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GeoIp2\Model\City Object
(
    [city:protected] => GeoIp2\Record\City Object
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            [validAttributes:protected] => Array
                (
                    [0] => confidence
                    [1] => geonameId
                    [2] => names
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [geoname_id] => 4509177
                    [names] => Array
                        (
                            [de] => Columbus
                            [en] => Columbus
                            [es] => Columbus
                            [fr] => Columbus
                            [ja] => コロンバス
                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
                        )

                )

        )

    [location:protected] => GeoIp2\Record\Location Object
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                    [1] => accuracyRadius
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                    [3] => longitude
                    [4] => metroCode
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                    [7] => postalConfidence
                    [8] => timeZone
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

        )

    [postal:protected] => GeoIp2\Record\Postal Object
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            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [code] => 43215
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        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
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                            [0] => confidence
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                            [2] => isoCode
                            [3] => names
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                    [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
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                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
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    [continent:protected] => GeoIp2\Record\Continent Object
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                    [2] => names
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
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                            [de] => Nordamerika
                            [en] => North America
                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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        )

    [country:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
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                            [de] => USA
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                            [es] => Estados Unidos
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                            [zh-CN] => 美国
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    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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                    [0] => queriesRemaining
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

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    [registeredCountry:protected] => GeoIp2\Record\Country Object
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            [validAttributes:protected] => Array
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                    [0] => confidence
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                    [2] => isInEuropeanUnion
                    [3] => isoCode
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
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                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                )

        )

    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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            [validAttributes:protected] => Array
                (
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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
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                    [5] => type
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            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [0] => en
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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    [traits:protected] => GeoIp2\Record\Traits Object
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            [validAttributes:protected] => Array
                (
                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
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                    [7] => isAnonymousVpn
                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
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                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
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                    [19] => staticIpScore
                    [20] => userCount
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                            [en] => Columbus
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                            [fr] => Columbus
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                            [pt-BR] => Columbus
                            [ru] => Колумбус
                            [zh-CN] => 哥伦布
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                )

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                    [geoname_id] => 6255149
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                            [es] => Norteamérica
                            [fr] => Amérique du Nord
                            [ja] => 北アメリカ
                            [pt-BR] => América do Norte
                            [ru] => Северная Америка
                            [zh-CN] => 北美洲
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                )

            [country] => Array
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                    [geoname_id] => 6252001
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                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
                        )

                )

            [location] => Array
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                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

            [postal] => Array
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                    [code] => 43215
                )

            [registered_country] => Array
                (
                    [geoname_id] => 6252001
                    [iso_code] => US
                    [names] => Array
                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
                            [ja] => アメリカ
                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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                )

            [subdivisions] => Array
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                    [0] => Array
                        (
                            [geoname_id] => 5165418
                            [iso_code] => OH
                            [names] => Array
                                (
                                    [de] => Ohio
                                    [en] => Ohio
                                    [es] => Ohio
                                    [fr] => Ohio
                                    [ja] => オハイオ州
                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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                )

            [traits] => Array
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                    [ip_address] => 216.73.216.110
                    [prefix_len] => 22
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        )

)

 country : United States
 city : Columbus
US
Array
(
    [as_domain] => amazon.com
    [as_name] => Amazon.com, Inc.
    [asn] => AS16509
    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)

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Automating Real Estate Lead Generation with Web Scraping and API Integration

Learn how Actowiz Solutions automates real estate lead generation using web scraping and API integration. Extract property data, streamline sales, and scale faster.

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Automating-Real-Estate-Lead-Generation-with-Web-Scraping-and-API-Integration

Introduction

The real estate industry is highly competitive, and accessing high-quality leads is crucial for success. Actowiz Solutions specializes in advanced web scraping and API integration to automate real estate lead generation. By extracting valuable data from multiple real estate websites in France, Actowiz Solutions enables businesses to enhance their sales and marketing strategies efficiently.

