Benefits of Scraping Data from Real Estate Website

With so much of growth in the recent times in real estate industry, it is likely that companies would want to create something different or use another method, so as to get desired benefits. Thus, it is best to go with the technological advancements and create real estate websites to get an edge over others in the industry. And to get all the information regarding website content, one can opt for real estate data scraping methods.

About real estate website scraping

Internet has become an important part of our daily lives and in industry marketing procedures too. With the use of website scraping one can easily scrape real estate listing from various websites. One just needs the help of experts and with proper software and tools; they can easily collect all the relevant real estate data from the required real estate websites and make a structured file containing the information. With internet becoming a valid platform for information and data submitted by numerous sources from around the globe, it is necessary to gather them all in one place for companies. In this way, the company can know what it lacks and work upon their strategies so as to gain profit and get to the top of the business world by taking one step at a time.

Uses of real estate website scraping

With proper use of website scraping one can collect and scrape the real estate listings which can help the company in the real estate market area. One can draw the attention of potential customers by designing the company strategies in such a way as contemplating the changing trends in the real estate global arena. All this is done with the help of the data collected from various real estate websites. With the help of proper website, one can collect the data and these get updated whenever new information gets into the web portal. In this way the company is kept updated about the various changes happening around the global market and thus, ensure in making plans regarding the company. This way one can plan ahead and take steps that can lead to the company gaining profits in future.

Thus, with the help of proper real estate website scraping one can be sure of getting all the information regarding real estate market. This way one can work upon making the company move as per the market trends and get a stronghold in real estate business.

Source: https://3idatascraping.wordpress.com/2013/09/25/benefit-of-scraping-data-from-real-estate-website/

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it
into a database.

Source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416

How to Generate Sales Leads Using Web Scraping Services

The first stage of any selling process is what is popularly known as “lead generation”. This phase is what most businesses place at the apex of their sales concerns. It is a driving force that governs decision-making at its highest levels, and influences business strategy and planning. If you are about to embark on an outbound sales campaign and are in the process of looking for leads, you would acknowledge the fact that lead generation process is of extreme importance for any business.

Different lead generation techniques have been used over and over again by companies around the world to satiate this growing business need. Newer, more innovative methods have also emerged to help marketers in this process. One such method of lead generation that is fast catching on, and is poised to play a big role for businesses in the coming years, is web scraping. With web scraping, you can easily get access to multiple relevant and highly customized leads – a perfect starting point for any marketing, promotional or sales campaign.

The prominence of Web Scraping in overall marketing strategy

At present, levels of competition have risen sky high for most businesses. For success, lead generation and gaining insight about customer behavior and preferences is an essential business requirement. Web scraping is the process of scraping or mining the internet for information. Different tools and techniques can be used to harvest information from multiple internet sources based on relevance, and the structured and organized in a way that makes sense to your business. Companies that provide web scraping services essentially use web scrapers to generate a targeted lead database that your company can then integrate into its marketing and sales strategies and plans.

The actual process of web scraping involves creating scraping scripts or algorithms which crawl the web for information based on certain preset parameters and options. The scraping process can be customized and tuned towards finding the kind of data that your business needs. The script can extract data from websites automatically, collate and put together a meaningful collection of leads for business development.

Lead Generation Basics

At a very high level, any person who has the resources and the intent to purchase your product or service qualifies as a lead. In the present scenario, you need to go far deeper than that. Marketers need to observe behavior patterns and purchasing trends to ensure that a particular person qualifies as a lead. If you have a group of people you are targeting, you need to decide who the viable leads will be, acquire their contact information and store it in a database for further action.

List buying used to be a popular way to get leads, but their efficacy has dwindled over time. Web scraping is the fast coming up as a feasible lead generation technique, allowing you to find highly focused and targeted leads in short amounts of time. All you need is a service provider that would carry out the data mining necessary for lead generation, and you end up with a list of actionable leads that you can try selling to.

How Web Scraping makes a substantial difference

With web scraping, you can extract valuable predictive information from websites. Web scraping facilitates high quality data collection and allows you to structure marketing and sales campaigns better. To drive sales and maximize revenue, you need strong, viable leads. To facilitate this, you need critical data which encompasses customer behavior, contact details, buying patterns and trends, willingness and ability to spend resources, and a myriad of other aspects critical to ascertain the potential of an entity as a rewarding lead. Data mining through web scraping can be a great way to get to these factors and identifying the leads that would make a difference for your business.

web-scraping-service

Crawling through many different web locales using different techniques, web scraping services pick up a wealth of information. This highly relevant and specialized information instantly provides your business with actionable leads. Furthermore, this exercise allows you to fine-tune your data management processes, make more accurate and reliable predictions and projections, arrive at more effective, strategic and marketing decisions and customize your workflow and business development to better suit the current market.

The Process and the Tools

Lead generation, being one of the most important processes for any business, can prove to be an expensive proposition if not handled strategically. Companies spend large amounts of their resources acquiring viable leads they can sell to. With web scraping, you can dramatically cut down the costs involved in lead generation and take your business forward with speed and efficiency. Here are some of the time-tested web scraping tools which can come in handy for lead generation –

•    Website download software – Used to copy entire websites to local storage. All website pages are downloaded and the hierarchy of navigation and internal links preserve. The stored pages can then be viewed and scoured for information at any later time.     Web scraper – Tools that crawl through bulk information on the internet, extracting specific, relevant data using a set of pre-defined parameters.

