Supply Adjustments to Demand Shocks in the Commercial Real Estate Market

This paper contributes a theoretical investigation of real estate supply adjustments in the commercial real estate market. Simple theoretical linkages between the goods market (the demand side) and the space market (the supply side) are developed and then used to explain the optimal supply decisions of space producers. Propositions relating to how space production decisions are made under conditions of demand certainty, demand uncertainty and free entry are derived from optimization models. Under demand certainty, the adjustment of space supply is shown to be affected by whether an exogenous shock is perceived as mild or disastrous. Under demand uncertainty, construction based on pent-up demand is shown to be suboptimal. Copyright American Real Estate and Urban Economics Association.

Tax Structure and Natural Vacancy Rates in the Commercial Real Estate Market

A number of studies have postulated that the Economic Recovery Tax Act of 1981 (ERTA 1981) was responsible for the dramatic overbuilding that occurred between 1981 and 1986, primarily because returns became less sensitive to "real" demand. While there has been much research on how equilibrium or "natural" vacancy rates in the real estate market are determined, beginning with Rosen and Smith's seminal paper in 1983, virtually none of this work has dealt with the impact of the tax environment. This study makes an initial attempt to answer this question with respect to equilibrium vacancies resulting from tenant (or owner) turnover. A formal model is developed that considers as an objective function the landlord's desire to maximize his/her after-tax equity returns in an environment of monopolistic competition in which individual projects face downward-sloping demand curves, owing to market conditions and a degree of heterogeneity among tenants in search costs or some other characteristic. The natural vacancy rate is shown not to depend directly upon the tax environment, but to depend indirectly upon it only to the extent that equilibrium market rents are lowered. The nature of the vacancy response depends critically upon the shape of the tenant demand response relationship upon its transition to a lower-rent region. This response is interactive with the degree of turnover and supply responsiveness within individual markets. Copyright 2003 by the American Real Estate and Urban Economics Association

Appraisal-Based Real Estate Returns under Alternative Market Regimes

In this article we use Monte Carlo simulation to study the statistical properties of real estate returns. We set up a model where transactions prices are noisy signals of true prices. We then consider a number of appraisal rules, derived from Bayesian and non-Bayesian theory, to estimate the current true price and rate of return. The class of exponential smoothing and Kalman filter rules perform well at both the disaggregate (returns on an individual property) and aggregate (returns on a real property portfolio) levels. A special case of exponential smoothing (α= 1.0) places all weight on current market data. Since this case eliminates smoothing, our results suggest that appraisers should place all weight on current data (no weight on past data) provided that they want to estimate returns rather than values. However, these results should be used with caution if sales prices are very noisy. Copyright American Real Estate and Urban Economics Association.

Pricing Mortgage-Backed Securities: Integrating Optimal Call and Empirical Models of Prepayment

Residential mortgage borrowers frequently appear to behave suboptimally with respect to their mortgage prepayment options. Many borrowers fail to exercise even well-into-the-money options while others prepay when the call option is out-of-the-money. To account for these apparently suboptimal prepayments, the recent trend in mortgage-backed securities research has been away from optimal call valuation models, in which the decision to exercise is determined endoge-nously, in favor of models in which prepayment behavior is exogenously specified based on empirical estimation. This paper develops a rational model of mortgage prepayment which incorporates both types of "non-optimal" prepayment and retains endogenous call. This enables the model to disentangle and compare the separate effects of the interest rate call, impeded by transaction costs, and of non-interest-rate driven prepayment. In addition, by recognizing heterogenous borrower transaction costs, the model presents a way to account more precisely for the varying prepayment lags associated with well-into-the-money call options and to account for the phenomenon of "burnout" within a mortgage pool. The paper includes an empirical test of the unbiasedness of the integrated pricing model by comparing simulated prices from our theoretical model to observed prices on traded Fannie Mae and GNMA securities. Copyright American Real Estate and Urban Economics Association.

Towards an end-to-end analysis and prediction system for weather, climate, and Marine applications in the Red Sea

The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more. © 2021 American Meteorological Societ

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants