{"id":"https://openalex.org/W3210175250","doi":"https://doi.org/10.1109/icccnt51525.2021.9579796","title":"Prediction of Rental Prices for Apartments in Brazil Using Regression Techniques","display_name":"Prediction of Rental Prices for Apartments in Brazil Using Regression Techniques","publication_year":2021,"publication_date":"2021-07-06","ids":{"openalex":"https://openalex.org/W3210175250","doi":"https://doi.org/10.1109/icccnt51525.2021.9579796","mag":"3210175250"},"language":"en","primary_location":{"id":"doi:10.1109/icccnt51525.2021.9579796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt51525.2021.9579796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5027926785","display_name":"Krish Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Krish Shah","raw_affiliation_strings":["Independent Researcher, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"Independent Researcher, Mumbai, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034733713","display_name":"Hetal Shah","orcid":"https://orcid.org/0000-0002-3685-4572"},"institutions":[{"id":"https://openalex.org/I4210142080","display_name":"Services Australia","ror":"https://ror.org/03gwaxw53","country_code":"AU","type":"government","lineage":["https://openalex.org/I2801453606","https://openalex.org/I4210142080","https://openalex.org/I4210163987"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hetal Shah","raw_affiliation_strings":["Aruma Services, Melbourne, Australia"],"affiliations":[{"raw_affiliation_string":"Aruma Services, Melbourne, Australia","institution_ids":["https://openalex.org/I4210142080"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017656704","display_name":"Akshay Zantye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akshay Zantye","raw_affiliation_strings":["Thadomal Shahani Engineering College, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"Thadomal Shahani Engineering College, Mumbai, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005514696","display_name":"Madhuri Rao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madhuri Rao","raw_affiliation_strings":["Thadomal Shahani Engineering College, Mumbai, India"],"affiliations":[{"raw_affiliation_string":"Thadomal Shahani Engineering College, Mumbai, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5027926785"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5931,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.66176734,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"07"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12546","display_name":"Smart Parking Systems Research","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12546","display_name":"Smart Parking Systems Research","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10632","display_name":"Housing Market and Economics","score":0.9869999885559082,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12392","display_name":"Sharing Economy and Platforms","score":0.9793999791145325,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.783055305480957},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.6892914772033691},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6293700337409973},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6119978427886963},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5394659042358398},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.5032250285148621},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.49375566840171814},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4903535842895508},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.4870219826698303},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4627651572227478},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46259015798568726},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.43250972032546997},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.41898220777511597},{"id":"https://openalex.org/keywords/real-estate","display_name":"Real estate","score":0.41153138875961304},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.34305626153945923},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3348395824432373},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2806183099746704},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.1515914797782898}],"concepts":[{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.783055305480957},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.6892914772033691},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6293700337409973},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6119978427886963},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5394659042358398},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.5032250285148621},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.49375566840171814},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4903535842895508},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.4870219826698303},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4627651572227478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46259015798568726},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.43250972032546997},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.41898220777511597},{"id":"https://openalex.org/C82279013","wikidata":"https://www.wikidata.org/wiki/Q684740","display_name":"Real estate","level":2,"score":0.41153138875961304},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34305626153945923},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3348395824432373},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2806183099746704},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.1515914797782898},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccnt51525.2021.9579796","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccnt51525.2021.9579796","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1501458856","https://openalex.org/W2044090912","https://openalex.org/W2058294106","https://openalex.org/W2099932395","https://openalex.org/W2266925404","https://openalex.org/W2574989679","https://openalex.org/W2766317890","https://openalex.org/W2775204485","https://openalex.org/W2796818356","https://openalex.org/W2806007788","https://openalex.org/W2892821018","https://openalex.org/W2950724674","https://openalex.org/W2982673924","https://openalex.org/W3003190408","https://openalex.org/W3007936059","https://openalex.org/W3019435264","https://openalex.org/W3084322042","https://openalex.org/W3094334168","https://openalex.org/W3121132206","https://openalex.org/W3124863041","https://openalex.org/W3161293515","https://openalex.org/W4245953385","https://openalex.org/W6675123363","https://openalex.org/W6764156884"],"related_works":["https://openalex.org/W2967733078","https://openalex.org/W3204430031","https://openalex.org/W3137904399","https://openalex.org/W4310492845","https://openalex.org/W2885778889","https://openalex.org/W2766514146","https://openalex.org/W2885516856","https://openalex.org/W4289703016","https://openalex.org/W3094138326","https://openalex.org/W4310224730"],"abstract_inverted_index":{"The":[0,15,46,106],"real":[1],"estate":[2],"industry":[3],"is":[4,20,83,112],"one":[5],"of":[6,17,109,148],"the":[7,18,22,36,40,86,92,110,146,152],"most":[8],"price-oriented":[9],"industries":[10],"and":[11,74,89,132,151],"tends":[12],"to":[13,90],"fluctuate.":[14],"objective":[16],"paper":[19,138],"predicting":[21],"rental":[23,41,141],"price":[24,42,142],"for":[25],"a":[26,31,98],"house.":[27],"In":[28],"this":[29],"study,":[30],"predictive":[32,81],"model":[33,82,95,144],"based":[34],"on":[35,101],"factors":[37],"that":[38],"influence":[39],"has":[43,48],"been":[44],"constructed.":[45],"dataset":[47,153],"thirteen":[49],"features.":[50],"Regression":[51],"techniques":[52],"such":[53,117],"as":[54,118],"Gradient":[55],"Boosting":[56,59],"regressor,":[57,60,63,67,70],"Ada":[58],"K-nearest":[61],"Neighbor":[62],"Partial":[64],"Least":[65],"Square":[66,124,129],"Random":[68],"Forest":[69],"Decision":[71],"Tree":[72],"regressor":[73,77],"Multilayer":[75],"Perceptron":[76],"were":[78],"applied.":[79],"A":[80],"built":[84],"using":[85,114],"regression":[87],"techniques,":[88],"pick":[91],"best":[93],"performing":[94,97],"by":[96],"comparative":[99],"analysis":[100],"their":[102],"performance":[103,115],"scores":[104],"obtained.":[105],"expected":[107],"outcome":[108],"models":[111],"measured":[113],"metrics":[116],"Mean":[119,123,128],"Absolute":[120],"Error":[121,125,130],"(MAE),":[122],"(MSE),":[126],"Root":[127],"(RMSE)":[131],"R-square":[133],"score":[134],"(R<sup>2</sup>)":[135],"metric.":[136],"This":[137],"explains":[139],"house":[140],"prediction":[143],"with":[145],"help":[147],"machine":[149],"learning":[150],"used":[154],"in":[155],"our":[156],"proposed":[157],"model.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
