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How do Non-Core Allocations Affect the Risk and Returns of Private Real Estate Funds?

The Journal of Real Estate Finance and Economics (2022)Cite this article

Abstract

This paper documents that funds with greater non-core allocations have higher market risk exposure, β, but lower returns. Additionally, it documents that one reason their returns are lower is because they poorly time their investment into these properties. Open-end private real estate funds have higher non-core allocations at the top of the market and lower allocations at the bottom. As such, these funds are disproportionately exposed to the downside of the market. Lastly, I find that reaching for yield and fund flow pressure are important determinants of this phenomenon. Funds buy relatively more non-core properties when either the market return expectations or their net queues are smaller. Buying more core properties when queues are larger enables managers to place capital quicker, but it also hurts existing investors by decreasing their market risk exposure at the time when it is the most desirable and beneficial.

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Notes

  1. I measure non-core allocations by summing the value of all non-core properties and dividing it by the total value of all properties in the fund’s extended portfolio. As noted in the Data and Summary Statistics below, this includes properties held by JV partners and as such may not entirely be reflected on the balance sheet of the fund. I designate a property as being non-core when it is in one of the following lifecycles: conversion, development, expansion, initial leasing, pre-development, or renovation. The only other lifecycle option for a property is to be classified as a stabilized, operating property. The lifecycle designations are predetermined and provided with the data from NCREIF. I do not evaluate other aspects of non-core characteristics such as geographic location or property type.

  2. Market risk exposure is the covariance of the fund’s returns with those of the market over a given time period divided by the variance of the market returns over the same time period. It is also known as β in a simple one-factor model framework such as the Capital Asset Pricing Model (CAPM).

  3. Reaching for yield is the act of investing in riskier assets for the purpose of obtaining higher expected returns in the hopes the higher returns will be incorrectly interpreted as α. As mentioned by Becker and Ivashina (2015), “The incentive to search for apparent α is a broad phenomenon that is not limited to any specific asset class, but it is likely to be more pronounced for illiquid and complex securities for which risk measurement is particularly problematic.” Real estate is one of the most illiquid asset classes. Additionally, because each property is unique and valuations are based mostly on appraisals, real estate is also one of the most complex asset classes. These characteristics make risk measurement difficult for real estate investments, potentially increasing the incentive to reach for yield relative to other asset classes.

  4. Core properties are more liquid than non-core properties, or properties in a non-core lifecycle. They are easier to value and there is less due diligence required to acquire them. Additionally, it takes longer to deploy capital with properties that are not yet core. The majority of the capitalized expenses are realized during the development or repositioning process, which can happen many months post acquisition.

  5. Non-core properties are exposed to some the following potential risks: entitlement risk, construction risk, leasing and sales risk, operating expense risk, credit risk, partnership risk, capital markets risk, pricing risk, event risk, and valuation risk. To the extent these risks are idiosyncratic, funds do not increase their market risk exposure by increasing their non-core allocations. However, to the extent these risks are systematic, funds increase their market risk exposure by increasing their non-core allocations.

  6. It is important to note, due to data limitations this analysis only includes one market downturn which was the Global Financial Crisis from 2008 to 2009.

  7. NCREIF is the leading collector of institutional real estate investment information and provides the primary industry benchmark for institutional investors. NCREIF represents roughly $500 billion in assets under management as of the fourth quarter 2015.

  8. The Townsend Group is the largest real estate adviser to institutional investors in the world with roughly $270 billion in assets under management as of the fourth quarter 2015.

  9. The NCREIF property-level data includes all properties their members report to NCREIF which includes those currently held by JV partners. As such, some of the properties in the data may not currently be on the balance sheet of the fund. The data include properties in all lifecycle categories including those I refer to as a non-core lifecycle (conversion, development, expansion, initial leasing, pre-development, or renovation) as well as those I refer to as a core lifecycle (operating). The NPI uses data that is a subset of the NCREIF property-level data. The NPI only includes apartment, hotel, industrial, office, and retail property types in an operating lifecycle. In this paper, I include all properties associated with the corresponding fund regardless of type or lifecycle.

