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How Does Local Economy Affect Commercial Property Performance?

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Abstract

Local economy should be an important determinant of commercial real estate (CRE) performance. This paper empirically examines how the economic conditions of a metropolitan area drive the performance of CRE in the area. This paper shows that areas with better economic conditions provide a higher total return on commercial properties than those with worse economic conditions. Further analysis indicates that both the income return and capital appreciation of CRE are significantly affected by the size of the economy (proxied as GDP level), while the capital return (but not income return) is significantly affected by the growth of the economy (proxied as GDP growth). The results are largely consistent in the Fama–MacBeth regression, the portfolio analysis, and the propensity score matching model, providing solid evidence on the important effects of local economy on CRE.

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Notes

  1. According to a National Association of Real Estate Investment Trusts (Nareit) report, “Estimating the Size of the Commercial Real Estate Market,” the total size of commercial real estate in the U.S. was estimated at $16 trillion in 2018. According to a Wall Street Journal article on Feb. 5, 2019, “Strong Economy, Cheap Money Boost Commercial Property Market,” the total value of commercial properties that sold for above $2.5 million was $562 billion in 2018, based on data from Real Capital Analytics (RCA).

  2. See the S&P/Case-Shiller U.S. National Home Price Index for residential properties, National Council of Real Estate Investment Fiduciaries Property Index, Real Capital Analytics’ Commercial Property Price Index, and CoStar’s Commercial Repeat Sales Index for commercial real estate properties, and National Association of Real Investment Trusts and CRSP-ZIMAN for real estate investment trusts.

  3. See Shearer et al. (2017), and an article from Business Insider on Jun 20, 2018, “The economies of the 40 biggest US cities, ranked from worst to best” at https://www.businessinsider.com/us-economy-by-metro-area-ranked-san-francisco-seattle-austin-2018-4, as well as an article from CityLab on Sep 26, 2017, “America's Most and Least Distressed Cities” at https://www.citylab.com/equity/2017/09/distressed-communities/541044/.

  4. The effects of national and regional economic conditions on the prices, vacancies, and constructions in the residential market are evident (e.g., Case et al., 2000).

  5. See “Commercial Real Estate Trends & Outlook, February 2020 report” by National Association of REALTORS, “Economic Impacts of Commercial Real Estate, 2021 U.S. Edition” by Commercial Real Estate Development Association, “2020 U.S. Real Estate Market Outlook” by the CBRE, and “2020 REIT and Economic Outlook” by the Nareit, among many others.

  6. In the long run, commercial properties should echo the nature and intensity of the desired economic activities in a region, given the rents of commercial spaces depend on the composition and level of industries, products, employment, and other elements of local economy. The cash flows from the commercial buildings should thus affect the values and performance of the real estate assets or portfolios.

  7. See Young et al. (2017) for a detailed review on the NCREIF Property Index (NPI).

  8. The GDP by metropolitan area data in BEA is available only since 2001. The data is available at https://www.bea.gov/data/gdp/gdp-metropolitan-area

  9. The Wharton Residential Land Use Regulation Index is available in Professor Joseph Gyourko’s webpage. http://real-faculty.wharton.upenn.edu/gyourko/land-use-survey/. When there are more than one WRLURI observations in each metropolitan area, the average is taken.

  10. The U.S. Census State and County Population Estimates 2000–2019 data is available in the Office of Scientific and Technical Information, U.S. Department of Energy. https://www.osti.gov/dataexplorer/biblio/dataset/1617641

  11. Figure 1 suggests that the time-series variations in GDP level and growth might be more pronounced than their cross-sectional variation. In the sample, the correlation between current and lagged log GDP level (GDP growth) is 0.9995 (0.4435). That is, a region that has a high GDP level (growth) this year is surely (likely) to be the one that is high in GDP level (growth) next year. Hence, the Fama–Macbeth two-stage model is adopted to address this issue. For robustness purpose, I also estimate the model using panel regressions with a year dummy variable and with heteroscedasticity-robust standard errors that are clustered at the firm-level. The untabulated results continue to hold and will be provided upon request.

