When the area of the lot increases and the land area increases, it is assumed that the cost of land will rise steadily. If the curve of land price is increasing at a slower percentage concerning the parcel size, the cost of splitting land could be the primary cause of the concavity. This paper utilizes a unique dataset to study the extent to which the area elasticity of prices changes throughout development as land shifts between raw and completed parcels. The results reveal the price elasticity regarding area growth as land parcels traverse through the stages of development. The results also reveal an inclination in the quantity-price (of lots) curve. As the number of lots bundled together in a transaction increases, the price of the whole transaction increases at a decrease rate.

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Notes

1. Munch (1976) provides an example of a monopsony holdout that explains how private land assembly results in rising unit costs, leading to land’s convex cost.
2. Isakson (2013) offers an overview of the academic literature regarding plottage and plattage and a comprehensive analysis of the appraisal literature.
3. Brownstone and DeVany (1991) examined the relation between the value of land and the size of parcels, employing a simple arbitrage equilibrium argument. It also demonstrates that the value-size relation can be described as, in fact, convex (concave) under the land assemblies (subdivision). Colwell & Sirmans (1993) provide three significant modifications of Brownstone and DeVany (1991).
4. Guntermann et al. (2015) examine the various econometric and modeling techniques employed to analyze land value.
5. Functional obsolescence occurs when a structure is no longer suited to its intended purpose or less attractive to its tenants/users because of technological advances or shifts in preferences like the need for fiber-optic technology or the need for green energy-efficient designs by those who live in space (Bokhari and Geltner in 2018).
6. Economic obsolescence is the process of the best and most effective utilization of the space changing away from the present structure’s usage or intensity. One example is the possibility of switching to residential or commercial use, which is more profitable than industrial use for the current structure (see Bokhari & Geltner, 2018).
7. Levkovich and others. (2018) has also observed that the depreciation rates for commercial real property in the Netherlands vary significantly between different property segments. The majority of properties built between 1960-1990 suffer adverse depreciation effects. However, buildings built before WWI may see an appreciation in value due to the vintage effect.
8. See the NAREIT website on “REITs by the Numbers” at https://www.reit.com/data-research/data/reits-numbers.
9. Recent research has revealed that U.S. REITs sell a small portion of their properties yearly. For instance, Eichholtz and Yonder (2015) provide evidence that an average US REIT selling five percent of its properties is based on the value in dollars between 2003 and 2010. Feng et al. (2022) found that most REITs employ a long-term investment strategy, holding most of their properties for an extended period. These results suggest that the average property age of REITs will remain relatively high in the near term, as it is an aggregate measurement of the property time of the portfolios held by REITs.
10. We also recognize that self-selection biases can be present as managers might opt to keep older properties. This bias in selection is addressed with the Heckman sampling technique. The results are essentially the same.For, see a list of REIT headquarters by location: https://www.reitsacrossamerica.com/us-reits-headquartered-state.
11. Mainly, CEOs who are older are more cautious about implementing a low-growth strategy (Child 1974) and restraining technology adoption (Kitchell, 1997), spending less money on R&D (Barker & Mueller, 2002), and making fewer acquisitions (Yim & Ooi, 2013) and taking fewer risk (Serfling & Serfling, 2014; Andreou et al., 2017; Belenzon et al. in 2019). In a recent study, Zhang Ooi and Zhang Ooi (2022) followed the property purchases of 150 REIT CEOs. They discovered that younger CEOs are more likely to be more frequent and more aggressive in acquisitions of properties to show the market that they can strike deals.
12. See the article, “Older condos plagued by high maintenance costs,” at https://www.marketwatch.com/story/older-condos-plagued-by-high-maintenance-costs-2014-06-12.
13. See a T.D. Insurance article titled “How property insurance is calculated” at https://www.tdinsurance.com/products-services/home-insurance/tips-advice/premium-calculations.
14. Compared to modern buildings, older structures are typically less energy efficient as they typically conform to outdated standards. See https://www.forbes.com/sites/pikeresearch/2016/04/13/energy-efficient-building/?sh=41a4e8d548f8.
15. Kenneth R. French’s Data Library: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
16. See https://www.eia.gov/consumption/commercial/data/2018/pdf/CBECS_2018_Building_Characteristics_Flipbook.pdf.
17. We mainly perform the time-series regression below on every REIT throughout the entire period.
18. Ri,t=b0+b1Rm,t+b2SMBt+b3HMLt+b4RMWt+b5CMAt+et,=0+1,+2+3+4+5+e where Ri,t, is the excess stock return of REIT i, Rm,t, is the risk-free stock return of the market, SMBt (Small minus Big), HMLt (High minus Low), MOMt (Momentum), and RMWt (Robust minus Weak), and CMAt (Conservative minus Aggressive) are the return to zero investment factor-mimicking portfolios designed to capture size, book-to-market effects, momentum, profitability. And risk to investment in the year and investment risk in the year. Then, we use the market return annually calculated by the SMB HML MoM, RMW, and CMA Risk factors and factors estimated to be loaded, called bs of the model, to calculate the expected returns Ri,t.
19. Leskinen et al. (2020) present a literature review regarding green certifications for commercial properties.
20. The number of observations in this regression is smaller than in other regressions because details on maintenance and repair costs are not available for a few REITs.
21. The literature suggests that the risk of REIT returns is different from other asset categories (e.g., Cotter & Stevenson (2006); Fei et al. (2010)). Furthermore, extreme risks for REITs are more severe than firms that do not have REITs (Zhou & Anderson, 2012). REIT pricing is influenced primarily by their unique risks (e.g., Ooi et al., 2009; Chiang et al., 2009; Cakici et al., 2014.) but not the distress risk (Shen, 2021).
22. The U.S. geographic regions determined by the National Council of Real Estate Investment Fiduciaries (NCREIF) are (1) N.E. (Northeast) comprising M.E., VT, NH, NY, CT, RI, MA, PA, NJ, DE; (2) M.E. (Mideast) which includes M.D., WV, VA, KY, NC, SC, DC; (3) S.E. (Southeast): comprising T.N., GA, FL, AL, MS; (4) EN (East et al.) comprising MI, IL, OH, IN, WI; (5) W.N. (West et al.) comprising M.N., IA, MO, KS, NE, S D, ND; (6) S.W. (Southwest) comprising TX, OK, AR, LA, (7) M.T. (Mountain): M.T., ID, WY, UT, CO, NM, AZ, NV; and (8) P.C. (Pacific) comprising W.A., OR, CA, AK, HI.
23. Check out some of the most recent asset pricing research studies from the REIT literature (e.g., Ling et al. (2019), Beracha et al. (2019b), Chen et al. (2020), Milcheva et al. (2021), Shen (2021), Shen et al. (2021) as well as Zhu as well as Lizieri (2022)) to perform similar testing.
24. We look at the equal-weighted monthly excess returns of portfolios’ stock to ensure robustness. We observe quantitatively comparable results but have removed them from the report to keep it short.
25. We employ our capital asset pricing (CAPM) model to ensure robustness. Fama as well as the French (1993) Five-factor model, and Fama and French (1993) five-factor well as the Fama the French (2015) five-factor model. We observe quantitatively comparable results for the simple coefficients and the statistical significance of the alphas when using these models. These results should be included in the paper due to their shortness.