•    Data grabber – Sifts through websites and databases fast and extracts all the information, which can be sorted and classified later.

•    Text extractor – Can be used to scrape multiple websites or locations for acquiring text content from websites and web documents. It can mine data from a variety of text file formats and platforms.

With these tools, web scraping services scrape websites for lead generation and provide your business with a set of strong, actionable leads that can make a difference.

Covering all Bases

The strength of web scraping and web crawling lies in the fact that it covers all the necessary bases when it comes to lead generation. Data is harvested, structured, categorized and organized in such a way that businesses can easily use the data provided for their sales leads. As discussed earlier, cold and detached lists no longer provide you with enough actionable leads. You need to look at various factors and consider them during your lead generation efforts –

•    Contact details of the prospect

•    Purchasing power and purchasing history of the prospect

•    Past purchasing trends, willingness to purchase and history of buying preferences of the prospect

•    Social markers that are indicative of behavioral patterns

•    Commercial and business markers that are indicative of behavioral patterns

•    Transactional details

•    Other factors including age, gender, demography, social circles, language and interests

All these factors need to be taken into account and considered in detail if you have to ensure whether a lead is viable and actionable, or not. With web scraping you can get enough data about every single prospect, connect all the data collected with the help of onboarding, and ascertain with conviction whether a particular prospect will be viable for your business.

Let us take a look at how web scraping addresses these different factors –

1. Scraping website’s

During the scraping process, all websites where a particular prospect has some participation are crawled for data. Seemingly disjointed data can be made into a sensible unit by the use of onboarding- linking user activities with their online entities with the help of user IDs. Documents can be scanned for participation. E-commerce portals can be scanned to find comments and ratings a prospect might have delivered to certain products. Service providers’ websites can be scraped to find if the prospect has given a testimonial to any particular service. All these details can then be accumulated into a meaningful data collection that is indicative of the purchasing power and intent of the prospect, along with important data about buying preferences and tastes.

2. Social scraping

According to a study, most internet users spend upwards of two hours every day on social networks. Therefore, scraping social networks is a great way to explore prospects in detail. Initially, you can get important identification markers like names, addresses, contact numbers and email addresses. Further, social networks can also supply information about age, gender, demography and language choices. From this basic starting point, further details can be added by scraping social activity over long periods of time and looking for activities which indicate purchasing preferences, trends and interests. This exercise provides highly relevant and targeted information about prospects can be constructively used while designing sales campaigns.

Check out How to use Twitter data for your business

3. Transaction scraping
Through the scraping of transactions, you get a clear idea about the purchasing power of prospects. If you are looking for certain income groups or leads that invest in certain market sectors or during certain specific periods of time, transaction scraping is the best way to harvest meaningful information. This also helps you with competition analysis and provides you with pointers to fine-tune your marketing and sales strategies.

get-results-from-your-lead-generation-campaign

Using these varied lead generation techniques and finding the right balance and combination is key to securing the right leads for your business. Overall, signing up for web scraping services can be a make or break factor for your business going forward. With a steady supply of valuable leads, you can supercharge your sales, maximize returns and craft the perfect marketing maneuvers to take your business to an altogether new dimension.

Source: https://www.promptcloud.com/blog/how-to-generate-sales-leads-using-web-scraping-services/

rvest: easy web scraping with R

rvest is new package that makes it easy to scrape (or harvest) data from html web pages, inspired by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%

  html_text() %>%

  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%

  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"   

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"   

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%

  .[[3]] %>%

  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

•    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

•    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

•    Detect and repair text encoding problems with guess_encoding() and repair_encoding().

•    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://blog.rstudio.org/2014/11/24/rvest-easy-web-scraping-with-r/

Custom Web Data Scraping Service

Data scraping services help companies and individuals alike to have access to on-demand data that is scraped from the web on the basis of individual requirements. Unlike the SaaS counterparts, a data scraping service such as PromptCloud can enable you to fetch scraped data in a clean, structured data format such that you don’t need to be involved at any stage in the process except for while giving your data scraping service request and taking delivery of the final scraped data.

A data scraping service fills a very important gap in the big data context as most of the options available are either DIY (open source data scraping) or SaaS based data scraping companies which, in most cases, fail to address the majority of the needs. For instance, someone who’s looking for a service for data scraping for custom requirements, and doesn’t want anything less than 99.9% accuracy in the final scraped data, both the above options (open source or SaaS) are unfavourable. This is so because unless someone is dedicatedly looking for structure changes in the list of source pages (which happen with 40% of all websites each month), it gets almost impossible to get accuracy levels of anywhere more than 90%.

A custom service for scraping web data such as PromptCloud can be your data partner if you know what you need to scrape from the web, and can deliver the scraped data automatically to you at a desired scraping frequency (near real-time, daily, weekly, monthly etc.), in the schema containing records of choice and in a format that you desire (we do XML, CSV, XLS, JSON).

All you need to do is participate only while briefing the requirements and then taking delivery of the desired data if you use our data scraping service offering. If you wish to discuss a data scraping use case.

Source: https://www.promptcloud.com/data-scraping-service/