Project Overview

Project-Overview

The goal of this project was to develop a powerful web scraper that extracts essential lead data from ten real estate websites in France. The data is then integrated into a front-end system via a secure API, ensuring seamless access for real estate professionals.

Key Challenges

  • Diverse Website Structures: Different real estate platforms have unique layouts and data formats.

  • Anti-Scraping Measures: Many websites employ CAPTCHAs, IP blocking, and bot-detection techniques.

  • Data Accuracy: Ensuring clean, duplicate-free, and well-structured data.

  • Scalability: The ability to handle high volumes of real estate listings efficiently.

Data Extraction Process

Data-Extraction-Process
Essential Data Fields

Actowiz Solutions designed the scraper to extract critical lead information, including:

✅ Full Name (Buyer/Seller/Agent)

✅ Email Address

✅ Phone Number

✅ Requirement Description

✅ Listing Type (Buy/Sell/Rent)

✅ Property Type (Apartment, House, Commercial, etc.)

✅ Location (City, Region, ZIP Code)

✅ Listing URL

Advanced Web Scraping Techniques

To overcome the challenges of anti-bot mechanisms, Actowiz Solutions implemented:

  • Proxies and IP Rotation: To avoid detection and bans.

  • Headless Browsing with Puppeteer & Selenium: To mimic real user behavior.

  • CAPTCHA Solving: Using automated solving techniques.

  • Data Structuring & Cleaning: Removing duplicates and standardizing formats.

API Development & Integration

API-Development-&-Integration

To make the scraped data accessible, a REST API was developed with the following key features:

API Endpoints

  • GET /leads → Fetch all real estate leads

  • GET /leads/{id} → Retrieve specific lead details

  • POST /leads → Manually add new leads (Optional)

  • DELETE /leads/{id} → Remove leads from the database

Security Features

  • JWT Authentication: Ensuring secure data access.

  • Rate Limiting: Preventing excessive API requests.

Front-End System Integration

Front-End-System-Integration

The extracted data was seamlessly integrated into the front-end system, allowing real estate professionals to:

  • Filter leads by property type, location, and listing type.

  • Automate follow-ups using email and phone contacts.

  • Export lead data in various formats (CSV, JSON, etc.).

Technology Stack

  • Backend: Python (Flask/Django/FastAPI) OR Node.js (Express.js/Nest.js)

  • Web Scraping: Scrapy, BeautifulSoup, Selenium, Puppeteer, Playwright

  • Database: MySQL, PostgreSQL, MongoDB

  • Security: JWT Authentication, Rate Limiting

Results & Business Impact

Results-&-Business-Impact
Increased Lead Generation Efficiency

By automating the lead collection process, real estate businesses reduced manual effort and increased efficiency by 80%.

Enhanced Data Accuracy

Data structuring and deduplication ensured 99% accuracy in collected leads, leading to improved sales conversions.

Improved Scalability

The system handled 100,000+ property listings daily without performance bottlenecks.

Why Choose Actowiz Solutions?

  • Expertise in web scraping for real estate data.

  • Advanced anti-bot handling for uninterrupted data collection.

  • Secure and scalable API integration for seamless front-end access.

Looking to automate real estate lead generation? Contact Actowiz Solutions today and streamline your property data extraction process!

Conclusion

Actowiz Solutions successfully developed and deployed an automated real estate lead generation system using advanced web scraping and API integration. The project enabled real estate professionals to access high-quality leads effortlessly, boosting sales and operational efficiency.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape → Structure → Analyze → Visualize
Explore Solutions Get a Custom Demo

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Ecommerce
Q-Commerce
Travel & OTA
Food Delivery
Real Estate
Social Media
Trusted by Global Leaders – Secured by International Standards

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & palniring

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price inights Top-slling SKUs

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Our Data Drives Impact - Real Client Stories

Blinkit | India (Relail Partner)

"Actow's helped us reduce out of ststack incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actow's helped us reduce out of ststack incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

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Real-Time Ride Fare Comparison: Uber vs DiDi vs Bolt Across 7 Countries

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