Citation for published version (APA): van Dijk, D. W. (2019). Commercial and residential real estate market liquidity.

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1 UvA-DARE (Digital Academic Repository) Commercial and residential real estate market liquidity van Dijk, D.W. Link to publication Citation for published version (APA): van Dijk, D. W. (2019). Commercial and residential real estate market liquidity. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam ( Download date: 09 Apr 2019

2 Chapter 1 Introduction What have the Romans ever done for us? Reg, Monty Python s Life of Brian In Ancient Rome, wealthy Romans bought and rented out apartments Insulae as an investment. This makes real estate one of the oldest asset classes. Although much has changed in the functioning of real estate markets and financial markets in a broader sense, people still invest in real estate. Professional investors trade office buildings, apartment complexes or industrial buildings on a, compared to the Romans, relatively frequent basis. In addition, a lot of ordinary people buy and sell houses to live in, which makes real estate the most important asset class for many households. An important aspect for professional investors and households is how prices change over time. Positive returns can boost the real economy, but negative returns can have a serious dampening effect. Related to these price changes is the liquidity of real estate markets. In the financial economics literature, market liquidity is usually defined as the ease at which an asset can be traded (Brunnermeier and Pedersen, 2009). The importance of a liquid market became apparent as early as the 17th century, when the formation of a liquid market to trade shares was crucial for the success of the first corporation, the Vereenigde Oostindische Compagnie or the Dutch East India Company (Dari-Mattiacci et al., 2017). 1

3 Real estate is an inherent illiquid asset class compared to, for example, stocks and bonds. Especially during the Global Financial Crisis (GFC) the importance of (the lack of) liquidity became clear. Prices fell tremendously, but, maybe even more importantly, investors and households were not able to sell their assets as quickly as desired. For investors, lower liquidity of their investment portfolio means that it is more difficult to rebalance their portfolio. For households, lower liquidity implies that they are not able to move if desired, which has consequences for labor mobility as well. This thesis explores several aspects of market liquidity of real estate assets. The main question is What is the role of market liquidity in real estate markets?. This question is divided into three sub-questions: (i) How can real estate market liquidity be measured?, (ii) What is the relationship between prices and market liquidity in real estate markets?, and (iii) How can market liquidity be used for a better understanding and monitoring of real estate markets?. This introduction will start with a view on the concept of real estate market liquidity and the relationship with prices based on basic economic theory. This will be followed by short discussion of the three sub-questions, how the chapters relate to these, and the main findings. 1.1 Market liquidity in real estate markets Central to the illiquidity of real estate is that real estate markets can be characterized as search markets, in which buyers and sellers need to be matched. Both properties and agents are heterogeneous. The former implies that it is difficult to assess the true value of an asset and the latter implies that agents can have different reservation prices for the same property. A match that is, a transaction can only occur if the reservation price of the buyer is at least equal to the reservation price of the seller. If this is not the case, the seller keeps the property on the market and waits until another buyer arrives with a reservation price that is high enough. Additionally, the seller can also choose to lower the reservation price or choose to withdraw the property from the market. For now, assume that lowering reservation prices and withdrawals do not occur. Generally, the frequency distribution of buyers and sellers will look like the top panel of Figure 1.1. Buyers have, on average, a lower reser- 2

