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Urban Land: Price Indices, Performance, and Leading Indicators

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Abstract

This study had two objectives: first, to evaluate the historical performance of urban land prices across 20 prominent U.S. metro markets; and second, to determine if urban land prices are a leading indicator for prices in the built environment. Using a time-varying econometric model with spatial controls, we constructed constant-quality metropolitan-level land price indices. We found that 1) from 2000 to 2017 (18 years) national residential and commercial-industrial urban land prices appreciated by 2.08% and 1.87% annually, respectively; 2) urban land prices exhibited greater volatility compared with improved property prices; 3) in many metro markets land prices began to decline before improved property prices leading up to the Great Recession; and 4) land prices were slower to recover after the Great Recession compared with prices in the built environment. Using Granger Causality tests on the national urban land market, we found evidence that from the peak of the market in 2007 through 2017, land prices were a leading indicator of prices in the built environment.

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Notes

  1. See Haughwout, Orr, and Bedoll, Federal Reserve Bank of New York, 2008

  2. These are the same 20 metro markets used by the S&P CoreLogic Case-Shiller Housing Indices and include Atlanta, Boston, Charlotte, Chicago, Cleveland, Dallas, Denver, Detroit, Las Vegas, Los Angeles, Miami, Minneapolis, New York, Phoenix, Portland, San Diego, San Francisco, Seattle, Tampa, and Washington, D.C.

  3. CoStar researches the likely use for each parcel of land included in a transaction which allowed us to categorize all land transactions in two broad categories—Residential or Commercial-Industrial. “Residential” use includes land proposed for low or multifamily housing. “Commercial-Industrial” use includes all land proposed for commercial or industrial uses including office, retail, manufacturing, and distribution.

  4. According to the S&P website www.us.spindices.com, “The S&P CoreLogic Case-Shiller Home Price Indices are the leading measures of U.S. residential real estate prices, tracking changes in the value of residential real estate both nationally as well as in 20 metropolitan regions.” The monthly indices use a repeat-sales construction methodology. This technique attempts to identify intertemporal change in housing prices assuming a constant-quality.

  5. As defined on the NCREIF website: www.ncreif.com, “The NCREIF Property Index (NPI) is a quarterly, unleveraged composite total return for private commercial real estate properties held for investment purposes only. All properties in the NPI have been acquired, at least in part, on behalf of tax-exempt institutional investors and held in a fiduciary environment.” The NCREIF appraisal-based appreciation index reports the intertemporal changes in values of the properties included in the index.

  6. CoStar Group, Inc., investigates and compiles commercial property and land transactions in most major metropolitan markets in the United States; however, they do not attempt to compile transactions of single-family resident lots or homes. Summaries of the transactions are provided to interested parties, such as appraisers, brokers, and developers, on a subscription basis. Given that some jurisdictions restrict public access to important transaction details, it is doubtful that CoStar captures the entire population of real estate transactions in each of the 20 metro markets; however, given their company objectives and their extensive efforts to obtain all transactions that are available, there is every reason to believe that they capture the large majority of transactions in the markets under investigation. We thank CoStar Group, Inc., for their generous assistance with the data.

  7. After obtaining the land transaction data from CoStar Group, Inc., initial filters were employed to screen the data for possible input errors and to prepare the data for analysis. These filters included a sales price minimum, a land area minimum and maximum, the availability of the latitude and longitude, the identification of the geographic area, the maximum distance from the CBD, and whether the land parcel is located in one of the 20 major markets. After the initial filtering, the data were further screened by trimming the top and bottom 1% of all transactions in each of the 20 major markets as indicated by the price per square foot of land area. This resulted in 149,989 transactions.

  8. We used aerial photography to identify the location, latitude and longitude, of all central business districts for each market. Other location data, latitude and longitude, were obtained from zip-codes.com, the National Transportation Authority, AggData, Wal-Mart Corporation, U.S. Census, and data-lists.com.

  9. Some of the decline in volume for 2017 may be because transaction data in 2017 had not been fully collected and verified as of the date of this analysis.

