Cálculo del volumen de reservas remanentes de un yacimiento

The price responsiveness to property supply always draws great interest among policy makers, particularly in the literature investigating house price bubbles. Over the last decade, we have witnessed a revolutionary shift in the nature of of- fice space demand from individual offices to collaborative space. On one hand, all major corporations (e.g. Facebook, Google, Ernst and Young, PricewaterhouseC- oopers) have been advocating for open and shared workspace and adopted work- from-home policies. On the other hand, smaller companies (especially ventures and sole traders) have been using shared office facilities to efficiently maximize networking opportunities offered by new providers of workspace. Moreover, less demand in office space is foreseen when more on-site tasks are assigned and more tedious work is superseded by automation. Facing all these changes, the ability of supply to adjust to new requirements and temporary mismatch (hence the presence of supply constraints in the office sector) can be used to predict the impact of a negative demand shock on property prices.

Supply constraints are generally classified into two main categories: regulatory and physical. Regulatory constraints are measured by the tightness of the devel- opment approval process, which is usually identified through surveys (Gyourko et al 2008 [99], Saks 2008 [150]). Saiz 2010([149]) introduced a new empirical strategy where land unavailability is measured to solve the endogenous problem, identifying the tightness of both regulatory and physical constraints of housing supply. Overall constraints are quantified by supply elasticity, which is mostly estimated using an urban growth-based econometric model.

We may argue that supply elasticity for offices should be positively correlated with the one in housing markets because the tightness of planning regulation and geographical barriers should not differ within the same area. However, lacking empirical evidence for non-residential markets, different dynamics of market com- petition between suppliers and divergent incentives to control the restrictiveness of supply constraints between different property markets motivate the focus of our study in office supply elasticity by metropolitan statistical area (hereafter MSA).

In fact, the higher proportion of informed investors in the office sector compared to the residential markets leads to a “strategically managed” availability of office space supply. In equilibrium, supply could be influenced by investors’ approach to control the flow of available office space as their strategy is rooted in the search and matching theory, initially applied in housing markets by Wheaton (1990[175]). Since lease contracts are long-term and have fixed rents, landlords

strategically keep aside a predefined amount of vacant space for high-profile ten- ants who will afford higher rents in the future, maximizing their profits. This amount of space may also vary over boom and bust cycles.

In our research, we define this situation as economic mismatch where a bid- ask rental gap exists until landlord and tenant’s requirements match and the vacant space is occupied. This mismatch situation may also be present in a long run equilibrium increasing the natural vacancy rate. Simultaneously, the vacant space in non-prime (i.e. class B or C) office buildings is due to its old- fashioned and worn-out physical design that requires a refurbishment to reach prime quality before their use can be guaranteed. We define this phenomenon asphysical mismatch and we argue that it can also increase the long-run natural vacancy rate due to long lease terms (even above 5-6 years) locking in tenants for predefined time periods in sometimes physically mismatched space. Unlike in housing markets where households act as both buyers and sellers and the searching process mainly affects the short-run disequilibrium, office landlords (i.e. sellers of a space rental service) with higher controlling power are capable of altering the long-run equilibrium of supply. As a consequence, we believe that equilibrium vacancy is highly important because it may distort the responsiveness of rents to office supply.

Moreover, the analogy of real estate and labour markets (where a well-established search and matching model can be applied) helps us to transfer the concept of un- employment to unoccupied space, distinguishing three types of vacancy: cyclical, structural and frictional. This set up also sheds light on the three components driving long-run vacancy: mismatch rate, search effort level (for structural) and demand of refurbishment (for frictional). We find that a search equilibrium does exist and we show that equilibrium vacancy should be determined at the time of the search equilibrium.

We initially build a conceptual framework to link supply elasticity and long-run vacancy. We then suggest an empirical strategy to identify economic mismatch (i.e. space in use which is available for re-let to new tenants instead of existing tenants), to quantify the search effort (i.e. relative size of available letting space listed), and to determine a simultaneous equilibrium in the market and the search and matching process using an error correction model.

Our empirical findings support the argument that a search equilibrium is essential to estimate office supply elasticity, which is found to be positively correlated with structural vacancy. As low structural vacancy implies less control by landlords, we argue that the price responsiveness to supply changes is almost completely explained by regulatory and geographical issues when office sectors are supply

inelastic.

This chapter is organized as follows: the next section provides a literature review related to supply constraints, vacancy and search equilibrium; section 3.4 presents our conceptual model. In section 3.5, we explain our empirical strategy including data description and error correction model framework. Sections 3.6 and 3.7 include main results, robustness tests and a discussion about supply elasticity rankings by MSA. Finally, we draw our conclusions and present further research directions in the last section.