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This dataset covers 31,490 verified records spread across 79+ districts in Uttar Pradesh. 31,490 records carry a verified phone number and 11,022 include a business website — directly usable for outreach, segmentation, and CRM import.
The highest-density districts are Mirzapur (6,358 listings), Mau (3,179 listings), Hardoi (2,119 listings), Shamli (1,589 listings), Barabanki (1,272 listings), Lucknow (1,060 listings), Shahjahanpur (908 listings), and Kasganj (795 listings).
Additional coverage spans Fatehpur (706), Baghpat (636), Aligarh (578), Etah (530), Bulandshahr (489), Pratapgarh (454), Hathras (424), Sonbhadra (397), Allahabad (374), Ballia (353), Kannauj (335), Hapur (318) , plus 59 more districts.
| Districts | Total | With mobile | With website |
|---|---|---|---|
| Mirzapur | 6,358 | 6,358 | 2,225 |
| Mau | 3,179 | 3,179 | 1,113 |
| Hardoi | 2,119 | 2,119 | 742 |
| Shamli | 1,589 | 1,589 | 556 |
| Barabanki | 1,272 | 1,272 | 445 |
| Lucknow | 1,060 | 1,060 | 371 |
| Shahjahanpur | 908 | 908 | 318 |
| Kasganj | 795 | 795 | 278 |
| Fatehpur | 706 | 706 | 247 |
| Baghpat | 636 | 636 | 223 |
| All districts (79) | 31,490 | 31,490 | 11,022 |
| Districts | Total | With mobile | With website |
|---|---|---|---|
| Mirzapur | 6,358 | 6,358 | 2,225 |
| Mau | 3,179 | 3,179 | 1,113 |
| Hardoi | 2,119 | 2,119 | 742 |
| Shamli | 1,589 | 1,589 | 556 |
| Barabanki | 1,272 | 1,272 | 445 |
| Lucknow | 1,060 | 1,060 | 371 |
| Shahjahanpur | 908 | 908 | 318 |
| Kasganj | 795 | 795 | 278 |
| Fatehpur | 706 | 706 | 247 |
| Baghpat | 636 | 636 | 223 |
| Aligarh | 578 | 578 | 202 |
| Etah | 530 | 530 | 186 |
| Bulandshahr | 489 | 489 | 171 |
| Pratapgarh | 454 | 454 | 159 |
| Hathras | 424 | 424 | 148 |
| Sonbhadra | 397 | 397 | 139 |
| Allahabad | 374 | 374 | 131 |
| Ballia | 353 | 353 | 124 |
| Kannauj | 335 | 335 | 117 |
| Hapur | 318 | 318 | 111 |
| Maharajganj | 303 | 303 | 106 |
| Budaun | 289 | 289 | 101 |
| Chitrakoot | 276 | 276 | 97 |
| Bhadohi | 265 | 265 | 93 |
| Chandauli | 254 | 254 | 89 |
| Sultanpur | 245 | 245 | 86 |
| Saharanpur | 235 | 235 | 82 |
| Deoria | 227 | 227 | 79 |
| Firozabad | 219 | 219 | 77 |
| Ghazipur | 212 | 212 | 74 |
| Amethi | 205 | 205 | 72 |
| Ayodhya | 199 | 199 | 70 |
| Gorakhpur | 193 | 193 | 68 |
| Rampur | 187 | 187 | 65 |
| Lalitpur | 182 | 182 | 64 |
| Bahraich | 177 | 177 | 62 |
| Ghaziabad | 172 | 172 | 60 |
| Bijnor | 167 | 167 | 58 |
| Basti | 163 | 163 | 57 |
| Sitapur | 159 | 159 | 56 |
| Kanpur Dehat | 155 | 155 | 54 |
| Ambedkar Nagar | 151 | 151 | 53 |
| Kushinagar | 148 | 148 | 52 |
| Varanasi | 144 | 144 | 50 |
| Agra | 141 | 141 | 49 |
| Auraiya | 138 | 138 | 48 |
| Amroha | 135 | 135 | 47 |
| Kaushambi | 132 | 132 | 46 |
| Etawah | 130 | 130 | 46 |
| Muzaffarnagar | 127 | 127 | 44 |
| Meerut | 125 | 125 | 44 |
| Kanpur Nagar | 122 | 122 | 43 |
| Kheri | 120 | 120 | 42 |
| Kanpur | 118 | 118 | 41 |
| Sambhal | 116 | 116 | 41 |
| Prayagraj | 114 | 114 | 40 |
| Shrawasti | 112 | 112 | 39 |
| Siddharthnagar | 110 | 110 | 39 |
| Sant Kabir Nagar | 108 | 108 | 38 |
| Unnao | 106 | 106 | 37 |
| Moradabad | 104 | 104 | 36 |
| Mahoba | 103 | 103 | 36 |
