Forecasting commercial rental values using ARIMA models

McGough, Tony; Tsolacos, Sotiris

doi:10.1108/14635789510147801

Cited by 58 publications

(43 citation statements)

References 16 publications

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“…One of the earliest attempts to explain the determinants of office rents was carried out by Guardiner and Henneberry, cited by Kiehela and Falkenbach [10]. In a study by McGough and Tsolacos [11] it was mentioned that the existing empirical work on the prediction of rental values is based mainly on dynamic specifications. A model of regional office rents was estimated over a period 1979 to 1984 and was used to forecast the rental values for 1985 [11].…”

Section: Determination Of Rental Levels and Earlier Studiesmentioning

confidence: 99%

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Forecasting office rentals in Gaborone in the short and long term

Kampamba¹,

Cloete²

2015

WIT Transactions on the Built Environment

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The purpose of the study was to forecast office rentals in the city of Gaborone, Botswana. A model was developed to assist in the evaluation of proposed investments and developments. The study was both qualitative and quantitative in nature. The sample was determined at 95% confidence level with a 5% margin of error and 40% chance that the picked respondents were dealing in offices. The data obtained from the 30 respondents was analysed using regression analysis models, Microsoft Excel and SPSS 16.0 package to determine the relationship between prosperity (GDP growth) and levels of rentals. The moving average method (MAM) of office rentals between 2008 and 2014 was used to derive the direction of rental values in the four office nodes in Gaborone. The findings indicate that rentals are expected to increase at various rates in the four areas under investigation. This study is the first of its kind in Botswana and should be useful to real estate professionals, financial institutions, tenants and academics.

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Section: Determination Of Rental Levels and Earlier Studiesmentioning

confidence: 99%

“…In a study by McGough and Tsolacos [11] it was mentioned that the existing empirical work on the prediction of rental values is based mainly on dynamic specifications. A model of regional office rents was estimated over a period 1979 to 1984 and was used to forecast the rental values for 1985 [11].…”

Section: Determination Of Rental Levels and Earlier Studiesmentioning

confidence: 99%

Forecasting office rentals in Gaborone in the short and long term

Kampamba¹,

Cloete²

2015

WIT Transactions on the Built Environment

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“…(1991), McGough and Tsolacos (1995) and Tse (1997) who argued the need for at least 50 sample observations to produce an adequate time-series model. Accordingly, the combination of both IPD and Scott's datasets extends rental series for 13 years for 1963-2010 period.…”

Section: Dependent Variablementioning

confidence: 99%

Examination of property forecasting models - accuracy and its improvement through combination forecasting

Jadevicius¹,

Sloan²,

Brown³

2012

Proceedings of the 19th Annual European Real Estate Society Conference - Edinburgh, Scotland

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This paper investigates property forecasting accuracy and its improvement. The research suggests that despite increased sophistication of property market modelling and forecasting, there still remains a degree of inaccuracy between model outputs and actual property market performance. Subsequently, the paper presents the principle of combination forecasting as a medium helping to achieve greater predictive outcomes. The research implements combination forecasting principle. It then assesses whether combination forecasts from different forecasting techniques are better than single model outputs. It examines which of them -combination or single forecast -fits the UK property market better, and which of these options forecasts best.

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“…The short-term dynamics are captured by performing 1 day ahead out-ofsample forecasts resulting from the estimation of the model with the previous 60 observations (i.e., data for approximately one quarter). The size of the rolling window is chosen in accordance with McGough and Tsolacos (1995) and Tse (1997) who suggest that the minimum number of observations needed for generating an ARMA model is 50 observations. The sample is rolled forward by including a new The SBC (Schwarz Bayesian information criterion) is calculated every year for all models up to five lags.…”

Section: Arma Modelsmentioning

confidence: 99%

Are Securitized Real Estate Returns More Predictable Than Stock Returns?

Serrano

Hoesli

2008

Book of Abstracts: 15th Annual European Real Estate Society Conference - Krakow, Poland

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This paper examines whether the predictability of securitized real estate returns differs from that of stock returns. It also provides a cross-country comparison of securitized real estate return predictability. In contrast to most of the literature on this issue, the analysis is not based on a multifactor asset pricing framework as such analyses may bias the results. We use a time series approach and thus create a level playing field to compare the predictability of the two asset classes. Forecasts are performed with ARMA and ARMA-EGARCH models and evaluated by comparing the entire empirical distributions of prediction errors, as well as with a trading strategy. The results, based on daily data for the 1990-2007 period, show that securitized real estate returns are generally more predictable than stock returns in countries with mature and well established REIT regimes. ARMA-EGARCH models are found to have portfolio outperformance potential even in the presence of transaction costs, with generally better results for securitized real estate than for stocks.

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Forecasting commercial rental values using ARIMA models

Cited by 58 publications

References 16 publications

Forecasting office rentals in Gaborone in the short and long term

Forecasting office rentals in Gaborone in the short and long term

Examination of property forecasting models - accuracy and its improvement through combination forecasting

Are Securitized Real Estate Returns More Predictable Than Stock Returns?

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