{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OYZTDXHBGK2CS4WYQ4NKODDI4H","short_pith_number":"pith:OYZTDXHB","schema_version":"1.0","canonical_sha256":"763331dce132b42972d8871aa70c68e1cc785589c797d4e046836da4173c9261","source":{"kind":"arxiv","id":"1909.00704","version":1},"attestation_state":"computed","paper":{"title":"Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CY","authors_text":"Daniela Paolotti, Francesco Bergadano, Giancarlo Ruffo, Roberto Bertilone","submitted_at":"2019-09-02T13:16:51Z","abstract_excerpt":"Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, obtaining significant structural information requires the intervention of a real estate expert, a professional appraiser. In this paper we propose a Web data acquisition methodology, and a Machine Learning model, that can be used to automatically evaluate real estate properties. T"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1909.00704","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2019-09-02T13:16:51Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"12b5b4aa08c4be3ef3b1695fef96f1a94d1006ae34122bfb5e7e9197b24b2468","abstract_canon_sha256":"1a4e15d69bb5073fd0acc2ffeddf8319f0950899887cc4702fecc5e4bc11d7b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:14:50.521205Z","signature_b64":"HuK3nXCWaaxoMsAZQsAKEH4Y5iJALvh9ngyongrBj/0MO4sGny2Vxs0CJwU8hvf2IoF2yHx6YYOCpo9jzbDMAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"763331dce132b42972d8871aa70c68e1cc785589c797d4e046836da4173c9261","last_reissued_at":"2026-07-05T03:14:50.520728Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:14:50.520728Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CY","authors_text":"Daniela Paolotti, Francesco Bergadano, Giancarlo Ruffo, Roberto Bertilone","submitted_at":"2019-09-02T13:16:51Z","abstract_excerpt":"Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, obtaining significant structural information requires the intervention of a real estate expert, a professional appraiser. In this paper we propose a Web data acquisition methodology, and a Machine Learning model, that can be used to automatically evaluate real estate properties. T"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.00704","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.00704/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1909.00704","created_at":"2026-07-05T03:14:50.520788+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.00704v1","created_at":"2026-07-05T03:14:50.520788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.00704","created_at":"2026-07-05T03:14:50.520788+00:00"},{"alias_kind":"pith_short_12","alias_value":"OYZTDXHBGK2C","created_at":"2026-07-05T03:14:50.520788+00:00"},{"alias_kind":"pith_short_16","alias_value":"OYZTDXHBGK2CS4WY","created_at":"2026-07-05T03:14:50.520788+00:00"},{"alias_kind":"pith_short_8","alias_value":"OYZTDXHB","created_at":"2026-07-05T03:14:50.520788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H","json":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H.json","graph_json":"https://pith.science/api/pith-number/OYZTDXHBGK2CS4WYQ4NKODDI4H/graph.json","events_json":"https://pith.science/api/pith-number/OYZTDXHBGK2CS4WYQ4NKODDI4H/events.json","paper":"https://pith.science/paper/OYZTDXHB"},"agent_actions":{"view_html":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H","download_json":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H.json","view_paper":"https://pith.science/paper/OYZTDXHB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.00704&json=true","fetch_graph":"https://pith.science/api/pith-number/OYZTDXHBGK2CS4WYQ4NKODDI4H/graph.json","fetch_events":"https://pith.science/api/pith-number/OYZTDXHBGK2CS4WYQ4NKODDI4H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H/action/storage_attestation","attest_author":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H/action/author_attestation","sign_citation":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H/action/citation_signature","submit_replication":"https://pith.science/pith/OYZTDXHBGK2CS4WYQ4NKODDI4H/action/replication_record"}},"created_at":"2026-07-05T03:14:50.520788+00:00","updated_at":"2026-07-05T03:14:50.520788+00:00"}