  10. I evaluate the relationship between non-core allocations and returns (net of fees) without any risk adjustment. Conventional wisdom suggests non-core allocations should be associated with both greater risk and greater return. I do not evaluate the relationship between non-core allocations and the risk-adjusted performance, or α. It is possible that non-core allocations could be associated with either positive or negative α, but I do not evaluate this topic in this paper. However, there are rational explanations why institutional investors could be interested in greater non-core allocations if they are associated with higher non-risk-adjusted expected returns but lower risk-adjusted expected returns (see Asness et al. (2012)). Specifically, many institutional investors are limited in their ability to self-lever, so they may be willing to pay a premium for greater market risk exposure if it is associated with higher expected returns even when the returns are lower risk-adjusted returns.

  11. Specifically, Fisher and Hartzell (2016) find that closed-end funds with development allocations between 10% to 33% performed worse on average than those that either had between 0% and 10% or greater than 33%. Fisher and Hartzell (2016) refer to properties that are in one of the following categories as development: pre-development, development, or initial lease-up. The alternative to this category is an existing property. It is also similar to what I refer to as being in a non-core lifecycle which includes properties in one of the following lifecycle stages: conversion, development, expansion, initial leasing, pre-development, or renovation. The alternative to being in one of these categories is being in a stabilized, operating lifecycle.

  12. The ideal specification for comparing the returns of core and non-core properties or funds would include investments which were randomly generated over the market cycle. The results of the return comparison could be biased if core and non-core properties or funds are systematically invested at different times over the market cycle. Gang et al. (2020) address this by including an acquisition period fixed effect. However, core and non-core properties returns are influenced by the market in systematically different ways due to the differences in their factor loadings, β’s. As such, an acquisition period fixed effect likely does not fully control for the nonrandom acquisitions of core and non-core properties. Similarly, it is likely that core and non-core funds are not started, nor have investments which happen, randomly over the market cycle. As such, to the extent there are larger investments in non-core funds or non-core funds have larger investments in non-core properties at the top of the market, they are likely to perform worse than core funds simply because of the timing in these investments.

  13. Importantly, these findings do not contradict the previous findings in other papers that core properties or funds achieve higher risk adjusted returns.

  14. In unreported results, I find that the empirical results provided in this paper are consistent for core and non-core funds, both when evaluated jointly as well as when evaluated in isolation.

  15. Investor sentiment is associated with high prices, low cap rates, and low expected returns.

  16. Managers create a negative externality from new investors to existing investors when they disproportionately acquire core properties when they have larger net queues. Acquiring more core properties enables new investors to enter the fund quicker and achieve their desired private real estate market exposure, but it also decreases the β of the fund, and thus, the market risk exposure of the existing investors.

  17. OPRE funds have negative net queues when their unfulfilled redemption requests become larger than unfulfilled capital commitments

  18. Non-core properties typically evolve into core, stabilized properties unless managers actively choose to reposition them. In some cases during a downturn, properties may stabilize at below-normal occupancy levels. However, in general, portfolios trend towards being fully core unless managers actively choose for it to do otherwise.

  19. Funds do not have to stop acquiring non-core properties even when their net queues become negative. Managers could still actively rebalance their portfolios by selling core properties and continuing to develop and reposition non-core properties even during a downturn. This would enable funds to maintain a more constant non-core allocation over the market cycle.

  20. It is more likely that investors could know which investments their capital will be placed into with younger, smaller funds. To address this potential concern, I evaluate the market reaching for yield and fund flow pressures to acquisition behavior relationships for only funds that have Total Net Assets of $1 Billion or larger and find consistent results.

  21. More specifically, Institutional Real Estate, Inc. defines commercial real estate as, “Buildings or land intended to generate a profit for investors, either from rental income or capital gain. Types of commercial real estate include office buildings, retail properties, industrial properties, apartments and hotels, as well as specialty niche property categories such as healthcare, student housing, senior housing, self-storage, data centers and farmland.” Single family rentals have recently become an important property type for institutional investment as well.

  22. Pension Real Estate Association (PREA) Investment Intentions Survey 2019

  23. Club deals are a hybrid between joint venture (JV) and commingled fund deals. Typically, JV deals have one investor with significant governance or approval rights, whereas commingled funds typically have multiple investors with little to no governance rights. In contrast, club deals typically have a limited number of like-minded investors with governance rights over acquisition, asset management, disposition, and financing decisions.

  24. The Financial Accounting Standards Board (FASB) regulates the valuation of both liquid and illiquid assets through Accounting Standards Codification (ASC) 820 - “Fair Value Measurement.” According to FASB ASC 820, the fair value of an asset is “the price that would be received to sell an asset or paid to transfer a liability in an orderly transaction between market participants at the measurement date.”

