探讨人工神经网络和传统回归模型在房地产定价中的预测能力:来自普里什蒂纳的证据

IF 1.6 Q3 BUSINESS, FINANCE Journal of Property Investment & Finance Pub Date : 2023-10-10 DOI:10.1108/jpif-06-2023-0051
Visar Hoxha
{"title":"探讨人工神经网络和传统回归模型在房地产定价中的预测能力:来自普里什蒂纳的证据","authors":"Visar Hoxha","doi":"10.1108/jpif-06-2023-0051","DOIUrl":null,"url":null,"abstract":"Purpose The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices. Design/methodology/approach The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al . (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model. Findings The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy. Practical implications The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation. Originality/value Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.","PeriodicalId":46429,"journal":{"name":"Journal of Property Investment & Finance","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the predictive power of ANN and traditional regression models in real estate pricing: evidence from Prishtina\",\"authors\":\"Visar Hoxha\",\"doi\":\"10.1108/jpif-06-2023-0051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Purpose The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices. Design/methodology/approach The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al . (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model. Findings The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy. Practical implications The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation. Originality/value Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.\",\"PeriodicalId\":46429,\"journal\":{\"name\":\"Journal of Property Investment & Finance\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Property Investment & Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/jpif-06-2023-0051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Property Investment & Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jpif-06-2023-0051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0

摘要

本研究的目的是检验线性、非线性和人工神经网络(ann)在预测房地产价格方面的效率。设计/方法/方法本研究使用了从科索沃共和国财产税部门获得的2020年至2022年1468笔房地产交易的数据集。从一个基本的线性回归模型开始,该研究解决了被忽视的非线性问题,采用了类似于Peterson和Flanagan(2009)以及McCluskey等人的策略。(2012),其中人工神经网络的预测作为普通最小二乘(OLS)模型中的附加回归量被纳入。研究结果表明,半对数和双对数模型的拟合优于OLS模型,而人工神经网络模型的拟合效果一般,这与传统的人工神经网络具有优越的预测能力的观点相反。这与普遍认为的人工神经网络具有优越的预测能力明显不同,揭示了对人工神经网络功效的潜在高估。该研究强调了在房地产价格预测中采用多种模型的重要性,揭穿了人工神经网络模型普遍适用性的概念。研究成果对从事房地产估价的学者和专业人士都有重大影响。值得注意的是,本研究率先在发展中国家首都的背景下对包括人工神经网络在内的各种模型进行了比较分析,从而为其在房地产价格预测中的有效性提供了一个新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Exploring the predictive power of ANN and traditional regression models in real estate pricing: evidence from Prishtina
Purpose The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices. Design/methodology/approach The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al . (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model. Findings The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy. Practical implications The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation. Originality/value Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
3.50
自引率
23.10%
发文量
33
期刊介绍: Fully refereed papers on practice and methodology in the UK, continental Western Europe, emerging markets of Eastern Europe, China, Australasia, Africa and the USA, in the following areas: ■Academic papers on the latest research, thinking and developments ■Law reports assessing new legislation ■Market data for a comprehensive review of current research ■Practice papers - a forum for the exchange of ideas and experiences
期刊最新文献
Client influence or the valuer's behavior? An empirical study of listed companies' valuation in Taiwan The influence mechanism of real estate enterprises' status on debt default risk Exploring the predictive power of ANN and traditional regression models in real estate pricing: evidence from Prishtina Real Estate Insights Back to the basics of sustainability The role of multi-family properties in hedging pension liability risk: long-run evidence
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1