  12. According to Pivo and Fisher (2011), in the NCREIF database, the total return that is computed by adding the income return and the capital return. Income return measures that portion of total return attributable to each property’s net operating income, which comes from the rent paid by the tenant, while capital return measures the change in each property’s market value from one period to the next.

  13. The average R-squared of the model is 0.518, while the average R-squared of a model without local economic condition variables is 0.438. Therefore, log GDP level and GDP growth add 8 percentage points to the predictive power on a model whose dependent variable is total return and independent variables are the control variables in Eq. (1). This is significant and deemed an important improvement.

  14. That is, ceteris paribus, the total return of CRE increases by 2.567 percentage points if the GDP level increases by 1%, and the total return increases by 0.334% if the GDP growth increases by 1%.

  15. The economic significance is computed by taking the estimated coefficients of log GDP level (GDP growth) and multiplying it by the unconditional standard deviation of log GDP level (GDP growth) and dividing by the unconditional standard deviation of total return.

  16. That is, ceteris paribus, the income return of CRE increases by 1.397 percentage points if the GDP level increases by 1%.

  17. That is, ceteris paribus, the capital return of CRE increases by 1.052 (0.339) percentage points if the GDP level (GDP growth) increases by 1%.

References

  • Addoum, J. M., Kumar, A., & Law, K. (2017). Slow diffusion of state-level information and return predictability. Working Paper. Cornell University, University of Miami, and Nanyang Technological University.

  • Alonso, W. (1964). Location and land use. Harvard.

    Book  Google Scholar 

  • Aizenman, J., & Jinjarak, Y. (2009). Current account patterns and national real estate markets. Journal of Urban Economics, 66(2), 75–89.

    Article  Google Scholar 

  • Beracha, E., & Skiba, H. (2013). Findings from a cross-sectional housing risk-factor model. The Journal of Real Estate Finance and Economics, 47(2), 289–309.

    Article  Google Scholar 

  • Beracha, E., Feng, Z., & Hardin, W. (2019a). REIT operational efficiency: Performance, risk, and return. Journal of Real Estate Finance and Economics, 58, 408–437.

    Article  Google Scholar 

  • Beracha, E., Feng, Z., & Hardin, W. (2019b). REIT operational efficiency and shareholder value. Journal of Real Estate Research, 414, 513–553.

    Article  Google Scholar 

  • Bernile, G., Korniotis, G., Kumar, A., & Wang, Q. (2015). Local business cycles and local liquidity. Journal of Financial and Quantitative Analysis, 50, 987–1010.

    Article  Google Scholar 

  • Blanchard, O., & Leigh, D. (2013). Growth forecast errors and fiscal multipliers. American Economic Review, 103(3), 117–120.

    Article  Google Scholar 

  • Bond, S. A., Shilling, J. D., & Wurtzebach, C. H. (2019). Commercial real estate market property level capital expenditures: An options analysis. The Journal of Real Estate Finance and Economics, 59(3), 372–390.

    Article  Google Scholar 

  • Case, B. (2014). Diversification benefits of real estate. In H. K. Baker & P. Chinloy (Eds.), Public real estate markets and investments. Oxford University Press.

    Google Scholar 

  • Case, B. (2015). What have 25 years of performance data taught us about private equity real estate? Journal of Real Estate Portfolio Management, 21(1), 1–20.

    Article  Google Scholar 

  • Case, K., Glaeser, E., & Parker, J. (2000). Real estate and the macroeconomy. Brookings Papers on Economic Activity, 2000(2), 119–162.

    Article  Google Scholar 

  • Chan, S. H., Erickson, J., & Wang, K. (2003). Real estate investment trusts: Structure, performance, and investment opportunities. Oxford University Press.

