.9 100 dist | 506 3.795043 2.10571 1.1296 12.1265 access | 506 9.549407 8.707259 1 24 tax | 506 408.2372 168.5371 187 711 ptratio | 506 18.45553 2.164946 12.6 22

  9. The Hedonic Model Ex-ante conjectures Since, What signs do we expect for j? • Positive Coefficients : 3 , 6 • Negative Coefficients: 1, 2 , 4, 7, 8 • Coefficients without conjecture: , 5

  10. OLS estimation regress 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 = 0.0000 Residual | 14652.221 497 29.48133 R-squared = 0.6570 -------------+------------------------------ Adj R-squared = 0.6515 Total | 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 -.1117576 nitox | -22.81088 4.160742 -5.48 0.000 -30.98569 -14.63607 rooms | 6.371512 .3923866 16.24 0.000 5.600571 7.142453 age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434 dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031 access | .272282 .072276 3.77 0.000 .1302777 .4142863 tax | -.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 ------------------------------------------------------------------------------

  11. OLS estimation regress 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 = 0.0000 Residual | 14652.221 497 29.48133 R-squared = 0.6570 -------------+------------------------------ Adj R-squared = 0.6515 Total | 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 -.1117576 nitox | -22.81088 4.160742 -5.48 0.000 -30.98569 -14.63607 rooms | 6.371512 .3923866 16.24 0.000 5.600571 7.142453 age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434 dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031 access | .272282 .072276 3.77 0.000 .1302777 .4142863 tax | -.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 ------------------------------------------------------------------------------

  12. Distance from downtown (dist): ------------------------------------------------------------------------------ value | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- crime | -.1834488 .0364887 -5.03 0.000 -.25514 -.1117576 nitox | -22.81088 4.160742 -5.48 0.000 -30.98569 -14.63607 rooms | 6.371512 .3923866 16.24 0.000 5.600571 7.142453 age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434 dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031 access | .272282 .072276 3.77 0.000 .1302777 .4142863 tax | -.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.

  13. Crime rate: ------------------------------------------------------------------------------ value | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- crime | -.1834488 .0364887 -5.03 0.000 -.25514 -.1117576 nitox | -22.81088 4.160742 -5.48 0.000 -30.98569 -14.63607 rooms | 6.371512 .3923866 16.24 0.000 5.600571 7.142453 age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434 dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031 access | .272282 .072276 3.77 0.000 .1302777 .4142863 tax | -.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

  14. The effects of pollution Pollution (nitox): ------------------------------------------------------------------------------ value | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- crime | -.1834488 .0364887 -5.03 0.000 -.25514 -.1117576 nitox | -22.81088 4.160742 -5.48 0.000 -30.98569 -14.63607 rooms | 6.371512 .3923866 16.24 0.000 5.600571 7.142453 age | -.0477499 .0141018 -3.39 0.001 -.0754564 -.0200434 dist | -1.335269 .2001468 -6.67 0.000 -1.728507 -.942031 access | .272282 .072276 3.77 0.000 .1302777 .4142863 tax | -.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.

  15. Suppose the government can implement a policy that reduces pollution in 5% in a certain neighborhood. What would be the expected 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 reduction produces an increas in the expected value of houses in $1140x (22810 * 0.05* x).

  16. For a neighborhood with average contamination (0.55ppm) this implies an increase in the value of houses of $627. • What is the social benefit of implementing this policy? Suppose each of the houses increases its value in $627 and that there are N hosues. Is the cost of reducing contamination in 5% greater than $627 N (this is more or less the maximum families should be willing to pay to have pollution decreased).

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