Leasing as a Lottery: Implications for Rational Building Surges and Increasing Vacancies

An expanded inventory demand framework is developed that focuses on the impacts of changes in the local leasing environment. We model the lease-up process as a lottery in which changes in turnover or absorption affect the probability of winning the leasing lottery. In this context, builders rationally respond to transitory, not just permanent, changes in the local market. Hence, factors such as temporary shocks to tenant turnover affect the decision to build. The magnitude of turnover-induced cycles can vary across markets depending upon the vintage of the existing building stock, the local absorption rate and the rent elasticity of demand for space. This framework, which refocuses attention on the local determinants of the developer's decision to build, hopefully will prove fruitful in future empirical efforts to explain development and vacancy behavior during the past decade.     

Transportation, Sorting and House Values

In this paper, we examine the importance of accessibility to employment and transportation system attributes for residential location choice, car ownership and house values. Using the 1980 Census of Housing and Journey to Work data merged with transportation system data, we find strong evidence of residential sorting based on employment location. We find that suburban areas with good commuter rail access to the CBD have significantly greater fractions of their labor force working in the CBD, own fewer cars and have higher house prices than similar neighborhoods and houses in census tracts without service. The house value premium is over 6.4%.     

National Vacancy Rates and the Persistence of Shocks in U.S. Office Markets

In this paper, we develop and estimate a model that decomposes the variance in office vacancy rates into market-specific, time-specific, and random components. The results indicate significant differences in natural vacancy rates across markets. We also find some persistence in deviations from these natural vacancy rates. The analysis is applied to both central business district (CBD) and suburban office markets. We find that natural vacancy rates differ across CBD markets and across suburban markets. Further, the persistence of disequilibrium in one CBD market seems to differ significantly from that in another. This is not shown to be true for suburban markets.     

The Suburban Housing Market: Effects of City and Suburban Employment Growth

Using a data set of over 88,000 housing sales, we find that city employment growth has a significant positive effect on suburban house values; this effect is largest for housing closest to the central business district and declines with increasing distance from it. City employment growth has a negative effect on the rate of suburban house construction; the magnitude of the effect increases with distance. Suburban employment growth has little aggregate effect on house prices, and there is less variation by distance. Suburban growth has a significant effect on construction rates, especially at locations near the urban fringe.     

Risk and Return within the Single-Family Housing Market

The trade-off between risk and return in equity markets is well established. This paper examines the existence of the same trade-off in the single-family housing market. That market is dominated by homeowners, who constitute about two-thirds of U.S. households. For them the choice about how much housing and what house to buy is a joint consumption-investment decision. Furthermore, owner-occupied housing is by nature a lumpy investment whose risk cannot be completely diversified. Does this consumption-investment link negate the risk-return trade-off within the single-family housing market? Theory suggests the link still holds. This paper supplies empirical evidence in support of that theoretical result.     

Estimating house price appreciation: A comparison of methods

Several parametric and nonparametric methods have been advanced over the years for estimating house price appreciation. This paper compares five of these methods in terms of predictive accuracy, using data from Montgomery County, Pennsylvania. The methods are evaluated on the basis of the mean squared prediction error and the mean absolute prediction error. A statistic developed by Diebold and Mariano is used to determine whether differences in prediction errors are statistically significant. We use the same statistic to determine the effect of sample size on the accuracy of the predictions. In general, parametric methods of estimation produce more accurate estimates of house price appreciation than nonparametric methods. And when the mean absolute prediction error is used as the criterion of accuracy, the repeat sales method produces the most accurate estimate among the parametric methods we tested. Finally, of the five methods we tested, the accuracy of the repeat sales method is least diminished by a reduction in sample size.