{"id":"https://openalex.org/W4386552484","doi":"https://doi.org/10.48550/arxiv.2309.00855","title":"DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal","display_name":"DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386552484","doi":"https://doi.org/10.48550/arxiv.2309.00855"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2309.00855","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2309.00855","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5049233960","display_name":"Weiwei Du","orcid":"https://orcid.org/0000-0002-0627-0314"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Wei-Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031992776","display_name":"Wei\u2010Yao Wang","orcid":"https://orcid.org/0000-0002-6551-1720"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wei-Yao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5040101293","display_name":"Wen-Chih Peng","orcid":"https://orcid.org/0000-0002-0172-7311"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Wen-Chih","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":67},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9381,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9381,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10632","display_name":"Housing Market and Economics","score":0.9237,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9068,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7570528},{"id":"https://openalex.org/C82279013","wikidata":"https://www.wikidata.org/wiki/Q684740","display_name":"Real estate","level":2,"score":0.6547904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.58118236},{"id":"https://openalex.org/C186027771","wikidata":"https://www.wikidata.org/wiki/Q4008379","display_name":"Valuation (finance)","level":2,"score":0.56264687},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5611716},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.37942377},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.18581817},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.12228498}],"mesh":[],"locations_count":3,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2309.00855","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2309.00855","pdf_url":"http://arxiv.org/pdf/2309.00855","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2309.00855","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2309.00855","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W610585721","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4306674287","https://openalex.org/W3170094116","https://openalex.org/W3107602296","https://openalex.org/W3046775127","https://openalex.org/W2961085424","https://openalex.org/W2746043536"],"abstract_inverted_index":{"The":[0,256],"marketplace":[1,240],"system":[2],"connecting":[3],"demands":[4],"and":[5,46,90,112,207,224,252],"supplies":[6],"has":[7],"been":[8],"explored":[9],"to":[10,37,75,114,170,174,231,234],"develop":[11],"unbiased":[12],"decision-making":[13],"in":[14,211],"valuing":[15],"properties.":[16],"Real":[17,131],"estate":[18,99,132,153,158],"appraisal":[19],"serves":[20],"as":[21,142],"one":[22],"of":[23,49,59,66,81,87,150,180,190],"the":[24,39,43,47,50,57,78,85,143,148,151,156,172,198,204,208,212],"high-cost":[25],"property":[26,188],"valuation":[27,54],"tasks":[28],"for":[29,68,105,129,154,177,201,219,222,226,246],"financial":[30,236],"institutions":[31],"since":[32],"it":[33],"requires":[34],"domain":[35,60,116,162],"experts":[36,61],"appraise":[38],"estimation":[40],"based":[41,146],"on":[42,147,186],"corresponding":[44],"knowledge":[45],"judgment":[48],"market.":[51],"Existing":[52],"automated":[53],"models":[55,245],"reducing":[56],"subjectivity":[58],"require":[62],"a":[63,124],"large":[64],"number":[65],"transactions":[67,82,179,192],"effective":[69],"evaluation,":[70],"which":[71],"is":[72,135,168,259],"predominantly":[73],"limited":[74,178,254],"not":[76],"only":[77],"labeling":[79],"efforts":[80],"but":[83],"also":[84],"generalizability":[86],"new":[88],"developing":[89],"rural":[91],"areas.":[92],"To":[93],"learn":[94],"representations":[95,159,173],"from":[96],"unlabeled":[97],"real":[98,152,157],"sets,":[100],"existing":[101],"self-supervised":[102,126],"learning":[103,127,167],"(SSL)":[104],"tabular":[106,202],"data":[107],"neglects":[108],"various":[109],"important":[110],"features,":[111],"fails":[113],"incorporate":[115],"knowledge.":[117,163],"In":[118],"this":[119],"paper,":[120],"we":[121],"propose":[122],"DoRA,":[123],"Domain-based":[125],"framework":[128],"low-resource":[130],"Appraisal.":[133],"DoRA":[134,195,230],"pre-trained":[136],"with":[137,160,238],"an":[138],"intra-sample":[139],"geographic":[140],"prediction":[141],"pretext":[144],"task":[145],"metadata":[149],"equipping":[155],"prior":[161],"Furthermore,":[164],"inter-sample":[165],"contrastive":[166],"employed":[169],"generalize":[171],"be":[175,232],"robust":[176],"downstream":[181],"tasks.":[182],"Our":[183],"benchmark":[184],"results":[185],"three":[187],"types":[189],"real-world":[191],"show":[193],"that":[194,248],"significantly":[196],"outperforms":[197],"SSL":[199],"baselines":[200],"data,":[203],"graph-based":[205],"methods,":[206],"supervised":[209],"approaches":[210],"few-shot":[213],"scenarios":[214],"by":[215],"at":[216,261],"least":[217],"7.6%":[218],"MAPE,":[220],"11.59%":[221],"MAE,":[223],"3.34%":[225],"HR10%.":[227],"We":[228],"expect":[229],"useful":[233],"other":[235],"practitioners":[237],"similar":[239],"applications":[241],"who":[242],"need":[243],"general":[244],"properties":[247],"are":[249],"newly":[250],"built":[251],"have":[253],"records.":[255],"source":[257],"code":[258],"available":[260],"https://github.com/wwweiwei/DoRA.":[262]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4386552484","counts_by_year":[],"updated_date":"2024-12-31T09:15:27.142294","created_date":"2023-09-09"}