| Raebareli | 101 | 101 | 35 |
| Bareilly | 99 | 99 | 35 |
| Jalaun | 98 | 98 | 34 |
| Banda | 96 | 96 | 34 |
| Azamgarh | 95 | 95 | 33 |
| Jhansi | 93 | 93 | 33 |
| Gautam Buddha Nagar | 92 | 92 | 32 |
| Farrukhabad | 91 | 91 | 32 |
| Mainpuri | 90 | 90 | 32 |
| Jaunpur | 88 | 88 | 31 |
| Balrampur | 87 | 87 | 30 |
| Hamirpur | 86 | 86 | 30 |
| Shravasti | 85 | 85 | 30 |
| Pilibhit | 84 | 84 | 29 |
| Gonda | 83 | 83 | 29 |
| Lakhimpur Kheri | 82 | 82 | 29 |
| Mathura | 78 | 78 | 27 |
| All districts (79) | 31,490 | 31,490 | 11,022 |
The Realtors database for Uttar Pradesh, India includes 31,490+ verified businesses with company name, address, city, state, phone, email, website and GPS coordinates. The file is delivered as a CSV ready for CRM import or BI analysis.
The Realtors database for Uttar Pradesh, India contains 31,490+ verified businesses as of 2026, each with phone, email, address, website and GPS coordinates. The full record count is shown on this page above.
The complete Realtors database for Uttar Pradesh, India is available on this page from Data2Sales AI as an instant CSV download. Every record carries verified contact details and is delivered immediately after checkout.
Each record includes: Business name, Street address, City, State / Region, Postal code, Country, Phone number, Email address and more. The file is delivered as a UTF-8 CSV that opens in Excel, Google Sheets or any standard CRM importer.
Every record is cross-referenced across public business registries, Google Maps and licensed directory partners before it ships. Closed businesses, disconnected phones and duplicates are stripped on every monthly refresh, so the dataset reflects the live realtors footprint in Uttar Pradesh, India.
Yes. The Realtors dataset is rebuilt on a rolling 30-day cycle. New entrants come in through registrar filings; closures are detected from negative signals and dropped before export.
Yes — the dataset is licensed for commercial use after purchase. Common use cases include cold outbound, geo-targeted advertising, market research, supplier sourcing and territory planning. Only publicly listed business contact information is included.
You receive a UTF-8 encoded CSV. It opens in Excel, Google Sheets, Power BI, Looker Studio and every major CRM (HubSpot, Salesforce, Zoho, Pipedrive). XLSX is available on request.
Within 30 seconds of successful payment. The CSV is generated fresh from the live database at purchase time. You also get 7 days of unlimited re-downloads from a secure link sent to your email.
Looking to reach world wide data decision-makers in Uttar Pradesh, India? This database aggregates 31,490+ verified records through the Data2Sales AI engine, pulling from verified public registries, official websites, social profiles, and licensed data partners. Dead numbers, closed storefronts, and duplicates are stripped before export — you receive a clean, CRM-ready CSV.
Last Update:
Jul 08, 2026 13:38 PM
Published:
May 11, 2026 02:29 AM
Rows (Regular):
14,171
Rows (Extended):
31,490
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Roll-up datasets — broader coverage at a higher tier are highlighted below.
