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Institutional real estate investors, leverage, and macroprudential regulation

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Institutional real estate investors, leverage, and macroprudential regulation


Manuel A. Muñoz 14 November 2020

Since the onset of the Global Crisis, the presence of institutional investors in housing markets has steadily increased over time. Real estate funds (REIFs) and other housing investment firms leverage large-scale buy-to-rent investments in real estate assets that, arguably, enable them to set prices in rental housing markets. A significant fraction of this funding is being provided in the form of direct lending.

In this column, I document the increasing presence of institutional investment in euro area real estate assets and provide a theoretical rationale for having countercyclical leverage regulation affecting institutional real estate investors as an effective tool to smooth aggregate lending and the property cycle. The aim is to contribute to the ongoing debate on how to strengthen the macroprudential policy framework for non-banks.1 

Recent trends in the real estate fund (and REITs) industry

The low-for-long interest rate environment has exerted a downward pressure on fixed income returns, thereby providing institutional investors with incentives to search for yield in alternative markets such as the real estate sector. Over the last decade, the increasing presence of institutional investors in housing markets – together with a tightening of lending standards – has revitalised rental housing markets, leading to higher rents and depressed homeownership rates (Gete and Reher 2018, Lambie-Hanson et al. 2019).

In recent years, market analysts and real estate experts have recurrently reported that a significant proportion of these investments is being leveraged via direct lending (i.e. lending that is not subject to regulatory LTV limits), often provided by debt funds. This has raised fears of a credit bubble building up in the debt fund industry.2

Recent empirical studies are consistent with these views. The findings presented in Tzur-Ilan (2019) suggest that investors are relatively more affected by LTV limits on mortgage loans than any other type of borrowers, signalling their heavy reliance on borrowing to purchase real estate assets. In addition, Hoesli et al. (2017) conclude that the Basel III framework has imposed a regulatory burden on real estate companies, thereby providing them with incentives to opt for funding sources other than bank lending. 

The euro area is among the group of economies where the increasing presence of institutional investors in housing markets has been more evident. Since 2013, institutional investment in euro area real estate assets has more than quadrupled in absolute terms and as a share of total housing investment (see Figure 1).

Figure 1 Real estate funds flows in the euro area

Institutional real estate investors, leverage, and macroprudential regulation 1

Note: This figure reports real estate funds flows (12-month flows) in the euro are both, in absolute terms and as a percentage of aggregate housing investment in the euro area. Time series are at quarterly frequency and have been plotted for the period 2012:III-2020:I. The figure is based on Battistini et al. (2018). 
Sources: ECB, Eurostat and own calculations.

Some other empirical studies have found that debt funds are among the most leveraged investment funds in Europe, with fund managers in leveraged funds reacting in a relatively more procyclical manner (than those in non-leveraged funds) and leverage reportedly amplifying financial fragility in the investment fund sector (e.g. van der Veer et al. 2017, Molestina Vivar et al. 2020). It is worth noting that real estate funds are generally not subject to leverage limits in the EU and that there is some uncertainty surrounding their actual leverage measures, among other reasons, due to the fact that investment funds often lever up synthetically through the use of derivatives.3


In Muñoz (2020), I develop a two-sector DSGE model that incorporates the corresponding key features of the real estate fund industry in the above mentioned context and calibrate it to quarterly euro area data in order to assess the effectiveness of dynamic leverage regulation (modelled as countercyclical LTV ratios) that limits the borrowing capacity of institutional real estate investors (i.e. LTV limits on commercial mortgages) in smoothing housing price and credit cycles.

