Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model

Forecasting the supply of commercial office buildings is critical for the economic development. Using the data from Hong Kong, the major factors influencing the supply of commercial office buildings such as gross domestic product, property prices, vacancy rates, interest rate are examined. Unlike th...

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Main Authors: Sing, Michael, Love, Peter, Wang, Xiangyu, Smith, J.
Other Authors: Paul Chynoweth
Format: Conference Paper
Published: RICS 2012 2012
Online Access:http://hdl.handle.net/20.500.11937/14135
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author Sing, Michael
Love, Peter
Wang, Xiangyu
Smith, J.
author2 Paul Chynoweth
author_facet Paul Chynoweth
Sing, Michael
Love, Peter
Wang, Xiangyu
Smith, J.
author_sort Sing, Michael
building Curtin Institutional Repository
collection Online Access
description Forecasting the supply of commercial office buildings is critical for the economic development. Using the data from Hong Kong, the major factors influencing the supply of commercial office buildings such as gross domestic product, property prices, vacancy rates, interest rate are examined. Unlike the investment in financial assets, commercial office building developments are characterized by a time lag from the decision to build until their completion. The lead-lag structure of these major factors in determining the supply of office buildings is identified. Using the technique of polynomial distributed lag, the lead-lag model is simulated, and provides a reliable forecast on the supply of office buildings. Considering the prevailing shortage of commercial office buildings as result of economic growth in Asia, the forecasts derived from the model are deemed to be useful for economists and property analysts to facilitate their investment decision-making in the future.
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format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T07:06:40Z
publishDate 2012
publisher RICS 2012
recordtype eprints
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spelling curtin-20.500.11937-141352017-01-30T11:41:39Z Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model Sing, Michael Love, Peter Wang, Xiangyu Smith, J. Paul Chynoweth Forecasting the supply of commercial office buildings is critical for the economic development. Using the data from Hong Kong, the major factors influencing the supply of commercial office buildings such as gross domestic product, property prices, vacancy rates, interest rate are examined. Unlike the investment in financial assets, commercial office building developments are characterized by a time lag from the decision to build until their completion. The lead-lag structure of these major factors in determining the supply of office buildings is identified. Using the technique of polynomial distributed lag, the lead-lag model is simulated, and provides a reliable forecast on the supply of office buildings. Considering the prevailing shortage of commercial office buildings as result of economic growth in Asia, the forecasts derived from the model are deemed to be useful for economists and property analysts to facilitate their investment decision-making in the future. 2012 Conference Paper http://hdl.handle.net/20.500.11937/14135 RICS 2012 restricted
spellingShingle Sing, Michael
Love, Peter
Wang, Xiangyu
Smith, J.
Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model
title Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model
title_full Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model
title_fullStr Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model
title_full_unstemmed Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model
title_short Forecasting Supply of Commerical Office Buildings Using Polynomial Distributed Lag Model
title_sort forecasting supply of commerical office buildings using polynomial distributed lag model
url http://hdl.handle.net/20.500.11937/14135