    Google Scholar 

  • Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of Political Economy, 81(3), 607–636.

    Article  Google Scholar 

  • Feng, Z., & Wu, Z. (2021). Local economy, asset location and REIT firm growth. The Journal of Real Estate Finance and Economics, Forthcoming.

  • Feng, Z., Hardin, W. G., & Wu, Z. (2019). Employee productivity and REIT performance. Real Estate Economics. Forthcoming.

  • Feng, Z., Pattanapanchai, M., Price, S. M., & Sirmans, C. F. (2021). Geographic diversification in real estate investment trusts. Real Estate Economics, 49(1), 267–286.

    Article  Google Scholar 

  • Gang, J., Peng, L., & Thibodeau, T. G. (2020). Risk and returns of income producing properties: Core versus noncore. Real Estate Economics, 48(2), 476–503.

    Article  Google Scholar 

  • Ghent, A., Torous, W., & Valkanov, R. (2019). Commercial real estate as an asset class. Annual Review of Financial Economics, 11, 153–171.

    Article  Google Scholar 

  • Ghosh, C., & Petrova, M. T. (2017). The impact of capital expenditures on property performance in commercial real estate. The Journal of Real Estate Finance and Economics, 55(1), 106–133.

    Article  Google Scholar 

  • Glaeser, E. L., Gyourko, J., & Saiz, A. (2008). Housing supply and housing bubbles. Journal of Urban Economics, 64(2), 198–217.

    Article  Google Scholar 

  • Gyourko, J., Hartley, J. S., & Krimmel, J. (2021). The local residential land use regulatory environment across us housing markets: Evidence from a new Wharton index. Journal of Urban Economics, 124, 103337.

    Article  Google Scholar 

  • Hardin, W. G., Jiang, X., & Wu, Z. (2017). Inflation illusion, expertise and commercial real estate. The Journal of Real Estate Finance and Economics, 55(3), 345–369.

    Article  Google Scholar 

  • Holtermans, R., & Kok, N. (2019). On the value of environmental certification in the commercial real estate market. Real Estate Economics, 47(3), 685–722.

    Article  Google Scholar 

  • Hoskins, N., Higgins, D., & Cardew, R. (2004). Macroeconomic variables and real estate returns: An international comparison. The Appraisal Journal, 72(2), 163.

    Google Scholar 

  • Howe, J., & Shilling, J. (1990). REIT advisor performance. Real Estate Economics, 18(4), 479–500.

    Article  Google Scholar 

  • Huang, X. (2015). Thinking outside the borders: Investors’ underreaction to foreign operations information. The Review of Financial Studies, 28(11), 3109–3152.

    Article  Google Scholar 

  • Korniotis, G., & Kumar, A. (2013). State-level business cycles and local return predictability. The Journal of Finance, 68(3), 1037–1096.

    Article  Google Scholar 

  • Ling, D., & Archer, W. (2017). Real estate principles: A value approach. McGraw-Hill Higher Education.

    Google Scholar 

  • Ling, D. C., Marcato, G., & McAllister, P. (2009). Dynamics of asset prices and transaction activity in illiquid markets: The case of private commercial real estate. The Journal of Real Estate Finance and Economics, 39(3), 359–383.

    Article  Google Scholar 

  • Ling, D., Naranjo, A., & Scheick, B. (2018). Geographic portfolio allocations, property selection and performance attribution in public and private real estate markets. Real Estate Economics, 46(2), 404–448.

    Article  Google Scholar 

  • Ling, D., Naranjo, A., & Scheick, B. (2019). Asset location, timing ability and the cross-section of commercial real estate returns. Real Estate Economics, 47(1), 263–313.

    Article  Google Scholar 

  • Ling, D., Wang, C., & Zhou, T. (2020a). Institutional common ownership and firm value: Evidence from real estate investment trusts. Real Estate Economics, Forthcoming.

