Predictive Rental Values Model for Low-Income Earners in Slums: the Case of Ijora, Nigeria

dc.authorid Misra, Sanjay/0000-0002-3556-9331
dc.authorscopusid 56968131000
dc.authorscopusid 56962766700
dc.authorscopusid 56669831200
dc.authorscopusid 56438006100
dc.authorwosid Okagbue, Hilary Izuchukwu/AAD-1102-2020
dc.authorwosid Misra, Sanjay/K-2203-2014
dc.contributor.author Iroham, Chukwuemeka O.
dc.contributor.author Misra, Sanjay
dc.contributor.author Emebo, Onyeka C.
dc.contributor.author Okagbue, Hilary, I
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:19:49Z
dc.date.available 2024-07-05T15:19:49Z
dc.date.issued 2023
dc.department Atılım University en_US
dc.department-temp [Iroham, Chukwuemeka O.] Covenant Univ, Dept Estate Management, Ota, Ogun State, Nigeria; [Misra, Sanjay] Atilim Univ, Dept Comp Engn, Ankara, Turkey; [Misra, Sanjay] Covenant Univ, Dept Elect & Informat Engn, Ota, Ogun State, Nigeria; [Emebo, Onyeka C.] Covenant Univ, Dept Comp & Informat Sci, Ota, Ogun State, Nigeria; [Okagbue, Hilary, I] Covenant Univ, Dept Math, Ota, Ogun State, Nigeria en_US
dc.description Misra, Sanjay/0000-0002-3556-9331 en_US
dc.description.abstract It is well known most often that values of properties tend to hike at the effluxion of time. This has necessitated the adoption of predictive models in interpreting outcomes in the property market in the future. Earlier studies have been oblivious of such models' outcomes as it affects any focal group, particularly the vulnerable. This present study focuses on the low-income earners found in the slum. The Ijora community in Lagos was the highlight of this study, particularly Ijora Badia and Ijora Oloye, regarded as slums according to the UNDP report. The entire fifty-two (52) local agents in the Ijora community were surveyed in cross-sectional survey research that entailed the questionnaire's issuance. The nexus of data collection, pre-processing, data analysis, algorithm application, and model evaluation resulted in retrieving rental values within the years 2010 and 2019 on two predominant residential property types of self-contain and one-bedroom flats found within the community. Three selected algorithms, Artificial Neural Network (ANN), Support Vector Machine, and Logistic Regression, were essentially used as classifiers but trained to predict the continuous values. These algorithms were implemented through the use of Python's SciKit-learn Library and RapidMiner. The findings revealed that though all three models gave accurate predictions, Logistic Regression was the highest with low error values. It was recommended that Logistic Regression be applied but with much data set of property values of low-income earners over much more period. This study will contribute to the Sustainable development goals(SDG) 11(Sustainable cities and communities) of the United Nations to benefit developing countries, especially in sub-Saharan Africa. en_US
dc.identifier.citationcount 2
dc.identifier.doi 10.1080/15623599.2021.1975021
dc.identifier.endpage 1435 en_US
dc.identifier.issn 1562-3599
dc.identifier.issn 2331-2327
dc.identifier.issue 8 en_US
dc.identifier.scopus 2-s2.0-85114622706
dc.identifier.scopusquality Q1
dc.identifier.startpage 1426 en_US
dc.identifier.uri https://doi.org/10.1080/15623599.2021.1975021
dc.identifier.uri https://hdl.handle.net/20.500.14411/2022
dc.identifier.volume 23 en_US
dc.identifier.wos WOS:000694604200001
dc.institutionauthor Mısra, Sanjay
dc.language.iso en en_US
dc.publisher Taylor & Francis Ltd en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 4
dc.subject Predictive algorithm en_US
dc.subject rental values en_US
dc.subject low-income earners en_US
dc.subject slum en_US
dc.subject Ijora en_US
dc.subject sustainability en_US
dc.subject SDG-11 en_US
dc.subject Nigeria en_US
dc.title Predictive Rental Values Model for Low-Income Earners in Slums: the Case of Ijora, Nigeria en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication
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