The impacts of brownfields on property values and private investment in Charlotte, North Carolina

Negative Perceptions of areas related to environmental contamination may have a substantial impact on the values of properties located close to brownfield sites and discourage new investment. The primary aim of this research is to look at the disamenity effects of brownfields on surrounding house values and private property investment in one post-industrial city---Charlotte, North Carolina. Conventional multivariate regression (Ordinary Least Squares) can conceal important local variations in the relationships among brownfields and property values and private investment. Geographically Weighted Regression (GWR), with the aid of Geographic Information Systems (GIS) mapping, can reveal this spatial nonstationarity. Another objective of this research is to compare the results of Geographically Weighted Regression models with the results obtained using conventional multivariate regression techniques (Ordinary Least Squares model) to investigate whether the use of GWR leads to improved statistical fit (explanatory power), as well as more accurate prediction. The GWR results show that there are significant local variations in the relationships between brownfields and property values and non-residential private investment. However, brownfields do not have any significant impacts on residential private investment. The results and analyses als0o indicate that Geographically Weighted Regression model outperforms the Ordinary Least Squares model in terms of explanatory power and predictive accuracy.