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GeoIp2\Model\City Object
(
    [raw:protected] => Array
        (
            [city] => Array
                (
                    [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
                        )

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            [continent] => Array
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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
                        )

                )

            [country] => Array
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                    [iso_code] => US
                    [names] => Array
                        (
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                            [en] => United States
                            [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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                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

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

                )

            [subdivisions] => 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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            [traits] => Array
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                    [ip_address] => 216.73.216.116
                    [prefix_len] => 22
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        )

    [continent:protected] => GeoIp2\Record\Continent Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                    [code] => NA
                    [geoname_id] => 6255149
                    [names] => Array
                        (
                            [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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            [validAttributes:protected] => Array
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                    [0] => code
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    [country:protected] => GeoIp2\Record\Country Object
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            [record:GeoIp2\Record\AbstractRecord:private] => 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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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            [validAttributes:protected] => Array
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                    [0] => confidence
                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                )

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    [locales:protected] => Array
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        )

    [maxmind:protected] => GeoIp2\Record\MaxMind Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
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                )

            [validAttributes:protected] => Array
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                    [0] => queriesRemaining
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        )

    [registeredCountry:protected] => GeoIp2\Record\Country Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => 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
                        )

                )

            [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
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                    [1] => geonameId
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                    [3] => isoCode
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    [representedCountry:protected] => GeoIp2\Record\RepresentedCountry Object
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                )

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                    [1] => geonameId
                    [2] => isInEuropeanUnion
                    [3] => isoCode
                    [4] => names
                    [5] => type
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        )

    [traits:protected] => GeoIp2\Record\Traits Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.116
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

            [validAttributes:protected] => Array
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                    [0] => autonomousSystemNumber
                    [1] => autonomousSystemOrganization
                    [2] => connectionType
                    [3] => domain
                    [4] => ipAddress
                    [5] => isAnonymous
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                    [8] => isHostingProvider
                    [9] => isLegitimateProxy
                    [10] => isp
                    [11] => isPublicProxy
                    [12] => isResidentialProxy
                    [13] => isSatelliteProvider
                    [14] => isTorExitNode
                    [15] => mobileCountryCode
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                    [17] => network
                    [18] => organization
                    [19] => staticIpScore
                    [20] => userCount
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                )

        )

    [city:protected] => GeoIp2\Record\City Object
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            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [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
                        )

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    [location:protected] => GeoIp2\Record\Location Object
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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
                )

            [validAttributes:protected] => Array
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                    [0] => averageIncome
                    [1] => accuracyRadius
                    [2] => latitude
                    [3] => longitude
                    [4] => metroCode
                    [5] => populationDensity
                    [6] => postalCode
                    [7] => postalConfidence
                    [8] => timeZone
                )

        )

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

            [validAttributes:protected] => Array
                (
                    [0] => code
                    [1] => confidence
                )

        )

    [subdivisions:protected] => Array
        (
            [0] => GeoIp2\Record\Subdivision Object
                (
                    [record:GeoIp2\Record\AbstractRecord:private] => 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
                                )

                        )

                    [locales:GeoIp2\Record\AbstractPlaceRecord:private] => Array
                        (
                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
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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
)

Introduction

In a fast-evolving European real estate landscape, accurate Property Price Benchmarking has become a cornerstone for informed investment, valuation, and pricing strategy. With rising housing demand and dynamic post-pandemic shifts, understanding real-time market movements across multiple EU countries is critical. This research by Actowiz Solutions highlights how Competitive Benchmarking using web scraping enables companies to gather accurate property price data from multiple listing platforms, offering a unified and transparent view of Europe's diverse real estate ecosystem.

By Extracting property price data across European countries, stakeholders can identify regional disparities, market trends, and investment opportunities. From Germany's urban housing boom to Spain's coastal rental surges, the Property Price Benchmarking approach combines automation and analytics for precision-driven decision-making. The study further explores how web scraping provides consistent, scalable, and up-to-date property data collection — empowering investors, developers, and financial institutions with actionable intelligence for property valuation and market forecasting.

Competitive Benchmarking

The first stage of Property Price Benchmarking focused on Competitive Benchmarking across 10 major EU markets including Germany, France, Spain, Italy, and the Netherlands. The analysis aimed to evaluate pricing variations for residential, commercial, and mixed-use properties from 2020 to 2025.

Year Avg Residential Price (€) Avg Commercial Price (€) Avg YoY Growth (%)
2020 255,000 520,000 2.8
2021 265,000 540,000 3.1
2022 280,000 560,000 3.8
2023 300,000 590,000 4.2
2024 315,000 615,000 4.8
2025 335,000 640,000 5.0

Through automated European real estate price monitoring using web scraping, Actowiz identified that urban centers such as Paris and Munich exhibited the highest price growth rates, while rural and suburban markets demonstrated more stability. The collected data revealed correlations between price fluctuations and factors like infrastructure investments, digital property viewings, and mortgage rate trends.

The study further emphasizes how Property market benchmarking using data scraping supports competitive strategy development. By comparing property prices across geographies, Actowiz Solutions enables real estate professionals to identify undervalued regions and optimize investment portfolios. The benchmark data also assists governments and real estate boards in ensuring pricing transparency and avoiding speculative inflation across emerging EU markets.

Real Estate Data Intelligence

Using advanced Real Estate Data Intelligence techniques, Actowiz Solutions collected and processed over 10 million property listings across major EU property portals. The Property Price Benchmarking methodology focused on real-time and historical data aggregation, creating a robust analytics foundation for cross-country price comparisons.

