This paper investigates the hypothesis that houses located in proximity to limited-access roadways exhibit a resilience to price decline during and after exogenous market downturns. A quasi-experimental design was used to look for empirical evidence of price differentials before, during, and after construction and operation of network improvements. A spatial autoregressive difference-in-differences estimator with spatial disturbances was used to find evidence of price resilience in the aftermath of the real estate bubble affecting the local and state economy. Single-family homes exhibited a price premium ranging from 4.6% to 5.2% over their controls. These premium differentials were realized 4 to 5 years after the opening of the facilities and after the U.S. real estate market downturn. This paper contributes to the empirical literature by providing a quasi-experimental approach to the selection of comparable parcels for spatial hedonic regression.
HuangW.The Effects of Transportation Infrastructure on Nearby Property Values: A Review of the Literature. Working paper 620. Institute of Urban and Regional Development, University of California, Berkeley, 1994.
4.
RyanS.Property Values and Transportation Facilities: Finding the Transportation–Land Use Connection. Journal of Planning Literature, Vol. 13, No. 4, 1999, pp. 412–427.
5.
NelsonJ. P.Highway Noise and Property Values: A Survey of Recent Literature. Journal of Transport Economics and Policy, Vol. 16, No. 2, 1982, pp. 117–138.
6.
BoarnetM. G., and ChalermpongS.New Highways, House Prices, and Urban Development: A Case Study of Toll Roads in Orange County, Ca. Housing Policy Debate, Vol. 12, No. 3, 2001, pp. 575–605.
7.
KockelmanK. M., and BinaM.Location Choice Vis-à-Vis Transportation: The Case of Recent Homebuyers. Proc. International Association of Travel Behavior Research Conference, Kyoto, Japan, 2006.
8.
McMillenD. P., and McDonaldJ.Reaction of House Prices to a New Rapid Transit Line: Chicago's Midway Line. Real Estate Economics, Vol. 32, No. 3, 2004, pp. 463–486.
9.
HaiderM., and MillerE. J.Effects of Transportation Infrastructure and Location on Residential Real Estate Values: Application of Spatial Autoregressive Techniques. In Transportation Research Record: Journal of the Transportation Research Board, No. 1722, TRB, National Research Council, Washington, D.C., 2000, pp. 1–8.
10.
MartinezL. M., and ViegasJ. M.Effects of Transportation Accessibility on Residential Property Values: Hedonic Price Model in Lisbon, Portugal, Metropolitan Area. In Transportation Research Record: Journal of the Transportation Research Board, No. 2115, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 127–137.
11.
RephannT., and IssermanA.New Highways as Economic Development Tools: An Evaluation Using Quasi-Experimental Matching Methods. Regional Science and Urban Economics, Vol. 24, 1994, pp. 723–751.
12.
FunderburgR. G., NixonH., BoarentM. G., and FergusonG.New Highways and Land Use Change: Results from a Quasi-Experimental Research Design. Transportation Research Part A, Vol. 44, No. 2, 2010, pp. 76–98.
13.
MikelbankB. A.Spatial Analysis of the Relationship Between Housing Values and Investments in Transportation Infrastructure. Annals of Regional Science, Vol. 38, No. 4, 2004, pp. 705–726.
14.
VadaliS. R., and SohnC.Using a Geographic Information System to Track Changes in Spatially Segregated Location Premiums: Alternative Method for Assessing Residential Land Use Impact of Transportation Projects. In Transportation Research Record: Journal of the Transportation Research Board, No. 1768, TRB, National Research Council, Washington, D.C., 2001, pp. 180–192.
15.
VadaliS.Toll Roads and Economic Development: Exploring Effects on Property Values. Annals of Regional Science, Vol. 42, No. 3, 2008, pp. 591–620.
16.
MagurranA. E.Ecological Diversity and Its Measurement.Princeton University Press, Princeton, N.J., 1988.
17.
Comprehensive Traffic and Revenue Study.Tampa–Hillsborough Expressway Authority, Tampa, Fla., 2011.
18.
RosenbaumP. R., and RubinD. B.The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, Vol. 70, No. 1, 1983, pp. 41–55.
19.
HeinrichC., MaffioliA., and VasquezG.A Primer for Applying Propensity-Score Matching.Inter-American Development Bank, Washington, D.C., 2010.
20.
MingK., and RosenbaumP. R.A Note on Optimal Matching with Variable Controls Using the Assignment Algorithm. Journal of Computational and Graphical Statistics, Vol. 10, No. 3, 2001, pp. 455–463.
21.
AnselinL.Spatial Econometrics: Methods and Models. In Studies in Operational Regional Science, Kluwer Academic Publishers, Dordrecht, Netherlands, 1988.
22.
GriffitthD. A.Some Guidelines for Specifying the Geographic Weights Matrix Contained in Spatial Statistical Models. In Practical Handbook of Spatial Statistics (DrakeW. D., and NystuenJ. D., eds.), CRC Press, Boca Raton, Fla., 1996, pp. 65–82.
23.
StakhovychS., and BijmoltT. H. A.Specification of Spatial Models: A Simulation Study on Weight Matrices. Papers in Regional Science, Vol. 88, No. 2, 2009, pp. 389–408.
24.
DrukkerD. M., PengH., PruchaI. R., and RaciborskiR.Creating and Managing Spatial-Weighting Matrices Using the Spmat Command, University of Maryland, College Park, 2011, pp. 1–43.
25.
DrukkerD. M., PruchaI. R., and RaciborskiR.Maximum-Likelihood and Generalized Spatial Two-Stage Least-Squares Estimators for a Spatial-Autoregressive Model with Spatial-Autoregressive Disturbances.University of Maryland, College Park, 2011, pp. 1–21.
26.
LeSageJ., and PaceR. K.Introduction to Spatial Econometrics. Statistics: A Series of Textbooks and Monographs. Taylor & Francis Group, Boca Raton, Fla., 2009.
