{
 "record_id": "de48aa9a311d709ef0a33453d7042001c2e721a6",
 "eligible": true,
 "exclusion_reason": null,
 "study": {
  "title": "Public transport, noise complaints, and housing: Evidence from sentiment analysis in Singapore",
  "year": 2021,
  "doi": "10.1111/jors.12524",
  "country": "Singapore",
  "region_city": "a public-housing (HDB) town in Singapore along new bus route no. 972",
  "sample_start": 2010,
  "sample_end": 2018,
  "property_type": "condo_apartment",
  "price_type": "transaction",
  "data_source": "HDB resale transaction records 2010-2018 in the study area; text-based noise complaint records scored with NLP sentiment analysis; GIS distances",
  "document_type": "journal",
  "estimators": [
   "OLS"
  ],
  "spatial_method": "building-block fixed effects",
  "time_fe": true,
  "location_fe": true,
  "se_type": "clustered",
  "n_obs_total": 1450,
  "ai_methods": [
   "NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1)"
  ],
  "notes": "Only Tables 5 and 6 are housing price regressions (log resale price of public HDB flats); Tables 2-4 model noise sentiment (not extracted). SEs clustered by building block, robust, in parentheses; * p<0.1, ** p<0.05, *** p<0.01. Distances measured in units of 100 m (Table 1). Launch (post bus-launch dummy) not extracted (time dummy); Launch x Near / Launch x Distance DiD interactions extracted as 'other'. Table 6 uses the subset of transactions matched to lagged noise sentiment (N=893); noise sentiment is an AI/NLP-derived score from complaint texts (mapped to noise). Unreported controls: unit layout, morphology, RC center (Table 6), morphology and RC center (Table 5)."
 },
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   "estimator": "OLS",
   "n_obs": 1450,
   "r2": 0.941,
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  },
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   "estimator": "OLS",
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   "r2": 0.919,
   "se_or_t_in_parentheses": "se",
   "sample_subset": "transactions matched with 12-month lagged noise sentiment; block and time FE"
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   "se_or_t_in_parentheses": "se",
   "sample_subset": "as column 1 plus interaction of noise sentiment with low/high floor dummy"
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