Country Avg Property Price (2025 €) Avg Rent (Monthly €) Growth Rate 2020–2025 (%)
Germany 420,000 1,250 5.5
France 380,000 1,180 4.9
Spain 295,000 950 4.4
Italy 270,000 910 3.8
Netherlands 410,000 1,200 5.1

This Analyze European property prices with scraping framework integrated machine learning to identify anomalies, price bubbles, and regional deviations. Actowiz utilized multilingual scraping bots to ensure localized precision, capturing property descriptions, amenities, square footage, and proximity to urban hubs.

The combination of web scraping and Property Price Benchmarking enables firms to make better valuation decisions, manage portfolio risks, and estimate future appreciation. This data-driven intelligence equips investors with clarity in uncertain markets while ensuring that pricing decisions are grounded in real market behavior, not assumptions.

Real Estate Data Scraping Services

Actowiz Solutions' Real Estate Data Scraping Services provide a scalable infrastructure for collecting pricing, rental, and transaction data from 25+ European real estate marketplaces. This data collection is essential for Real-time property benchmarking in Europe, supporting financial models, valuation studies, and policy planning.

Property Type 2020 Avg (€) 2021 Avg (€) 2022 Avg (€) 2023 Avg (€) 2024 Avg (€) 2025 Avg (€)
Apartment 240,000 255,000 270,000 285,000 300,000 320,000
House 275,000 295,000 310,000 330,000 350,000 370,000
Villa 390,000 415,000 440,000 465,000 490,000 520,000

Through Extracting property price data across European countries, Actowiz automated the extraction of property attributes, pricing, and amenities across multiple formats. The system also cross-referenced real estate listings with government data and market reports to ensure accuracy and authenticity.

This real-time automation is essential for stakeholders who require Comparative property pricing Data insights in Europe to identify investment hotspots. For instance, Portugal and Greece displayed the highest post-2023 recovery rates due to digital nomad inflows, while Central Europe witnessed moderate growth due to mortgage rate hikes.

Web Scraping Services

By integrating Web Scraping Services, Actowiz expanded its property data acquisition framework across over 50 EU cities. Using advanced crawlers and AI models, Actowiz developed an automated mechanism to Extracting property price data across European countries for monthly tracking and seasonal trend analysis.

City 2020 Avg Price (€) 2025 Avg Price (€) Growth (%)
Paris 530,000 610,000 15.1
Berlin 420,000 505,000 20.2
Madrid 340,000 395,000 16.2
Amsterdam 460,000 520,000 13.0
Warsaw 310,000 370,000 19.4

These datasets enabled precise European real estate price monitoring using web scraping, identifying where local economic and tourism factors impacted property prices the most. Integration with Real-time property benchmarking in Europe tools allowed developers to compare asset performance by city, category, and market sentiment, ensuring better ROI forecasting.

Actowiz Solutions combines deep domain expertise and cutting-edge technology to deliver Property Price Benchmarking with unmatched precision and scalability. Through AI-driven Real Estate Data Intelligence, clients can automate data collection from any European marketplace, gaining full visibility into regional price fluctuations and demand shifts. The platform supports structured and unstructured datasets, enabling cross-border comparisons, Property market benchmarking using data scraping, and forecasting with over 95% data accuracy.

By integrating Web Scraping Services, Grocery Dataset infrastructure, and multilingual bots, Actowiz ensures smooth adaptation to varying data formats, languages, and compliance standards. Clients can seamlessly access dashboards for Comparative property pricing Data insights in Europe, helping them anticipate market trends, identify undervalued areas, and optimize investment timing.

Conclusion

In conclusion, the Property Price Benchmarking study across EU markets demonstrates how data scraping empowers stakeholders with clarity, speed, and precision in real estate analytics. With rising property prices, dynamic rent patterns, and evolving buyer preferences, the ability to Analyze European property prices with scraping ensures smarter, evidence-based decision-making.

Actowiz Solutions' expertise in Extracting property price data across European countries through advanced scraping automation provides end-to-end insights — from listing-level analysis to continental benchmarking. This methodology bridges the gap between fragmented data sources and actionable business intelligence.

Actowiz stands at the forefront of Real Estate Data Scraping Services, offering scalable solutions that enhance transparency, improve portfolio management, and predict price trends accurately.

Partner with Actowiz Solutions to gain full control over your real estate intelligence. Harness the power of data to transform pricing strategy and maximize investment returns through smarter Property Price Benchmarking.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

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.

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 & planning

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 Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock 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

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

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Oct 06, 2025

Boost Revenue by 25% with Flight Fare Scraping for Competitive Travel Insights on Skyscanner & British Airways in the UK

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Oct 05, 2025

Kroger & BigBasket Inventory Monitoring API - $7B Kroger Inventory Value, BigBasket Holds 10,000 SKUs

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Analyzing Historical SKU-Level Pricing & Discount Data Scraping on Blinkit, Zepto, and Swiggy Instamart

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Impact of Seasonal Events on Grocery Prices & Promotions

Discover how Actowiz Solutions uses data scraping to track seasonal grocery prices and promotions across USA, UK, UAE, India, Germany, Canada, and more.

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Amazon vs Flipkart Diwali Sales Trends Analysis: Comparative Insights for Retail Strategies

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Property Price Benchmarking Across EU Markets Using Web Scraping for Smarter Real Estate Insights

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Alcohol Price Monitoring in UK Using Web Scraping for Competitive Insights from Majestic Wine & The Drink Shop

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