Predicting private property prices using neural network
The objective of this academic study is to experiment and choose suitable economic indicators that are relevant to the Singapore economy to forecast the property price in Singapore. With this selected economic indicators, the study aim to establish the private property prices in Singapore with the h...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/10356/46128 |
| _version_ | 1811692601085001728 |
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| author | Ng, Brian Theng Koon. |
| author2 | Quah Tong Seng |
| author_facet | Quah Tong Seng Ng, Brian Theng Koon. |
| author_sort | Ng, Brian Theng Koon. |
| collection | NTU |
| description | The objective of this academic study is to experiment and choose suitable economic indicators that are relevant to the Singapore economy to forecast the property price in Singapore. With this selected economic indicators, the study aim to establish the private property prices in Singapore with the help of Artificial Neural Networks (ANN). The predictive and generalization ability of the Artificial Neural Networks (ANN) will be used to explore private property price in Singapore. ANN is used in this forecast particularly due to its ability to handle non linear problem and give a good prediction. Similar experiments ANN have been carried out in Europe by [1] (E. Worzala et al, 1995) as regression methods which are used before by [2] (M.J. Bailey et al, 1963) in the 1960s are not a good enough predictor for the property price in a market (Singapore) that is much more volatile today compared to the past due to globalization . The historical data of these indicators which are found to be ideal will be used as input and will be used to train the Artificial Neural Networks (ANN), the trained Artificial Neural Networks (ANN) will be able to infer from the training based on the input indicators.This predictive ability of the trained ANN can be used by the government or economic planners in Singapore for further studies such as modeling the likely effects of new economic policies to adjust the property prices when there’s a need to intervene the property market. The housing index which is found on Bloomberg and SISV are used, the price is derived from resale transactions on the actual market. [3] (Bourassa et al., 2008) and [4] (N. García 2004) have shown that these data are usable for research in this area. |
| first_indexed | 2024-10-01T06:38:22Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/46128 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:38:22Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | ntu-10356/461282019-12-10T10:48:16Z Predicting private property prices using neural network Ng, Brian Theng Koon. Quah Tong Seng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The objective of this academic study is to experiment and choose suitable economic indicators that are relevant to the Singapore economy to forecast the property price in Singapore. With this selected economic indicators, the study aim to establish the private property prices in Singapore with the help of Artificial Neural Networks (ANN). The predictive and generalization ability of the Artificial Neural Networks (ANN) will be used to explore private property price in Singapore. ANN is used in this forecast particularly due to its ability to handle non linear problem and give a good prediction. Similar experiments ANN have been carried out in Europe by [1] (E. Worzala et al, 1995) as regression methods which are used before by [2] (M.J. Bailey et al, 1963) in the 1960s are not a good enough predictor for the property price in a market (Singapore) that is much more volatile today compared to the past due to globalization . The historical data of these indicators which are found to be ideal will be used as input and will be used to train the Artificial Neural Networks (ANN), the trained Artificial Neural Networks (ANN) will be able to infer from the training based on the input indicators.This predictive ability of the trained ANN can be used by the government or economic planners in Singapore for further studies such as modeling the likely effects of new economic policies to adjust the property prices when there’s a need to intervene the property market. The housing index which is found on Bloomberg and SISV are used, the price is derived from resale transactions on the actual market. [3] (Bourassa et al., 2008) and [4] (N. García 2004) have shown that these data are usable for research in this area. Bachelor of Engineering 2011-06-29T04:00:12Z 2011-06-29T04:00:12Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/46128 en Nanyang Technological University 84 p. application/msword |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Ng, Brian Theng Koon. Predicting private property prices using neural network |
| title | Predicting private property prices using neural network |
| title_full | Predicting private property prices using neural network |
| title_fullStr | Predicting private property prices using neural network |
| title_full_unstemmed | Predicting private property prices using neural network |
| title_short | Predicting private property prices using neural network |
| title_sort | predicting private property prices using neural network |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/46128 |
| work_keys_str_mv | AT ngbrianthengkoon predictingprivatepropertypricesusingneuralnetwork |