Academic journal article Economic Inquiry

Precaution and Liquidity in the Demand for Housing

Academic journal article Economic Inquiry

Precaution and Liquidity in the Demand for Housing

Article excerpt

We exploit cross-sectional mortgage data to investigate the importance of liquidity constraints and a precautionary motive in the demand for housing. Households that are not liquidity constrained consume housing services essentially as the life cycle hypothesis suggests but with a significant precautionary component. Households that are liquidity constrained, in terms of not meeting standard loan-to-value or payments-to-income constraints, are similar to unconstrained households in most respects, including the precautionary motive, but they respond somewhat less to fluctuations in their lifetime income--suggesting some influence of bank-induced liquidity constraints. We additionally find, however, that banks enforce liquidity constraints only weakly. (JEL D91, D12, R21)

I. INTRODUCTION

The benchmark theory in the economics of consumption is the life cycle or permanent income hypothesis (LCPI) based on the work of Modigliani and Brumberg [1954] and Friedman [1957]. Its empirical validity is sometimes questionable, especially when compared against alternatives that incorporate liquidity constraints or the precautionary motive for saving. See, for instance, Deaton [1992] for a survey. Rejection of the LCPI may, however, be just a manifestation of the poor quality of consumption data. At the micro level, these data are typically subject to the reporting biases and omissions inherent in consumer surveys. There are further difficulties in measuring a desired stream of consumption services: nondurable consumption goods are a small fraction of total consumption and, in the case of food essentials, hardly a consumer choice variable; durable consumption goods have the feature that they are lumpy and that the consumption services at any point in time are hard to measure.

We propose to test the LCPI at the micro level with a quite different type of data. The data are obtained from the Residential Mortgage Finance Database collected by the National Association of Realtors. They are based on actual home purchases and the associated mortgage transactions reported by realtors and thus avoid some of the reporting biases of the consumer surveys. More important, they allow us to accurately identify households that, in principle, have instantaneous access to additional liquidity by lowering the down payment for the house purchase or increasing the size of the loan. Further, the purchase of a house provides an accurate measure of a relatively large fraction of consumption services obtained for a medium to long horizon and measured at the time of the consumption decision. Although our data also have some serious shortcomings, they allow a look at the LCPI from an unconventional vantage point.

Perhaps the most influential study employing the standard consumer survey in testing the LCPI at the micro level is that of Hall and Mishkin [1982]. It examines food expenditure from the Panel Study of Income Dynamics. One of their key results is that consumption tracks income more closely than would be expected under the LCPI. They demonstrate that this result could be explained by assuming a group of liquidity-constrained consumers, consuming their income at each point in time, of 20% of the total sample. Some of the drawbacks of this study are the use of food expenditures to represent consumption, the underreporting of income, and absence of proper wealth data inherent in the consumer survey, and the associated inability to individually separate liquidity-constrained consumers from the rest of the sample.

Several previous studies have attempted to improve on this way of testing the LCPI by examining other data sets. Bernanke [1984] used data on durable consumption goods (automobiles) to overcome the drawbacks of using food consumption in the Hall and Mishkin study. His results support the LCPI but are still suspect because of possible reporting biases and the lack of good wealth and savings observations. …

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