  25. Importantly, the time-varying market risk exposure, \(\tilde {\beta }_{i,t}\), could also be a function of the time-varying cash holdings of the fund. However, OPRE funds hold relatively little cash in their portfolios. The median cash holding over this period was a little over 3%. Additionally, cash holdings were not found to be significant in explaining market risk exposure when evaluated empirically. It becomes an empirical question as to whether this model adequately captures the time-varying β and fund characteristics relationships in the data.

  26. The sample is survivorship bias free to the extent that NCREIF members are required to report their quarterly data regardless of performance. Members can not discretionarily choose to report.

  27. It is important to note this includes all properties NCREIF members report to NCREIF which includes properties currently being held by joint venture partners. As such, some of these properties may not currently be on the balance sheet of the fund.

  28. In addition to the NPI return, I also evaluated the results using the following NCREIF index returns as proxies for the real estate market return: the NFI - Open End Diversified Core Equity (NFI-ODCE), the NFI - Open End Equity (NFI-OE), and the NCREIF Transaction Based Index (NTBI). The results from each analysis was consistent with those provided in this paper.

  29. The empirical approach I use evaluates whether β is a function of non-core allocations and has a linear relationship with it. However, it is possible that non-core allocations have a non-linear relationship with β. I evaluate whether this is the case later in this section. Leverage is believed to have a non-linear relationship with the market risk exposure such that \(\beta _{L}=\frac {\beta _{U}}{\left (1-LTV\right )}\) (see Hamada (1972)). Based on this theoretical relationship, and as shown in Figure 3 in the Appendix, the leverage to market risk exposure relationship starts to become non-linear around 50% to 60% leverage. 95% of observations are below 57% leverage. Additionally, the results discussed in this section are consistent when observations with a leverage larger than 50% are removed from the sample.

  30. It is possible leverage correlates positively with both non-core allocations and market risk exposure and not including it would cause an omitted variable bias. I provide additional analysis in the Appendix which further controls for the effects of leverage and the results are consistent. Specifically, I separate the data into bottom and top median non-core and leverage observations and rerun the tests. Additionally, I complete the original analysis using fixed effects models with individual slopes and the results are similarly consistent.

  31. I evaluate different NPI return lags as potential proxies for the market return expectation. The results are provided in Table 13 and discussed in Appendix: A: Market Return Expectations of the Appendix. As noted, I find that the two period lagged NPI return is the best proxy for market return expectation.

  32. The PREA Consensus Forecast provides the results from surveying industry professionals on their expectations of the expected return for the private commercial real estate market.

References

  • Arnold, TR, Ling, DC, & Naranjo, A (2017). Waiting to be called: The impact of manager discretion and dry powder on private equity real estate returns. The Journal of Portfolio Management, 43, 23–43.

    Article  Google Scholar 

  • Arnold, TR, Ling, DC, & Naranjo, A (2019). Private equity real estate funds: returns, risk exposures, and persistence. The Journal of Portfolio Management, 45, 24–42.

    Article  Google Scholar 

  • Asness, CS, Frazzini, A, & Pedersen, LH (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68, 47–59.

    Article  Google Scholar 

  • Barber, BM, & Yasuda, A (2017). Interim fund performance and fundraising in private equity. Journal of Financial Economics, 124, 172–194.

    Article  Google Scholar 

  • Barberis, N, & Shleifer, A (2003). Style investing. Journal of Financial Economics, 68, 161 -199.

    Article  Google Scholar 

  • Becker, B, & Ivashina, V (2015). Reaching for yield in the bond market. The Journal of Finance, 70, 1863–1902.

    Article  Google Scholar 

  • Berk, JB, Green, RC, & Naik, V (1999). Optimal investment, growth options, and security returns. The Journal of Finance, 54, 1553–1607.

    Article  Google Scholar 

  • Bollen, NPB, & Busse, JA (2001). On the timing ability of mutual fund managers. The Journal of Finance, 56, 1075–1094.

    Article  Google Scholar 

  • Brown, GW, Gredil, OR, & Kaplan, SN (2019). Do private equity funds manipulate reported returns?. Journal of Financial Economics, 132, 267–297.

    Article  Google Scholar 

  • Brown, KC, Van Harlow, W, & Zhang, H. (2009). Staying the course. The role of investment style consistency in the performance of mutual funds, Working Paper.

