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Revisiting supply and demand indexes in real estate

Author

Listed:
  • Dorinth van Dijk
  • David Geltner
  • Alex van de Minne

Abstract

In this paper we disentangle reservation prices of buyers and sellers for commercial real estate at the city level. To do so, we further develop and extend the cite{fisher03,fisher07} methodology to a repeat sales indexing framework. This has the advantage that it takes care of all unobserved heterogeneity, which is an important consideration in commercial real estate. Furthermore, it allows for the construction of supply and demand indexes without the need for many property characteristics or assessed values. A key innovation in our methodology, which also enables granular index production, is our use of a Bayesian, structural time series model for index estimation. By introducing these new methodological developments, we are able to estimate reliable, robust supply and demand indexes for all major metropolitan areas in the US. Here we focus on two very different urban markets: New York and Phoenix. Consistent with the notion of pro-cyclical liquidity, we find that buyers' reservation prices move much more extremely and earlier than sellers' reservation prices. Our results show that the demand indexes in both New York and Phoenix went down a full year earlier than the supply indexes during the crisis.

Suggested Citation

  • Dorinth van Dijk & David Geltner & Alex van de Minne, 2018. "Revisiting supply and demand indexes in real estate," ERES eres2018_41, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2018_41
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    References listed on IDEAS

    as
    1. Genesove, David & Han, Lu, 2012. "Search and matching in the housing market," Journal of Urban Economics, Elsevier, vol. 72(1), pages 31-45.
    2. William Goetzmann & Liang Peng, 2006. "Estimating House Price Indexes in the Presence of Seller Reservation Prices," The Review of Economics and Statistics, MIT Press, vol. 88(1), pages 100-112, February.
    3. Wheaton, William C, 1990. "Vacancy, Search, and Prices in a Housing Market Matching Model," Journal of Political Economy, University of Chicago Press, vol. 98(6), pages 1270-1292, December.
    4. Jeff Fisher & David Geltner & Henry Pollakowski, 2007. "A Quarterly Transactions-based Index of Institutional Real Estate Investment Performance and Movements in Supply and Demand," The Journal of Real Estate Finance and Economics, Springer, vol. 34(1), pages 5-33, January.
    5. Jeffrey Fisher & Dean Gatzlaff & David Geltner & Donald Haurin, 2003. "Controlling for the Impact of Variable Liquidity in Commercial Real Estate Price Indices," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(2), pages 269-303, June.
    6. Paul E. Carrillo & Eric R. Wit & William Larson, 2015. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the United States and the Netherlands," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(3), pages 609-651, September.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    8. Marc Francke, 2010. "Repeat Sales Index for Thin Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 41(1), pages 24-52, July.
    9. David Genesove & Christopher Mayer, 2001. "Loss Aversion and Seller Behavior: Evidence from the Housing Market," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(4), pages 1233-1260.
    10. Sheharyar Bokhari & David Geltner, 2011. "Loss Aversion and Anchoring in Commercial Real Estate Pricing: Empirical Evidence and Price Index Implications," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 39(4), pages 635-670, December.
    11. Koehler, Elizabeth & Brown, Elizabeth & Haneuse, Sebastien J.-P. A., 2009. "On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses," The American Statistician, American Statistical Association, vol. 63(2), pages 155-162.
    12. Dorinth W. van Dijk & Marc K. Francke, 2018. "Internet Search Behavior, Liquidity and Prices in the Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 46(2), pages 368-403, June.
    13. Gatzlaff, Dean H & Haurin, Donald R, 1997. "Sample Selection Bias and Repeat-Sales Index Estimates," The Journal of Real Estate Finance and Economics, Springer, vol. 14(1-2), pages 33-50, Jan.-Marc.
    14. Marc K. Francke & Alex Minne, 2017. "The Hierarchical Repeat Sales Model for Granular Commercial Real Estate and Residential Price Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 55(4), pages 511-532, November.
    15. Paul E. Carrillo, 2013. "To Sell or Not to Sell: Measuring the Heat of the Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 41(2), pages 310-346, June.
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    Cited by:

    1. Robert J. Hill & Miriam Steurer, 2020. "Commercial Property Price Indices and Indicators: Review and Discussion of Issues Raised in the CPPI Statistical Report of Eurostat (2017)," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 736-751, September.
    2. Bahar Öztürk & Dorinth van Dijk & Frank van Hoenselaar & Sander Burgers, 2018. "The relation between supply constraints and house price dynamics in the Netherlands," DNB Working Papers 601, Netherlands Central Bank, Research Department.
    3. Gianluca Marcato & Anupam Nanda, 2022. "Asymmetric Patterns of Demand-Supply Mismatch in Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 64(3), pages 440-472, April.
    4. Dorinth van Dijk, 2019. "Local Constant-Quality Housing Market Liquidity Indices," DNB Working Papers 637, Netherlands Central Bank, Research Department.

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    More about this item

    Keywords

    Commercial Real Estate; Liquidity; Price index;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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