{"title":"Modeling real estate dynamics using survival analysis","authors":"Diana Minzat, Mihaela Breaban, H. Luchian","doi":"10.1109/SYNASC.2018.00042","DOIUrl":null,"url":null,"abstract":"This article introduces an adapted version of survival analysis for predicting the period of time a property will stay on market from the listing date to the sale agreement. Survival analysis is a method developed for medical research, in which the dependent variable is the survival time of a patient. Generalizing, the method can be applied in most problems where the dependent variable is time - in our case, the time a property stays on market before selling. Experimental results show that survival analysis brings some advantages when compared to regression analysis on our problem, not only in terms of prediction accuracy: survival curves offer descriptive quantitative views on the influence specific house features have on the variable of interest - the time on market.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2018.00042","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This article introduces an adapted version of survival analysis for predicting the period of time a property will stay on market from the listing date to the sale agreement. Survival analysis is a method developed for medical research, in which the dependent variable is the survival time of a patient. Generalizing, the method can be applied in most problems where the dependent variable is time - in our case, the time a property stays on market before selling. Experimental results show that survival analysis brings some advantages when compared to regression analysis on our problem, not only in terms of prediction accuracy: survival curves offer descriptive quantitative views on the influence specific house features have on the variable of interest - the time on market.