  • Chen, Y, & Liang, B (2007). Do market timing hedge funds time the market?. Journal of Financial and Quantitative Analysis, 42, 827–856.

    Article  Google Scholar 

  • Chung, J-W, Sensoy, BA, Stern, L, & Weisbach, MS (2012). Pay for performance from future funds flow: The case of private equity. Review of Financial Studies, 25, 3259–3304.

    Article  Google Scholar 

  • Dichev, ID (2007). What are stock investors’ actual historical returns? Evidence from dollar-weighted returns. American Economic Review, 97, 386–401.

    Article  Google Scholar 

  • Dichev, ID, & Yu, G. (2011). Higher risk, lower returns: What hedge fund investors really earn. Journal of Financial Economics, 100, 248–263.

    Article  Google Scholar 

  • Fisher, LM, & Hartzell, DJ (2016). Class differences in real estate private equity fund performance. The Journal of Real Estate Finance and Economics, 52, 327–346.

    Article  Google Scholar 

  • Florance, A, Miller, N, Peng, R, & Spivey, J (2010). Slicing, dicing, and scoping the size of the us commercial real estate market. Journal of Real Estate Portfolio Management, 16, 101–118.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Geltner, D (2015). Real estate price indices and price dynamics: an overview from an investments perspective. Annual Review of Financial Economics, 7, 615–633.

    Article  Google Scholar 

  • Hamada, RS (1972). The effect of the firm’s capital structure on the systematic risk of common stocks. The Journal of Finance, 27, 435–452.

    Article  Google Scholar 

  • Hochberg, YV, Ljungqvist, A, & Vissing-jørgensen, A (2014). Informational holdup and performance persistence in venture capital. The Review of Financial Studies, 27, 102–152.

    Article  Google Scholar 

  • Huang, J, Sialm, C, & Zhang, H (2011). Risk shifting and mutual fund performance. The Review of Financial Studies, 24, 2575–2616.

    Article  Google Scholar 

  • Jiang, GJ, Yao, T, & Yu, T (2007). Do mutual funds time the market? Evidence from portfolio holdings. Journal of Financial Economics, 86, 724–758.

    Article  Google Scholar 

  • Kaplan, SN, & Schoar, A (2005). Private equity performance: returns, persistence, and capital flows. The Journal of Finance, 60, 1791–1823.

    Article  Google Scholar 

  • Lewellen, J (1999). The time-series relations among expected return, risk, and book-to-market. Journal of Financial Economics, 54, 5–43.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ling, DC, & Naranjo, A (2015). Returns and information transmission dynamics in public and private real estate markets. Real Estate Economics, 43, 163–208.

    Article  Google Scholar 

  • Ling, DC, Naranjo, A, & Scheick, B (2014). Investor sentiment, limits to arbitrage and private market returns. Real Estate Economics, 42, 531–577.

    Article  Google Scholar 

  • Pagliari, J (2017). Another take on real estate’s role in mixed-asset portfolio allocations. Real Estate Economics, 45, 75–132.

    Article  Google Scholar 

  • Pagliari, J (2020). Real estate returns by strategy: Have value-added and opportunistic funds pulled their weight?. Real Estate Economics, 48, 89–134.

    Article  Google Scholar 

  • Pagliari, J, Scherer, KA, & Monopoli, RT (2005). Public versus private real estate equities: a more refined, long-term comparison. Real Estate Economics, 33, 147–187.

    Article  Google Scholar 

  • Riddiough, TJ, Moriarty, M, & Yeatman, PJ (2005). Privately versus publicly held asset investment performance. Real Estate Economics, 33, 121–146.

    Article  Google Scholar 

  • Rossi, A. (2019). Decreasing returns or reversion to the mean? The case of private equity fund growth. Working Paper.

  • Sing, TF, Tsai, I-C, & Chen, M-C (2016). Time-varying betas of us reits from 1972 to 2013. The Journal of Real Estate Finance and Economics, 52, 50–72.

    Article  Google Scholar 

  • Tobin, J (1969). A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking, 1, 15–29.

    Article  Google Scholar 

  • Wermers, R. (2012). Matter of style. The causes and consequences of style drift in institutional portfolios. Working Paper.