4 vation price than sellers, but there are some buyers with a higher reservation price than some sellers. If these are matched, a transaction will occur. The shaded area depicts such situations and indicates the number of transactions. The average transaction price is indicated by P 0. When the economic situation changes, buyers and sellers may adjust their reservation prices. Obviously, this has consequences for the observed average transaction price. It will also have an impact on the number of transactions if the reservation prices of buyers and sellers move differently over the cycle. If they would move in parallel, prices may change but the number of transactions would be equal across time. Empirically, there is ample evidence that prices and transaction volume are co-moving. In other words, the reservation prices of buyers and sellers have to move in a different manner over time. In a boom, both transaction volume and prices are high, while in a bust transaction volume and prices are low. The only way this can occur, is when the reservation prices of buyers and sellers in an up market are further apart and in a down market are closer together. 1 Combined with the fact that reservation prices tend to move up (down) in a boom (bust), this implies that buyers change their reservation prices quicker than sellers. These situations are presented in the middle and bottom panels in Figure 1.1. There can be multiple reasons why buyers and sellers move their reservation prices differently, these will be discussed in Section 1.3. It might be more informative to look at the cumulative distributions of buyers and sellers reservation prices, which are shown in Figure 1.2. When looking at the cumulative distributions, the x-axis shows the equilibrium transaction price and the y-axis the number of transactions. If the axes are flipped, a classical diagram with downward-sloping demand and upward-sloping supply curves is obtained (Figure 1.3). In the initial situation, the equilibrium price and quantity are indicated by P 1 and Q 1. In case of a boom, the demand curve will shift more to the right than the supply curve, as buyers respond quicker than sellers. The result is that the new equilibrium price and quantity, P 2 and Q 2, will both be higher than the old price and quantity. Related to the number of transactions is the time on market (TOM). The expected TOM of an asset is related to the number of buyers with 1 Note that potential sellers can also choose to keep their properties of the market, which is equivalent to having very high reservation prices. This would also imply that the reservation prices of buyers and sellers are further apart, which results in fewer transactions. 3

5 Figure 1.1: Buyers and sellers reservation price distributions at different points in the cycle consistent with pro-cyclical liquidity. Demand Supply N ormal market P 0 Reservation P rices Boom P 0 P 1 Reservation P rices Bust P 0 P 1 P 2 Reservation P rices 4

6 Figure 1.2: Cumulative distributions of buyers and sellers reservation prices. T ransactions Buyers Sellers Q 1 P 1 Reservation P rices Figure 1.3: Pro-cyclical liquidity in a classical supply and demand diagram. Reservation P rices S 1 S 2 P 2 P 1 D 1 D 2 Q 1 Q 2 T ransactions 5

7 an appropriate reservation price (i.e. buyers with a sufficiently high reservation price). In case the reservation prices of buyers in Figure 1.1 moves more to the right, there are simply more buyers with an appropriate reservation price and the probability that a successful match occurs increases. Intuitively, when the proportion of suitable buyers increases, the time to find a buyer and the TOM decreases. As such, there is an inverse relationship between the probability of sale and the TOM (Fisher et al., 2003). A different perspective on the pro-cyclicality between liquidity measured by the TOM and prices can be obtained when looking at the real estate market as a simplified Robinson Crusoe production economy. 2,3 Suppose that asset owners want to sell their assets. In doing so, they face a trade-off between the TOM and the price. Setting a lower (higher) reservation price will result in a lower (higher) transaction price, but also in a lower (higher) TOM. Production curves in two market situations (boom and bust) together with the indifference curves are presented in Figure 1.4. In a bust, the production function is less efficient as sellers will hold properties off the market, which is similar to keeping their reservation prices too high. In a boom, more properties are on the market and more transactions occur, resulting in a more efficient, steeper, production function and quicker price discovery. 4 Because sellers prefer a higher price and a lower TOM, the sellers isoquants are increasing towards the northwest. As such, for sellers the utility maximizing TOMs and prices are pro-cyclical. 2 In the original example, Robinson Crusoe can choose to spend 24 hours per day by either gathering coconuts or to enjoy leisure time. 3 Note that pro-cyclicality in this case implies a high (low) TOM and a low (high) price. 4 A potential analogue with the original Robinson Crusoe example would be a technological shock in which it becomes easier to gather coconuts, e.g. the introduction of a ladder. 6

8 P rice Figure 1.4: Pro-cyclical liquidity in a Robinson Crusoe economy. U2 U1 Boom P 2 Bust P 1 T OM 2 T OM 1 T ime on Market 7

