تئوری نقدینگی در بازار دارایی واقعی مسکونی

A “hot” real estate market is one where prices are rising, average selling times are short, and the volume of transactions is higher than the norm. “Cold” markets have the opposite characteristics: prices are falling, liquidity is poor, and volume is low. This paper provides a theory to match these observed correlations. I show that liquidity can be good while prices are high because the opportunity cost of failing to complete a transaction is high for both buyers and sellers. I also show how state-varying liquidity depends on the absence of smoothly functioning rental markets.

Consider the problem facing someone who has decided to sell their house. The selling price depends on the type of house, on market conditions and on luck. A seller can affect the outcome by carefully choosing the list price that is seen by potential house buyers but that choice also affects the probability of sale and the expected time-till-sale. Researchers are beginning to form a consensus on the best way to estimate the determinants of this second process,2 but there has been little analysis of the trade-off per se and little analysis of the process by which an individual reacts to a change in market conditions. This paper uses a locus of feasible combinations of the expected sale price and of the probability of sale to describe the trade off created by a given set of market conditions. Fig. 1 demonstrates that a change in market conditions has an ambiguous effect on an individual seller unless one is willing to add restrictions. A change in market conditions is usually expected to change the value of a house. A change in market conditions may change the ease of selling and, if so, it may also change the seller’s selling strategy and the ultimate selling price. This paper focusses on decomposing the full effect of a change in market conditions into two parts: a level effect which mimics the effects of an increase in the value of a house and a slope effect which causes a seller to substitute between price and the probability of sale. Some adding-up conditions restrict the range of permissible outcomes and can be used to test whether observed behavior satisfies necessary conditions for optimal decision-making.The trade off between the selling price and the probability of sale is presumed to exist in a markets because of transaction costs. Unfortunately, these costs are difficult to quantify directly and, consequently, their link to studies of a real estate market is usually weak. Some researchers, e.g. Glower et al. (1998); focus on the actions of sellers with different tastes but, with few restrictions on tastes available, few predictions were possible. By focussing on aspects of behavior which are payoff-relevant, instead of being specific to a selling mechanism, I offer general insights into the market process. An appendix shows how the parameters of sellers’ objective function might, in principle, be deduced from the coefficients estimated from a common specification of two regression equations. Many indicators of market conditions have been proposed in informal and formal studies of real estate markets, including – the unemployment rate or total employment, – the level of mortgage interest rates, – the change in, or volatility of, interest rates, – the ratio of listings to sales, sometimes known as “inventory”, – the ratio of new listings to sales, – the rate of increase in prices, – the number of sales, and, perhaps because of a lack of testable hypotheses and diverse data sources, trend variables. Some indicators (i.e. unemployment rate, interest rate, rate of increase in prices) suggest that a change primarily alters value or buyers’ willingness to pay while other indicators (i.e. volatility of interest rates, inventory, sales) point to a change in the flow of buyers. These indicators may also be classified as internal or external where, for example, the number of sales would be an internal indicator because it can be expected to change as a market adjusts to changes in an external indicator such as the interest rates. Whether any of these classification schemes is informative should be decided by empirical analysis. These schemes are not relevant to this paper since I study how a single seller reacts to the market conditions, however they may be determined. I show that all reactions represent a combination of two types of effects. The conclusion discusses how the behavior of a group of individuals can be integrated into an equilibrium model.

From the perspective of a single seller, all changes in market conditions change either the level or the slope of the price-probability locus. I show that all effects on payoff-relevant dimensions of a seller’s behavior can be summarized as a combination of those which appear to change the value of a house and those which cause the seller to voluntarily change the selling strategy so as to substitute between selling price and probability of sale. The magnitudes of these effects may differ across selling mechanisms or markets but the theory imposes cross-equation restrictions that can be exploited to improve the efficiency of a chosen econometric procedure or to test whether sellers are making an optimal decision. An appendix offers a simple example showing how sellers’ preferences could be derived from a common specification of regression equations. The proofs of these results use ideas commonly associated with the Slutsky Equation which is the basis for the complete set of testable predictions for individual behavior (Mas-Colell et al., 1995). I consider how individual behavior responds, in both static and dynamic models, to changes in market conditions outside of the individual’s control. With the few exceptions noted above, most empirical studies of time-on-market and of selling price omit measures of market conditions. I suggest that incorporating such measures would add to the analysis of the market process and our understanding. In general, the market price adjusts toward an equilibrium by some combination of changing the price that each seller offers (and each buyer bids) and changing the selection of participants active in a market. A stochastic rationing process, such as that which exists in a real estate market and is explored above, implies that the first process is always relevant. Demand theory offers a concept which could be used to study the second process: the indirect utility function. If U(.; σ) is the utility function of a type σ seller then equation(16) W(ν,τ,σ)=U(pS∗(ν,τ,σ),λ∗(ν,τ,σ);σ)W(ν,τ,σ)=U(pS∗(ν,τ,σ),λ∗(ν,τ,σ);σ) Turn MathJax on shows the value of that type of seller actively trying to sell under market conditions described by (ν, τ). Using this function and a modified Roy’s Identity, it may be possible to combine a general model of selection with the price and probability choices of active sellers.