1 / 16

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.

roanna-moon
Download Presentation

The Effect of Pollution on the Value of Houses

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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).

More Related