Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Development and Prediction of a Construction Price Index in Egypt
Cost overrun is one of the most recurring issues in the Egyptian construction industry. The effect is not only on the construction industry ,but also influences the overall economy of a country like Egypt. Therefore, a construction performance index and a construction price index could be an integra...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
AUC Knowledge Fountain
2023
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| Summary: | Cost overrun is one of the most recurring issues in the Egyptian construction industry. The effect is not only on the construction industry ,but also influences the overall economy of a country like Egypt. Therefore, a construction performance index and a construction price index could be an integral indicator of the performance of the industry. These indices can reduce construction companies’ losses and decrease the effect of any sudden economic depression on the construction industry. Several research were conducted to predict construction indices. However, many uncertainties disturb the prediction of these indices. The trend in the prediction models for construction indices, construction materials and construction companies’ stock prices, is developing artificial intelligence and machine learning models. These models help decision makers take the most suitable response for any sudden changes. The fundamental challenge with this generic model is to find influencing indicators for that model.
This research aims to use data envelopment analysis (DEA) to develop a construction performance index from predictions of the construction companies’ stock market and construction price index from construction materials prices. Four different models are used to predict Portland cement prices, Steel reinforcement bars, and stock prices of companies in the Egyptian construction industry such as Acrow Misr for Metallic Scaffoldings, Nasr Company Civil Works, National Cement Company, Sinai Cement Company, and Suez Cement Company. The best performing model is selected and used
Page | 4
in the proposed construction price indices. The first model applies the artificial neural networks (ANNs) to predict the future prices of major construction stock prices and construction materials. Moreover, the second and third models use linear regression and support vector regression using python language. The fourth model uses mono variant Long Short-Term Memory (LSTM) to predict the construction stock prices and construction material based on its historical performance in the last 10 years. Historical data for all the mentioned above construction materials and construction companies’ stocks and macroeconomic indicators in Egypt is from January 2010 to June 2020.
The inputs of the proposed models are other Egyptian construction companies' stock prices and leading economic indicators such as the US Dollar to Egyptian pound exchange rate, Consumer price index, Producer price index (PPI), Unemployment rate, Inflation rate, Interest rate, Construction GDP, Lending rate, Money supply M2, Crude oil prices, GDP growth rate, Foreign reserves ,and Gold reserves. The proposed construction indices might be useful tools for construction market stakeholders to predict and quantify the fluctuations in the construction market . Decision makers can use these indices to identify suitable measures to prevent any sudden disruptions that could negatively impact the financial stability of construction stakeholders. |
|---|