Randal Verbrugge

The Puzzling Divergence of Rents and User Costs, 1980-2004

This paper demonstrates that, in the context of U.S. housing data, rents and ex ante user costs diverge markedly—in both growth rates and levels—for extended periods of time, a seeming failure of arbitrage and a puzzle from the perspective of standard capital theory. The tremendous volatility of even appropriately-smoothed ex ante annual user cost measures implies that such measures are unsuitable for inclusion in official price statistics. The divergence holds not only at the aggregate level, but at the metropolitan-market level as well, and is robust across different house price and rent measures. But transactions costs matter: the large persistent divergences did not imply the presence of unexploited profit opportunities. In particular, even though detached housing is readily moved between owner and renter markets, and the detached-unit rental market is surprisingly thick, transactions costs would have prevented risk-neutral investors from earning expected profits by buying a property to rent out for a year, and would have prevented risk-neutral homeowners from earning expected profits by selling their homes and becoming renters for a year. Finally, computing implied appreciation as a residual yields a house price forecast with huge errors; but either longer-horizon or no-real-capital-gains forecasts— which turn out to have similar forecast errors—imply a far less divergent user cost measure which might ultimately be useful for official price statistics. Some conjectures are offered.

Reconciling user costs and rental equivalence: Evidence from the US consumer expenditure survey

Previous research [Verbrugge, Randal, 2008a. The puzzling divergence of aggregate rents and user costs, 1980-2004. The Review of Income and Wealth 54(4), 671-699] demonstrated that housing rents and ex ante user costs diverge markedly for extended periods of time, a finding with profound implications for income and inflation measurement. But the primary data sources in that study were various indexes, based upon largely disjoint data sources, constructed using different aggregation techniques, and each subject to various criticisms. This raised doubts about the quality of the comparison. The relationship between user costs and rents might well be much tighter at the micro level; after all, house prices and rents (and their growth rates) can vary dramatically within cities, and rents are notoriously sticky. Furthermore, the use of indexes precludes both cross-sectional and dollar cost comparisons. In this study, we use Consumer Expenditure Interview Survey (CE) data to examine the relationship between user costs and rents at the individual unit-level, in dollars, using unit-level information on house value, rent, taxes, and the like. This allows us to accurately estimate unit-specific user costs and to control for unobservables like structure and neighborhood quality. We also make the point that in theory, after-tax user costs should equal net rent, i.e., expected rental income, rather than gross rent. Our findings are striking. In keeping with most previous research, we find tremendous divergence between conventional measures of user costs and net rents, thus ruling out index construction errors as a possible explanation. This divergence does not result from a faulty rent measure: we find that reported rents are sensible, in that they move similarly to official rent indexes, and are not simply out-of-pocket expenses. Instead, and most perplexing, we find a surprisingly close correspondence between net rents and a particular estimate of user costs, one implicitly assuming zero transactions costs and constructed using an appreciation measure that is both theoretically suspect and empirically a poor predictor of actual appreciation.

To difference or not to difference: a Monte Carlo investigation of inference in vector autoregression models

It is often unclear whether time series displaying substantial persistence should be modelled as a vector autoregression in levels (perhaps with a trend term) or in differences. The impact of this decision on inference is examined here using Monte Carlo simulation. In particular, the size and power of variable inclusion (Granger causality) tests and the coverage of impulse response function confidence intervals are examined for simulated vector autoregression models using a variety of estimation techniques. We conclude that testing should be done using differenced regressors, but that overdifferencing a model yields poor impulse response function confidence interval coverage; modelling in Hodrick-Prescott filtered levels yields poor results in any case. We find that the lag-augmented vector autoregression method suggested by Toda and Yamamoto (1995) – which models the level of the series but allows for variable inclusion testing on changes in the series – performs well for both Granger causality testing and impulse response function estimation.

INTERACTIVE-AGENT ECONOMIES: AN ELUCIDATIVE FRAMEWORK AND SURVEY OF RESULTS

This paper is helpful for understanding interactive-agent economies. It presents a user-friendly framework for exploring the implications of economic interaction, and demonstrates its wide applicability. Agents repeatedly face binary choices and receive i.i.d. shocks; payoffs depend upon ones own action and shocks, as well as upon the actions of a subset of other agents. Simulations explore how properties change as the number of agents, the number of neighbors, and the degree of interaction change. Key results in this framework include the following: The law of large numbers can readily be postponed or defeated, stationarity and ergodicity are trivial to deduce, any equilibrium distribution may be attained in “globally interactive†economies, continuous- and discrete-time analogues generally behave very similarly, dynamics can be sensitive to details of the interaction structure (implying that popular mean-field approximations are not always appropriate), and substantial persistence is typical.

Do the Consumer Price Index's Utilities Adjustments for Owners’ Equivalent Rent Distort Inflation Measurement?

The Consumer Price Index (CPI) is an important social index number, central to monetary policy, well being measurement, optimal pricing, and tax and contract escalation. Shelter costs have a large weight in the CPI, so their movements receive much attention. The CPI incorporates two shelter indexes: Rent, covering renters, and Owners’ Equivalent Rent (OER), covering owners. Between 1999 and 2006, Rent and OER inflation twice diverged markedly; this occurred again recently. Because these indexes share a common data source—a large sample of market rents—such divergence often prompts questions about CPI methods, particularly the OER utilities adjustment. (This adjustment is necessary to produce an unbiased OER index, because many market rents include utilities, but OER is a rent-of-shelter concept.) The utilities adjustment procedure is no smoking gun. It was not the major cause of these divergences, and it generates no long-run inflation measurement bias. Nonetheless, it increases OER inflation volatility and can drive OER inflation far from its measurement goal in the short run. This article develops a theory of utilities adjustment and outlines a straightforward improvement of Bureau of Labor Statistics procedures that eliminates their undesirable properties. The short-run impact on inflation measurement can be very sizable.