Conclusion
This paper offers an econometrically sound analysis of the effect of wind turbines on
property values in Rhode Island. With a sample of 48,554 transactions, we estimate a suite of
18
DD models that examine property impacts due to proximity, viewshed, and type and location of
turbine. Because our sample time period includes the housing boom and bust, we control for
city-level price fluctuations and allow the implicit value of housing characteristics to vary by
year and city, following the advice of Boyle et al. (2012). Broadly, the results suggest that there
is no statistical evidence for negative property value impacts of wind turbines. Both the whole
sample analysis and the repeat sales analysis indicate that houses within half a mile had
essentially no price change PC. These results are consistent with Hoen et al. (2013), who
examine impacts of large wind farms in nine states. However, the results are not unequivocal.
First, some models do suggest negative impacts; however, these are often incongruent with other
coefficient estimates in the same model. Second, many important coefficient estimates have large
standard errors. As time goes on and there are more PC transactions observed, we hope to update
this analysis and improve accuracy and consistency of the estimates.
In the past (and likely going forward), proposed wind energy projects have been fervently
opposed by homeowners surrounding the turbine site. There are several possible reasons why
these stated preferences may be different than preferences revealed through housing market
choices, such as we found in this analysis. First, stated preference is completely in the abstract
and losses and gains are never realized. Hence, people may behave strategically to try and
influence outcomes even if they are not willing to pay for it. Lang (2013b) finds a similar
inconsistency with stated beliefs about climate change and what internet search records reveal
about people’s interests. Second, wind energy is still relatively new in the United States,
especially farms and individual turbines that are in close proximity to residential development. It
could be that local opposition is driven by fear of the unknown, but that once reality sets in (i.e.,
the turbines are built) people care much less. Third, there could be a process of preference-based
sorting occurring in the housing market in which people who dislike the turbines move away and
those that are indifferent or even enjoy the turbines move near.7 Importantly, these location shifts
of certain homeowners may not affect housing prices if there are enough potential buyers who
are indifferent or prefer to live near turbines.
ConclusionThis paper offers an econometrically sound analysis of the effect of wind turbines onproperty values in Rhode Island. With a sample of 48,554 transactions, we estimate a suite of 18DD models that examine property impacts due to proximity, viewshed, and type and location ofturbine. Because our sample time period includes the housing boom and bust, we control forcity-level price fluctuations and allow the implicit value of housing characteristics to vary byyear and city, following the advice of Boyle et al. (2012). Broadly, the results suggest that thereis no statistical evidence for negative property value impacts of wind turbines. Both the wholesample analysis and the repeat sales analysis indicate that houses within half a mile hadessentially no price change PC. These results are consistent with Hoen et al. (2013), whoexamine impacts of large wind farms in nine states. However, the results are not unequivocal.First, some models do suggest negative impacts; however, these are often incongruent with othercoefficient estimates in the same model. Second, many important coefficient estimates have largestandard errors. As time goes on and there are more PC transactions observed, we hope to updatethis analysis and improve accuracy and consistency of the estimates.In the past (and likely going forward), proposed wind energy projects have been ferventlyopposed by homeowners surrounding the turbine site. There are several possible reasons whythese stated preferences may be different than preferences revealed through housing marketchoices, such as we found in this analysis. First, stated preference is completely in the abstractand losses and gains are never realized. Hence, people may behave strategically to try andinfluence outcomes even if they are not willing to pay for it. Lang (2013b) finds a similarinconsistency with stated beliefs about climate change and what internet search records revealabout people’s interests. Second, wind energy is still relatively new in the United States,especially farms and individual turbines that are in close proximity to residential development. Itcould be that local opposition is driven by fear of the unknown, but that once reality sets in (i.e.,the turbines are built) people care much less. Third, there could be a process of preference-basedsorting occurring in the housing market in which people who dislike the turbines move away andthose that are indifferent or even enjoy the turbines move near.7 Importantly, these location shiftsof certain homeowners may not affect housing prices if there are enough potential buyers whoare indifferent or prefer to live near turbines.
การแปล กรุณารอสักครู่..
