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Combining Revealed and Stated Preference Data to Estimate Preferences for Residential Amenities: A GMM Approach

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  • Daniel J. Phaneuf
  • Laura O. Taylor
  • John B. Braden

Abstract

We show how stated preference information obtained from a choice experiment, and revealed preference information based on housing market transactions, can be combined via generalized method of moments (GMM) estimation. Specifically, we use a moment condition matching the predicted marginal willingness to pay (WTP) from a first-stage hedonic model to the marginal WTP formula implied by the choice experiment utility function. This is coupled with other moments from the choice experiment to produce GMM-based estimates of parameters that reflect the strengths of each data source. Our application values remediation of a contaminated site in Buffalo, New York, and we find evidence in support of estimates arising from our approach.

Suggested Citation

  • Daniel J. Phaneuf & Laura O. Taylor & John B. Braden, 2013. "Combining Revealed and Stated Preference Data to Estimate Preferences for Residential Amenities: A GMM Approach," Land Economics, University of Wisconsin Press, vol. 89(1), pages 30-52.
    • Handle: RePEc:uwp:landec:v:89:y:2013:i:1:p:30-52
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    2. Adelina Gschwandtner & Jose Eduardo Ribeiro & Cesar Revoredo-Giha & Michael Burton, 2021. "Combining Stated and Revealed Preferences for valuing Organic Chicken Meat," Studies in Economics 2113, School of Economics, University of Kent.
    3. Melstrom, Richard T., 2022. "Residential demand for sediment remediation to restore water quality: Evidence from Milwaukee," Journal of Environmental Economics and Management, Elsevier, vol. 116(C).
    4. Caplan, Arthur J. & Akhundjanov, Sherzod B. & Toll, Kristopher, 2021. "Measuring heterogeneous preferences for residential amenities," Regional Science and Urban Economics, Elsevier, vol. 87(C).
    5. Balbontin, C. & Ortúzar, J. de D. & Swait, J.D., 2015. "A joint best–worst scaling and stated choice model considering observed and unobserved heterogeneity: An application to residential location choice," Journal of choice modelling, Elsevier, vol. 16(C), pages 1-14.
    6. Chen, Kee-Kuo & Ho, Hui-Ping & Chang, Ching-Ter, 2015. "Estimating attributes importance for container shipping industry by closing the listening gap with maximum convergent validity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 79(C), pages 145-163.
    7. GLUMAC Brano & DES ROSIERS François, 2018. "Real estate and land property automated valuation systems: A taxonomy and conceptual model," LISER Working Paper Series 2018-09, Luxembourg Institute of Socio-Economic Research (LISER).
    8. Wiktor L. Adamowicz & Klaus Glenk & Jürgen Meyerhoff, 2014. "Choice modelling research in environmental and resource economics," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 27, pages 661-674, Edward Elgar Publishing.
    9. Traoré, Sidnoma, 2019. "Residential location choice in a developing country: What matter? A choice experiment application in Burkina Faso," Forest Policy and Economics, Elsevier, vol. 102(C), pages 1-9.
    10. Bottero, Marta & Bravi, Marina & Caprioli, Caterina & Dell'Anna, Federico, 2023. "Combining Revealed and Stated Preferences to design a new urban park in a metropolitan area of North-Western Italy," Ecological Modelling, Elsevier, vol. 483(C).

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    JEL classification:

    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling

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