IDEAS home Printed from https://ideas.repec.org/a/ire/issued/v10n012007p93-118.html

Indices for Investment Benchmarking and Return Performance Analysis in Private Real Estate

Author

Listed:
  • David Geltner

    (Department of Economics, Chinese University of Hong Kong, Hong Kong)

  • Peiling Wei

    (George Macomber Professor of Real Estate Finance, Department of Urban Studies & Planning, Massachusetts, Institute of Technology, Cambridge, MA 02139)

  • David C. Ling

    (William D. Hussey Professor of Real Estate , Department of Finance & Real Estate, Warrington College of Business Administration, University of Florida,ainesville, FL 32611-7160)

Abstract

In U.S. commercial real estate markets, a single index product produced by the National Council of Real Estate Investment Fiduciaries (NCREIF) has been used for both asset class research and for agent evaluation benchmarking. While the NCREIF Index is an invaluable tool for the U.S. real estate investment industry, in some respects its construction is not optimized for either research or the benchmarking function, though the gap between the reality and the ideal is arguably greatest on the research side. This paper first discusses the purpose of the ideal research index, the optimal depth and breadth of market coverage, and the type of information included in the research index database. This discussion is followed by an analysis of the ideal agent evaluation benchmark index, including the definition, purpose, and uses of evaluation benchmarking, the characteristics of the ideal benchmark index, market coverage and information onsiderations, and a comparison of benchmark indices in the private real estate market to comparable indices used to benchmark public securities.

Suggested Citation

  • David Geltner & Peiling Wei & David C. Ling, 2007. "Indices for Investment Benchmarking and Return Performance Analysis in Private Real Estate," International Real Estate Review, Global Social Science Institute, vol. 10(1), pages 93-118.
  • Handle: RePEc:ire:issued:v:10:n:01:2007:p:93-118
    as

    Download full text from publisher

    File URL: https://www.gssinst.org/irer/wp-content/uploads/2020/10/vol-10-no-1-indices-for-investment-benchmarking-and-return-performance.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Geltner, David & Goetzmann, William, 2000. "Two Decades of Commercial Property Returns: A Repeated-Measures Regression-Based Version of the NCREIF Index," The Journal of Real Estate Finance and Economics, Springer, vol. 21(1), pages 5-21, July.
    2. David Geltner & David C. Ling, 2006. "Considerations in the Design and Construction of Investment Real Estate Research Indices," Journal of Real Estate Research, American Real Estate Society, vol. 28(4), pages 411-444.
    3. Dirk Brounen & Piet Eichholtz & David Ling, 2007. "Trading Intensity and Real Estate Performance," The Journal of Real Estate Finance and Economics, Springer, vol. 35(4), pages 449-474, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sampagnaro, Gabriele & Battaglia, Francesca, 2010. "Reliability and Heterogeneity of Real Estate Indexes and their Impact on the Predictability of Returns," MPRA Paper 23378, University Library of Munich, Germany.
    2. David C. Ling & Chongyu Wang & Tingyu Zhou, 2022. "Asset productivity, local information diffusion, and commercial real estate returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 89-121, March.
    3. Jeffrey Fisher & David C. Ling & Andy Naranjo, 2009. "Institutional Capital Flows and Return Dynamics in Private Commercial Real Estate Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(1), pages 85-116, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Deng, Yongheng & McMillen, Daniel P. & Sing, Tien Foo, 2014. "Matching indices for thinly-traded commercial real estate in Singapore," Regional Science and Urban Economics, Elsevier, vol. 47(C), pages 86-98.
    3. William Goetzmann & Eduardas Valaitis, 2006. "Simulating Real Estate in the Investment Portfolio: Model Uncertainty and Inflation Hedging," Yale School of Management Working Papers amz2476, Yale School of Management, revised 01 May 2006.
    4. Marc Francke & Lyndsey Rolheiser & Alex Minne, 2025. "Estimating Census Tract House Price Indexes: A New Spatial Dynamic Factor Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 70(3), pages 483-514, April.
    5. Erwin Diewert & Chihiro Shimizu, 2020. "Alternative Land‐Price Indexes for Commercial Properties in Tokyo," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(4), pages 784-824, December.
    6. Sampagnaro, Gabriele & Battaglia, Francesca, 2010. "Reliability and Heterogeneity of Real Estate Indexes and their Impact on the Predictability of Returns," MPRA Paper 23378, University Library of Munich, Germany.
    7. David C. Ling & Chongyu Wang & Tingyu Zhou, 2022. "Asset productivity, local information diffusion, and commercial real estate returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 89-121, March.
    8. Sheharyar Bokhari & David Geltner, 2012. "Estimating Real Estate Price Movements for High Frequency Tradable Indexes in a Scarce Data Environment," The Journal of Real Estate Finance and Economics, Springer, vol. 45(2), pages 522-543, August.
    9. Martin Hoesli & Jon Lekander & Witold Witkiewicz, 2004. "New International Evidence on Real Estate as a Portfolio Diversifier," Journal of Real Estate Research, American Real Estate Society, vol. 26(2), pages 161-206.
    10. Zhenguo Lin & Kerry D. Vandell, 2006. "Illiquidity and Pricing Biases in the Real Estate Market," Working Paper 8577, USC Lusk Center for Real Estate.
    11. Piet Eichholtz & Erkan Yönder, 2015. "CEO Overconfidence, REIT Investment Activity and Performance," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(1), pages 139-162, March.
    12. David Geltner & Anil Kumar & Alex M. Van de Minne, 2025. "Estimating Commercial Property Fundamentals from REIT data," The Journal of Real Estate Finance and Economics, Springer, vol. 71(3), pages 453-475, October.
    13. William Goetzmann & Liang Peng, 2003. "Estimating Indices in the Presence of Seller Reservation Prices," Yale School of Management Working Papers ysm352, Yale School of Management, revised 01 May 2003.
    14. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    15. Liang Peng, 2020. "Benchmarking Local Commercial Real Estate Returns: Statistics Meets Economics," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(4), pages 1004-1029, December.
    16. Robert J. Hill & Alicia N. Rambaldi & Michael Scholz, 2021. "Higher frequency hedonic property price indices: a state-space approach," Empirical Economics, Springer, vol. 61(1), pages 417-441, July.
    17. 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.
    18. Palmer, Karen & Walls, Margaret, 2015. "Can Benchmarking and Disclosure Laws Provide Incentives for Energy Efficiency Improvements in Buildings?," RFF Working Paper Series dp-15-09, Resources for the Future.
    19. Willem P Sijp & Anastasios Panagiotelis, 2024. "Estimating granular house price distributions in the Australian market using Gaussian mixtures," Papers 2404.05178, arXiv.org.
    20. Füss, Roland & Ruf, Daniel, 2021. "Bank systemic risk exposure and office market interconnectedness," Journal of Banking & Finance, Elsevier, vol. 133(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ire:issued:v:10:n:01:2007:p:93-118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: IRER Graduate Assistant/Webmaster (email available below). General contact details of provider: https://www.gssinst.org/gssinst/index.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.