9 1.2 How can real estate market liquidity be measured? Ametefe et al. (2016) identify five dimensions of real estate market liquidity: tightness, depth, resilience, breadth, and immediacy. This thesis will discuss most of these characteristics, how they differ over the cycle, and how these cyclical movements relate to price movements. Market tightness refers to the costs related to taking a round-trip (i.e. simultaneously buy and sell or sell and buy). Market depth measures the extent to which trading can occur without affecting prices. After a while, more trading will affect prices more, the magnitude by which this happens is called resilience. The breadth refers to the overall size of all trades. Finally, immediacy relates to the discount or premium related to selling or buying quickly. Chapter 2 focuses on the TOM of the Dutch housing market. This measure is mostly related to the immediacy characteristic of market liquidity. From an investors perspective, a lower expected TOM is related to more immediacy (lower costs of selling quickly). Practitioners and policymakers frequently use the average TOM of sold properties as a market liquidity indicator. This chapter shows that the average TOM can be misleading, mainly due to two reasons. Firstly, in calculating the average TOM, only properties that are sold are considered. A seller might also choose to withdraw the property. If many sellers choose to do so, this is also an indication of an illiquid market. If, for example, the probability of a withdrawal increases during some periods, the average TOM might give a wrong signal about liquidity. Secondly, houses are heterogeneous assets: no house is exactly the same. Some houses, usually more homogeneous properties like apartments, transact quicker. The main aim of this chapter is to construct a measure for market liquidity that corrects for these features. Novel features of the presented method include that the liquidity indices can be created reliably up to the end of the sample (until the most recent data comes in) and that indices can be constructed in markets where transactions or withdrawals occur infrequently. Chapter 3 develops a model of reservation prices of buyers and sellers in the US commercial real estate market to obtain a measure for market tightness. In this model, reservation price dynamics are the root of price and liquidity changes in the market. The model builds 8

10 on the empirical fact that liquidity and prices are highly pro-cyclical in real estate. The model in this chapter is an extension of the model of Fisher et al. (2003). Their model is extended in a repeat-sales structural time series framework. This makes it possible to estimate reliable, robust investor supply and demand indices for granular markets. The difference between the central tendencies of these reservation prices can be used as a measure for market tightness the first aspect of market liquidity. This measure be viewed as an analogue to the bid-ask spread, which is commonly used as a market liquidity measure in the stock market. Chapter 4 employs a volume-based measure for market liquidity and relates it to the breadth of the Dutch housing market. More specifically, the rate of sale i.e. the number of transactions divided by the number of houses for sale is used as a measure for liquidity. The rate of sale can be viewed as an ex post sale probability and is very much related to the TOM and the difference between average buyers and sellers reservation prices. The chapter further proposes a leading measure for market tightness based on internet search behavior. More specifically, the measure is the ratio of the number of clicks of all listed properties in this region divided by the number of listed properties. Conceptually, clicks are related to demand in the market and the number of listed properties to supply. The ratio is then defined as market tightness (i.e. demand / supply). Intuitively, houses in more popular regions should receive more clicks than houses in less popular regions and markets in the more popular regions should be tighter. 1.3 What is the relationship between prices and market liquidity in real estate markets? In the literature, there are roughly three theories that focus on the commonality between real estate liquidity and transaction prices (De Wit et al., 2013). The first theory is related to search models with asymmetric information, the second theory to anchoring and other behavioral explanations, and the third theory relates liquidity to mortgage markets. The theories are not mutually exclusive and can be very much overlapping. All chapters in this thesis will discuss the commonality between price and liquidity movements in real estate markets. Central to this is the lead-lag relationship that tends to exist between these. The first theory (asymmetric information models) focuses on the fact 9