  • Ling, D. C., Wang, C., & Zhou, T. (2020b). Asset productivity, local information diffusion, and commercial real estate returns. Working Paper. University of Florida, Concordia University, and Florida State University.

  • Loutskina, E., & Strahan, P. (2015). Financial integration, housing, and economic volatility. Journal of Financial Economics, 115(1), 25–41.

    Article  Google Scholar 

  • Mian, A., & Sufi, A. (2012). The effects of fiscal stimulus: Evidence from the 2009 cash for clunkers program. The Quarterly Journal of Economics, 127(3), 1107–1142.

    Article  Google Scholar 

  • Mills, E. (1967). An aggregative model of resource allocation in a metropolitan centre. American Economic Review, 57(2), 197–210.

    Google Scholar 

  • Muth, R. (1969). Cities and housing. University of Chicago Press.

    Google Scholar 

  • Naranjo, A., & Ling, D. C. (1997). Economic risk factors and commercial real estate returns. The Journal of Real Estate Finance and Economics, 14(3), 283–307.

    Article  Google Scholar 

  • Nicholson, J. R., & Stevens, J. A. (2021). REIT operational efficiency: External advisement and management. The Journal of Real Estate Finance and Economics, 1–25.

  • Peng, L. (2016). The risk and return of commercial real estate: A property level analysis. Real Estate Economics, 44(3), 555–583.

    Article  Google Scholar 

  • Peng, L. (2020). Performance and acquisitions: Evidence from commercial real estate institutional investors. Working Paper. The Pennsylvania State University.

  • Pirinsky, C., & Wang, Q. (2006). Does corporate headquarters location matter for stock returns? The Journal of Finance, 61(4), 1991–2015.

    Article  Google Scholar 

  • Pivo, G., & Fisher, J. (2010). Income, value, and returns in socially responsible office properties. Journal of Real Estate Research, 32(3), 243–270.

    Article  Google Scholar 

  • Pivo, G., & Fisher, J. D. (2011). The walkability premium in commercial real estate investments. Real Estate Economics, 39(2), 185–219.

    Article  Google Scholar 

  • Plazzi, A., Torous, W., & Valkanov, R. (2008). The cross-sectional dispersion of commercial real estate returns and rent growth: Time variation and economic fluctuations. Real Estate Economics, 36(3), 403–439.

    Article  Google Scholar 

  • Plazzi, A., Torous, W., & Valkanov, R. (2010). Expected returns and expected growth in rents of commercial real estate. The Review of Financial Studies, 23(9), 3469–3519.

    Article  Google Scholar 

  • Saiz, A. (2008). On local housing supply elasticity. Working Paper. The University of Pennsylvania.

  • Shearer, R., Friedhoff, A., Shah, I., & Berube, A. (2017). Metro monitor: An index of inclusive economic growth in the 100 largest US metropolitan areas. Brookings Institution.

    Google Scholar 

  • Smajlbegovic, E. (2019). Regional economic activity and stock returns. Journal of Financial and Quantitative Analysis, 54(3), 1051–1082.

    Article  Google Scholar 

  • Wheaton, W. C., Baranski, M. S., & Templeton, C. A. (2009). 100 years of commercial real estate prices in Manhattan. Real Estate Economics, 37(1), 69–83.

    Article  Google Scholar 

  • Young, M. S., Fisher, J. D., & D’Alessandro, J. (2017). New NCREIF value index and operations measures. Journal of Real Estate Literature, 25(1), 221–235.

    Article  Google Scholar 

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Acknowledgements

I am grateful to William Hardin, Chongyu Wang, Zhonghua Wu, and Tingyu Zhou for their insightful comments.

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  1. The University of Texas at El Paso, 500 W. University Ave, El Paso, TX, 79968, USA

    Zifeng Feng

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Feng, Z. How Does Local Economy Affect Commercial Property Performance?. J Real Estate Finan Econ 65, 361–383 (2022). https://doi.org/10.1007/s11146-021-09848-y

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