زیان گریزی، محدودیت های حقوق صاحبان سهام و رفتار فروشنده در بازار املاک و مستغلات

I develop an estimation strategy that can point identify the effects of loss aversion and equity constraints on selling prices using a long panel of data from the San Francisco Bay Area real estate market. I find strong evidence that owners facing nominal losses on their housing investments and owners with high LTV ratios sell for higher prices, on average, and the effects are larger than previously thought. I also present new empirical findings that support the theory that down-payment constraints or other institutional details of the mortgage market drive the relationship between LTV and prices. The results have implications for understanding how local housing market variables such as prices and volume are determined in slow markets.

This paper contributes to the literature on the role of loss aversion and equity constraints in determining how local housing markets operate (Genesove and Mayer, 1997, Genesove and Mayer, 2001, Lamont and Stein, 1999 and Engelhardt, 2003). Sellers who are averse to selling their house for less than they initially paid face unique incentives when making decisions such as whether and when to sell, and what prices to accept. Those with little or negative equity in their house are also thought to evaluate housing decisions differently given the way that the mortgage market operates. In a market downturn, the effects of loss aversion and equity constraints become more pronounced because more homeowners see their homes depreciate in value. Thus, understanding their effects on homeowner behavior is essential for understanding key features of cold housing markets such as declines in sales volume, a relatively large inventory of unsold homes on the market at any given time, and gradually declining prices. While I discuss other studies that directly address the effects of these constraints on sales volume, this paper revisits the challenge of estimating effects on prices with a richer dataset. The effect of down-payment constraints on selling behavior is best understood through the following example. Suppose a family has a house that is initially worth $100,000 and an outstanding mortgage of $85,000. The family wants to move for an exogenous reason, and the purchase of a new house requires a minimum down payment of 10%. If housing prices stay the same or increase, the family could sell the house and would likely have enough cash to make a down payment on a new house. However, if prices fall by 10%, the family would only have enough to make a down payment of $5000 (ignoring moving costs), and the family may be better off staying rather than moving. Alternatively, the family could list their price at an above-average price (“fishing”) and hope to eventually match with a buyer who has a relatively high valuation of the house. Of course this strategy would tend to involve keeping the house on the market for longer than average. To the extent that sellers with low equity use this strategy, there should be a negative relationship between list prices and equity for low or negative levels of equity, but not necessarily for higher levels. Sellers with high equity have enough cash to make a down payment, and so if the costs to keeping a house on the market are sufficiently high, these sellers have less of an incentive to fish. I also discuss and simulate a simple model that generates the same prediction between equity constraints and price, but does not rely on down-payment constraints. The model shows that it is relatively less costly for sellers with low equity, as measured by loan-to-value (LTV) ratios, to wait for higher prices on average because the option to default on their mortgage is relatively more attractive. Estimating the effects of potential losses and equity position on prices is difficult because these variables are non-linear functions of unobserved house characteristics. In a seminal paper, Genesove and Mayer (henceforth, GM) address these identification issues using a clever estimation procedure that bounds the true effects of loss aversion and equity constraints. They find an effect of equity position on list and sales prices, and in the lower bound, GM find no statistically significant effect of loss aversion on the sales prices. They do, however, find a significant effect of loss aversion on the list price. Whether loss aversion carries through to the actual transaction prices remains in part an open question. It is possible, as GM note, that since loss aversion is a psychological reluctance to sell, its effect may quickly diminish with learning or exposure to market conditions. The main contribution of this paper is to use a rich dataset to develop a closely related econometric model that is less parametric than GM, and can point identify the effects of loss aversion and equity constraints on actual selling prices in a more diverse sample of housing transactions. Whereas GM estimate their model on a sample of condominium sales, I use a dataset that provides details of every housing transaction that occurred in the San Francisco metropolitan area over a 18 year period. In a first stage, I restrict the sample to houses that sold at least two times during periods when prices were rising rapidly and it is reasonable to assume that sellers do