Download references

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Authors and Affiliations

  1. Lusk Center of Real Estate, University of Southern California, 319 Ralph & Gold Lewis Hall, 650 Childs Way, Los Angeles, CA, 90089, USA

    Spencer J. Couts

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I am grateful for comments and advice from two anonymous referees, Zahi Ben-David, Brad Cannon, Moussa Diop, Jeffrey Fisher, Richard Green, Andrei Gonçalves, Dongxu Li, Crocker Liu, Haoyang Liu, C. Jack Liebersohn, David Ling, Greg MacKinnon, Gianluca Marcato (discussant), Andri Rabetanety (discussant), Andrea Rossi, René Stulz, Chongyu Wang, Michael Weisbach, and Lu Zhang as well as seminar participants at the 2020 Real Estate Finance and Investment Symposium, the 2020 UNC Commercial Real Estate Data Association Real Estate Research Symposium, the 3rd Annual REALPAC/Ryerson Canadian Commercial Real Estate Research Symposium (2020), and the Annual Private Markets Research Conference (2019). All errors are my own.

Appendices

Appendix A: Market Return Expectations

In this section, I evaluate different proxies for the market return expectations for the private real estate market. Ideally, I would use the PREA Consensus Forecast value produced by the Pension Real Estate Association (PREA).Footnote 32 However, the first observation of the PREA Consensus Forecast is the second quarter of 2010. As such, I evaluate the lagged NPI returns as potential proxies in order to obtain a proxy that covers the entire sample period. In doing so, I regress the PREA Consensus Forecast returns on four different sets of lagged NPI returns both individually and jointly and choose the one that correlates most with the PREA values. The results to this analysis are provided in Table 13 below.

Table 13 Market Return Expectations
Full size table

As shown, the parameter on the two-period lagged NPI return is .97. Additionally, a one standard deviation increase in the NPI return is associated with a 78% standard deviation increase in the PREA Consensus Forecast return. In all, this evidence suggests the lagged two period NPI return acts as a good proxy for investor expectations about the market return.

Appendix B: Robustness Tests

The results provided in this Section of the Appendix supplement those found in the main body of the paper. Several robustness tests were completed by changing the empirical specifications used in the main body of the paper. Sections B.1 Non-core Allocation and Leverage Loadings (groupings) and B.2 Non-core Allocation and Leverage Loadings (individual slopes) provide additional analysis on the relationship between returns and non-core allocations and leverage. These sections evaluate the influence of using median buckets and fixed-effects with individual slopes. Section B.3 Return Expectations, Capital Flows, and Acquisitions (individual slopes) provides additional analysis on the relationships between acquisition behavior and expected market returns, net queues, and fund flows. Section B.4 Non-core Allocations and the Market Cycle provides further analysis on the relationship between non-core allocations and the market cycle by separating funds into core and non-core strategies. Section B.5 Leverage and Market Risk Exposure provides additional analysis on the linearity of the relationship between leverage and the market risk exposure.

B.1 Non-core Allocation and Leverage Loadings (groupings)

In this subsection, I further evaluate the relationship between risk exposure and non-core allocations. The primary analysis for this relationship is provided in Non-core Allocations, Risk, and Returns and Table 2 above. In this analysis, I categorize funds into groups depending on whether their prior period non-core allocations and leverage were below median or above median. This provides the following four groups of funds: (i) below median non-core and below median leverage funds, (ii) below median non-core above median leverage funds, (iii) above median non-core and below median leverage funds, and (iv) above median non-core and above median leverage funds. Since leverage and non-core allocations are both believed to increase market risk exposure, it is important to isolate their respective effects where possible. Additionally, it is possible that high leverage funds also tend to have more non-core allocations. Sorting them into low and high median categories greater isolation of their separate effects. Univariate regressions are then completed by regressing the returns of the funds in the different categories on the returns of the NPI. The results are reported in Table 14.

Table 14 Characteristic-based Factor Loadings
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As shown in Table 14, funds in low-high and high-low categories have a greater market factor loading than those in the low-low category by approximately the same magnitude. Additionally, those funds in the high-high category have a greater market factor loading than funds in the low-high and high-low categories by approximately the same as well. Additionally, the number of funds in the respective categories suggests not all funds are categorized into either low-low or high-high categories. Approximately 40% of the funds having less than the median amount of leverage have more than the median amount of non-core allocations. Additionally, approximately 40% of the funds with less than the median amount of non-core allocations have more than the median amount of leverage. In all, this evidence further supports the previously discussed evidence that the non-core allocation is an important fund characteristic that explains market risk exposure.

