{"title":"基于灰色系统理论的廉租住房供应量预测","authors":"Yichun Li","doi":"10.1109/BIFE.2013.70","DOIUrl":null,"url":null,"abstract":"In this paper, we use grey GM (1, 1) model to predict low rental housings' supply quantity in Beijing from 2013 to 2015.Firstly, we give a brief introduction of renewal grey GM (1, 1) model and its methodology. Secondly, we use per capital floor space of urban residential buildings and non-agricultural population as basic data to predicts Beijing's total housing supply from 2014 to 2016. Finally, we use grey GM (1, 1) model predict the number and the area of low rent housings' supply target in Beijing.","PeriodicalId":165836,"journal":{"name":"Business Intelligence and Financial Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory\",\"authors\":\"Yichun Li\",\"doi\":\"10.1109/BIFE.2013.70\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we use grey GM (1, 1) model to predict low rental housings' supply quantity in Beijing from 2013 to 2015.Firstly, we give a brief introduction of renewal grey GM (1, 1) model and its methodology. Secondly, we use per capital floor space of urban residential buildings and non-agricultural population as basic data to predicts Beijing's total housing supply from 2014 to 2016. Finally, we use grey GM (1, 1) model predict the number and the area of low rent housings' supply target in Beijing.\",\"PeriodicalId\":165836,\"journal\":{\"name\":\"Business Intelligence and Financial Engineering\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Business Intelligence and Financial Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIFE.2013.70\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Business Intelligence and Financial Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIFE.2013.70","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction of Low-Rent Housing's Supply Quantity Based on Grey Systems Theory
In this paper, we use grey GM (1, 1) model to predict low rental housings' supply quantity in Beijing from 2013 to 2015.Firstly, we give a brief introduction of renewal grey GM (1, 1) model and its methodology. Secondly, we use per capital floor space of urban residential buildings and non-agricultural population as basic data to predicts Beijing's total housing supply from 2014 to 2016. Finally, we use grey GM (1, 1) model predict the number and the area of low rent housings' supply target in Beijing.
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