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Location, location, location: The 3L Approach to house price determination

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  • Kiel, Katherine A.
  • Zabel, Jeffrey E.

Abstract

The immobility of houses means that their location affects their values. This explains the common belief that three things determine the price of a house: location, location, and location. We use this notion to develop the 3L Approach to house price determination. That is, prices are determined by the Metropolitan Statistical Area (MSA), town, and street where the house is located. This study creates a unique data set based on data from the American Housing Survey (AHS) consisting of small 'clusters' of housing units with information on structure and resident characteristics that is merged with census tract-level attributes. We use these data to test the 3L Approach: we find that all three levels of location are significant when estimating the house price hedonic equation. This indicates that the concept of "neighborhood" is multifaceted; individuals care about their very local surroundings such as the general upkeep of their street and possibly their neighbors' characteristics (cluster variables), and a broader area such as the school district and/or the town that accounts for school quality and crime rates (tract variables). We show that price indices and evidence of discrimination and prejudice in the housing market are affected if all three levels of location are not included in the house price hedonic model.

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  • Kiel, Katherine A. & Zabel, Jeffrey E., 2008. "Location, location, location: The 3L Approach to house price determination," Journal of Housing Economics, Elsevier, vol. 17(2), pages 175-190, June.
    • Handle: RePEc:eee:jhouse:v:17:y:2008:i:2:p:175-190
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    Cited by:

    1. Leung, Tin Cheuk & Tsang, Kwok Ping, 2012. "Love thy neighbor: Income distribution and housing preferences," Journal of Housing Economics, Elsevier, vol. 21(4), pages 322-335.
    2. Lee Chun Chang & Hui-Yu Lin, 2012. "The Impact of Neighborhood Characteristics on Housing Prices-An Application of Hierarchical Linear Modeling," International Journal of Management and Sustainability, Conscientia Beam, vol. 1(2), pages 31-44.
    3. Gouriéroux, Christian & Laferrère, Anne, 2009. "Managing hedonic housing price indexes: The French experience," Journal of Housing Economics, Elsevier, vol. 18(3), pages 206-213, September.
    4. Gibbons, Steve & Overman, Henry G. & Patacchini, Eleonora, 2015. "Spatial Methods," Handbook of Regional and Urban Economics, Elsevier.
    5. Ioannides, Yannis M., 2015. "Neighborhoods to nations via social interactions," Economic Modelling, Elsevier, vol. 48(C), pages 5-15.
    6. Yannis M. Ioannides, 2010. "Neighborhood Effects and Housing," Discussion Papers Series, Department of Economics, Tufts University 0747, Department of Economics, Tufts University.
    7. Carlos Pestana Barros & Zhongfei Chen & Luis A. Gil-Alana, 2012. "Housing sales in urban Beijing," Applied Economics, Taylor & Francis Journals, vol. 44(34), pages 4495-4504, December.
    8. Sebastian Johann, 2015. "Development of refurbishment concepts - The case of multi-family houses from the 1970s in Western Germany," ERES eres2015_71, European Real Estate Society (ERES).
    9. Kim, Young Se & Rous, Jeffrey J., 2012. "House price convergence: Evidence from US state and metropolitan area panels," Journal of Housing Economics, Elsevier, vol. 21(2), pages 169-186.
    10. Barros, Carlos Pestana & Gil-Alana, Luis A. & Chen, Zhongfei, 2014. "The housing market in Beijing and delays in sales: A fractional polynomial survival model," Economic Modelling, Elsevier, vol. 42(C), pages 296-300.
    11. Todd H. Kuethe & Roman Keeney, 2012. "Environmental Externalities and Residential Property Values: Externalized Costs along the House Price Distribution," Land Economics, University of Wisconsin Press, vol. 88(2), pages 241-250.
    12. Katherine Kiel, 2006. "Environmental Contamination and House Values," Working Papers 0601, College of the Holy Cross, Department of Economics.

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