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Wireless Towers and Home Values: An Alternative Valuation Approach Using a Spatial Econometric Analysis

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Abstract

This is the first study to use an hedonic spatial autoregressive model to assess the impact of wireless communication towers on the value of residential properties. Using quantile analyses based on minimum distances between sold properties and visible and non-visible towers, we examine the relationship between property values and wireless tower proximity and visibility within various specified radii for homes sold after tower construction. For properties located within 0.72 kilometers of the closest tower, results reveal significant social welfare costs with values declining 2.46% on average, and up to 9.78% for homes within tower visibility range compared to homes outside tower visibility range; in aggregate, properties within the 0.72-kilometer band lose over $24 million dollars.

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Notes

  1. Wireless devices include special feature phones, smartphones, and tablets.

  2. CTIA defines a cell site as the location of wireless antenna and network communications equipment necessary to provide wireless service in a geographic area (CTIA 2015).

  3. In their paper, the authors refer to wireless towers as cellular phone base stations.

  4. Sold properties data draw from the Gulf Coast Multiple Listing Service, Inc., a wholly owned subsidiary of the Mobile Area Association of Realtors, Inc.

  5. These data draw from the U.S. Federal Communication Commission’s Antenna Structure Registration database, available at http://wireless.fcc.gov/antenna/index.htm?job=home.

  6. These data draw from the U.S. Census Bureau, available at http://www.census.gov.

  7. The Viewshed tool is available as part ESRI ArcGIS® software package.

  8. Digital elevation maps draw from publicly available information hosted by the Geospatial Data Gateway of the U.S. Department of Agriculture’s Natural Resources Conservation Service.

  9. An anonymous referee observed that every property within a 1 km radius of a tower is also within the towers’ viewshed. We believe that this unusual result is consistent with the average height of a wireless tower in our dataset of approximately 60 m, and, more importantly, with the fact that our property sales data draw from a fairly flat coastal geographical area (i.e., the average housing elevation of our sample ≈ 11 m above sea level).

  10. There is a quadratic relationship between the logarithm of the property price and the number of bedrooms. We evaluate the semi-elasticities at the mean values of the number of bedrooms as reported in Table 2.

  11. This figure was calculated using equation (4). Let \( {\widehat{\boldsymbol{y}}}_1 \) be a column vector (5828 × 1) of predicted housing prices obtained by evaluating exp(Xβ) at the average values of all of the price predictors with D = 1 (sold after tower construction) and \( {\widehat{\boldsymbol{y}}}_0 \) the predicted housing prices counterpart with D = 0 (sold before tower construction). We define the change in welfare of each household i within Sample 1, as the element-by-element subtraction ΔW i = \( {\widehat{y}}_{1 i} \) - \( {\widehat{y}}_{0 i} \). Finally, the aggregate welfare impact was obtained by taking the sum of the elements of the column vector ΔW, i.e., \( {\sum}_{i=1}^{5,828}\varDelta {W}_i=-24,081,385 \).

  12. We calculate a 10% increase in the average minimum distance for houses in Sample 1 as 0.49 km ∙ 0.1 ≈ 50 m. A 0.59% increase in the average housing price of Sample 1 is $163,008.8 ∙ 0.0059 ≈ $ 961.80.

  13. The U.S. Census Bureau list of metropolitan statistical areas ranks Mobile County, Alabama at number 127. Data available at http://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=bkmk.

  14. The Port of Mobile is home to the twelfth busiest port in the U.S., and ninth busiest port along the Gulf Coast, ranked by cargo tonnage handled as reported by the U.S. Department of Transportation, available at http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_statistics/html/table_01_57.html.

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Authors and Affiliations

  1. Department of Economics and Finance, Mitchell College of Business, University of South Alabama, 5811 USA South Drive, Room 314, Mobile, Alabama, 36688, USA

    Ermanno Affuso

  2. Department of Economics and Finance, Mitchell College of Business, University of South Alabama, 5811 USA South Drive, Room 126, Mobile, Alabama, 36688, USA

    J. Reid Cummings

  3. Department of Economics and Finance, Mitchell College of Business, University of South Alabama, 5811 USA South Drive, Room 312, Mobile, Alabama, 36688, USA

    Huubinh Le

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  1. Ermanno Affuso
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  2. J. Reid Cummings
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  3. Huubinh Le
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Correspondence to Ermanno Affuso.

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Affuso, E., Reid Cummings, J. & Le, H. Wireless Towers and Home Values: An Alternative Valuation Approach Using a Spatial Econometric Analysis. J Real Estate Finan Econ 56, 653–676 (2018). https://doi.org/10.1007/s11146-017-9600-9

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