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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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                            [de] => Nordamerika
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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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                        (
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                            [zh-CN] => 美国
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            [postal] => Array
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            [registered_country] => Array
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                    [iso_code] => US
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                        (
                            [de] => USA
                            [en] => United States
                            [es] => Estados Unidos
                            [fr] => États Unis
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                            [pt-BR] => EUA
                            [ru] => США
                            [zh-CN] => 美国
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            [subdivisions] => Array
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                                    [es] => Ohio
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                                    [pt-BR] => Ohio
                                    [ru] => Огайо
                                    [zh-CN] => 俄亥俄州
                                )

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            [traits] => Array
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                    [ip_address] => 216.73.216.115
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        )

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

                )

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

        )

    [locales:protected] => Array
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        )

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

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

        )

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

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

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

    [traits:protected] => GeoIp2\Record\Traits Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [ip_address] => 216.73.216.115
                    [prefix_len] => 22
                    [network] => 216.73.216.0/22
                )

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

        )

    [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
                        )

                )

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

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

    [location:protected] => GeoIp2\Record\Location Object
        (
            [record:GeoIp2\Record\AbstractRecord:private] => Array
                (
                    [accuracy_radius] => 20
                    [latitude] => 39.9625
                    [longitude] => -83.0061
                    [metro_code] => 535
                    [time_zone] => America/New_York
                )

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

        )

    [postal:protected] => GeoIp2\Record\Postal Object
        (
            [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
                                )

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                            [0] => en
                        )

                    [validAttributes:protected] => Array
                        (
                            [0] => confidence
                            [1] => geonameId
                            [2] => isoCode
                            [3] => names
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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
)
Using-Rental-Data-Analytics-to-Optimize-Property-Listings-for-Realtors

Introduction

In the competitive real estate market, realtors must continuously adapt to changing trends and demands. Rental Data Analytics has become an essential tool for real estate professionals to gain a competitive edge by making data-driven decisions. This case study explores how Actowiz Solutions helped realtors leverage Real Estate Data Insights and Optimize Property Listings to maximize engagement, improve pricing strategies, and drive higher conversions.

Client Background

lient-Background

A prominent real estate firm specializing in rental properties across urban and suburban markets was facing challenges in listing optimization. Despite having a vast inventory of rental properties, their conversion rates were low, and properties remained vacant for extended periods. The client sought a solution to leverage Rental Market Analysis and enhance their listing performance.

Challenges Faced

Challenges-Faced

The client encountered several challenges, including:

  • Lack of Data-Driven Insights: Traditional listing methods were ineffective in attracting prospective tenants.

  • Inefficient Pricing Strategies: Without accurate Real Estate Pricing Strategy, rental properties were either overpriced or undervalued.

  • Poor Listing Engagement: Listings were not optimized to appear in searches, leading to low visibility.

  • Unstructured Market Data: The absence of real-time Rental Property Data Trends made it difficult to understand market fluctuations.

  • Delayed Rental Decisions: Slow market analysis resulted in delayed decision-making, affecting overall occupancy rates.

Solution: Implementing Rental Data Analytics

Solution-Implementing-Rental-Data-Analytics

Actowiz Solutions developed a tailored approach to address these challenges using AI in Real Estate Listings and Real Estate Market Intelligence. The key steps included:

1. Data Collection & Market Analysis

Actowiz Solutions implemented a robust Rental Market Analysis framework that gathered data from multiple sources, including:

  • Rental listing platforms

  • Market trends and historical rental data

  • Tenant search behaviors

  • Competitor rental pricing

2. Dynamic Pricing for Maximum ROI

By leveraging Rental Price Optimization, Actowiz Solutions provided realtors with:

  • AI-driven pricing models to set competitive rates

  • Predictive analytics for rental price fluctuations

  • Comparative price analysis across similar properties

3. Optimized Property Listings

To enhance property visibility, the solution focused on Property Listing Optimization through:

  • SEO-driven descriptions to improve search rankings

  • High-quality images and virtual tour integrations

  • Smart categorization based on tenant preferences

4. Real-Time Monitoring and Market Intelligence

Using Housing Market Data Analytics, Actowiz Solutions enabled realtors to:

  • Track rental demand in real-time

  • Identify emerging rental property trends

  • Adjust strategies based on seasonal variations

5. Smart Property Management with Data Insights

A Smart Property Management system was integrated to:

  • Automate listing updates based on market trends

  • Provide actionable insights for targeted marketing campaigns

  • Enhance tenant experience with data-driven personalization

Results & Benefits

Results-&-Benefits

With the implementation of Data-Driven Property Listings, the client achieved significant improvements, including:

  • 25% Increase in Property Views: Enhanced listing optimization resulted in higher online engagement.

  • 30% Faster Lease Conversions: Data-driven pricing strategies reduced the time properties stayed on the market.

  • 15% Higher Rental Revenue: Optimized pricing ensured rental properties were priced competitively without undervaluation.

  • Real Estate Investment Insights: Gained in-depth knowledge on profitable property investments.

  • Improved Tenant Targeting: Personalized listings attracted the right tenants, leading to higher satisfaction rates.

Client Testimonial

"Actowiz Solutions transformed our rental property strategy! Their Rental Data Analytics helped us optimize pricing, improve listing visibility, and significantly boost tenant engagement. In just a few months, we saw a 25% increase in property views and a 30% faster lease conversion rate. Their Real Estate Market Intelligence gave us the insights we needed to stay ahead of competitors. Highly recommended!"

- Director of Property Management

Conclusion

By implementing Rental Data Analytics, Actowiz Solutions helped realtors Optimize Property Listings, improve engagement, and drive rental conversions. With Real Estate Market Intelligence, Rental Property Data Trends, and Smart Property Management, the client successfully transformed their approach to property listings. Realtors looking to stay ahead must embrace the power of analytics to enhance their market positioning.

Want to optimize your rental listings with data analytics? Contact Actowiz Solutions today!

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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Infographics
Report
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Scraping 250K Restaurant Menus: How Actowiz Solutions Decoded Diwali Dining Trends Across India

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Competitive Product Pricing on Tesco & Argos Using Data Scraping to Uncover 30% Weekly Price Fluctuations in the UK Market

Discover how Competitive Product Pricing on Tesco & Argos using data scraping uncovers 30% weekly price fluctuations in UK market for smarter retail decisions.

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Airline Ticket Price Trends - Scrape Airline Ticket Price Trend and Track 20–35% Market Volatility in U.S. & EU

Discover how Scrape Airline Ticket Price Trend uncovers 20–35% market volatility in U.S. & EU, helping airlines analyze seasonal fare fluctuations effectively.

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Quick Commerce Trend Analysis Using Data Scraping - Insights from Nana Direct & HungerStation in Saudi Arabia

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