Values of environmental landscape amenities during the 2000–2006 real estate boom and subsequent 2008 recession

Abstract

This research suggests that consumers' marginal willingness to pay for environmental landscape attributes, such as water view, developed open space and forest-land open space, decreased during the 2008 recession compared to the 2000–2006 real estate boom. Estimates were obtained from a spatial hedonic housing price model after controlling for household location patterns and structural differences between the periods. Because the decline in amenity values was probably due to a temporary deterioration in economic conditions, the amenity values will probably rebound with economic recovery. Thus, development decisions based on the lower estimated amenity values measured during a temporary deterioration in economic conditions may be determined suboptimal post-economic recovery.

Keywords:

• environmental amenity
• landscape
• real estate boom
• recession
• spatial hedonic model

Acknowledgements

The views expressed here do not necessarily represent those of the University of Tennessee. The authors wish to thank the co-editors of the special issue, Drs. Oueslati and Salanié and two anonymous referees for helpful comments and suggestions. The authors also thank the Metro Planning Department, Davidson County and the Davidson County Tax Assessor's Office for providing the individual parcel data.

Notes

1. A detailed description of the buffer can be found in the Study Areas and Data section.

2. The details of the model description immediately follow.

3. Previous studies suggest that the BIC outperforms other information criterion, e.g. the Akaike Information Criterion (AIC) and the Posterior Information Criterion (PIC), in models with large sample sizes (Wang and Bessler 2005, 2009).

4. In general, no consensus exists on which weights are most appropriate for any econometric study (Anselin 1988), and the selection of appropriate weight matrices remains a challenge to practitioners (Le Gallo and Ertur 2003). Florax and Rey (1995) discuss some problems that may arise if spatial weight matrices are poorly selected. In an ex post analysis, when the two and four nearest neighbours for the spatial weight matrix were used, both spatial error and lag were significant in both periods. While it is difficult to conclude that the W matrix used in this study is the best of all possible neighbourhood specifications, the Wald tests for the second and third sets of regressions using the two and four nearest neighbour weight matrices are encouraging in this respect.

5. To compare the difference between results using the sub-sample of repeated sales and full sample of sales transactions, the same model was estimated using the full sample. The results using the full sample indicate that the effects of landscape variables on housing prices are not qualitatively different but quantitatively different with minor variations between the two periods. The results using the full sample are available upon request.