11 that real estate assets are highly heterogeneous, for which the market values are difficult to assess. As such, news regarding the asset values is not priced instantaneously. Due to the institutional structure of real estate markets, information regarding the supply of houses (e.g. listing prices, sold properties, and recent transaction values) is known to the public. The number of buyers or other demand-side characteristics, such as income, are not known (instantaneously) to sellers. Because of this, sellers adapt more gradually, pro-cyclical liquidity occurs, and consummated transaction prices will change gradually. The second theory explains the relationship with behavioral arguments. The most important argument is that sellers anchor their reservation prices to the price they paid when buying the property. When markets are going up, this implies that sellers keep their reservation prices too low. Conversely, when market are going down, the reservation prices are kept too high (i.e. sellers are loss-averse). The third theory relates to downpayment and negative equity constraints. In an up-market, a homeowner is more likely to have made a profit on the current home. Therefore, if this homeowner would like to move up the housing ladder, he would have enough money left for the downpayment for the next, more expensive, house. Therefore, current homeowners are more likely to move on to a more expensive home in a booming market. This initiates a chain of transactions and the result is that there will be more transactions in booms. Conversely, in a bust, current homeowners will have less (or even negative) equity to make a new downpayment on a more expensive house. In this case, the chain is less likely to be initiated, and fewer transactions will occur. In Chapter 2 the empirical commonality between price changes and changes in the TOM is discussed. The results suggest a strong commonality between transaction prices and the TOM: when prices are high / increasing, the TOM is low / decreasing and vice versa. Granger causality tests show that changes in the TOM lead changes in transaction prices. Additionally, the relationship between list prices and the TOM is examined: setting a higher list price results in a higher TOM or slower sale. Chapter 3 shows that time-varying liquidity has profound implications for price measurements such as price indices that market participants tend to look at. Chapter 3 constructs buyer and seller reservation price indices. In tracking the relative changes of these, there is a particularly interesting interpretation of the demand side index. Consider 10

12 the case where sellers match the buyers reservation prices at all times. Here the liquidity of the market would be constant over time. As such, the demand reservation price index has a constant-liquidity price index interpretation. Consequently, the difference between the demand-side index and the observed transaction price price index is the effect that liquidity has on price measurements in real estate. Chapter 4 provides insights in the relationship between homebuyers internet search behavior, housing prices, and liquidity. The theoretical and empirical model in this chapter allows for a three-way interaction between these variables. This simultaneous causality is important to account for, since higher price levels might also affect liquidity. Furthermore, the two measures for liquidity (one based on transaction data and one on internet search behavior) are, for obvious reasons, also related. This chapter specifically looks at the intertemporal connections between these variables and regional differences. 1.4 How can market liquidity be used for a better understanding and monitoring of real estate markets? Real estate markets are of vital importance for the economy, financial markets, and financial stability. Movements in real estate asset prices are closely followed to ensure that financial institutions remain resilient to future shocks. Moreover, big swings in asset prices can pose substantial risks for households, which heavily invested usually with debt in the real estate asset class. Commercial real estate is an important asset class for public and non-public investment institutions such as pension funds, large real estate funds, and small private investors. Hence, understanding the real estate market is important to assess the stability of the financial system and contributes to sustainable economic growth. Real estate liquidity plays an important role in the understanding and monitoring of the real estate market. Therefore, it is essential to understand and track real estate liquidity. The three chapters in this thesis help to better understand and monitor liquidity and real estate markets in general. Additionally, the proposed indicators can be used as forecasting instruments. Chapter 2 provides a novel way to measure the TOM for regional markets. A frequently used measure for liquidity used by policymakers 11

13 is the average (seller) TOM: how long has a house been on the market before before it is sold? This chapter shows that it is important to correct the TOM for features such as censoring and housing quality. To monitor the market situation, it is of vital importance to correctly measure the situation. The chapter shows that the usually employed average TOM underestimates market liquidity in good times and overestimates market liquidity in bad times. Furthermore, the constructed constant-quality indices lead both price indices and the simple average TOM, and can therefore be useful for both monitoring and forecasting purposes. Chapter 3 shows that a more comprehensive view of the market can be obtained by tracking demand and supply reservation prices separately. Particularly, the demand-side index provides a joint metric of price and liquidity changes and is therefore interesting for monitoring purposes. Also, since liquidity is an important part of these demand (constant-liquidity) indices, they tend to lead observed transaction price indices, which can make them useful for forecasting purposes as well. Chapter 4 presents a model where liquidity and prices are linked to internet search behavior. As shown in the empirical literature and chapters 2 and 3, liquidity itself is a leading indicator of prices. This chapter shows how to lead the leading indicator. Since internet search popularity measures preparatory steps that potential home buyers take before the actual purchase, the indicator based on this behavior is shown to be useful in explaining changes in liquidity and prices. Furthermore, the chapter shows that it is likely that sellers respond more slowly to changes in the market. This is an important feature of the housing market and real estate markets in general. Behavioral patterns like these are essential for a thorough comprehension of the real estate market. 12