In this quantitative business cycle model, households, real estate funds, and final goods-producing firms interact in a real, closed, decentralised and time-discrete economy. The model features two frictions which closely interconnect credit and housing markets in the economy and amplify the effects of exogenous supply and demand shocks to the real economy: collateral constraints à la Iacoviello (2005) – which affect the borrowing capacity of the two types of indebted agents (i.e. impatient households and real estate funds) – and monopolistic competition in the segment of the rental housing market operated by institutional investors.4 The motivation for the latter is twofold. From the demand side of the rental housing market, renter households and firms exhibit a preference for variety at the aggregate level.5 From the supply side, purchasing a large amount of housing with a common characteristic (e.g. the neighbourhood), grants the REIF capacity to set the price in the market of the corresponding rental housing variety.

Countercyclical leverage limits and real estate funds

The macroprudential authority is assumed to have two policy instruments at hand; dynamic LTV policy rules that limit the borrowing capacity of impatient households and those that limit the borrowing capacity of REIFs. A key contribution of Muñoz (2020) is its assessment on the effectiveness and workings of the LTV rule that affects the borrowing capacity of REIFs. 

The quantitative analysis finds that the optimised LTV rule that affects the borrowing limit of REIFs is more effective in smoothing property prices and the credit cycle (and the credit-to-output gap) than the optimised rule that limits the borrowing capacity of impatient households. This is despite noticeably larger stocks of housing and borrowing held by impatient households as compared to real estate funds. Moreover, if the aim of the prudential authority is to minimise the asymptotic variance of a credit gap or that of property prices, the best option is to solely have a countercyclical and highly responsive LTV rule that affects REIFs’ borrowing decisions in place (i.e. the LTV ratio that limits the borrowing capacity of impatient households should not be countercyclical). This finding is remarkably robust across key alternative specifications and calibrations of the model. The underlying reason behind such an important and unexpectedly robust finding relates to the strong interconnectedness of REIFs with various sectors of the economy.

Such policy rule operates through the following transmission mechanism; a tightening of the REIFs’ LTV limit in the face of a positive exogenous shock restricts funds’ borrowing capacity and, thus, their activity. Fund managers eventually find it optimal to demand less property housing and supply less rental housing services to both, renter households and final goods producing firms. Consequently, property prices soar less abruptly and the share of patient households’ supply in rental housing markets increases, thereby exerting a downward pressure on the competitive rental housing price. That is, countercyclical LTV ratios affecting the borrowing capacity of REIFs have the potential to smooth lending, property prices, and reference (i.e. competitive) rental housing prices over the cycle.

Figure 2 reports the impulse responses of key selected aggregates to a positive (non-housing) technology shock for the only case in which the two jointly optimised LTV policy rules are countercyclical (i.e. where the macroeconomic indicator whose variance is minimised by the prudential authority is the reference rental housing price). Interestingly, even in this case, the optimised LTV policy rule which directly affects REIFs (i.e. green dotted line) seems to be more effective in stabilising aggregates of the real economy – such as output, employment or final consumption – than the one directly affecting impatient households (i.e. red starred line). This result holds for each of the different types of technology and housing demand shocks that hit this model economy, the kinds of exogenous shocks that have been shown to explain the bulk of the variability in housing investment and housing prices (Iacoviello and Neri 2010).

Figure 2 Impulse responses to a positive non-housing productivity shock 

Institutional real estate investors, leverage, and macroprudential regulation 2

Note: Variables are expressed in percentage deviations from the steady state. The blue solid line refers to the baseline scenario. The red starred line corresponds to the optimised LTV ratio on residential mortgages (impatient households) scenario. The green dotted line relates to the optimised LTV ratio on commercial mortgages (institutional investors) scenario. The black diamond line makes reference to the jointly-optimised LTV limits on residential and commercial mortgages scenario. Optimised LTV policy rules have been obtained by means of the osr (i.e., optimal simple rule) command in dynare. HH and REIF stand for impatient households and institutional investors (i.e., real estate funds), respectively.

Policy implications

Despite the comparatively low fraction of property and debt held by REIFs, optimised countercyclical LTV rules directly affecting their borrowing limit are more effective in smoothing property prices, credit and business cycles than the well investigated optimised LTV limits restricting the borrowing capacity of (indebted) households. Moreover, if the sole objective of the macroprudential authority is to tame the housing price and credit cycle, the best option is to have a countercyclical LTV rule affecting REIFs’ borrowing limit in place (i.e. the LTV rule limiting households’ borrowing capacity should not be countercyclical).

