A Regime‐Switching Approach to Modeling Rental Prices of U.K. Real Estate Sectors

Füss, Roland; Stein, Michael; Zietz, Joachim

doi:10.1111/j.1540-6229.2011.00316.x

Cited by 9 publications

(6 citation statements)

References 53 publications

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“…Similarly, Harrami and Paulsson (2017) investigated rent modelling for the Swedish office market and found that the GDP was useful in predicting the direction of the real-estate market. Similar studies in the UK and the USA (Füss, Stein & Zietz, 2012;Tsolacos, 2006;Frankel & Saravelos, 2012;Buehler & Almeida, 2016) report that, in order to create predictive models, identifying the "right" set of variables that combine to trigger changes in the market was the first step. In particular, Buehler and Almeida (2016), noted that the risk of downturns in the commercial real-estate prices in USA cities was attributable to several macroeconomic indicators, including inflation rates, bond yields, consumer confidence, and employment.…”

Section: Literature Reviewmentioning

confidence: 99%

Macroeconomic leading indicators of listed property price movements in Nigeria and South Africa

Oladeji¹,

Yacim

Wall

et al. 2020

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Section: Literature Reviewmentioning

confidence: 99%

Macroeconomic leading indicators of listed property price movements in Nigeria and South Africa

Oladeji¹,

Yacim

Wall

et al. 2020

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“…Because the two purchasing groups are under different conditions and presumably hold different goals for the property, it would be expected that the models would differ to some degree. This difference could hold a particular significance in light of some models using rents instead of effective property values (16,17,19). In addition, the number of observations for some models was particularly low.…”

Section: Conclusion and Future Considerationsmentioning

confidence: 98%

Location and Transportation Effects on Nonresidential Real Estate Price Regressions in Windsor, Ontario, Canada

Gingerich

Maoh

Anderson

2013

Transportation Research Record

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In this paper, land prices for nonresidential real estate listings in Windsor, Ontario, Canada, are analyzed with the use of ordinary least squares regression models. Location and transportation attributes are used in the analysis to explain observed land prices. Seven models were estimated to control the heterogeneity in the land use types. The results show differences in factors explaining land prices of the models, indicating that caution should be used when land use types are aggregated together. The role of transportation carried mixed results. Rail had a positive effect for industrial properties but was negative for commercial and food services. Transit had a positive effect on vacant land. Direct proximity to highway ramps had a negative correlation for vacant land, but indirect variables such as potential (residential) accessibility and time to the central business district indicated the positive effects that roads and highways had. Testing showed that although spatial autocorrelation was present in the price data, the independent regressors used in the modeling partially mitigated that effect. Multicollinearity and heteroscedasticity were also accounted for throughout the modeling process. The results obtained provide a useful account of various spatial and transportation-related phenomena for a midsize Canadian metropolitan area. Moreover, the empirical analysis is particularly valuable given the lack of modeling done on the commercial and industrial prices when compared with that done for residential properties.

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“…As the market moves from one period to another, a regime switch occurs that changes the equilibrium natural occupancy in that market. This technique has been applied in London apartments and U.K. office, retail, and industrial real estate markets (Farrelly & Sanderson, 2005; Füss, Stein, & Zietz, 2011). More recently, regime switching has been applied to long-run valuation in the Paris office market (Bruneau & Cherfouh, 2015).…”

Section: Natural Occupancy Estimation: Constant and Varyingmentioning

confidence: 99%

Natural Occupancy Shifts in Hotel Markets

Gallagher¹,

Corgel

2018

Cornell Hospitality Quarterly

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The natural occupancy rate underpins room price adjustment models for hotel market evaluations; however, models that assume that this rate is time invariant and everywhere constant may introduce error when forecasting future real room rates and benchmarking market strength. We use monthly STR room rate and occupancy data for five large U.S. metropolitan hotel markets to estimate natural occupancy differences in time and across markets. The notion of a time-varying natural occupancy aligns with changing market equilibrium. When aggregating over the entire sample, long-run average occupancy is reasonably good approximation of estimated constant natural occupancy. We use a Markov switching model to determine the likely equilibrium occupancy for unique periods in time, and these results inform Tse and Fischer's model to estimate time-and market-specific natural occupancy. Using the estimated natural occupancy rates for prevailing market regimes produces better fitting predictions than either constant natural occupancy or long-run average occupancy. Accordingly, they are most appropriate for practical applications.

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A Regime‐Switching Approach to Modeling Rental Prices of U.K. Real Estate Sectors

Cited by 9 publications

References 53 publications

Macroeconomic leading indicators of listed property price movements in Nigeria and South Africa

Macroeconomic leading indicators of listed property price movements in Nigeria and South Africa

Location and Transportation Effects on Nonresidential Real Estate Price Regressions in Windsor, Ontario, Canada

Natural Occupancy Shifts in Hotel Markets

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