Application of Artificial Intelligence in Estimating the Trend of Residential Rent Fee
碩士 === 國立臺灣科技大學 === 營建工程系 === 107 === To build a predicting model of house rental fee in Taipei city, this study utilizes artificial intelligence (back-propagation neural network and support vector machine) to construct the model, in which the information of several important factors are obtained fr...
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| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2019
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| Online Access: | http://ndltd.ncl.edu.tw/handle/jpm7sd |
| Summary: | 碩士 === 國立臺灣科技大學 === 營建工程系 === 107 === To build a predicting model of house rental fee in Taipei city, this study utilizes artificial intelligence (back-propagation neural network and support vector machine) to construct the model, in which the information of several important factors are obtained from the Internet (2013. Jan. ~ 2017.Mar.). These rental data are then combined with the open data such as information of economy factors as the input of the predicting model. 12 districts of Taipei are selected to demonstrate the proposed method, in which the prediction of the seasonal average rental fee for last season of 2016 and the first season of 2017 is provided. Results indicate that back-propagation neural network is a more suitable tool in the case of apartment. On the other hand, support vector machine may deliver a more promising estimation in the case of studio when the data size is more than 1000.
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