Predictable or Not? Forecasting Office Markets with a Simultaneous Equation Approach

Abstract

The main objective of this paper is to elucidate the capability of time-series regression models to capture and forecast movements in occupancy patterns, rental rates and construction activity. The model presented is a three-stage simultaneous equation model. The first stage incorporates the office space market in terms of occupied space and absorption of new space. The second stage captures the adjustment of office rents to changing market conditions and the third stage specifies the supply response to market signals in terms of construction of new office space. The standard simultaneous model is subsequently modified to account for the specific characteristics using the New York market as a case study. The results demonstrate that the market reacts efficiently and predictably to changes in market conditions. The significance of the estimated parameters underscores the general validity and robustness of the simultaneous equation approach in modeling real estate markets. The modifications of the standard model, notably the inclusion of sublet space in the rent equation, contributed considerably to improving the explanatory power of the model. Finally, we test whether a non-linear function performs better than the original linear approach and find mixed evidence based on the limited empirical dataset of this study.