not face potential losses or equity constraints. During these hot markets, the econometric model predicts that unobserved quality of a house only affects prices linearly because the equity constraint and potential loss variables are zeroed out. Thus, I can estimate unobserved quality for this sample of houses using simple panel data estimation methods, where I follow GM in treating unobserved quality as a fixed effect. The estimator that I use is a more flexible version of the repeat sales estimator described in Shiller (1991); I use locally linear regression to allow the time effect to vary by house. In the second stage I restrict the first stage sample to houses that have an additional sale during the market downturn when equity constraints and loss aversion may affect selling behavior. For the transaction price during the cold market, unobserved quality has the usual non-linear effect that complicates GM's estimation strategy. However, I can recover point estimates of the effects of loss aversion and equity constraints using least squares on the restricted sample, where I substitute the estimate of unobserved quality from the first stage into the model. As a whole, my results largely support the findings in GM in a larger, more diverse sample of housing transactions. The key difference is that I find larger effects. I find that a seller facing a 10% prospective nominal loss receives a 3.55% higher price, on average, while a seller with a 100% LTV ratio receives a 3.3% higher price than a seller with an 80% LTV ratio, on average. In addition, I present a number of new findings. I find that the effects of loss aversion and equity constraints are smaller for homes surrounded by similar houses, possibly because competition makes it more difficult for sellers to negotiate higher prices. I also find that transaction prices of foreclosed properties do not display sensitivity to the LTV ratio. This is expected if the theories discussed above are driving the results since the sellers of foreclosed properties do not face the same constraints as the delinquent owner. This result supports the claim that LTV is not proxying for some unobserved characteristic of the home. I also find that failing to control for loss and LTV in a repeat sales estimator overstates prices that a non-credit constrained seller expects to receive. The results imply that selling prices do not adjust as quickly to deteriorating fundamentals because sellers facing equity constraints and nominal losses are reluctant to set lower prices. This is one explanation for the large inventory of unsold homes in markets where home prices are falling: buyers are unwilling to pay prices that include premiums for loss aversion and equity constraints. In addition, popular home price indexes like Case–Shiller do not capture changes in search behavior that accompany a market downturn, and so an analysis of selling prices alone can understate the severity of a market downturn. This paper proceeds as follows. Section 2 reviews the related empirical literature, and discusses GM's results. Section 3 discusses the theoretical literature that motivates my empirical strategy and also presents a new theory to motivate the effects of equity position. Section 4 describes my unique dataset and presents summary statistics. In Section 5, I describe my empirical model and discuss how it differs from GM. 6 and 7 present the estimation strategy and the results, as well as a discussion of how my estimates compare to GM, and why they differ in some cases. Finally, Section 8 concludes by summarizing the results and presenting directions for future research.

Using richer data, this paper has built upon the insights of GM to provide stronger evidence that loss aversion and equity constraints affect seller behavior. To my knowledge, my estimation strategy is the first to point identify the effects of loss aversion and equity constraints on actual transaction prices. I find that both loss aversion and equity constraints affect housing prices, and that the effects are larger than previously thought. I also provide stronger evidence that the relationship between these seller characteristics and prices is causal. These results, when combined with the results in Genesove and Mayer (2001), imply that sellers facing losses and equity constraints select higher reservation prices on average and realize a higher price conditional on a sale occurring.29 Evidently, loss aversion is not a brief hope for a positive return that the market quickly corrects. My findings combined with the results from other studies discussed in Section 2 suggest that during market downturns, sellers become locked-in to their homes because of loss aversion and equity constraints. This slows down the market. Selling prices do not drop as quickly because sellers are reluctant to accept lower prices; homes sit on the market for longer because many sellers are “fishing” for high prices, as shown in GM; and sales volume slows down because owners delay selling altogether to avoid nominal losses on their housing investments. Future work will try to generate these reduced form findings using a structural dynamic search model. Estimating this type of model could allow for interesting counterfactuals such as eliminating loss aversion and reducing outstanding mortgage balances.