  10. Geltner (1993, 1998), Diaz and Wolverton (1998), Lai and Wang (1998), and Gatzlaff and Geltner (1998).

  11. See Davis and Heathcote (2007), Davis and Palumbo (2008), and Davis (2009).

  12. Sometime known as the matched-pairs method in other industries (see Triplett 2006).

  13. See Bailey et al. (1963), Case and Shiller (1989), Clapp et al. (1991), and Wong et al. (2018).

  14. This methodology was established by Court (1939) and further advanced by Griliches and Adelman (1961), Griliches (1971), and Rosen (1974). There is a large body of literature relating to hedonic price analysis and the accompanying benefits and challenges as it relates to improved properties (see, for example, Case and Shiller (1989), Fisher et al. (1994), Gatzlaff and Ling (1994), Knight et al. (1995), Munneke and Slade (2000, 2001), and Fisher et al. (2003), Sirmans and Slade (2012), Nichols et al. (2013), and Slade (2014)). See Albouy et al. (2018) for a recent application of hedonic price analysis to estimate a cross-sectional index of transaction-based land values in U.S. metropolitan markets.

  15. See Griliches and Adelman (1961), Griliches (1971), Berndt et al. (1995), Fisher et al. (1994), and Knight et al. (1995).

  16. Divisia (1925) pioneered the concept of a chained index construction technique, while Griliches and Adelman (1961) and Griliches (1971) explored this concept within the context of hedonic price analysis. Others have investigated time-varying parameter techniques to study intertemporal price changes for personal computers (see Berndt et al. 1995), housing (see Hill and Scholz 2017; Knight et al. 1995), and commercial properties (see Munneke and Slade 2001); however, this study is the first to construct land price indices using time-varying parameter methods.

  17. See, for example, Colwell and Sirmans (1978, 1980), Colwell and Munneke (1999), Thornes and McMillen (1997), and Colwell and Munneke (2003).

  18. See Linneman (1980), Weirick and Ingram (1990), and de Leeuw (1993).

  19. See Thornes and McMillen (1997) and Munneke et al. (2018).

  20. See Colwell and Sirmans (1978, 1980) and Colwell and Munneke (1997).

  21. Appendix Table 8 provides the regression output for a sample period. An adjusted R-square of 0.74 indicates a good fit of the model.

  22. The indices begin in 2000. CoStar Group, Inc., did not collect land transactions data in the early periods of the study for Charlotte, Cleveland, and Minneapolis; therefore, we were unable to construct separate land price indices for these three markets. Also, the paucity of residential land transactions in Dallas, San Francisco, and Tampa prohibited construction of residential land price indices. In addition, the paucity of commercial-industrial land transactions in Detroit prohibited construction of a commercial-industrial land price index in this metro market.

  23. Although not precisely related to the current investigation of the time series properties of real estate indices, Holly et al. (2011) provide a nice method for analyzing the spatial and temporal diffusion of shocks in housing prices in the UK.

  24. A failure to reject the null hypothesis of the unit root is a necessary condition for formal cointegration tests.

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Funding

This work was supported by the USAA Real Estate, the James Passey Professorship, and the Peery Institute of Financial Services at the BYU Marriott School of Business. The standard disclaimer applies.

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Authors and Affiliations

  1. USAA Real Estate, San Antonio, TX, USA

    Mark Fitzgerald & Will McIntosh

  2. Property Reserve, Inc., Salt Lake City, UT, USA

    David J. Hansen

  3. BYU Marriott School of Business, Provo, UT, USA

    Barrett A. Slade

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  1. Mark Fitzgerald
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  2. David J. Hansen
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  3. Will McIntosh
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  4. Barrett A. Slade
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Correspondence to Barrett A. Slade.

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Appendix

Appendix

Table 8 Sample chained regression output residential land composite 20 (national market) Jan 2000 through Dec 2000 (12 months) 5144 observations
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Fitzgerald, M., Hansen, D.J., McIntosh, W. et al. Urban Land: Price Indices, Performance, and Leading Indicators. J Real Estate Finan Econ 60, 396–419 (2020). https://doi.org/10.1007/s11146-019-09696-x

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