B.2 Non-core Allocation and Leverage Loadings (individual slopes)

In this subsection, I further evaluate the relationship between risk exposure and non-core allocations. The primary analysis for this relationship is provided in Non-core Allocations, Risk, and Returns and Table 2 above. In this analysis, I evaluate the relationship using a fixed-effects model with individual-specific slopes. It is possible funds adjust their market risk exposure through unobservable ways other than non-core allocations or leverage and they change those activities in a way that correlates with how they change their non-core allocations. Using a fixed-effects model with individual-specific slopes accounts for the possibility of these unobserved variables. Table 15 provides the results from this analysis.

Table 15 Non-core Allocations and Market Risk Exposure (Individual Slopes)
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As shown in Table 15, the results from using a fixed-effects model with individual-specific slopes are consistent with those in the main body of this paper. Specifically, both non-core allocations and leverage are important fund characteristics which explain time-varying market risk exposure.

B.3 Return Expectations, Capital Flows, and Acquisitions (individual slopes)

In this subsection, I further evaluate the relationship between acquisition activity, market return expectations, net queues, and fund flows. The primary analysis for this relationship is provided in Non-core Allocation Drivers and Table 9 above. In this analysis, I evaluate the relationship using a fixed-effects model with individual-specific slopes. It is possible funds adjust their acquisition activity due to unobservable conditions other than net queues, fund flows and return expectations and that these conditions correlate with either net queues, fund flows, or return expectations. Using a fixed-effects model with individual-specific slopes accounts for the possibility of these unobserved variables. Table 15 provides the results from this analysis.

As shown in Table 16, the results from using a fixed-effects model with individual-specific slopes are consistent with those in the main body of this paper.

Table 16 Return Expectations, Capital Flows, and Acquisitions (Individual Slopes)
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B.4 Non-core Allocations and the Market Cycle

In this subsection, I further evaluate the relationship between non-core allocations and the market cycle. The primary analysis for this relationship is provided in Non-core Allocations and the Market Cycle and Table 7 above. In this analysis, I break the analysis into core and non-core funds to evaluate whether these two types of funds evolve their allocations differently. Normalized non-core allocations are regressed on market and fund returns separately. The ideal specification evaluates the relative weight each fund puts on non-core allocations relative to the maximum it is willing to take on. As such the non-core allocations are normalized by dividing each observation by the maximum non-core allocation for the given fund.

As shown, the results for both core and non-core funds are consistent with the findings in Table 7 of the main body of the paper. Interestingly, based on the magnitude of the coefficients in the tables, it appears core funds have a slightly greater tendency to style drift than non-core funds. This finding is likely offset in the overall returns of the funds though by the fact that non-core funds have larger magnitudes of non-core allocations. It is not surprising that the coefficients associated with the non-core fund subsample are only slightly statistically significant. The sample size is significantly smaller.

Table 17 Non-core Allocations over the Market Cycle
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B.5 Leverage and Market Risk Exposure

In this subsection, I evaluate the theoretical relationship between leverage and market risk exposure. In particular, I evaluate the concern that higher levels of leverage lead to a non-linear relationship with the market risk exposure. Figure 3 provides a graphical representation of this relationship assuming the market risk exposure to leverage relationship is as follows: \(\beta _{L}=\frac {\beta _{U}}{\left (1-LTV\right )}\) and the unlevered market risk exposure, βu, equals one. As shown, the relationship starts to become non-linear at around 50% to 60% and even at that point the relationship is marginally non-linear. As mentioned above, 90% of observations in the sample have a leverage below 48% and 95% of observations have a leverage of below 57%. Additionally, the results are consistent when all observations with a leverage above 50% are removed from the data.

Fig. 3
figure 3

Leverage and Market Risk Exposure. This figure graphically depicts the relationship between market risk exposure, β, and leverage under the assumption they have the following mathematical relationship and the unlevered market risk exposure, βu, equals one: \(\beta _{L}=\frac {\beta _{U}}{\left (1-LTV\right )}\)

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Couts, S.J. How do Non-Core Allocations Affect the Risk and Returns of Private Real Estate Funds?. J Real Estate Finan Econ (2022). https://doi.org/10.1007/s11146-022-09886-0

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Keywords

  • Asset pricing
  • Risk factors
  • Factor loadings
  • Commercial real estate

JEL Classification

  • G11
  • G12
  • G13
  • G14
  • G23
  • R33