These results shed light on some of the potential avenues for strengthening the macroprudential policy framework for non-banks. There are at least two policy instruments that could be considered to manage this type of funds’ leverage-induced procyclicality in practice and which are still not in place: (dynamic) limits on REIFs’ leverage and countercyclical LTV limits on non-bank lending. 

On a separate issue, the quantitative analysis notes that such (quantity) regulation would allow for reference prices in rental housing markets to increase less abruptly during the boom, an issue that policymakers in several countries of the euro area have attempted to handle via price regulation (an alternative that could generate price distortions).

Author’s note: The views expressed in this column are those of the author and do not necessarily reflect the views of the ECB or the Eurosystem.


Battistini, N, J Le Roux, M Roma and J Vourdas, (2018), “The state of the housing market in the euro area“, ECB Economic Bulletin Articles, 7.

Bénassy-Quéré, A, and B Weder di Mauro (eds) (2020), Europe in the Time of Covid-19, A VoxEU.org ebook, CEPR Press.

Gete, P, and M Reher (2018), “Mortgage Supply and Housing Rents“, The Review of Financial Studies 31 (12): 4884-4911.

Hoesli, M, S Milcheva, and S Moss (2017), “Is Financial Regulation Good or Bad for Real Estate Companies? – An Event Study“, The Journal of Real Estate Finance and Economics 5 (1/2), 1 – 39.

Iacoviello, M (2005), “House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle”, American Economic Review 95, 739-764.

Iacoviello, M and S Neri (2010), “Housing Market Spillovers: Evidence from an Estimated DSGE Model“, American Economic Journal: Macroeconomics 2(2): 125-164.

Lambie-Hanson, L, W Li, and M Slonkosky (2019), “Institutional Investors and the U.S. Housing Recovery“, Working Papers 19-45, Federal Reserve Bank of Philadelphia.

Molestina Vivar, L, M Wedow and C Weistroffer (2020), “Burned by leverage? Flows and fragility in bond mutual funds“, ECB Working Paper Series 2413.

Muñoz, M A (2020), “Macroprudential policy and the role of institutional investors in housing markets”, ECB Working Paper Series 2454.

Perotti, E (2020), “The coronavirus shock to financial stability”, VoxEU.org, 27 March.

Tzur-Ilan, N (2019), “The Real Consequences of LTV Limits on Housing Choices“, Working Paper.

van der Veer, K, A Levels, C Lambert, L Molestina Vivar, C Weistroffer, R Chaudron and R de Sousa van Stralen (2017), “Developing Macroprudential Policy for Alternative Investment Funds“, ECB Occasional Paper Series 202.


1 Total assets of the euro area non-banking sector have doubled over the last decade, with the size of the investment fund industry expanding at a relatively higher pace and its interconnectedness with other segments of the financial sector and the real economy being well documented. Moreover, the short-term impact of the COVID-19 shock on the financial sector has highlighted the potential of the investment fund sector to trigger episodes of severe market volatility and price dislocations (e.g. Bénassy-Quéré and Weder di Mauro 2020, Perotti 2020). However, the macroprudential policy framework for non-banks is still moderately developed, when compared to that for banks.

2 See, for example, “Real Estate: post-crisis boom draws to a close”, Financial Times, 18 June; “Rise of private debt creates fears of a bubble“, Financial Times, 13 April.

3 Real estate funds operating in the European Union generally fall within the category of funds that are subject to the AIFMD (Alternative Investment Fund Managers Directive), for which no leverage limits apply.

4 The other segment of the rental housing market is operated by patient households, which supply homogeneous rental housing services under perfect competition.

5 Housing markets are, in practice, segmented according to some of their main features (location, type of construction, style, etc).

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