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Modelling office market dynamics: panel estimation and comparison of US metropolitan areas

Modelling office market dynamics: panel estimation and comparison of US metropolitan areas . Steven Devaney, Patric Hendershott, and Bryan MacGregor University of Aberdeen, Scotland. 1. Introduction. The dynamics of the property space market, specifically US office markets.

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Modelling office market dynamics: panel estimation and comparison of US metropolitan areas

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  1. Modelling office market dynamics: panel estimation and comparison of US metropolitan areas Steven Devaney, Patric Hendershott, and Bryan MacGregor University of Aberdeen, Scotland

  2. 1. Introduction • The dynamics of the property space market, specifically US office markets. • Error Correction Model (ECM): • long run relationship; and • short run adjustments of rents, vacancy rate and development to fundamental (shock) variables and to lagged disequilibrum. • Panel: trade-off between adding cycles from cross section and differing local processes • Explaining cross section variation in vacancy rate.

  3. 2. Literature • Rental adjustment:Extensive literature linking changes in rent to changes in vacancies. • ECM: Hendershott, MacGregor &Tse(2002, REE). • Three equation system (rent, vacancy rate and dev’t): • Englund, Gunnelin, Hendershott and Soderberg(2007, REE); • Hendershott, Lizieri and MacGregor (2010, JREFE). • Panel (rent only): • UK regional rents - Hendershott, MacGregor and White (2002, JREFE); • US metropolitan areas- Brounenand Jennen (2009a, JREFE) and Ibanez and Pennington-Cross (2012, JREFE) • European cities - Mouzakisand Richards (2007, JPR) and Brounen and Jennen (2009b, JREFE) • Panel (3 equation system): Hendershott, Jennen and MacGregor (2012) • Explaining vacancy rate in cross section: mainly 1980s and 1990s.

  4. 3. Data • Main source: CBRE-EA – to whom, many thanks. • 57 MSAs over 1987-2010; 18 over 1981-2010. • Effective rent indices estimated by CBRE-EA and deflated here using MSA or regional CPI. • Stock and vacancy rates reflect ‘competitive’ multi-tenanted offices in each location. • Employment is finance and other office services.

  5. 4. Model – long run . • Demand is a function of rent and employment: • Equate demand to occupied supply at natural vacancy rate: • Convert to logs and solve for equilibrium rent: • Estimate as: • Price and income elasticities:

  6. 5. Model – short run • Three adjustment equations to bring market back to equilibrium: rent; vacancy rate; development: • driven by: autoregressive terms; shock variables; lagged rent and vacancy rate adjustments • development has longer lags • As an illustration, rent: • Estimated as: • Three estimates of natural vacancy rate. • From rent:

  7. 6. Results – long run

  8. 7. Results – short run rent

  9. 8. Results – short run vacancy rate

  10. 9. Results – short run development

  11. 10. Estimates of the natural vacancy rate

  12. 11.Explaining the natural vacancy rate • Explanations are linked to: search process of tenants and landlords; desire of landlords to hold an inventory to take advantage of market changes, linked to: • heterogeneity in the occupier base; • tenant mobility (including lease length) and holding costs; • heterogeneity in the stock (increased tenant search costs); • expected growth and volatility of demand (higher values mean higher option values for vacant space); • land use regulation and physical constraints (supply elasticity); • length of the development period; and • competitiveness of the local real estate market. • Challenges in identifying and obtaining robust proxies. • Early results point to importance of option values.

  13. 12.Explaining the natural vacancy rate

  14. 13. Conclusion and further work • The basic modelling framework works well and produces robust results. • Refine adjustment equations to improve v* estimates. • Consider asymmetric adjustments. • Need to estimate a constrained system with a single estimate of natural vacancy rate. • Many of the cross-section explanatory variables are correlated (positively & negatively), so need to extract factors. • Consider time varying natural vacancy rates. • Consider cross-section variations in long and short run space market adjustments to employment and supply.

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