Wind and Property

I.               The Effect of Wind Development on Local Property Values-REPP

Overview

Wind energy is the fastest growing domestic energy resource. It is expected that this fast growth rate continue. Opponents of wind energy have raised claims that within the view shed of turbines property values will decrease.

Methodology

1. Project Selection Criteria

This report focuses on wind farms that use modern technology that is likely to be installed over the next several years. Newer and more modern turbines are larger turbine blades, but reduced mechanical noise. This study analyzes twenty-seven wind farms of 10MW or larger that were installed between 1998 and 2001.

2. Data Compilation

Once the study sites were selected, data was collected for each. This happened in a variety of ways either through phone calls, on-line resources, e-mail, government offices, or other collection methods. Some records were not kept or could not be accessed. Out of the original twenty-seven sites, only ten were used based on availability of data.

3. View Shed Definition

For this study, “the area in which potential property value effects are being tested for is termed the “view shed.”” Three sources of view shed definitions were used: The U.S. Department of Agriculture, The Sinclair-Thomas Matrix, and interviews with industry experts.

4. Comparable Criteria

“A set of neighboring communities outside of the view shed [were] selected to evaluate trends in residential house sales prices without the potential effects of wind farms on property values.” Five criteria were used to select the comparable communities: population, median household income, ratio of income to poverty level, and median value of owner-occupied housing units.

5. Analysis

After a review of other literature on the topic, “a simple linear regression analysis on property sales price as a function of time” was chosen to analyze the data. Three models were used to analyze the data. Case One compared the view shed sales prices and the comparable community sales prices for the entire period of the study. Case Two looks at sales prices only in the view shed. However it compares the sales prices from before the wind farm came on-line and after the wind farm came on-line. Case Three compares view shed and comparable community prices only after the wind farm came on-line.

Site Reports

A.   Riverside County, California

The topography of this site has a wide range. Elevations go from 450 feet to 2,500 feet. 3,067 wind turbines were installed over more than ten miles. In all three models average monthly prices grew slower in the comparable community. This means that there was no significant negative impact to property values for this site.

B.    Madison County, New York

Two of the ten wind farms are located in Madison County: Madison wind farm and Fenner wind farm. Each was analyzed separately, but shared the same comparable area. Madison wind farm has seven turbines and Fenner wind farm has twenty turbines. Five out of the six models from both wind farms showed that average sale prices grew slower in the comparable view shed. However, “the explanatory power of the model is very poor…there is no significant evidence in these cases that the presence of wind farms had a negative effect on residential property values.”

C.    Carson County, Texas

This wind farm is located among large expanses of very flat agricultural land. It has 80 turbines. In all of the models monthly average sale prices grew faster in the view shed than in the comparable area. This shows that for this area, there was no significant negative impact on property values from the wind turbines.

D.   Bennington County, Vermont

The Searsburg wind farm is located on a ridge and is comprised of eleven wind turbines. The turbine towers are visible above the trees, but the blades are painted black making them very difficult to see. Again, all three regression models showed no significant evidence of negative impacts on property values from wind turbines.

E.    Kewaunee County, Wisconsin

This area has some small hills and elevation changes, but is mostly agricultural land. There are two major wind farm projects, which are spread over three areas within a five-mile radius of one another. There are 31 turbines. The data from all three farms was grouped together. In all three models, “sales prices grew faster in the view shed than in the comparable area, indicating that there is no significant evidence that the presence of the wind farms had a negative effect on residential property values. However, the fit of linear regression is poor for all cases analyzed.”

F.    Somerset County, Pennsylvania

Somerset County has two major wind farms: Somerset and Green Mountain. Somerset has six turbines on a ridge crest, while Green Mountain has eight turbines. “In all three of the regression models, monthly average sales prices grew faster in the view shed than in the comparable area, indicating that there is no significant evidence that the presence of the wind farms had a negative effect on residential property values.”

G.   Buena Vista County, Iowa

This area is another very flat agricultural area. There are two wind farms scattered among homes and agricultural land.  Project Storm Lake I has 150 towers and Project Storm Lake II has 107 towers. Storm Lake City was used for the second analysis, but not for the first analysis. In analysis one, all three models showed faster growing sales prices in the view shed. In analysis two, all three models showed slower growth of sales prices in the view shed.

H.   Kern County, California

This area has large mountains, and is known for its wind farm development. Between 1981 and 2002 3,569 turbines were installed. One regression model showed that sales prices grew faster in the view shed, while the other two did not.

I.      Fayette County, Pennsylvania

There are ten turbines in this area located on a ridge. The area is very hilly and densely populated with trees. For this analysis the model is very poor and there is no significant evidence that the presence of the wind farms had a negative effect on property values.

Beck, F., Kostiuk, D., Sterzinger, G. (2003). The Effect of Wind Development on Local Property Values. Renewable Energy Policy Project. Retrieved from: http://www.repp.org/