An econometric model for forecasting private construction investment in Hong Kong

Ng, S. Thomas; Fan, Ryan Y.C.; Wong, Johnny

doi:10.1080/01446193.2011.570356

Cited by 16 publications

(1 citation statement)

References 46 publications

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“…While the combination of models have their limitations, this approach is supported by literature as it increases forecasting accuracy [79]. The agglomeration economic model [80] and the private construction forecasting model [81] was used to estimate jobs creation and construction & investment generated. Furthermore, the econometric forecasting was restricted to a time frame of 6 years, as longer time frames were shown to involve risks and contribute to inaccuracy [82].…”

Section: A1 the Econometric Modelmentioning

confidence: 99%

Economically Incentivising Smart Urban Regeneration. Case Study of Port Louis, Mauritius

Allam

Newman

2018

Smart Cities

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Port Louis, the capital city of Mauritius, has been the preferred city for hosting the judicial, political and business activities of the country for the past two centuries. However, new policies have created nine new smart cities in greenfield locations within 10 km from Port Louis, so the capital city is facing economic decline as it is losing businesses, as well as administrative functions. This loss equates to an erosion in municipal revenue along with a reduced interest in contributing to the development of the city; all of which takes a toll on its urban economic landscape, as well as on the broader Mauritian economy. This paper builds from the findings of a focus group study to propose a smart urban regeneration model for the City of Port Louis, which could enable the old city to be restored and regenerated rather than redeveloped in modernist architecture, as has happened in the new smart cities model. A smart urban regeneration model is proposed backed by the pillars of smart infrastructure, culture, metabolism and governance. The proposed model is applied to the context of Port Louis to generate an urban regeneration scheme. The potential benefits in terms of financial outcomes, investment attraction and job creation are explored through a combined application of econometric forecasting models. The results support positive figures of both investment and job creation, and the findings of this study aim at informing and providing the governing bodies of Port Louis with a tangible solution for revamping the centuries-old capital city, as well as demonstrating to the world that smart cities can mean sensitive urban regeneration.Keywords: urban regeneration; economics; culture; port louis; smart cities; fiscal measures IntroductionNested in the North-West littoral side of the Island of Mauritius and bounded inland by a mountain range, Port Louis extends over an area of 46.7 km 2 . With a total population of 119,333 inhabitants as at December 2016, the capital city of Mauritius is the most densely populated geographical district of the island with 2954 P/km 2 [1]. Port Louis hosts the only trade port of the island and has been the main administrative centre from a judicial, political and business perspective for more than two centuries. However, within the last decade, there has been an emergence of Techno-parks and privately-owned smart cities across the island which opened a new competitive landscape for businesses and administrative functions [2]. There have been notable highlighted risks that the loss in business and administrative functions from both the state and privately-owned organisations will eventually lead to an erosion in municipal revenue [3]. Lesser financial input will trickle down to lesser budget availability not only for city development but also for its maintenance. This might lead to an increase in urban decay, as has been the case with Detroit in USA [4]. In fact, at the moment more than 95% of municipal revenue is used for administrative ends such as salaries for the employe...

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Section: A1 the Econometric Modelmentioning

confidence: 99%

Economically Incentivising Smart Urban Regeneration. Case Study of Port Louis, Mauritius

Allam

Newman

2018

Smart Cities

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Mathematical Methods—Statistics and Forecasting

Haidar

2015

Construction Program Management – Decision Making and Optimization Techniques

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Given the improvement of data available, advanced modeling techniques, and computer programs, the design, construction, and the delivery of programs are likely to be more accessible with improved accuracy at every level of the program. This chapter will provide an introduction for statistics applications and forecasting methods to familiarize program managers with their applications as they have been proven applicable and apprehendable for various aspects of a program delivery. Statistics with its wide scope is a very relevant tool for program management. It is concerned with scientific methods for collecting, organizing, summarizing, presenting, and analyzing data as well as with drawing valid conclusions and making reasonable decisions on the basis of such analysis. In a narrower sense, the term statistics is used to denote the data themselves or numbers derived from the data. A forecast is a prediction, projection, or estimate of some future activity, event, or occurrence. When historical data are available, some proven statistical forecasting methods have been developed for using these data to forecast future demand. Such a method assumes that historical trends will continue, so management then needs to make any adjustments to reflect current changes in the construction industry and the management of programs. Statistics AnalysisStatistics appertaining to program management involves finding out the best sample and populations required to solve a wide range of problems. Thus, we speak of recruitment and employment statistics, on-site accident statistics and statistics related to types of construction, leverage risk provisions, and forecasting statistics analysis.Programs are large and in many cases involve huge data. A program involving 400 schools in a developed country will involve mega studies related to the number of students per year, the age of the students, and the demographic distribution of the students which will involve the design, the size, and the type of schools. Then, it is

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