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The Journal of Real Estate Finance and Economics

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18-07-2020

The Dynamics of Liquidity in Commercial Property Markets: Revisiting Supply and Demand Indexes in Real Estate

Authors: Dorinth W. van Dijk, David M. Geltner, Alex M. van de Minne

Published in: The Journal of Real Estate Finance and Economics | Issue 3/2022

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Abstract

A common definition of liquidity in real estate investment is the ability to sell property assets quickly at full value, as reflected by transaction volume. The present paper makes methodological and conceptual contributions in the study and understanding of liquidity. First, we extend the Fisher et al. (Real Estate Economics, 31(2), 269–303, 2003) Fisher et al. (The Journal of Real Estate Finance and Economics, 34(1), 5–33, 2007) methodology for the separate tracking of changes in reservation prices on the demand (potential buyers) and supply (potential sellers) sides of the asset market. We show how to apply the methodology to a repeat sales indexing framework, allowing application to typical commercial property transaction price datasets, which lack appraisal valuations or complete data regarding property characteristics. We also use a Bayesian, structural time series approach to estimate the indexes. These methodological enhancements enable much more granular supply and demand index estimation, including at the metropolitan level. Second, we propose a Liquidity Metric based on the indexes, and show that the normal liquidity dynamic in commercial property asset markets is “pro-cyclical”, that is, price and trading volume tend to move together, with demand tending to lead supply. Additionally, we observe an “anomalous” dynamic that occurs about 25 percent of the time, in which the Liquidity Metric declines while consummated prices are rising. This anomalous dynamic is often associated with the end of a period of rapid price growth.

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Leon Walras (1834-1910): “Elements d’economie politique pure”, Paris, 1874.

See for example Chapter 7 in: Geltner et al. (2014).

Appraisal based indicators would be similarly incomplete, as they are based on observed transaction prices.

Traders in the stock market may not view that market as being “constantly liquid”. But compared to the frictions and time required for trading real assets, from the perspective of private real estate markets, effectively the stock market is “always liquid”.

Such behavior is much less common in real estate markets. However, a famous instance of sell-off behavior in real estate was the neighborhood “tipping point” phenomenon of housing market racial segregation in U.S. cities in the 1950s and 60s.

See for example: Genesove and Han (2012), Carrillo et al. (2015), and Van Dijk and Francke (2018).

Goetzmann and Peng (2006) present a slightly different approach to develop supply and demand indexes that provides similar results. The FGP methodology was employed by the MIT Center for Real Estate to produce and publish during 2006-2011 a quarterly-updated set of demand and supply indexes based on the National Council of Real Estate Investment Fiduciaries (NCREIF) population of properties.

The markets are Boston (BOS), Chicago (CHI), Los Angeles (LA), New York City (NYC), San Francisco (SF), Seattle (SEA), and Washington D.C. (DC). The website URL is http://pricedynamicsplatform.mit.edu/.

We will not pursue the relationship between bargaining power, list prices, and information asymmetries here since this would result in difficulties of identification of the reservation price changes. See Carrillo (2013) and Han and Strange (2015) for a discussion on bargaining power and Guren (2018) on the role of asking prices in search markets. Suffice it to say that buyers and sellers consider the current market conditions when deciding on their reservation prices. For a discussion on the uncertainty of the sellers’ information in search models, see Anenberg (2016).

The distribution of sellers might also move, but it is generally accepted that buyers respond more quickly than sellers, see Genesove and Han (2012), Carrillo et al. (2015), and Van Dijk and Francke (2018). So in our case the buyer reservation prices move more to the right than seller reservation prices. As noted in FGGH, this is the criterion that determines pro-cyclical variable liquidity (price and volume moving together), in effect, buyers driving the price movements.

The conditions for the identifiability of this “probit σ” in our context are discussed later on.

For properties that are not sold in a given time period, the IMR would be calculated as the fraction of the probability density and (1-cumulative density). We don’t need this intermediate outcome for further derivations and therefore will refrain from a discussion on this.

Anchoring should now be captured by the supply and demand indexes. Take the example of a bust, supply will decrease by less compared to the case of no anchoring, hence the supply and demand indexes will be further apart and liquidity will go down by more. In case of speculation, this should go the other way around: a relatively modest drop in demand in a bust compared to the case without speculation and vice versa in a boom.

The SSR and MSE are equivalent, but the individual squared errors are different. However, since we are eventually interested in the MSE, we can safely assume $E(\varepsilon _{i,t}^{2}|S_{i,t}=1)=\frac {1}{2}E(\varepsilon _{i,fir}^{2}|S_{i,t}=1)+\frac {1}{2}E(\varepsilon _{i,sec}^{2}|S_{i,t}=1)$, where fir is the first sale and sec is the second sale.

Note that we estimate the coefficient on the difference of the IMR. However, the restriction σ₂₄ = σ₁₃ = σ_ε,η implies that the coefficient on the difference (σ_ε,η) is the same as the coefficient on the level of the IMR of the first and second sale.

When a property has more than 2 sales, for example 3 sales, this would result in 4 observations in the hedonic model with 2 pair fixed effects. Hence the second sale enters twice.

In case one would want to include bargaining power in the model, the price index β_t and other coefficients α, could be replaced by weighted averages of the buyers’ and sellers’ valuations: $\beta _{t}=w_{t}{\beta _{t}^{b}}+(1-w_{t}){\beta _{t}^{s}}$ and α = w_tα^b + (1 − w_t)α^s. Here, w_t could be the (time-varying) bargaining power of buyers. In that case the buyers’ reservation price index would be $\hat {{\beta _{t}^{b}}}=\hat {\beta _{t}}+w_{t}\hat {\sigma }\hat {\gamma _{t}}$. We will leave such an extension for further research and assume that $w_{t}=\frac {1}{2}$.

Once a property is captured in the database, it remains in the data, even if a subsequent sale price is below $2,500,000.

Many owner-occupied properties, so-called “corporate real estate”, are effectively not in the investible universe.

Another option would be to present marginal effects. The supply and demand indexes, however, require the “raw” coefficients. Therefore, we present the coefficients instead.

The correlation between the probability of sale in New York and Phoenix is 0.92 in (index) levels and 0.66 in first differences.

Recall, however, that this without including sales of distressed properties.

Both Granger causality analyses are significant at the 1% level and are based on a VAR model with 4 lags estimated separately for each market.

Note again that this is apart from distressed properties.

The original FGP methodology, NCREIF based indexes published by the MIT/CRE are somewhat noisy. For our purposes here we have smoothed the indexes using a five-quarter centered moving average. This does not induce a lag bias, but we do lose both the first and last two quarters of the history.

Because transaction volume can be somewhat noisy at the quarterly frequency, we only consider the price-liquidity dynamic to be “anomalous” if it continues for at least two consecutive quarters. Also note that while the anomalous periods are characterized by sellers driving the price movement, this is not inconsistent with the earlier fundamental point that buyers drive liquidity. Indeed, the anomalous periods are characterized by declining liquidity precisely because fewer buyers are willing to pay the prices sellers are wanting.

These six metros have sufficient data to yield very robust and reliable results with minimal noise.

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Title: The Dynamics of Liquidity in Commercial Property Markets: Revisiting Supply and Demand Indexes in Real Estate
Authors: Dorinth W. van Dijk
David M. Geltner
Alex M. van de Minne
Publication date: 18-07-2020
Publisher: Springer US
Published in: The Journal of Real Estate Finance and Economics / Issue 3/2022
Print ISSN: 0895-5638
Electronic ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-020-09782-5

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