{"id":"https://openalex.org/W4410772540","doi":"https://doi.org/10.1145/3727353.3727388","title":"Research on Relationship Between Aging Population Distribution and Real Estate Market Dynamics based on Neural Networks","display_name":"Research on Relationship Between Aging Population Distribution and Real Estate Market Dynamics based on Neural Networks","publication_year":2025,"publication_date":"2025-01-10","ids":{"openalex":"https://openalex.org/W4410772540","doi":"https://doi.org/10.1145/3727353.3727388"},"language":"en","primary_location":{"id":"doi:10.1145/3727353.3727388","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727388","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727388","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727388","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115969345","display_name":"Yiqiu Tang","orcid":"https://orcid.org/0009-0008-8105-0312"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiqiu Tang","raw_affiliation_strings":["School of Professional Studies, Columbia University, New York, New York State, USA"],"raw_orcid":"https://orcid.org/0009-0008-8105-0312","affiliations":[{"raw_affiliation_string":"School of Professional Studies, Columbia University, New York, New York State, USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010599110","display_name":"Shenghan Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shenghan Zhao","raw_affiliation_strings":["The Department of Economics, Cornell University, New York, New York State, USA"],"raw_orcid":"https://orcid.org/0009-0008-5665-8414","affiliations":[{"raw_affiliation_string":"The Department of Economics, Cornell University, New York, New York State, USA","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"197","last_page":"203"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10632","display_name":"Housing Market and Economics","score":0.8654999732971191,"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"}},"topics":[{"id":"https://openalex.org/T10632","display_name":"Housing Market and Economics","score":0.8654999732971191,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6633597612380981},{"id":"https://openalex.org/keywords/real-estate","display_name":"Real estate","score":0.6110590100288391},{"id":"https://openalex.org/keywords/dynamics","display_name":"Dynamics (music)","score":0.6091398000717163},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.48827600479125977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4750659167766571},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.41532421112060547},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4133964478969574},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.294863760471344},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.20507830381393433},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.123883455991745},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08024924993515015},{"id":"https://openalex.org/keywords/demography","display_name":"Demography","score":0.07343444228172302},{"id":"https://openalex.org/keywords/sociology","display_name":"Sociology","score":0.0574299693107605}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6633597612380981},{"id":"https://openalex.org/C82279013","wikidata":"https://www.wikidata.org/wiki/Q684740","display_name":"Real estate","level":2,"score":0.6110590100288391},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.6091398000717163},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.48827600479125977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4750659167766571},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.41532421112060547},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4133964478969574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.294863760471344},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.20507830381393433},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.123883455991745},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08024924993515015},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.07343444228172302},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0574299693107605},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3727353.3727388","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727388","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727388","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3727353.3727388","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3727353.3727388","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3727353.3727388","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8","score":0.4699999988079071}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410772540.pdf","grobid_xml":"https://content.openalex.org/works/W4410772540.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W2943519290","https://openalex.org/W3092942821","https://openalex.org/W3096282345","https://openalex.org/W3129538679","https://openalex.org/W3138471714","https://openalex.org/W3182639363","https://openalex.org/W4200272551","https://openalex.org/W4220671561","https://openalex.org/W4224218983","https://openalex.org/W4250831972","https://openalex.org/W4308308022"],"related_works":["https://openalex.org/W2267291520","https://openalex.org/W1570557499","https://openalex.org/W2338356869","https://openalex.org/W2339666525","https://openalex.org/W2229866233","https://openalex.org/W2611341084","https://openalex.org/W3123266604","https://openalex.org/W1161845713","https://openalex.org/W2256089030","https://openalex.org/W2993281843"],"abstract_inverted_index":{"This":[0],"study":[1,18],"aims":[2,19],"to":[3,20,69,102,111,139],"analyze":[4],"the":[5,17,22,25,34,71,75,78,85,109,113,130,145],"relationship":[6],"between":[7,84],"demographic":[8],"patterns":[9],"and":[10,59,91],"real":[11],"estate":[12],"market":[13,104,155],"movements.":[14],"In":[15,143],"particular,":[16],"examine":[21],"relation":[23],"with":[24,51],"help":[26],"of":[27,77,115],"a":[28,40],"neural":[29],"network-based":[30],"model.":[31],"Considering":[32],"that":[33,82,129],"model":[35,133,146],"in":[36,153],"question":[37],"is":[38,97],"using":[39],"deep":[41],"learning":[42,56],"approach":[43],"with,":[44],"one":[45],"long":[46],"short-term":[47],"memory":[48],"network":[49],"enhanced":[50],"several":[52],"optimization":[53],"techniques,":[54],"adaptive":[55],"rate":[57],"methods":[58],"data":[60],"augmentation":[61],"techniques.":[62],"Collectively,":[63],"these":[64],"strategies":[65],"provide":[66],"sufficient":[67],"means":[68],"understand":[70],"potential":[72,110],"impacts":[73],"for":[74,150],"dynamics":[76],"housing":[79,120],"market,":[80],"those":[81],"run":[83],"population":[86,118],"age":[87],"structure,":[88],"migration":[89],"demographics":[90],"economic":[92],"indicators.":[93],"The":[94,125],"model's":[95],"timeliness":[96],"validated":[98],"through":[99],"its":[100],"ability":[101],"match":[103],"trends":[105],"tightly,":[106],"while":[107],"providing":[108],"minimize":[112],"impact":[114],"an":[116],"aging":[117],"on":[119],"prices":[121],"from":[122],"structural":[123],"angles.":[124],"empirical":[126],"analysis":[127],"showed":[128],"proposed":[131],"LSTM":[132],"provides":[134],"more":[135],"reliable":[136],"predictions":[137],"compared":[138],"conventional":[140],"statistical":[141],"methods.":[142],"addition,":[144],"demonstrates":[147],"excellent":[148],"generalization":[149],"application":[151],"robustness":[152],"different":[154],"conditions.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}